jict-140507041214050704120414CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information and Communication Technologyjict2717-0411101720251765MelikaKalhorMitraMirzarezaeeTouraj BaniRostam1017202511910.66224/jict.45512.17.65.1http://jour.aicti.ir/fa/Article/45512http://jour.aicti.ir/fa/Article/Download/45512http://jour.aicti.ir/fa/Article/Download/45512http://jour.aicti.ir/fa/Article/Download/45512http://jour.aicti.ir/fa/Article/Download/45512http://jour.aicti.ir/fa/Article/Download/45512http://jour.aicti.ir/fa/Article/Download/45512http://jour.aicti.ir/fa/Article/Download/45512[1] Majumder, Navonil and Poria, Soujanya and Gelbukh, Alexander and Cambria, Erik, "Deep Learning-Based Document Modeling for Personality Detection from Text," IEEE Intelligent Systems, vol. 32, pp. 74-79, 03 2017. [2]Xue, Di and Hong, Zheng and Guo, Shize and Gao, Liang and Wu, Lifa and Zheng, Jinghua and Zhao, Nan, "Personality Recognition on Social Media With Label Distribution Learning," IEEE Access, vol. 5, pp. 13478-13488, 2017. [3]J. Š. Matej Gjurković, "Reddit: A Gold Mine for Personality Prediction," in Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media, New Orleans, Louisiana, USA, 2018, pp. 87-97. [4] Faidh Ilzam Nur Haq and Erwin Budi Setiawan, "Implementasi Naive Bayes Classifier untuk Prediksi Kepribadian Big Five pada Twitter Menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) dan Term Frequency-Relevance Frequency (TF-RF)," vol. 6(2), p. 9785–9795, 2019. [5]Sandhya Katiyar, Sanjay Kumar, Himdweep Walia, "Personality Prediction from Stack Overflow by using Naïve Bayes Theorem in Data Mining," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 9, no. 3, pp. 1555-1559, 2020. [6] Tomas Mikolov and Wen-tau Yih and Geoffrey Zweig, Linguistic Regularities in Continuous Space Word Representations, NAACL, 2013. [7] David M. Blei & Andrew Y. Ng & Michael I. Jordan , "Latent dirichlet allocation," The Journal of Machine Learning Research, vol. 3, p. 993–1022, 2003. [8] Kishan Das,Himanshu Prajapati, "PERSONALITY IDENTIFICATION BASED ON MBTI DIMENSIONS USING NATURAL LANGUAGE PROCESSING," International Journal of Creative research Thoughts, vol. 8(6), pp. 1653-1657, 2020. [9] Kazemeini, Amirmohammad and Fatehi, Samin and Mehta, Yash and Eetemadi, Sauleh and Cambria, Erik, "Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles," 07 2020. [10] Kamal El-Demerdash and Reda A. El-Khoribi and Mahmoud A. and Sherif Abdou, "Deep learning based fusion strategies for personality prediction," Egyptian Informatics Journal, vol. 23, pp. 47-53, 2022. [11]Sakdipat Ontoum, Jonathan H. Chan, "Personality Type Based on Myers-Briggs Type Indicator with Text Posting Style by using Traditional and Deep Learning," CoRR, vol. abs/2201.08717, 2022. MojganFarhoodi10172025627210.66224/jict.46478.17.65.62http://jour.aicti.ir/fa/Article/46478http://jour.aicti.ir/fa/Article/Download/46478http://jour.aicti.ir/fa/Article/Download/46478http://jour.aicti.ir/fa/Article/Download/46478http://jour.aicti.ir/fa/Article/Download/46478http://jour.aicti.ir/fa/Article/Download/46478http://jour.aicti.ir/fa/Article/Download/46478http://jour.aicti.ir/fa/Article/Download/46478[1] S. Kusal, S. Patil, J. Choudrie, K. Kotecha, D. Vora, and I. Pappas, “A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions.” arXiv, Apr. 26, 2022. Accessed: Oct. 15, 2023. [Online]. Available: http://arxiv.org/abs/2205.03235[2] A. Kumar and A. Jaiswal, “Systematic literature review of sentiment analysis on Twitter using soft computing techniques,” Concurrency and Computation, vol. 32, no. 1, p. e5107, Jan. 2020, doi: 10.1002/cpe.5107.[3] N. Sabri, A. Edalat, and B. Bahrak, “Sentiment analysis of persian-english code-mixed texts,” in 2021 26th International Computer Conference, Computer Society of Iran (CSICC), IEEE, 2021, pp. 1–4. Accessed: Oct. 15, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9420605/[4] K. Sailunaz and R. Alhajj, “Emotion and sentiment analysis from Twitter text,” Journal of Computational Science, vol. 36, p. 101003, 2019.[5] P. Mehta and S. Pandya, “A review on sentiment analysis methodologies, practices and applications,” International Journal of Scientific and Technology Research, vol. 9, no. 2, pp. 601–609, 2020.[6] B. Pang and L. Lee, “Sentiment Polarity Dataset Version 2.0,” Part of the Natural Language Tool Kit, for the Python computer language, 2002.[7] K. Topal and G. Ozsoyoglu, “Movie review analysis: Emotion analysis of IMDb movie reviews,” in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2016, pp. 1170–1176. Accessed: Oct. 15, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7752387/[8] “Yelp.” Accessed: Oct. 30, 2023. [Online]. Available: https://www.yelp.com/dataset/challenge[9] E. Vaziripour, C. Giraud-Carrier, and D. Zappala, “Analyzing the political sentiment of tweets in Farsi,” in Proceedings of the International AAAI Conference on Web and Social Media, 2016, pp. 699–702. Accessed: Oct. 25, 2023. [Online]. Available: https://ojs.aaai.org/index.php/ICWSM/article/view/14791[10] Z. B. Nezhad and M. A. Deihimi, “Twitter sentiment analysis from Iran about COVID 19 vaccine,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 16, no. 1, p. 102367, 2022.[11] H. Abdi Khojasteh, E. Ansari, and M. Bohlouli, “Large-Scale Colloquial Persian 0.5,” https://iasbs.ac.ir/~ansari/lscp/, Feb. 2020, Accessed: Oct. 25, 2023. [Online]. Available: https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3195[12] M. Heidari and P. Shamsinejad, “Producing an instagram dataset for persian language sentiment analysis using crowdsourcing method,” in 2020 6th International Conference on Web Research (ICWR), IEEE, 2020, pp. 284–287.[13] M. Panahandeh Nigjeh and S. Ghanbari, “Leveraging ParsBERT for cross-domain polarity sentiment classification of Persian social media comments,” Multimedia Tools and Applications, pp. 1–18, 2023.[14] A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N project report, Stanford, vol. 1, no. 12, p. 2009, 2009.[15] H. Poursepanj, J. Weissbock, and D. Inkpen, “uOttawa: system description for SemEval 2013 task 2 sentiment analysis in twitter,” in Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2013, pp. 380–383. Accessed: Oct. 15, 2023. [Online]. Available: https://aclanthology.org/S13-2062.pdf[16] B. Velichkov et al., “SU-FMI: System description for SemEval-2014 task 9 on sentiment analysis in Twitter,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 590–595. Accessed: Oct. 15, 2023. [Online]. Available: https://aclanthology.org/S14-2103.pdf[17] P. Nakov, A. Ritter, S. Rosenthal, F. Sebastiani, and V. Stoyanov, “SemEval-2016 Task 4: Sentiment Analysis in Twitter.” arXiv, Dec. 03, 2019. doi: 10.48550/arXiv.1912.01973.[18] S. Mohammad, F. Bravo-Marquez, M. Salameh, and S. Kiritchenko, “Semeval-2018 task 1: Affect in tweets,” in Proceedings of the 12th international workshop on semantic evaluation, 2018, pp. 1–17. Accessed: Oct. 15, 2023. [Online]. Available: https://aclanthology.org/S18-1001/[19] A. Rogers, A. Romanov, A. Rumshisky, S. Volkova, M. Gronas, and A. Gribov, “RuSentiment: An enriched sentiment analysis dataset for social media in Russian,” in Proceedings of the 27th international conference on computational linguistics, 2018, pp. 755–763. Accessed: Oct. 15, 2023. [Online]. Available: https://aclanthology.org/C18-1064/[20] S.-Y. Chen, C.-C. Hsu, C.-C. Kuo, Ting-Hao, Huang, and L.-W. Ku, “EmotionLines: An Emotion Corpus of Multi-Party Conversations.” arXiv, May 30, 2018. Accessed: Oct. 15, 2023. [Online]. Available: http://arxiv.org/abs/1802.08379[21] S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, and R. Mihalcea, “MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations.” arXiv, Jun. 04, 2019. Accessed: Oct. 15, 2023. [Online]. Available: http://arxiv.org/abs/1810.02508[22] D. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade, and S. Ravi, “GoEmotions: A Dataset of Fine-Grained Emotions.” arXiv, Jun. 02, 2020. Accessed: Oct. 15, 2023. [Online]. Available: http://arxiv.org/abs/2005.00547[23] F. Rustam, I. Ashraf, A. Mehmood, S. Ullah, and G. S. Choi, “Tweets classification on the base of sentiments for US airline companies,” Entropy, vol. 21, no. 11, p. 1078, 2019.[24] R. Asgarnezhad and S. A. Monadjemi, “Persian sentiment analysis: feature engineering, datasets, and challenges,” Journal of applied intelligent systems & information sciences, vol. 2, no. 2, pp. 1–21, 2021.[25] S. Alimardani and A. Aghaie, “Opinion mining in Persian language using supervised algorithms,” 2015, Accessed: Apr. 22, 2024. [Online]. Available: https://www.sid.ir/paper/332700/en[26] S. A. A. Asli, B. Sabeti, Z. Majdabadi, P. Golazizian, R. Fahmi, and O. Momenzadeh, “Optimizing annotation effort using active learning strategies: A sentiment analysis case study in persian,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, 2020, pp. 2855–2861.[27] A. Hatefi Ghahfarrokhi and M. Shamsfard, “Tehran stock exchange prediction using sentiment analysis of online textual opinions,” Intell Sys Acc Fin Mgmt, vol. 27, no. 1, pp. 22–37, Jan. 2020, doi: 10.1002/isaf.1465.[28] T. S. Ataei, K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, “Pars-absa: an aspect-based sentiment analysis dataset for Persian,” arXiv preprint arXiv:1908.01815, 2019.[29] K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, “Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022, pp. 7056–7060.[30] M. E. Basiri and A. Kabiri, “Words Are Important: Improving Sentiment Analysis in the Persian Language by Lexicon Refining,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 17, no. 4, pp. 1–18, Dec. 2018, doi: 10.1145/3195633.[31] A. Khodaei, A. Bastanfard, H. Saboohi, and H. Aligholizadeh, “Deep Emotion Detection Sentiment Analysis of Persian Literary Text,” 2022, Accessed: Oct. 15, 2023. [Online]. Available: https://www.researchsquare.com/article/rs-1796157/latest[32] M. Shirghasemi, M. H. Bokaei, and M. Bijankhan, “The impact of active learning algorithm on a cross-lingual model in a Persian sentiment task,” in 2021 7th International Conference on Web Research (ICWR), IEEE, 2021, pp. 292–295. Accessed: Apr. 22, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9443156/[33] P. Hosseini, A. A. Ramaki, H. Maleki, M. Anvari, and S. A. Mirroshandel, “SentiPers: A Sentiment Analysis Corpus for Persian.” arXiv, Jan. 01, 2021. Accessed: Oct. 15, 2023. [Online]. Available: http://arxiv.org/abs/1801.07737[34] J. P. R. Sharami, P. A. Sarabestani, and S. A. Mirroshandel, “Deepsentipers: Novel deep learning models trained over proposed augmented persian sentiment corpus,” arXiv preprint arXiv:2004.05328, 2020.[35] S. M. Mohammad and F. Bravo-Marquez, “WASSA-2017 Shared Task on Emotion Intensity.” arXiv, Aug. 11, 2017. Accessed: Oct. 15, 2023. [Online]. Available: http://arxiv.org/abs/1708.03700MajidAkhshabi1017202512515010.66224/jict.47672.17.65.125http://jour.aicti.ir/fa/Article/47672http://jour.aicti.ir/fa/Article/Download/47672http://jour.aicti.ir/fa/Article/Download/47672http://jour.aicti.ir/fa/Article/Download/47672http://jour.aicti.ir/fa/Article/Download/47672http://jour.aicti.ir/fa/Article/Download/47672http://jour.aicti.ir/fa/Article/Download/47672http://jour.aicti.ir/fa/Article/Download/47672[1] کلاته، رحمانی و چهارده چریکی، معصومه. "هوش مصنوعی و کاربرد آن در حسابداری مالی"، کنکاش، صص 135-140. 1389.[2] حشمدار، اکرم، کردی، مراد، "بررسی اثربخشی سیستم‌های هوش مصنوعی در کارکردهای منابع انسانی"، فصلنامه پژوهش‌های معاصر در علوم مدیریت و حسابداری، سال چهارم، شماره 12، بهار1401.[3] زیودار، زهره، "کاربرد تکنیک¬های هوش مصنوعی در حوزه حسابداری و مالی"، فصلنامه رویکردهای پژوهشی نوین در مدیریت و حسابداری، سال ششم، شماره 84، صص 1572-1557، 1401.[4] ترابی، علیرضا، احمدی¬زاد، آرمان و اصغرنیا، مرتضی، "فرصتها و چالشها و روندهای جهانی فناوری تنظیم گری (مرور مبانی نظری)"، فصلنامه توسعه تکنولوژی صنعتی، شماره 8، صص 3-16، 1401.[5] استخریان، امیررضا و آزرم، الهام، "مرور سیستماتیک مطالعات مرتبط با هوش مصنوعی و اینترنت اشیاء در شرکت‌های کوچک و متوسط (SME)"، اولین کنفرانس بین المللی برق و کامپیوتر، مکانیک و هوش مصنوعی، 1400.[6] کارکن بیرق، حبیب، "نگرش انتقادی به مسئه هوش مصنوعی"، نشریه ذهن، شماره 37، صص 59-84، 1388.[7] بافنده، علی و شمول، داوود، "هوش مصنوعی و کاربرد آن در مدیریت"، فروغ تدبیر، سال ششم، شماره 15، صص 44-49، 1387.[8] اخشابی، مجید و درتاج، فریبرز، "شناسایی متغیرهای میانجی در رابطه میان آموزش موسیقی و افزایش خلاقیت، فصلنامه علمی پژوهشی علوم روانشناختی، شماره ۲۱، صص ۸۶۶-۸۵۳، 1401.[9] نصیری مفخم، فریا و روغنی¬زاده، راضیه، "ساخت هوشمند موسیقی با الگوریتم ژنتیک مبتنی بر جهش یکنواخت موتیف"، فصلنامه هوش محاسباتی در مهندسی برق، دوره 5، شماره 4، صص 102 – 85، 1393.[10] G. Papadopoulos, and G. Wiggins, “AI Methods for Algorithmic Composition: A Survey, a Critical view and Future Prospects”, AISB Symposium on Musical Creativity, Edinburgh, UK, 1999.[11] B. Johanson, and R. Poli, R, “GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters”, Genetic Prog. Proc. of the Third Annual Conf. CSRP, 1998.[12] N. Lazar, and K. Chithra, “Comprehensive bibliometric mapping of publication trends in the development of Building Sustainability Assessment Systems”, Environment, Development and Sustainability, 23(4), 4899–4923, 2021.[13] J. S. Downie, “Music information retrieval”, Annual review of information science and technology, 37(1), 295-340, 2003.[14] D. Deutsch, “Music recognition”, Psychological Review, 76(3), 300–307, 1969.[15] J. A. Moorer. “Music and computer composition”, Commun, ACM 15(2), 104–113, 1972.[16] D. Gregory, and W. L. Sims, “Music Preference Analysis with Computers”, Journal of Music Therapy, 24(4), 203–212, 1987.[17] S. Dixon, “Beat induction and rhythm recognition”, In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence, AI 1997, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 1342, 1997.[18] X. Liu. “Research on Music Genre Recognition Based on Improved Naive Bayes Algorithm”, Mobile Information Systems, 2022(1), 1909928, 2022.[19] T. Ren, F. Wang, and H. Wang, “A sequential naive Bayes method for music genre classification based on transitional information from pitch and beat”, Statistics and Its Interface, 13(3), 361-371, 2020.[20] D. S. Rahardwika, E. H. Rachmawanto, C.A. Sari, C. Irawan, D. P. Kusumaningrum, and S.L. Trusthi, “Comparison of SVM, KNN, and NB classifier for genre music classification based on metadata”, In 2020 international seminar on application for technology of information and communication (iSemantic), 12-16. 2020.[21] R. Malheiro, R. Panda, P. Gomes, and R.P. Paiva, “Emotionally-relevant features for classification and regression of music lyrics”, IEEE Transactions on Affective Computing, 9(20), 240-254, 2016.[22] J. Bai, K. Luo, J. Peng, J. Shi, Y. Wu, L. Feng, ... and Y. Wang, “Music emotions recognition by cognitive classification methodologies”, In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), 121-129, 2017.[23] F. Kurniawan, “Automatic music classification for Dangdut and campursari using Naïve Bayes”, In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, 1-6, 2011.[24] Z. Fu, G. Lu, K. M. Ting, and D. Zhang, “Learning naive Bayes classifiers for music classification and retrieval”, In 2010 20th international conference on pattern recognition, 4589-4592, 2010.[25] R. Rajan, and B. S. Mohan, “Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors”, International Journal of Pattern Recognition and Artificial Intelligence, 35(13), 2151014, 2021.[26] M. Sudarma, M, and I. G. Harsemadi, “Design and analysis system of KNN and ID3 algorithm for music classification based on mood feature extraction”, International Journal of Electrical and Computer Engineering, 7(1), 486, 2017.[27] Y. Li, X. Li, Z. Lou, and C. Chen, “Long short-term memory-based music analysis system for music therapy”, Frontiers in Psychology, 13, 928048, 2022.[28] L. Wang, H. Zhu, X. Zhang, S. Li, and W. Li, “Transfer learning for music classification and regression tasks using artist tags”, In Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers, 81-89. Springer Singapore, 2020.[29] D. H. Lee, J. W. Park, and Y. S. Seo, “Multiple Regression-Based Music Emotion Classification Technique”, KIPS Transactions on Software and Data Engineering, 7(6), 239-248, 2018.[30] K. Choi, G. Fazekas, M. Sandler, and K. Cho, “Convolutional recurrent neural networks for music classification”, In 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP), 2392-2396, 2017.[31] Y. Deng, Y. Lv, M. Liu, and Q. Lu, “A regression approach to categorical music emotion recognition”, In 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), 257-261, 2015.[32] H. Wu, and L. Zhu, Adaptive classification method of electronic music based on improved decision tree, International Journal of Arts and Technology, 15(1), 1-12, 2024.[33] V. Pavan, A. S. Vickram, and R. Dhanalakshmi, “Efficient prediction of music genre using support vector machine and decision tree”, In AIP Conference Proceedings, AIP Publishing, 2853(1), 2024.[34] G. M. Bressan, B. C. de Azevedo, and E. A. S. Lizzi, “A decision tree approach for the musical genres classification”, Applied Mathematics & Information Sciences, 11(6), 1703-1713, 2017.[35] V. Pavan, and R. Dhanalakshmi, “Performance evaluation of music genre prediction system using convolutional neural network and random forest”, In AIP Conference Proceedings, AIP Publishing, 2853(1), 2024.[36] Z. Dai, and X. Huang, “Electronic Dance Music Classification Based on Machine Learning Methods”, In 2022 International Conference on Electronics and Devices, Computational Science (ICEDCS), IEEE, 351-354, 2022.[37] S. V. Thambi, K. T. Sreekumar, C. S. Kumar, C. S. and P. R. Raj, “Random forest algorithm for improving the performance of speech/non-speech detection”, In 2014 First International Conference on Computational Systems and Communications (ICCSC), IEEE, 28-32, 2014.[38] A. Gómez de Silva Garza, and E. Herrera González, “Music Style Analysis Using the Random Forest Algorithm”, In DS 73-2 Proceedings of the 2nd International conference on Design Creativity, 2, 343-350, 2012.[39] C. Wang, and X. Geng, “Simulation of music style classification model based on support vector machine algorithm”, In Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023), SPIE, 12703, 266-272, 2023.[40] M. Xiang, “Music Vocal Spectrum Classification Model based on Improved SVM Algorithm”, In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), IEEE, 1244-1247, 2022.[41] E. Egivenia, G. Ryanie Setiawan, S. Shania Mintara, and D. Suhartono, “Classification of explicit music content based on lyrics, music metadata, and user annotation”, In Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology, 265-270, 2021.[42] X. Wang, “Research on recognition and classification of folk music based on feature extraction algorithm”, Informatica, 44(4), 2020.[43] B. A. Achar, N. D. Aiyappa, B. Akshaj, M. N. Thippeswamy, and N. Pillay, “Activity-Based Music Classifier: A Supervised Machine Learning Approach for Curating Activity-Based Playlists”, In Emerging Research in Computing, Information, Communication and Applications: ERCICA 2018, 2, 185-198, 2019.[44] F. Mahardhika, H. L. H. S. Warnars, and Y. Heryadi, “Indonesian's dangdut music classification based on audio features”, In 2018 Indonesian association for pattern recognition international conference (INAPR), IEEE, 99-103, 2018.[45] B. K. Khonglah, and S. M. Prasanna, “Speech/music classification using vocal tract constriction aspect of speech”, In 2015 Annual IEEE India Conference (INDICON), IEEE, 1-6, 2015.[46] C. Lim, and J. H. Chang, “Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion”, IET signal processing, 6(4), 335-340, 2012.[47] C. Lim, S. R. Lee, and J. H. Chang, “Efficient implementation of an SVM-based speech/music classifier by enhancing temporal locality in support vector references”, IEEE Transactions on Consumer Electronics, 58(3), 898-904, 2012.[48] Y. Yuan, and J. Liu, “Music Classification and Identification Based on Convolutional Neural Network”, Computer-Aided Design and Applications, 21(S18), 205–221, 2024.[49] S. K. Mahanta, N. J. Basisth, E. Halder, A. F. U. R. Khilji, and P. Pakray, “Exploiting cepstral coefficients and CNN for efficient musical instrument classification”, Evolving Systems, 1-13, 2023.[50] W. B. Zulfikar, Y. A. Gerhana, A. Y. P. Almi, D. S. A. Maylawati, and M. I. Al Amin, “Mood of Song Detection Using Mel Frequency Cepstral Coefficient and Convolutional Neural Network with Tuning Hyperparameter”, In 2023 11th International Conference on Cyber and IT Service Management (CITSM), IEEE, 1-6, 2023.[51] W. Peng, Y. Tang, and Y. Ouyang, “Design of Computer-Aided Music Generation Model Based on Artificial Intelligence Algorithm”, In International Conference on Computational Finance and Business Analytics, Cham: Springer Nature Switzerland, 229-237, 2023.[52] S. Li, and Y. Sung, “MelodyDiffusion: chord-conditioned melody generation using a transformer-based diffusion model”, Mathematics, 11(8), 1915, 2023.[53] J. Zhang, “Music feature extraction and classification algorithm based on deep learning”, Scientific Programming, 2021(1), 1651560, 2021.[54] A. Arronte Alvarez, and F. Gómez, “Motivic pattern classification of music audio signals combining residual and LSTM networks”, International Journal of Interactive Multimedia and Artificial Intelligence, 6(6), 208–214, 2021.[55] W. Bian, J. Wang, B. Zhuang, J. Yang, S. Wang, and J. Xiao, “Audio-based music classification with DenseNet and data augmentation”, In PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part III 16, 56-65, 2019.[56] Y. M. Costa, L. S. Oliveira, and J. C. N. Silla, “An evaluation of convolutional neural networks for music classification using spectrograms”, Applied soft computing, 52, 28-38, 2017.[57] C. Senac, T. Pellegrini, F. Mouret, and J. Pinquier, “Music feature maps with convolutional neural networks for music genre classification”, In Proceedings of the 15th international workshop on content-based multimedia indexing, 1-5, 2017.[58] K. K. Khine, and C. Su, “Acoustic Scene Classification Using Deep C-RNN Based on Log Mel Spectrogram and Gammatone Frequency Cepstral Coefficients Features”, In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), IEEE, 1-6, 2024.[59] Q. He, “A Music Genre Classification Method Based on Deep Learning”, Mathematical Problems in Engineering, 2022(1), 9668018, 2022.[60] H. Mukherjee, A. Dhar, M. Ghosh, S. M. Obaidullah, K. C. Santosh, S. Phadikar, and K. Roy, “Music chord inversion shape identification with LSTM-RNN”, Procedia Computer Science, 167, 607-615, 2020.[61] J. Jakubik, “Evaluation of gated recurrent neural networks in music classification tasks”, In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology–ISAT 2017: Part I, Springer International Publishing, 27-37, 2018.[62] W. Zhao, Y. Zhou, Y. Tie, and Y. Zhao, “Recurrent neural network for MIDI music emotion classification”, In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, 2596-2600, 2018.[63] J. Yi, Y. Zhu, J. Xie, and Z. Chen, “Cross-modal variational auto-encoder for content-based micro-video background music recommendation”, IEEE Transactions on Multimedia, 25, 515-528, 2021.[64] R. A. Sadek, A. A. Khalifa, and M. M. Elfattah, “Detecting digital stimulant music using bidirectional deep long short term memory”, In 2021 9th international Japan-Africa conference on electronics, communications, and computations (JAC-ECC), IEEE, 150-154, 2021.[65] S. Deepak, and B. G. Prasad, “Music Classification based on Genre using LSTM”, In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, 985-991, 2020.[66] A. Ycart, and E. Benetos, “Learning and evaluation methodologies for polyphonic music sequence prediction with LSTMs”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 1328-1341, 2020.[67] S. I. Kang, and S. Lee, Improvement of speech/music classification for 3GPP EVS Based on LSTM, Symmetry, 10(11), 605, 2018.[68] R. J. M. Quinto, R. O. Atienza, and N. M. C. Tiglao, “Jazz music sub-genre classification using deep learning”, In TENCON 2017-2017 IEEE Region 10 Conference, IEEE, 3111-3116, 2017.[69] Z. Li, Q. Huang, X. Yang, Q. Chen, and L. Zhang, “Automatic Composition System Based on Transformer-XL”, Applied Sciences, 14(13), 5765, 2024.[70] P. Kaushik, S. P. S. Rathore, K. Chahal, S. Saraf, G. S. Chauhan, and P. Kumar, “RhythmQuest: Unifying Indian Music Classification and Prediction with Hybrid Deep Learning Techniques”, In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE, 2, 1-6, 2024.[71] M. Ashraf, F. Abid, I. U. Din, J. Rasheed, M. Yesiltepe, S. F. Yeo, and M. T. Ersoy, “A hybrid cnn and rnn variant model for music classification”, Applied Sciences, 13(3), 1476, 2023.[72] J. Wang, “Research on the Integration Path of College Vocal Music Teaching and Traditional Music Culture Based on Deep Learning”, Applied Mathematics and Nonlinear Sciences, 2023.[73] J. Dai, “Intelligent Music Style Classification System Based on K-Nearest Neighbor Algorithm and Artificial Neural Network”, In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE, 531-543, 2022.[74] Y. Yi, X. Zhu, Y. Yue, and W. Wang, “Music genre classification with LSTM based on time and frequency domain features”, In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), IEEE, 678-682, 2021.[75] M. Ashraf, G. Geng, X. Wang, F. Ahmad, and F. Abid, “A globally regularized joint neural architecture for music classification”, IEEE Access, 8, 220980-220989, 2020.[76] C. Chen, and Q. Li, “A multimodal music emotion classification method based on multifeature combined network classifier”, Mathematical Problems in Engineering, 2020(1), 4606027, 2020.[77] H. Tang, and N. Chen, “Combining CNN and broad learning for music classification”, IEICE TRANSACTIONS on Information and Systems, 103(3), 695-701, 2020.[78] Q. D. A. P. Bayu, S. Suyanto, and A. Arifianto, “Hierarchical SVM-kNN to classify music emotion”, In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 5-10, 2019.[79] C. Wu, “Music Therapy Music Selection Based on Big Data Analysis”, Applied Mathematics and Nonlinear Sciences, 9(1), 2024.[80] C. Yin, “Application of Recommendation Algorithms Based on Social Relationships and Behavioral Characteristics in Music Online Teaching”, International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 19(1), 1-18, 2024.[81] H. Chen, “Music Transcription Based on Deep Learning”, Proceedings of 2023 International Conference on New Trends in Computational Intelligence, NTCI 2023, 62–65, 2024.[82] L. Tian, and F. Liu, “Design of Music Heritage Database System Based on Collaborative Filtering Algorithm”, In Advances in Artificial Intelligence, Big Data and Algorithms, IOS Press, 438-443, 2023.[83] Y. Niu, “Collaborative Filtering‐Based Music Recommendation in Spark Architecture”, Mathematical Problems in Engineering, 2022(1), 9050872, 2022.[84] J. Sun, “Personalized music recommendation algorithm based on spark platform”, Computational Intelligence and Neuroscience, 2022(1), 7157075, 2022.[85] M. Lu, and F. Deng, “[Retracted] Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching”, Security and Communication Networks, 2021(1), 3140341, 2021.[86] Ning, Hui, Li, Qian, “Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm”, Complexity, 6643888, 2020.[87] M. Sunitha, and T. Adilakshmi, “Music recommendation system with user-based and item-based collaborative filtering technique”, In Networking Communication and Data Knowledge Engineering, Springer Singapore, 1, 267-278, 2018.[88] H. Y. Chang, S. C. Huang, and J. H. Wu, “A personalized music recommendation system based on electroencephalography feedback”, Multimedia Tools and Applications, 76, 19523-19542, 2017.[89] J. Chen, Y. Liu, and D. Li, “Dynamic group recommendation with modified collaborative filtering and temporal factor”, Int. Arab J. Inf. Technol., 13(2), 294-301, 2016.[90] H. Magadum, H. K. Azad, and H. Patel, “Music recommendation using dynamic feedback and content-based filtering”, Multimedia Tools and Applications, 1-20, 2024.[91] J. Patel, A. A. Padaria, A. Mehta, A. Chokshi, J. D. Patel, and R. Kapdi, “ConCollA-A Smart Emotion-based Music Recommendation System for Drivers”, Scalable Computing: Practice and Experience, 24(4), 919-939, 2023.[92] Y. Mao, G. Zhong, H. Wang, and K. Huang, “Music-CRN: An efficient content-based music classification and recommendation network”, Cognitive Computation, 14(6), 2306-2316, 2022.[93] A. Niyazov, E. Mikhailova, and O. Egorova, “Content-based music recommendation system”, In 2021 29th Conference of Open Innovations Association (FRUCT), IEEE, 274-279, 2021.[94] N. Yadav, A. Kumar Singh, and S. Pal, “Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system”, Computational Intelligence, 38(4), 1232-1257, 2022.[95] M. Garanayak, S. K. Nayak, K. Sangeetha, T. Choudhury, and S. Shitharth, “Content and Popularity-Based Music Recommendation System”, International Journal of Information System Modeling and Design (IJISMD), 13(7), 1-14, 2022.[96] F. Narducci, M. De Gemmis, and P. Lops, “A general architecture for an emotion-aware content-based recommender system”, In Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems, 3-6, 2015.[97] M. Soleymani, A. Aljanaki, F. Wiering, and R. C. Veltkamp, “Content-based music recommendation using underlying music preference structure” In 2015 IEEE international conference on multimedia and expo (ICME), IEEE, 1-6, 2015.[98] B. Stasiak, and M. Papiernik, “Melody-based approaches in music retrieval and recommendation systems”, In Multimedia Services in Intelligent Environments: Recommendation Services, Heidelberg: Springer International Publishing, 125-153, 2013.[99] D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. Gómez, and P. Herrera, “Semantic audio content-based music recommendation and visualization based on user preference examples”, Information Processing & Management, 49(1), 13-33, 2013.[100] D. Bogdanov, and H. Boyer, “How much metadata do we need in music recommendation?: A subjective evaluation using preference sets”, In Klapuri A, Leider C, editors. 12th International Society for Music Information Retrieval Conference (ISMIR 2011); 2011 Oct 24-28; Miami, USA. Montréal: ISMIR; 2011. p. 97-102. International Society for Music Information Retrieval (ISMIR), 2011.[101] G. B. Mohan, R. P. Kumar, S. Korrayi, M. Harshitha, B. S. S. Chaithanya, K. Saiteja, and G. V. Rohan, “Emotion-Based Music Recommendation System-A Deep Learning Approach”, In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), IEEE, 1-7, 2024.[102] C. Li, and X. Zuo, “Classical music recommendation algorithm on art market audience expansion under deep learning”, Journal of Intelligent Systems, 33(1), 20230351, 2024.[103] H. Liu, and C. Zhao, “A Deep Learning Algorithm for Music Information Retrieval Recommendation System”, Computer-Aided Design and Applications, 21(S13), 1–16, 2024.[104] N. Narkhede, S. Mathur, A. Bhaskar, and M. Kalla, M, “Music genre classification and recognition using convolutional neural network”, Multimedia Tools and Applications, 1-16, 2024.[105] A. Kumble, S. Medatati, and A. Bhatt, “Emotion-Based Music Recommendation”, In International Conference on Advanced Computational and Communication Paradigms, Singapore: Springer Nature Singapore, 147-155, 2023.[106] C. Kumar, M. Kumar, and A. Jindal, “Session-based song recommendation using recurrent neural network”, In Machine Intelligence Techniques for Data Analysis and Signal Processing: Proceedings of the 4th International Conference MISP 2022, Singapore: Springer Nature Singapore, 1, 719-728, 2023.[107] P. Linlin, “Tchaikovsky music recommendation algorithm based on deep learning”, Mobile Information Systems, 2022(1), 1265451, 2022.[108] J. Xia, “Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing”, International journal of grid and utility computing, 13(2-3), 183-194, 2022.[109] H. Gao, “Automatic recommendation of online music tracks based on deep learning”, Mathematical Problems in Engineering, 2022(1), 5936156, 2022.[110] S. Wang, C. Xu, A. S. Ding, and Z. Tang, “A novel emotion-aware hybrid music recommendation method using deep neural network”, Electronics, 10(15), 1769, 2021.[111] J. W. Chang, C. Y. Chiou, J. Y. Liao, Y. K. Hung, C. C. Huang, K. C. Lin, and Y. H. Pu, “Music recommender using deep embedding-based features and behavior-based reinforcement learning”, Multimedia Tools and Applications, 1-28, 2021.[112] L. Danyang, and G. Mingxin, “Music Recommendation Method Based on Multi-Source Information Fusion”. Data Analysis and Knowledge Discovery, 5(2), 94-105, 2021.[113] S. R. Chavare, C. J. Awati, and S. K. Shirgave, “Smart recommender system using deep learning”, In 2021 6th international conference on inventive computation technologies (ICICT), IEEE, 590-594, 2021.[114] S. Joshi, T. Jain, and N. Nair, “Emotion based music recommendation system using LSTM-CNN architecture”, In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, 01-06, 2021.[115] A. Elbir, and N. Aydin, “Music genre classification and music recommendation by using deep learning”, Electronics Letters, 56(12), 627-629, 2020.[116] A. Ferraro, D. Bogdanov, J. Yoon, K. Kim, and X. Serra, X. “Automatic playlist continuation using a hybrid recommender system combining features from text and audio”, In Proceedings of the ACM Recommender Systems Challenge, 1-5, 2018.[117] H. T. Zheng, J. Y. Chen, N. Liang, A. K. Sangaiah, Y. Jiang, and C. Z. Zhao, “A deep temporal neural music recommendation model utilizing music and user metadata”, Applied Sciences, 9(4), 703, 2019.[118] S. Oramas, O. Nieto, M. Sordo, and X. Serra. “A deep multimodal approach for cold-start music recommendation”, In Proceedings of the 2nd workshop on deep learning for recommender systems, 32-37, 2017.[119] A. H. Nabizadeh, A. M. Jorge, S. Tang, and Y. Yu, “Predicting user preference based on matrix factorization by exploiting music attributes”, In Proceedings of the ninth international c* conference on computer science & software engineering, 61-66, 2016.[120] A. Vall, M. Skowron, P. Knees, and M. Schedl, “Improving Music Recommendations with a Weighted Factorization of the Tagging Activity”, In ISMIR, 65-71, 2015.[121] M. S. Reddy, and T. Adilakshmi, “Music recommendation system based on matrix factorization technique-SVD”, In 2014 international conference on computer communication and informatics, IEEE, 1-6, 2014.[122] S. V. Reddy, V. R. Krishna, R. J. Sapkal, J. Dhanke, S. P. Waghmare, and K. Kumar, “A cutting-edge artificial intelligence paradigm for entertainment-infused music recommendations”, Entertainment Computing, 51, 100717, 2024.[123] S. Mukhopadhyay, A. Kumar, D. Parashar, and M. Singh, “Enhanced Music Recommendation Systems: A Comparative Study of Content-Based Filtering and K-Means Clustering Approaches”, Revue d'Intelligence Artificielle, 38(1), 2024.[124] M. Nuo, X. Han, and Y. Zhang, “Contrastive learning-based music recommendation model”, In International Conference on Neural Information Processing, Singapore: Springer Nature Singapore, 370-382, 2023. [125] V. Omowonuola, B. Wilkerson, and S. Kher, “Hybrid Context-Content Based Music Recommendation System”, In Proceedings of the Future Technologies Conference, Cham: Springer International Publishing, 121-132, 2022.[126] J. P. Verma, P. Bhattacharya, A. S. Rathor, J. Shah, and S. Tanwar, “Collaborative filtering-based music recommendation in view of negative feedback system”, In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021, Singapore: Springer Nature Singapore, 447-460, 2022.[127] T. Zhang, and S. Liu, “[Retracted] Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph”, Security and Communication Networks, 2022(1), 5889724, 2022.[128] P. Magron, and C. Févotte, “Neural content-aware collaborative filtering for cold-start music recommendation”, Data Mining and Knowledge Discovery, 36(5), 1971-2005, 2022.[129] J. Singh, and V. K. Bohat, “Neural network model for recommending music based on music genres”, In 2021 International Conference on Computer Communication and Informatics (ICCCI), IEEE, 1-6, 2021.[130] M. Pulis, and J. Bajada, “Siamese Neural Networks for Content-based Cold-Start Music Recommendation”, In Proceedings of the 15th ACM Conference on Recommender Systems, 719-723, 2021.[131] A. N. Arnold, and S. Vairamuthu, “Music recommendation using collaborative filtering and deep learning”, Int. J. Innov. Technol. Explor. Eng. (IJITEE), 8(7), 964-968, 2019.[132] F. Fessahaye, L. Perez, T. Zhan, R. Zhang, C. Fossier, R. Markarian, ... and P. Oh, “T-recsys: A novel music recommendation system using deep learning”, In 2019 IEEE international conference on consumer electronics (ICCE), IEEE, 1-6, 2019.[133] D. Ayata, Y. Yaslan, and M. E. Kamasak, “Emotion based music recommendation system wearable physiological sensors”, IEEE transactions on consumer electronics, 64(2), 196-203, 2018.[134] P. Darshna, “Music recommendation based on content and collaborative approach & reducing cold start problem”, In 2018 2nd international conference on inventive systems and control (ICISC), IEEE, 1033-1037, 2018.[135] J. H. Su, W. Y. Chang, and V. S. Tseng, “Effective social content-based collaborative filtering for music recommendation”, Intelligent Data Analysis, 21(S1), S195-S216, 2017.[136] D. Sánchez-Moreno, A. B. G. González, M. D. M. Vicente, V. F. L. Batista, and M. N. M. García, “A collaborative filtering method for music recommendation using playing coefficients for artists and users”, Expert Systems with Applications, 66, 234-244, 2016.[137] R. Cheng, and B. Tang, “A music recommendation system based on acoustic features and user personalities”, In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, Revised Selected Papers, Springer International Publishing, 20, 203-213, 2016.[138] X. Wang, and Y. Wang, “Improving content-based and hybrid music recommendation using deep learning”, In Proceedings of the 22nd ACM international conference on Multimedia, 627-636, 2014.[139] J. Kim, K. Kim, K. H. You, and J. H. Lee, “An approach for music recommendation using content-based analysis and collaborative filtering”, International Information Institute (Tokyo), Information, 15(5), 1985, 2012.[140] C. Musto, G. Semeraro, P. Lops, M. De Gemmis, and F. Narducci, “Leveraging social media sources to generate personalized music playlists”, In E-Commerce and Web Technologies: 13th International Conference, EC-Web 2012, Vienna, Austria, September 4-5, 2012. Proceedings 13, Springer Berlin Heidelberg, 112-123, 2012.[141] N. Hariri, B. Mobasher, and R. Burke, “Using social tags to infer context in hybrid music recommendation”, In Proceedings of the twelfth international workshop on Web information and data management, 41-48, 2012.[142] G. Richard, P. Chouteau, and B. Torres, “A fully differentiable model for unsupervised singing voice separation”, In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 946-950, 2024.[143] T. Zhang, “Application of Unet-SE-Bisru Algorithm for Music Signal Processing in Music Source Separation”, Scalable Computing: Practice and Experience, 25(4), 2960-2972, 2024.[144] M. Schwabe, and M. Heizmann, “Improved Separation of Polyphonic Chamber Music Signals by Integrating Instrument Activity Labels”, IEEE Access, 11, 42999-43007, 2023.[145] K. Schulze-Forster, G. Richard, L. Kelley, C. S. Doire, and R. Badeau, “Unsupervised music source separation using differentiable parametric source models”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1276-1289, 2023.[146] Y. Zhu, K. Olszewski, Y. Wu, P. Achlioptas, M. Chai, Y. Yan, and S. Tulyakov, “Quantized gan for complex music generation from dance videos”, In European Conference on Computer Vision, Cham: Springer Nature Switzerland, 182-199, 2022.[147] J. Luo, X. Yang, S. Ji, and J. Li, “MG-VAE: deep Chinese folk songs generation with specific regional styles”, In Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers, Springer Singapore, 93-106, 2020.[148] R. Zhang, J. Bai, X. Guan, and Q. Li, “Music Source Separation Method Based on Unet Combining SE and BiSRU”, Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), 49(11), 2021.[149] T. P. Chen, and L. Su, “Attend to Chords: Improving Harmonic Analysis of Symbolic Music Using Transformer-Based Models”, Trans. Int. Soc. Music. Inf. Retr., 4(1), 1-13, 2021.[150] H. S. Choi, J. Lee, and K. Lee, “Spec2spec: Towards the general framework of music processing using generative adversarial networks”, Acoustical Science and Technology, 41(1), 160-165, 2020.[151] S. Tahmasebi, T. Gajȩcki, and W. Nogueira, “Design and evaluation of a real-time audio source separation algorithm to remix music for cochlear implant users, 2020, Frontiers in Neuroscience, 14, 434, 2020.[152] H. Liu, L. Xie, J. Wu, and G. Yang, “Channel-wise subband input for better voice and accompaniment separation on high resolution music”, arXiv preprint arXiv:2008.05216, 2020.[153] C. Lordelo, E. Benetos, S. Dixon, S. Ahlbäck, and P. Ohlsson, “Adversarial unsupervised domain adaptation for harmonic-percussive source separation”, IEEE Signal Processing Letters, 28, 81-85, 2020.[154] B. Bhattarai, Y. R. Pandeya, and J. Lee, “Parallel stacked hourglass network for music source separation”, IEEE Access, 8, 206016-206027, 2020[155] V. S. Kadandale, J. F. Montesinos, G. Haro, and E. Gómez, “Multi-channel u-net for music source separation”, In 2020 IEEE 22nd international workshop on multimedia signal processing (MMSP), IEEE, 1-6, 2020.[156] E. Manilow, G. Wichern, P. Seetharaman, and J. Le Roux, “Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity”, In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE, 45-49, 2019.[157] O. Slizovskaia, L. Kim, G. Haro, and E. Gomez, “End-to-end sound source separation conditioned on instrument labels”, In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 306-310, 2019.[158] P. Seetharaman, G. Wichern, S. Venkataramani, and J. Le Roux, “Class-conditional embeddings for music source separation”, In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 301-305, 2019.[159] F. Lluís, J. Pons, and X. Serra, “End-to-end music source separation: Is it possible in the waveform domain?”, Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019-September, 4619–4623, 2018.[160] R. Kumar, Y. Luo, and N. Mesgarani, “Music Source Activity Detection and Separation Using Deep Attractor Network”, In Interspeech, 347-351, 2018.[161] Z. C. Fan, Y. L. Lai, and J. S. R. Jang, “SVSGAN: singing voice separation via generative adversarial network”, In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 726-730, 2018.[162] Y. Luo, Z. Chen, J. R. Hershey, J. Le Roux, and N. Mesgarani, “Deep clustering and conventional networks for music separation: Stronger together”, In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 61-65, 2017.[163] P. Chandna, M. Miron, J. Janer, and E. Gómez, “Monoaural audio source separation using deep convolutional neural networks”, In Latent Variable Analysis and Signal Separation: 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings 13, Springer International Publishing, 258-266, 2017.[164] M. Miron, J. Janer Mestres, and E. Gómez Gutiérrez, “Generating data to train convolutional neural networks for classical music source separation”, In Lokki T, Pätynen J, Välimäki V, editors. Proceedings of the 14th Sound and Music Computing Conference; 2017 Jul 5-8; Espoo, Finland. Aalto: Aalto University, 227-33, 2017.[165] S. I. Mimilakis, K. Drossos, T. Virtanen, and G. Schuller, G. “A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation”, In 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 1-6, 2017.[166] M. Miron, J. Janer Mestres, and E. Gómez Gutiérrez, “Monaural score-informed source separation for classical music using convolutional neural networks”, In Hu X, Cunningham SJ, Turnbull D, Duan Z. ISMIR 2017, 18th International Society for Music Information Retrieval Conference, 2017 Oct 23-27, Suzhou, China, [Canada]: ISMIR, 55-62, 2017.[167] Y. Fang, and T. Gui-fa, “Visual music score detection with unsupervised feature learning method based on k-means”, International Journal of Machine Learning and Cybernetics, 6, 277-287, 2015.[168] Y. Chen, and H. Zheng, “The application of HMM algorithm based music note feature recognition teaching in universities”, Intelligent Systems with Applications, 20, 200277, 2023.[169] J. Calvo-Zaragoza, A. H. Toselli, and E. Vidal, “Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation”, Pattern Analysis and Applications, 22, 1573-1584, 2019.[170] G. J. Mysore, P. Smaragdis, and B. Raj, “Non-negative hidden Markov modeling of audio with application to source separation”, In International Conference on Latent Variable Analysis and Signal Separation, Berlin, Heidelberg: Springer Berlin Heidelberg, 140-148, 2010.[171] K. Siedenburg, M. R. Schädler, and D. Hülsmeier, “Modeling the onset advantage in musical instrument recognition”, The journal of the Acoustical Society of America, 146(6), EL523-EL529, 2019.[172] C. Jeyalakshmi, B. Murugeshwari, and M. Karthick, “HMM and K-NN based automatic musical instrument recognition”, In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), IEEE, 350-355, 2018.[173] J. Calvo-Zaragoza, A. H. Toselli, and E. Vidal, “Handwritten music recognition for mensural notation: Formulation, data and baseline results”, In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, 1, 1081-1086, 2017.[174] J. Calvo-Zaragoza, A. H. Toselli, and E. Vidal, E, “Early handwritten music recognition with hidden markov models”, In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, 319-324, 2016.[175] E. Nakamura, P. Cuvillier, A. Cont, N. Ono, and S. Sagayama, “Autoregressive hidden semi-Markov model of symbolic music performance for score following”, In 16th International Society for Music Information Retrieval Conference (ISMIR), 2015.[176] A. S. Krishna, P. V. Rajkumar, K. P. Saishankar, and M. John, “Identification of carnatic raagas using hidden markov models”, In 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI), IEEE, 107-110, 2011.[177] J. T. Fang, and C. W. Yang, “SINGLE-CHANNEL MUSIC SOURCE SEPARATION BY HARMONIC STRUCTURE MODEL AND SUPPORT VECTOR MACHINE”, 電機工程學刊, 29(2), 43-51, 2022.[178] Y. Liao, H. Xu, and K. Xu, “MusicGAIL: A Generative Adversarial Imitation Learning Approach for Music Generation”, In CAAI International Conference on Artificial Intelligence, Singapore: Springer Nature Singapore, 505-516, 2023.[179] F. C. A. Ferano, A. Zahra, and G. P. Kusuma, “Stacking ensemble learning for optical music recognition”, Bulletin of Electrical Engineering and Informatics, 12(5), 3095-3104, 2023.[180] X. Pan, B. Song, C. Liu, and H. Zhang, “Music detecting and recording system based on support vector machine”, In 2019 International Conference on Communications, Information System and Computer Engineering (CISCE), IEEE, 244-248, 2019.[181] D. Johnson, and G. Tzanetakis, “Guitar model recognition from single instrument audio recordings”, In 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), IEEE, 370-375, 2015.[182] S. Kolozali, M. Barthet, G. Fazekas, and M. Sandler, “Automatic ontology generation for musical instruments based on audio analysis”, IEEE transactions on audio, speech, and language processing, 21(10), 2207-2220, 2013.[183] B. Bhattarai, and J. Lee, “Korean Pansori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding”. Applied Sciences, 14(2), 492, 2024.[184] E. Hulme, D. Marshall, K. Sidorov, and A. Jones, “Acoustic Classification of Guitar Tunings with Deep Learning”, In Proceedings of the 11th International Conference on Digital Libraries for Musicology, 6-14, 2024.[185] D. Kozlov, M. Bakulin, S. Pavlov, A. Zuev, M. Krylova, and I. Kharchikov, “Learning Properties of Holomorphic Neural Networks of Dual Variables”, In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 1-5, 2023.[186] M. Scarpiniti, E. Sigismondi, D. Comminiello, and A. Uncini, “A U-Net Based Architecture for Automatic Music Transcription”, In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 1-6, 2023.[187] W. Huang, Y. Yu, H. Xu, Z. Su, and Y. Wu, “Hyperbolic music transformer for structured music generation”, IEEE Access, 11, 26893-26905, 2023.[188] M. Zehren, M. Alunno, and P. Bientinesi, “High-quality and reproducible automatic drum transcription from crowdsourced data”, Signals, 4(4), 768-787, 2023.[189] Y. Telila, T. Cucinotta, and D. Bacciu, “Automatic Music Transcription using Convolutional Neural Networks and Constant-Q Transform”, In Ital-IA, 526-531, 2023.[190] R. Zheng, X. Liu, and M. D. Butala, “Multi-Instrument Polyphonic Melody Transcription Based on Deep Learning”, In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), IEEE, 762-767, 2023.[191] I. S. Wahbi, H. T. Bahbouh, and K. M. Yahia, “Transcription of Arabic and Turkish Music Using Convolutional Neural Networks”, In 2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), IEEE, 5, 1-5, 2023.[192] J. Park, K. Choi, S. Oh, L. Kim, and J. Park, “Note-level singing melody transcription with transformers”, Intelligent Data Analysis, 27(6), 1853-1871, 2023.[193] Y. Wang, “Piano automatic transcription based on transformer”, Journal of Intelligent & Fuzzy Systems, (Preprint), 1-8, 2023.[194] J. Zhou, T. Niu, H. Zhu, and X. Wang, “CoCoFormer: A controllable feature-rich polyphonic music generation method”, In Summit on Music Intelligence, Singapore: Springer Nature Singapore, 95-107, 2023.[195] P. Li, T. Song, and J. Hu, “Audio classification based on audio WSOLA and CNN algorithm”, In Second International Conference on Electronic Information Technology (EIT 2023), SPIE, 12719, 1145-1149, 2023.[196] C. Gupta, H. Li, and M. Goto, “Deep learning approaches in topics of singing information processing”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2422-2451, 2022.[197] C. de la Fuente, J. J. Valero-Mas, F. J. Castellanos, and J. Calvo-Zaragoza, “Multimodal image and audio music transcription”, International Journal of Multimedia Information Retrieval, 11(1), 77-84, 2022.[198] C. Weiß, and G. Peeters, “Comparing deep models and evaluation strategies for multi-pitch estimation in music recordings”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2814-2827, 2022.[199] H. Cuesta, and E. Gómez Gutiérrez, “Voice assignment in vocal quartets using deep learning models based on pitch salience”, Transactions of the International Society for Music Information Retrieval, 5(1), 99-112, 2022.[200] D. Stefani, S. Peroni, and L. Turchet, L. “A comparison of deep learning inference engines for embedded real-time audio classification”, In Proceedings of the International Conference on Digital Audio Effects, DAFx, MDPI (Multidisciplinary Digital Publishing Institute), 3, 256-263, 2022.[201] C. Hernandez-Olivan, I. Zay Pinilla, C. Hernandez-Lopez, and J. R. Beltran, “A comparison of deep learning methods for timbre analysis in polyphonic automatic music transcription”, Electronics, 10(7), 810, 2021.[202] F. Simonetta, S. Ntalampiras, and F. Avanzini, “Audio-to-score alignment using deep automatic music transcription”, In 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), IEEE, 1-6, 2021.[203] P. Steiner, A. Jalalvand, S. Stone, and P. Birkholz, “Feature engineering and stacked echo state networks for musical onset detection”, In 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 9537-9544, 2021.[204] X. Li, K. Wang, J. Soraghan, and J. Ren, “Fusion of Hilbert-Huang transform and deep convolutional neural network for predominant musical instruments recognition”, In Artificial Intelligence in Music, Sound, Art and Design: 9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Springer International Publishing, Proceedings 9, 80-89, 2020.[205] E. Demirel, S. Ahlbäck, and S. Dixon, “Automatic lyrics transcription using dilated convolutional neural networks with self-attention”, In 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 1-8, 2020.[206] Y. T. Wu, B. Chen, and L. Su, “Multi-instrument automatic music transcription with self-attention-based instance segmentation”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2796-2809, 2020.[207] D. Yu, H. Duan, J. Fang, and B. Zeng, “Predominant instrument recognition based on deep neural network with auxiliary classification”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 852-861, 2020.[208] D. Stoller, S. Durand, and S. Ewert, “End-to-end lyrics alignment for polyphonic music using an audio-to-character recognition model”, In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 181-185, 2019.[209] C. D. Carthen, V. Le, R. Kelley, T. J. Kozubowski, and F. C. Harris Jr, “Rewind: An Automatic Music Transcription Web Application”, INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, 20, 2017.[210] G. Burlet, and A. Hindle, “Isolated guitar transcription using a deep belief network”, PeerJ Computer Science, 3, e109, 2017.[211] S. Sigtia, E. Benetos, and S. Dixon, “An end-to-end neural network for polyphonic piano music transcription”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(5), 927-939, 2016.[212] Y. Ojima, E. Nakamura, K. Itoyama, and K. Yoshii, “Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models”, APSIPA Transactions on Signal and Information Processing, 7, e14, 2018.[213] E. Nakamura, E. Benetos, K. Yoshii, and S. Dixon, “Towards complete polyphonic music transcription: Integrating multi-pitch detection and rhythm quantization”, In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 101-105, 2018.[214] D. Cazau, Y. Wang, O. Adam, Q. Wang, and G. Nuel, “Improving Note Segmentation in Automatic Piano Music Transcription Systems with a Two-State Pitch-Wise HMM Method”, In ISMIR, 523-530, 2017.[215] A. McLeod, R. Schramm, M. Steedman, and E. Benetos, “Automatic transcription of polyphonic vocal music”, Applied Sciences, 7(12), 1285. 2017.[216] D. Cazau, Y. Wang, M. Chemillier, and O. Adam, “An automatic music transcription system dedicated to the repertoires of the marovany zither”, Journal of New Music Research, 45(4), 343-360, 2016.[217] E. Nakamura, K. Itoyama, and K. Yoshii, “Rhythm transcription of MIDI performances based on hierarchical Bayesian modelling of repetition and modification of musical note patterns”, In 2016 24th European Signal Processing Conference (EUSIPCO), IEEE, 1946-1950, 2016.[218] D. Cazau, G. Revillon, J. Krywyk, and O. Adam, “An investigation of prior knowledge in Automatic Music Transcription systems”, The Journal of the Acoustical Society of America, 138(4), 2561-2573, 2015.[219] E. Benetos, and T. Weyde, “Explicit duration hidden markov models for multiple-instrument polyphonic music transcription”, 2013.[220] Y. K. Suprapto, and Y. Triwidyastuti, “Saron music transcription based on rhythmic information using hmm on gamelan orchestra”, TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(1), 103-117, 2015.[221] E. Benetos, and S. Dixon, “A shift-invariant latent variable model for automatic music transcription”, Computer Music Journal, 36(4), 81-94, 2012.[222] C. T. Lee, Y. H. Yang, and H. H. Chen, “Multipitch estimation of piano music by exemplar-based sparse representation”, IEEE Transactions on Multimedia, 14(3), 608-618, 2012.[223] M. Nakano, J. Le Roux, H. Kameoka, T. Nakamura, N. Ono, and S. Sagayama, “Bayesian nonparametric spectrogram modeling based on infinite factorial infinite hidden Markov model”, In 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE, 325-328, 2011.[224] E. Benetos, and S. Dixon, “Joint multi-pitch detection using harmonic envelope estimation for polyphonic music transcription”, IEEE Journal of Selected Topics in Signal Processing, 5(6), 1111-1123, 2011.[225] F. J. Cañadas Quesada, N. Ruiz Reyes, P. Vera Candeas, J. J. Carabias, and S. Maldonado, “A multiple-F0 estimation approach based on Gaussian spectral modelling for polyphonic music transcription”, Journal of New Music Research, 39(1), 93-107, 2010.[226] M. Akbari, J. Liang, and H. Cheng, “A real-time system for online learning-based visual transcription of piano music”, Multimedia tools and applications, 77, 25513-25535, 2018.[227] I. Barbancho, G. Tzanetakis, A. M. Barbancho, and L. J. Tardón, “Discrimination between ascending/descending pitch arpeggios” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(11), 2194-2203, 2018.[228] M. Fujieda, T. Murakami, and Y. Ishida, “An approach to multi-pitch estimation using a support vector machine”, International Journal of Computers and Applications, 33(3), 202-210, 2011.[229] A. Tjahyanto, Y. K. Suprapto, M. H. Purnomo, and D. P. Wulandari, “Fft-based features selection for javanese music note and instrument identification using support vector machines”, In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), IEEE, 1, 439-443, 2012.[230] J. Mycka, A. Żychowski, and J. Mańdziuk, “Toward human-level tonal and modal melody harmonizations”, Journal of Computational Science, 67, 101963, 2023.[231] A. Wiafe, C. Nutrokpor, E. Owusu, F. A. Kastriku, and I. Wiafe, “Using genetic algorithms for music composition: implications of early termination on aesthetic quality”, International Journal of Information Technology, 14(4), 1875-1881, 2022.[232] O. Lopez-Rincon, O. Starostenko, and A. Lopez-Rincon, A. “Algorithmic music generation by harmony recombination with genetic algorithm”, Journal of Intelligent & Fuzzy Systems, 42(5), 4411-4423, 2022.[233] M. Farzaneh, and R. Mahdian Toroghi, “Music generation using an interactive evolutionary algorithm”, In Pattern Recognition and Artificial Intelligence: Third Mediterranean Conference, MedPRAI 2019, Istanbul, Turkey, December 22–23, 2019, Springer International Publishing, Proceedings 3, 207-217, 2020.[234] A. Papadopoulos, P. Roy, and F. Pachet, “Assisted lead sheet composition using flowcomposer”, In Principles and Practice of Constraint Programming: 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Springer International Publishing, Proceedings 22, 769-785, 2016.[235] D. M. Hofmann, “A genetic programming approach to generating musical compositions”, In Evolutionary and Biologically Inspired Music, Sound, Art and Design: 4th International Conference, EvoMUSART 2015, Copenhagen, Denmark, April 8-10, 2015, Springer International Publishing, Proceedings 4, 89-100, 2015.[236] S. Chakrabarty, S. Roy, and D. De, “Pervasive diary in music rhythm education: a context-aware learning tool using genetic algorithm”, In Advanced Computing, Networking and Informatics-Volume 1: Advanced Computing and Informatics Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014), Cham: Springer International Publishing, 669-677, 2014.[237] D. Matić, “A genetic algorithm for composing music”, Yugoslav Journal of Operations Research, 20(1), 157-177, 2010.[238] S. Ji, X. Yang, J. Luo, and J. Li, “Rl-chord: Clstm-based melody harmonization using deep reinforcement learning”, IEEE Transactions on Neural Networks and Learning Systems, 2023.[239] D. R. Vemula, S. K. Tripathi, N. K. Sharma, M. M. Hussain, U. R. Swamy, B. L. Polavarapu, “Music Generation Using Deep Learning”, In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, Springer, Singapore, 1007, 2023.[240] M. Kopel, D. Antczak, and M. Walczyński, “Generating Music for Video Games with Real-Time Adaptation to Gameplay Pace”, In Asian Conference on Intelligent Information and Database Systems, Singapore: Springer Nature Singapore, 261-272, 2023.[241] S. Banerjee, M. Rath, T. Swain, and Samant, T, “Music generation using time distributed dense stateful char-RNNs”, In 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 1-5, 2022.[242] S. Sajad, S. Dharshika, and M. Meleet, “Music generation for novices using Recurrent Neural Network (RNN)”, In 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), IEEE, 1-6, 2021.[243] G. Hadjeres, and F. Nielsen, “Anticipation-RNN: Enforcing unary constraints in sequence generation, with application to interactive music generation”, Neural Computing and Applications, 32(4), 995-1005, 2020.[244] K. Goel, R. Vohra, and J. K. Sahoo, “Polyphonic music generation by modeling temporal dependencies using a rnn-dbn”, In Artificial Neural Networks and Machine Learning–ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Springer International Publishing, Proceedings 24, 217-224, 2014.[245] Y. Bai, “A music generation model based on Bi-LSTM”, In International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 12969, 430-435, 2024.[246] J. Li, L. Han, X. Wang, Y. Wang, J. Xia, Y. Yang, ... and H. Yan, “A hybrid neural network model based on optimized margin softmax loss function for music classification”, Multimedia Tools and Applications, 83(15), 43871-43906, 2024.[247] F. Li, “Chord-based music generation using long short-term memory neural networks in the context of artificial intelligence”, The Journal of Supercomputing, 80(5), 6068-6092, 2024.[248] A. Kasif, S. Sevgen, A. Ozcan, and C. Catal, “Hierarchical multi-head attention LSTM for polyphonic symbolic melody generation”, Multimedia Tools and Applications, 83(10), 30297-30317, 2024.[249] Z. Zhang, Y. Yu, and A. Takasu, “Controllable lyrics-to-melody generation”, Neural Computing and Applications, 35(27), 19805-19819, 2023.[250] M. N. Mohanty, V. Tripathy, and R. K. Pattanaik, “Music Regeneration with RNN Architecture Using LSTM”, In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), IEEE, 538-542, 2023.[251] C. Shen, V. Z. Yao, and Y. Liu, “Everybody Compose: Deep Beats To Music”, In Proceedings of the 14th Conference on ACM Multimedia Systems, 353-357, 2023.[252] H. Bihani, S. Bothe, A. Acharya, T. Desai, and P. Joglekar, “Automatic Music Melody Generation Using LSTM and Markov Chain Model”, In International Conference on Information and Communication Technology for Intelligent Systems, 249-257, 2023.[253] P. Gupta, and P. Dwivedi, “Advancements in Music Generation: Leveraging RNN and LSTM Networks for Automated Composition”, In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), IEEE, 434-439, 2023.[254] S. S. Patil, S. H. Patil, A. M. Pawar, R. Shandilya, A. K. Kadam, R. B. Jadhav, and M. S. Bewoor, “Music Generation Using RNN-LSTM with GRU”, In 2023 International Conference on Integration of Computational Intelligent System (ICICIS), IEEE, 1-5, 2023.[255] G. Keerti, A. N. Vaishnavi, P. Mukherjee, A. S. Vidya, G. S. Sreenithya, and D. Nayab, “Attentional networks for music generation”, Multimedia Tools and Applications, 81(4), 5179-5189, 2022.[256] J. Pan, S. Yu, Z. Zhang, Z. Hu, and M. Wei, “The Generation of Piano Music Using Deep Learning Aided by Robotic Technology”, Computational Intelligence and Neuroscience, 2022(1), 8336616, 2022.[257] C. De Boom, S. Van Laere, T. Verbelen, and B. Dhoedt, “Rhythm, chord and melody generation for lead sheets using recurrent neural networks”, In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Springer International Publishing, Proceedings, Part II, 454-461, 2020.[258] J. Singh, and A. Ratnawat, “Algorithmic Music Generation for the stimulation of Musical Memory in Alzheimer’s”, In 2018 4th International Conference on Computing Communication and Automation (ICCCA), IEEE, 1-4, 2018.[259] M. Pezzat-Morales, H. Perez-Meana, and T. Nakashika, “Fast Jukebox: Accelerating Music Generation with Knowledge Distillation”, Applied Sciences, 13(9), 5630, 2023.[260] L. Comanducci, D. Gioiosa, M. Zanoni, F. Antonacci, and A. Sarti, “Variational Autoencoders for chord sequence generation conditioned on Western harmonic music complexity”, EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 24, 2023.[261] J. Gillick, J. Yang, C. E. Cella, and D. Bamman, “Drumroll Please: Modeling Multi-Scale Rhythmic Gestures with Flexible Grids”, Transactions of the International Society for Music Information Retrieval, 4(1), 156-167, 2021.[262] Y. Q. Lim, C. S. Chan, and F. Y. Loo, “Style-conditioned music generation”, In 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 1-6, 2020.[263] T. Wang, J. Liu, C. Jin, J. Li, and S. Ma, “An intelligent music generation based on Variational Autoencoder”, In 2020 International Conference on Culture-oriented Science & Technology (ICCST), IEEE, 394-398, 2020.[264] Z. Ziegler, and A. Rush, “Latent normalizing flows for discrete sequences”, In International Conference on Machine Learning, PMLR, 7673-7682, 2019.[265] Y. A. Wang, Y. K. Huang, T. C. Lin, S. Y. Su, and Y. N. Chen, “Modeling melodic feature dependency with modularized variational auto-encoder”, In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 191-195, 2019.[266] R. Yang, T. Chen, Y. Zhang, and G. Xia, “Inspecting and interacting with meaningful music representations using VAE”, Proceedings of the International Conference on New Interfaces for Musical Expression, 307–312, 2019.[267] J. Kang, S. Poria, and D. Herremans, “Video2music: Suitable music generation from videos using an affective multimodal transformer model”, Expert Systems with Applications, 249, 123640, 2024.[268] Y. Xin, “MusicEmo: transformer-based intelligent approach towards music emotion generation and recognition”, Journal of Ambient Intelligence and Humanized Computing, 1-11, 2024.[269] P. Li, T. M. Liang, Y. M. Cao, X. M. Wang, X. J. Wu, and L. Y. Lei, “A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning”, Applied Intelligence, 54(1), 80-94, 2024.[270] Y. Zhang, X. Lv, Q. Li, X. Wu, Y. Su, and H. Yang, “An automatic music generation method based on RSCLN_Transformer network”, Multimedia Systems, 30(1), 4, 2024.[271] Q. Zhu, A. Shankar, and C. Maple, “Grey wolf optimizer based deep learning mechanism for music composition with data analysis”, Applied Soft Computing, 153, 111294, 2024.[272] D. Dalmazzo, K. Déguernel, and B. L. Sturm, “The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies”, In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), Cham: Springer Nature Switzerland, 52-66, 2024.[273] N. Venkatachalam, “MusiciAI: A Hybrid Generative Model for Music Therapy using Cross-Modal Transformer and Variational Autoencoder”, In 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), IEEE, 1176-1180, 2024.[274] B. S. Krishna, S. S. R. S. Chiluvuri, S. S. H. Kosuri, and S. S. H. Chimbili, “The Application of Long Short-Term Memory and Transformers for Music Generation”, In 2023 IEEE International Conference on Big Data (BigData), IEEE, 4475-4478, 2023.[275] W. Xu, J. McAuley, S. Dubnov, and H. W. Dong, “Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls”, In 2023 IEEE International Conference on Big Data (BigData), IEEE, 4512-4517, 2023.[276] S. Li, and Y. Sung, “Transformer-based seq2seq model for chord progression generation”, Mathematics, 11(5), 1111, 2023.[277] B. Cao, T. Fukumori, and Y. Yamashita, “Multi-Instruments Music Generation Based on Chord Input”, In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), 1082-1083, 2023.[278] M. Araneda-Hernandez, F. Bravo-Marquez, D. Parra, and R. F. Cádiz, “MUSIB: musical score inpainting benchmark”, EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 19, 2023.[279] X. Li, Y. Yan, J. Soraghan, Z. Wang, and J. Ren, “A music cognition–guided framework for multi-pitch estimation”, Cognitive computation, 15(1), 23-35, 2023.[280] L. Xu, Y. Chen, and L. Zhang, “Intelligent Composition Based on Fibonacci Sequence and Hidden Markov Model”, In Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology, 1-6, 2023.[281] S. Ji, and X. Yang, “Emomusictv: Emotion-conditioned symbolic music generation with hierarchical transformer vae”, IEEE Transactions on Multimedia, 26, 1076-1088, 2023.[282] S. Li, and Y. Sung, “INCO-GAN: variable-length music generation method based on inception model-based conditional GAN”, Mathematics, 9(4), 387, 2021.[283] J. Huang, X. Huang, L. Yang, and Z. Tao, “Dance-Conditioned Artistic Music Generation by Creative-GAN”, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 107(5), 836-844, 2024.[284] H. Wang, S. Hao, C. Zhang, X. Wang, and Y. Chen, “Motif transformer: Generating music with motifs”, IEEE Access, 11, 63197-63204, 2023.[285] N. Imasato, K. Miyazawa, C. Duncan, and T. Nagai, “Using a language model to generate music in its symbolic domain while controlling its perceived emotion”, IEEE Access, 11, 52412-52428, 2023.[286] G. Wu, S. Liu, and X. Fan, “The power of fragmentation: a hierarchical transformer model for structural segmentation in symbolic music generation”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1409-1420, 2023.[287] S. L. Wu, and Y. H. Yang, “MuseMorphose: Full-song and fine-grained piano music style transfer with one transformer VAE”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1953-1967, 2023.[288] L. Mou, Y. Sun, Y. Tian, Y. Sun, Y. Liu, Z. Zhang, ... and R. Jain, “MemoMusic 3.0: considering context at music recommendation and combining music theory at music generation”, In 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 296-301, 2023.[289] X. Wei, J. Chen, Z. Zheng, L. Guo, L. Li, and D. Wang, “A Multi-Scale Attentive Transformer for Multi-Instrument Symbolic Music Generation”, arXiv preprint arXiv, 2305.16592, 2023.[290] J. Tang, L. Yin, and J. Yu, “Partially Trained Music Generation based on Transformer”, In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 1133-1138, 2023.[291] W. Huang, Y. Xue, Z. Xu, G. Peng, and Y. Wu, “Polyphonic music generation generative adversarial network with Markov decision process”, Multimedia Tools and Applications, 81(21), 29865-29885, 2022.[292] S. Adkins, P. Sarmento, and M. Barthet, “LooperGP: a loopable sequence model for live coding performance using guitarpro tablature”, In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) Cham: Springer Nature Switzerland, 3-19, 2023.[293] P. Sarmento, A. Kumar, Y. H. Chen, C. J. Carr, Z. Zukowski, and M. Barthet, “GTR-CTRL: instrument and genre conditioning for guitar-focused music generation with transformers”, In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), Cham: Springer Nature Switzerland, 260-275, 2023.[294] Y. Zou, P. Zou, Y. Zhao, K. Zhang, R. Zhang, and X. Wang, “Melons: generating melody with long-term structure using transformers and structure graph”, In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 191-195, 2022.[295] B. Yu, P. Lu, R. Wang, W. Hu, X. Tan, W. Ye, ... and T. Y. Liu, “Museformer: Transformer with fine-and coarse-grained attention for music generation”, Advances in Neural Information Processing Systems, 35, 1376-1388, 2022.[296] Z. Hu, Y. Liu, G. Chen, and Y. Liu, “Can machines generate personalized music? a hybrid favorite-aware method for user preference music transfer”, IEEE Transactions on Multimedia, 25, 2296-2308, 2022.[297] C. Jin, T. Wang, X. Li, C. J. J. Tie, Y. Tie, S. Liu, ... and S. Huang, “A transformer generative adversarial network for multi‐track music generation”, CAAI Transactions on Intelligence Technology, 7(3), 369-380, 2022.[298] X. Yang, Y. Yu, and X. Wu, “Double linear transformer for background music generation from videos”, Applied Sciences, 12(10), 5050, 2022.[299] Y. Tie, T. Wang, C. Jin, X. Li, and P. Yang, “Wireless Communications and Mobile Computing Multitrack Music Generation Network Based on Music Rules”, Wireless Communications and Mobile Computing, 2022(1), 5845689, 2022.[300] W. Y. Hsiao, J. Y. Liu, Y. C. Yeh, and Y. H. Yang, “Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs”, In Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 178-186, 2021.[301] S. Dai, Z. Jin, C. Gomes, and R. B. Dannenberg, “Controllable deep melody generation via hierarchical music structure representation”, Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021, 143–150, 2021.[302] D. Makris, K. R. Agres, and D. Herremans, “Generating lead sheets with affect: A novel conditional seq2seq framework”, In 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021.[303] A. Muhamed, L. Li, X. Shi, S. Yaddanapudi, W. Chi, D. Jackson, ... and A. J. Smola, “Symbolic music generation with transformer-gans”, In Proceedings of the AAAI conference on artificial intelligence, 35(1), 408-417, 2021.[304] A. Liutkus, O. Cıfka, S. L. Wu, U. Simsekli, Y. H. Yang, and G. Richard, “Relative positional encoding for transformers with linear complexity”, In International Conference on Machine Learning, 7067-7079, 2021.[305] S. Di, Z. Jiang, S. Liu, Z. Wang, L. Zhu, Z. He, ... and S. Yan, “Video background music generation with controllable music transformer”, In Proceedings of the 29th ACM International Conference on Multimedia, 2037-2045, 2021.[306] J. Jiang, G. G. Xia, D. B. Carlton, C. N. Anderson, and R. H. Miyakawa, “Transformer vae: A hierarchical model for structure-aware and interpretable music representation learning”, In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 516-520, 2020.[307] C. Gan, D. Huang, P. Chen, J. B. Tenenbaum, and A. Torralba, “Foley music: Learning to generate music from videos”, In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Springer International Publishing, Proceedings, Part XI 16, 758-775, 2020.[307] K. Choi, C. Hawthorne, I. Simon, M. Dinculescu, and J. Engel, “Encoding musical style with transformer autoencoders”, In International conference on machine learning, 1899-1908, 2020.[308] C. Donahue, H. H. Mao, Y. E. Li, G. W. Cottrell, and J. McAuley, “LakhNES: Improving multi-instrumental music generation with cross-domain pre-training”, arXiv preprint arXiv:1907.04868, 2019.[309] L. Zheng, and C. Li, “Real-Time Emotion-Based Piano Music Generation using Generative Adversarial Network (GAN)”, IEEE Access, 2024.[310] L. Chen, L. Shen, D. Yu, Z. Wang, K. Qian, B. Hu, ... and Y. Yamamoto, “Multi-Track Music Generation with WGAN-GP and Attention Mechanisms”, In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), 606-607, 2023.[311] G. Barnabò, G. Trappolini, L. Lastilla, C. Campagnano, A. Fan, F. Petroni, and F. Silvestri, “Cycledrums: automatic drum arrangement for bass lines using cyclegan”, Discover Artificial Intelligence, 3(1), 4, 2023.[312] D. Tian, J. Chen, Z. Gao, and G. Pan, “Linear Transformer-GAN: A Novel Architecture to Symbolic Music Generation”, In International Conference on Artificial Neural Networks, Cham: Springer Nature Switzerland, 451-463, 2023.[313] X. Li, and Y. Niu, “[Retracted] Research on Chord‐Constrained Two‐Track Music Generation Based on Improved GAN Networks”, Scientific Programming, 2022(1), 5882468, 2022.[314] P. L. Tomaz Neves, J. Fornari, and J. Batista Florindo, “Self-attention generative adversarial networks applied to conditional music generation”, Multimedia Tools and Applications, 81(17), 24419-24430, 2022.[315] J. Hu, Z. Ge, and X. Wang, “[Retracted] The Psychological Education Strategy of Music Generation and Creation by Generative Confrontation Network under Deep Learning”, Computational Intelligence and Neuroscience, 2022(1), 3847415, 2022.[316] Y. Xu, W. Wang, H. Cui, M. Xu, and M. Li, “Paralinguistic singing attribute recognition using supervised machine learning for describing the classical tenor solo singing voice in vocal pedagogy”, EURASIP Journal on Audio, Speech, and Music Processing, 2022(1), 8, 2022.[317] X. Wang, C. Jin, and W. Zhao, “Beijing opera synthesis based on straight algorithm and deep learning”, Advances in Multimedia, 2018(1), 5158164, 2018.[318] F. Maccarini, M. Oudin, M. Giraud, and F. Levé, “Co-creative orchestration of Angeles with layer scores and orchestration plans”, In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), Cham: Springer Nature Switzerland, 228-245, 2024.[319] L. Highams, and J. I. Olszewska, “Markov-chain-based musical creative intelligent agent passing successfully the Turing Test”, In 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES), 000215-000220, 2023.[320] P. Bodily, and D. Ventura, “Steerable Music Generation which Satisfies Long-Range Dependency Constraints”, Transactions of the International Society for Music Information Retrieval, 5(1), 71-87, 2022.[321] M. Navarro-Caceres, H. G. Oliveira, P. Martins, and A. Cardoso, “Integration of a music generator and a song lyrics generator to create Spanish popular songs”, Journal of Ambient Intelligence and Humanized Computing, 11(11), 4421-4437, 2020.[322] S. S. Garani, and H. Seshadri, “An algorithmic approach to South Indian classical music”, Journal of Mathematics and Music, 13(2), 107-134, 2019.[323] D. Williams, V. J. Hodge, L. Gega, D. Murphy, P. I. Cowling, and A. Drachen, “AI and automatic music generation for mindfulness”, In 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience. York, 2019.[324] P. Roy, and F. Pachet, “Enforcing meter in finite-length markov sequences”, In Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 854-861, 2013.[325] E. Gale, O. Matthews, B. D. L. Costello, and A. Adamatzky, “Beyond markov chains, towards adaptive memristor network-based music generation”, International Journal of Unconventional Computing, 10(3), 181–197, 2013.[326] W. Schulze, and B. Van Der Merwe, “Music generation with Markov models”, IEEE MultiMedia, 18(03), 78-85, 2011.[327] L. Jing, “Evolutionary Deep Learning for Sequential Data Processing in Music Education”, Informatica, 48(8), 2024.[328] W. Wang, J. Li, Y. Li, and X. Xing, “Style-conditioned music generation with Transformer-GANs”, Frontiers of Information Technology & Electronic Engineering, 25(1), 106-120, 2024.[329] Q. Liang, and Y. Wang, “Drawlody: Sketch-Based Melody Creation with Enhanced Usability and Interpretability”, IEEE Transactions on Multimedia, 2024[330] Y. Xu, “Enhancing Music Generation With a Semantic-Based Sequence-to-Music Transformer Framework”, International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-19, 2024.[331] C. Ford, A. Noel-Hirst, S. Cardinale, J. Loth, P. Sarmento, E. Wilson, ... and N. Bryan-Kinns, “Reflection Across AI-based Music Composition”, In Proceedings of the 16th Conference on Creativity & Cognition, 398-412, 2024.[332] C. Hsu, and R. Greer, “Bridging Subjectivity and Objectivity in Evaluation of Machine-Generated Jazz Music: A Multimetric Approach”, In 2023 IEEE International Symposium on Multimedia (ISM), 232-237, 2023.[333] A. Kurniawati, E. M. Yuniarno, Y. K. Suprapto, and A. N. Ikhsan Soewidiatmaka, “Automatic note generator for Javanese gamelan music accompaniment using deep learning”, International Journal of Advances in Intelligent Informatics, 9(2), 2023.[334] H. Wang, S. Han, G. Li, and B. Zhao, “A Hybrid Neural Network for Music Generation Using Frequency Domain Data”, In International Conference on Artificial Intelligence in China, Singapore: Springer Nature Singapore, 219-228, 2022.[335] H. W. Dong, K. Chen, S. Dubnov, J. McAuley, and T. Berg-Kirkpatrick, “Multitrack music transformer”, In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1-5, 2023.[336] J. Grekow, “Generating Polyphonic Symbolic Emotional Music in the Style of Bach Using Convolutional Conditional Variational Autoencoder”, IEEE Access, 11, 93019–93031, 2023.[337] Y. U. Jinming, C. H. E. N. Zhuang, and H. A. I. Han, “Multi-style Chord Music Generation Based on Artificial Neural Network”, Journal of Donghua University (English Edition), 40(4), 2023.[338] K. Gupta, K. Srivastava, K. Singh, and R. Pillai, “Music Generation and Retrieval Using LSTM and VAE”, In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-6, 2023.[339] M. Majidi, and R. M. Toroghi, “A combination of multi-objective genetic algorithm and deep learning for music harmony generation”, Multimedia Tools and Applications, 82(2), 2419-2435, 2023.[340] S. L. Wu, and Y. H. Yang, “Compose & Embellish: Well-structured piano performance generation via a two-stage approach”, In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1-5, 2023.[341] X. Liao, X. Li, Q. Xu, H. Wu, and Y. Wang, “Improving ant collaborative filtering on sparsity via dimension reduction”, Applied Sciences, 10(20), 7245, 2020.[342] J. Min, Z. Liu, L. Wang, D. Li, M. Zhang, and Y. Huang, “Music generation system for adversarial training based on deep learning”, Processes, 10(12), 2515, 2022.[343] C. Jin, F. Wu, J. Wang, Y. Liu, Z. Guan, and Z. Han, “MetaMGC: a music generation framework for concerts in metaverse”, EURASIP journal on audio, speech, and music processing, 2022(1), 31, 2022.[344] S. Tanberk, D. B. Tükel, “Style-specific Turkish pop music composition with CNN and LSTM network”, In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 000181-000185, 2021.[345] G. Li, S. Ding, and Y. Li, “Novel LSTM-gan based music generation”, In 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP), 1-6, 2021.[346] S. R. Hwang, and Y. C. Park, “Chord-based stepwise Korean Trot music generation technique using RNN-GAN”, The Journal of the Acoustical Society of Korea, 39(6), 622-628, 2020.[347] M. Dua, R. Yadav, D. Mamgai, and S. Brodiya, “An improved RNN-LSTM based novel approach for sheet music generation”, Procedia Computer Science, 171, 465-474, 2020.[348] A. Ç. Yiiksel, M. M. Karci, and A. Ş. Uyar, “Automatic music generation using evolutionary algorithms and neural networks”, In 2011 International Symposium on Innovations in Intelligent Systems and Applications, 354-358, 2011. hoseinsarlakAlirezaSheikh10172025738710.66224/jict.47692.17.65.73http://jour.aicti.ir/fa/Article/47692http://jour.aicti.ir/fa/Article/Download/47692http://jour.aicti.ir/fa/Article/Download/47692http://jour.aicti.ir/fa/Article/Download/47692http://jour.aicti.ir/fa/Article/Download/47692http://jour.aicti.ir/fa/Article/Download/47692http://jour.aicti.ir/fa/Article/Download/47692http://jour.aicti.ir/fa/Article/Download/47692[1]Anderson ET, Simester DI (2014) Reviews without a purchase: Low ratings, loyal customers, and deception. J. Mark. Res. 51:249–269. https://doi.org/10.1509/jmr.13.0209 [2]Cao H (2020) Online review manipulation by asymmetrical firms: Is a firm’s manipulation of online reviews always detrimental to its competitor? Inf. Manag. 57:103244. https://doi.org/10.1016/j.im.2019.103244 [3]Chatterjee S, Goyal D, Prakash A, Sharma J (2021) Exploring healthcare/health-product e-commerce satisfaction: A text mining and machine learning application. J. Bus. Res. 131:815–825. https://doi.org/10.1016/j.jbusres.2020.10.043 [4]Chen J, Yang Y, Liu H (2021) Mining bilateral reviews for online transaction prediction: A relational topic modeling approach. Inf. Syst. Res. 32:541–560. https://doi.org/10.1287/ISRE.2020.0981 [5]Dong X, Lian Y (2021) A review of social media-based public opinion analyses: Challenges and recommendations. Technol. Soc. 67:101724. https://doi.org/10.1016/j.techsoc.2021.101724 [6]Ho SM, Hancock JT (2019) Context in a bottle: Language-action cues in spontaneous computer-mediated deception. Comput. Human Behav. 91:33–41. https://doi.org/10.1016/j.chb.2018.09.008 [7]Huang G, Liang H (2021) Uncovering the effects of textual features on trustworthiness of online consumer reviews: A computational-experimental approach. J. Bus. Res. 126:1–11. https://doi.org/10.1016/j.jbusres.2020.12.052 [8]Hussain S, Guangju W, Jafar RMS, Ilyas Z, Mustafa G, Jianzhou Y (2018) Consumers’ online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Comput. Human Behav. 80:22–32. https://doi.org/10.1016/j.chb.2017.09.019 [9]Ke Z, Liu D, Brass DJ (2020) Do online friends bring out the best in us? The effect of friend contributions on online review provision. Inf. Syst. Res. 31:1322–1336. https://doi.org/10.1287/isre.2020.0947 [10]Kumar N, Venugopal D, Qiu L, Kumar S (2019) Detecting anomalous online reviewers: An unsupervised approach using mixture models. J. Manag. Inf. Syst. 36:1313–1346. https://doi.org/10.1080/07421222.2019.1661089 [11]Kumar N, Venugopal D, Qiu L, Kumar S (2018) Detecting review manipulation on online platforms with hierarchical supervised learning. J. Manag. Inf. Syst. 35:350–380. https://doi.org/10.1080/07421222.2018.1440758 [12]Lamb Y, Cai W, McKenna B (2020) Exploring the complexity of the individualistic culture through social exchange in online reviews. Int. J. Inf. Manage. 54:102198. https://doi.org/10.1016/j.ijinfomgt.2020.102198 [13]Lee SY, Qiu L, Whinston A (2018) Sentiment manipulation in online platforms: An analysis of movie tweets. Prod. Oper. Manag. 27:393–416. https://doi.org/10.1111/poms.12805 [14]Lei Z, Yin D, Zhang H (2021) Focus within or on others: The impact of reviewers’ attentional focus on review helpfulness. Inf. Syst. Res. 32:801–809. https://doi.org/10.1287/ISRE.2021.1007 [15]Li L, Lee KY, Lee M, Yang SB (2020) Unveiling the cloak of deviance: Linguistic cues for psychological processes in fake online reviews. Int. J. Hosp. Manag. 87:102468. https://doi.org/10.1016/j.ijhm.2020.102468 [16]Li L, Yang L, Zhao M, Liao M, Cao Y (2022) Exploring the success determinants of crowdfunding for cultural and creative projects: An empirical study based on signal theory. Technol. Soc. 70:102036. https://doi.org/10.1016/j.techsoc.2022.102036 [17]Pang H, Liu J, Lu J (2022) Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat. Technol. Soc. 70:102004. https://doi.org/10.1016/j.techsoc.2022.102004 [18]Qiao D, Lee SY, Whinston AB, Wei Q (2020) Financial incentives dampen altruism in online prosocial contributions: A study of online reviews. Inf. Syst. Res. 31:1361–1375. https://doi.org/10.1287/isre.2020.0949 [19]Sporer SL (1997) The less travelled road to truth: verbal cues in deception detection in accounts of fabricated and self-experienced events. Appl. Cogn. Psychol. 11:373–397. https://doi.org/10.1002/(SICI)1099-0720(199710)11:53.0.CO;2-0 [20]Wang H, Du R, Shen W, Qiu L, Fan W (2022a) Product reviews: a benefit, a burden, or a trifle? How seller reputation affects the role of product reviews. MIS Q. Manag. Inf. Syst 46:1243–1272. https://doi.org/10.25300/misq/2022/15660 [21]Wang Q, Zhang W, Li J, Mai F, Ma Z (2022b) Effect of online review sentiment on product sales: The moderating role of review credibility perception. Comput. Human Behav. 133:107272. https://doi.org/10.1016/j.chb.2022.107272 [22]Wang Y, Goes P, Wei Z, Zeng D (2019) Production of online word-of-mouth: Peer effects and the moderation of user characteristics. Prod. Oper. Manag. 28:1621–1640. https://doi.org/10.1111/poms.13007 [23]Wu C, Mai F, Li X (2021) The effect of content depth and deviation on online review helpfulness: Evidence from double-hurdle model. Inf. Manag. 58:103408. https://doi.org/10.1016/j.im.2020.103408 [24]Wu PF (2019) Motivation crowding in online product reviewing: A qualitative study of amazon reviewers. Inf. Manag. 56:103163. https://doi.org/10.1016/j.im.2019.04.006 [25]Xu W, Zhang C(2018) Sentiment, richness, authority, and relevance model of information sharing during social Crises—the case of #MH370 tweets Comput. Human Behav. 89:199–206. https://doi.org/10.1016/j.chb.2018.07.041 [26]Yang, Y, Zhang, K, 2022. sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics. Inf. Syst. Res. https://doi.org/10.1287/isre.2022.1124 [27]Yu Y, Khern-am-nuai W, Pinsonneault A (2022) When paying for reviews pays off: The case of performance-contingent monetary incentives. MIS Q. Manag. Inf. Syst 46:609–626. https://doi.org/10.25300/MISQ/2022/15488 [28]Zhang D, Zhou L, Kehoe JL, Kilic IY (2016) What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. J. Manag. Inf. Syst. 33:456–481. https://doi.org/10.1080/07421222.2016.1205907 [29]Zhang W, Du Y, Yoshida T, Wang Q (2018) DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Inf. Process. Manag. 54:576–592. https://doi.org/10.1016/j.ipm.2018.03.007 [30]Zhang W, Xie R, Wang Q, Yang Y, Li J (2022) A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence. Decis. Support Syst. 157:113765. https://doi.org/10.1016/j.dss.2022.113765 [31] Zhou Y, Lv X, Wang L, Li J, Gao X (2023) What increases the risk of gamers being addicted? An integrated network model of personality–emotion–motivation of gaming disorder. Comput. Human Behav. 141:107647. https://doi.org/10.1016/j.chb.2022.107647 [32] Zhuang M, Cui G, Peng L (2018) Manufactured opinions: The effect of manipulating online product reviews. J. Bus. Res. 87:24–35. https://doi.org/10.1016/j.jbusres.2018.02.016 [33] Lakshami, S., Muthumani, S.(2017). Building Brand Power. IOP Conference Series: Materials Science and Engineering. 197, https://iopscience.iop.org/article/10.1088/1757-899X/197/1/01 Seyyede ZahraAftabiSaeedFarzi10172025204010.66224/jict.47950.17.65.20http://jour.aicti.ir/fa/Article/47950http://jour.aicti.ir/fa/Article/Download/47950http://jour.aicti.ir/fa/Article/Download/47950http://jour.aicti.ir/fa/Article/Download/47950http://jour.aicti.ir/fa/Article/Download/47950http://jour.aicti.ir/fa/Article/Download/47950http://jour.aicti.ir/fa/Article/Download/47950http://jour.aicti.ir/fa/Article/Download/47950[1]B. Patra, ‘A survey of Community Question Answering’, CoRR, abs/1705.04009, May 2017, [Online]. Available: http://arxiv.org/abs/1705.04009.[2]D. Hoogeveen, L. Wang, T. Baldwin, and K. M. Verspoor, ‘Real and Misflagged Duplicate Question Detection in Community Question-Answering’, 2018.[3]A. Figueroa, ‘Automatically generating effective search queries directly from community question-answering questions for finding related questions’, Expert Syst. Appl., vol. 77, pp. 11–19, Jul. 2017, doi: 10.1016/j.eswa.2017.01.041.[4]I. Srba and M. Bielikova, ‘A Comprehensive Survey and Classification of Approaches for Community Question Answering’, ACM Trans. Web, vol. 10, no. 3, pp. 1–63, Aug. 2016, doi: 10.1145/2934687.[5] M. Sulír and M. Regeci, ‘Software Engineers’ Questions and Answers on Stack Exchange’, in 2022 IEEE 16th International Scientific Conference on Informatics (Informatics), Nov. 2022, pp. 304–310, doi: 10.1109/Informatics57926.2022.10083403.[6]M. Asaduzzaman, A. S. Mashiyat, C. K. Roy, and K. A. Schneider, ‘Answering questions about unanswered questions of Stack Overflow’, in 2013 10th Working Conference on Mining Software Repositories (MSR), May 2013, pp. 97–100, doi: 10.1109/MSR.2013.6624015.[7]S. A. Bhaskar, R. Rungta, J. Route, E. Nyberg, and T. Mitamura, ‘Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 462–470, doi: 10.18653/v1/W19-5049.[8]A. Merchant, N. Shenoy, A. Bharali, and A. K. M, ‘Identifying Similar Questions in the Medical Domain Using a Fine-tuned Siamese-BERT Model’, in 2022 IEEE 19th India Council International Conference (INDICON), Nov. 2022, pp. 1–6, doi: 10.1109/INDICON56171.2022.10040144.[9]A. B. Abacha and D. Demner-Fushman, ‘A question-entailment approach to question answering’, BMC Bioinformatics, vol. 20, no. 1, Dec. 2019, doi: 10.1186/s12859-019-3119-4.[10]Z. Xu and H. Yuan, ‘Forum Duplicate Question Detection by Domain Adaptive Semantic Matching’, IEEE Access, vol. 8, pp. 56029–56038, 2020, doi: 10.1109/ACCESS.2020.2982268.[11]L. Wang, L. Zhang, and J. Jiang, ‘Duplicate Question Detection With Deep Learning in Stack Overflow’, IEEE Access, vol. 8, pp. 25964–25975, 2020, doi: 10.1109/ACCESS.2020.2968391.[12]D. D. Koswatte and S. Hettiarachchi, ‘Optimized Duplicate Question Detection in Programming Community Q&A Platforms using Semantic Hashing’, in 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), Aug. 2021, pp. 375–380, doi: 10.1109/ICIAfS52090.2021.9606030.[13] V.-T. Nguyen, A.-C. Le, and H.-N. Nguyen, ‘A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems’, Int. J. Mach. Learn. Comput., vol. 11, no. 3, pp. 194–201, May 2021, doi: 10.18178/ijmlc.2021.11.3.1035.[14] R. Zhang, Q. Zhou, B. Wu, W. Li, and T. Mo, ‘What do questions exactly ask? mfae: duplicate question identification with multi-fusion asking emphasis’, in Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, 2020, pp. 226–234, doi: 10.1137/1.9781611976236.26.[15] D. Hoogeveen, A. Bennett, Y. Li, K. Verspoor, and T. Baldwin, ‘Detecting Misflagged Duplicate Questions in Community Question-Answering Archives’, Proc. Int. AAAI Conf. Web Soc. Media, vol. 12, no. 1, pp. 112–120, Jun. 2018, doi: 10.1609/icwsm.v12i1.15011.[16] P. K. Roy, S. Saumya, J. P. Singh, S. Banerjee, and A. Gutub, ‘Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review’, CAAI Trans. Intell. Technol., vol. 8, no. 1, pp. 95–117, Mar. 2023, doi: 10.1049/cit2.12081.[17] N. Othman, R. Faiz, and K. Smaïli, ‘Enhancing Question Retrieval in Community Question Answering Using Word Embeddings’, Procedia Comput. Sci., vol. 159, pp. 485–494, 2019, doi: 10.1016/j.procs.2019.09.203.[18] G. Zhou, L. Cai, J. Zhao, and K. Liu, ‘Phrase-based translation model for question retrieval in community question answer archives’, ACL-HLT 2011 - Proc. 49th Annu. Meet. Assoc. Comput. Linguist. Hum. Lang. Technol., vol. 1, pp. 653–662, 2011.[19] K. Mrini et al., ‘A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding’, in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021, pp. 1505–1515, doi: 10.18653/v1/2021.acl-long.119.[20] N. S. Tawfik and M. R. Spruit, ‘Evaluating sentence representations for biomedical text: Methods and experimental results’, J. Biomed. Inform., vol. 104, no. February, p. 103396, Apr. 2020, doi: 10.1016/j.jbi.2020.103396.[21] M. Faseeh, M. A. Khan, N. Iqbal, F. Qayyum, A. Mehmood, and J. Kim, ‘Enhancing User Experience on Q&A Platforms: Measuring Text Similarity Based on Hybrid CNN-LSTM Model for Efficient Duplicate Question Detection’, IEEE Access, vol. 12, no. January, pp. 34512–34526, 2024, doi: 10.1109/ACCESS.2024.3358422.[22] V. Nguyen, S. Karimi, and Z. Xing, ‘Combining Shallow and Deep Representations for Text-Pair Classification’, ALTA 2021 - Proc. 19th Work. Australas. Lang. Technol. Assoc., pp. 68–78, 2021.[23] S. Ghasemi and A. Shakery, ‘Harnessing the Power of Metadata for Enhanced Question Retrieval in Community Question Answering’, IEEE Access, vol. 12, no. May, pp. 65768–65779, 2024, doi: 10.1109/ACCESS.2024.3395449.[24] A. M. Monea, ‘Medical Question Entailment based on Textual Inference and Fine-tuned BioMed-RoBERTa’, in 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP), 2021, pp. 319–326, doi: 10.1109/ICCP53602.2021.9733687.[25] S. Yadav, V. Pallagani, and A. Sheth, ‘Medical Knowledge-enriched Textual Entailment Framework’, in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 1795–1801, doi: 10.18653/v1/2020.coling-main.161.[26] T. R. Goodwin and D. Demner-Fushman, ‘Bridging the Knowledge Gap: Enhancing Question Answering with World and Domain Knowledge’, CoRR abs/1910.07429, Oct. 2019, [Online]. Available: http://arxiv.org/abs/1910.07429.[27] A. B. Abacha, D. Demner-fushman, and U. S. N. Library, ‘Recognizing Question Entailment for Medical Question Answering’, in AMIA Annual Symposium Proceedings, 2016, pp. 310–318.[28] T. Baldwin, H. Liang, B. Salehi, D. Hoogeveen, Y. Li, and L. Duong, ‘UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures’, in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016, pp. 851–856, doi: 10.18653/v1/S16-1131.[29] A. B. Abacha and D. Demner-Fushman, ‘On the Summarization of Consumer Health Questions’, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 2228–2234, doi: 10.18653/v1/P19-1215.[30] C. dos Santos, L. Barbosa, D. Bogdanova, and B. Zadrozny, ‘Learning Hybrid Representations to Retrieve Semantically Equivalent Questions’, in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2015, vol. 2, no. 1, pp. 694–699, doi: 10.3115/v1/P15-2114.[31] D. A. Prabowo and G. Budi Herwanto, ‘Duplicate Question Detection in Question Answer Website using Convolutional Neural Network’, in 2019 5th International Conference on Science and Technology (ICST), Jul. 2019, no. 1, pp. 1–6, doi: 10.1109/ICST47872.2019.9166343.[32] A. Agrawal, R. Anil George, S. S. Ravi, S. Kamath S, and A. Kumar, ‘ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 533–540, doi: 10.18653/v1/W19-5059.[33] N. Tawfik and M. Spruit, ‘UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 493–499, doi: 10.18653/v1/W19-5053.[34] V. Nguyen, S. Karimi, and Z. Xing, ‘ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 478–487, doi: 10.18653/v1/W19-5051.[35] D. Bandyopadhyay, B. Gain, T. Saikh, and A. Ekbal, ‘IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 517–522, doi: 10.18653/v1/W19-5056.[36] K. Mrini, F. Dernoncourt, W. Chang, E. Farcas, and N. Nakashole, ‘Joint Summarization-Entailment Optimization for Consumer Health Question Understanding’, in Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, 2021, pp. 58–65, doi: 10.18653/v1/2021.nlpmc-1.8.[37] W. Zhu et al., ‘PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 380–388, doi: 10.18653/v1/W19-5040.[38] M. Sarrouti, A. B. Abacha, and D. Demner-Fushman, ‘Multi-Task Transfer Learning with Data Augmentation for Recognizing Question Entailment in the Medical Domain’, in 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), Aug. 2021, pp. 339–346, doi: 10.1109/ICHI52183.2021.00058.[39] H. Zhou, X. Li, W. Yao, C. Lang, and S. Ning, ‘DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering’, in Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 437–445, doi: 10.18653/v1/W19-5046.[40] A. Shang, X. Zhu, M. Danner, and M. Rätsch, ‘Unsupervised question-retrieval approach based on topic keywords filtering and multi-task learning’, Comput. Speech Lang., vol. 87, no. March, p. 101644, Aug. 2024, doi: 10.1016/j.csl.2024.101644.[41] K. Jiang, G. Jin, Z. Zhang, R. Cui, and Y. Zhao, ‘Incorporating external knowledge for text matching model’, Comput. Speech Lang., vol. 87, no. March, p. 101638, Aug. 2024, doi: https://doi.org/10.1016/j.csl.2024.101638.[42] Y. Sun and J. Song, ‘Research on question retrieval method for community question answering’, Multimed. Tools Appl., vol. 82, pp. 24309–24325, 2023, doi: https://doi.org/10.1007/s11042-023-14458-2.[43] D. Hoogeveen, K. M. Verspoor, and T. Baldwin, ‘CQADupStack: A Benchmark Data Set for Community Question-Answering Research’, in Proceedings of the 20th Australasian Document Computing Symposium, Dec. 2015, pp. 1–8, doi: 10.1145/2838931.2838934.[44] S. Z. Aftabi, S. M. Seyyedi, M. Maleki, and S. Farzi, ‘ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems’, IEEE Access, vol. 12, pp. 17137–17151, 2024, doi: 10.1109/ACCESS.2024.3358287.[45] C. Y. Lin, ‘Rouge: A package for automatic evaluation of summaries’, Proc. Work. text Summ. branches out (WAS 2004), no. 1, pp. 25–26, 2004, [Online]. Available: papers2://publication/uuid/5DDA0BB8-E59F-44C1-88E6-2AD316DAEF85.[46] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, ‘BERTScore: Evaluating Text Generation with BERT’, CoRR abs/1904.09675, pp. 1–43, Apr. 2019, [Online]. Available: http://arxiv.org/abs/1904.09675.[47] K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu, ‘MPNet: Masked and Permuted Pre-training for Language Understanding’, Adv. Neural Inf. Process. Syst., vol. 2020-Decem, no. NeurIPS, pp. 1–11, Apr. 2020, [Online]. Available: http://arxiv.org/abs/2004.09297.SinaDamiMohammadAliSanagoo ye Moharrer10172025416110.66224/jict.48488.17.65.41http://jour.aicti.ir/fa/Article/48488http://jour.aicti.ir/fa/Article/Download/48488http://jour.aicti.ir/fa/Article/Download/48488http://jour.aicti.ir/fa/Article/Download/48488http://jour.aicti.ir/fa/Article/Download/48488http://jour.aicti.ir/fa/Article/Download/48488http://jour.aicti.ir/fa/Article/Download/48488http://jour.aicti.ir/fa/Article/Download/48488[1] S. Tam, R. BenSaid and Ö. Ö. Tanriöver, "A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification," IEEE Access, vol. 9, Mar. 2021 [2] F. Huang, X. Li, C. Yuan, S. Zhang, J. Zhang and S. Qiao, "Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, Sept. 2022 [3] L. Zhang, S. Wang and B. Liu, "Deep learning for sentiment analysis: A survey," wires data mining and knowledge discovery, vol. 8, Aug. 2018 [4] M. Dong, Y. Li, X. Tang, J. Xu, S. Bi and Y. Cai, " Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification," IEEE Access, vol. 8, jan. 2020 [5] L. Yue, W. Chen, X. Li, W. Zuo and M. Yin, "A survey of sentiment analysis in social media," Knowledge and Information Systems, vol. 60, July. 2018 [6] M. Shukla and A. Kumar, "An Experimental Analysis of Deep Neural Network Based Classifiers for Sentiment Analysis Task," IEEE Access, vol. 11, Apr. 2023 [7] M. Birjali, M. Kasri and A. B. Hssane, "A comprehensive survey on sentiment analysis: Approaches, challenges and trends," Knowledge-Based Systems, vol. 226, Aug. 2021 [8] M. U. Salur and I. Aydin, "A Novel Hybrid Deep Learning Model for Sentiment Classification," IEEE Access, vol. 8, Mar. 2020 [9] D. W. O. Otter, J. R. Medina and J. K. Kalita, " A Survey of the Usages of Deep Learning for Natural Language Processing," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, Apr. 2020 [10] F. A. Acheampong, H. N. Mensah and W. Chen, "Transformer models for text‑based emotion detection: a review of BERT‑based approaches," Artificial Intelligence Review, vol. 54, Feb. 2021 [11] K. Berahmand, F. Daneshfar, E. S. Salehi, Y. Li and Y. Xu, " Autoencoders and their applications in machine learning: a survey," Artificial Intelligence Review, vol. 57, Feb. 2024 [12] I. Kanwal, F. Wahid, S. Ali, A. UrRehman, A. Alkhayyat and A. Al-Radaei, "Sentiment Analysis Using Hybrid Model of Stacked Auto-Encoder-Based Feature Extraction and Long Short Term Memory-Based Classification Approach," IEEE Access, vol. 11, Sept. 2023 [13] G. M. Shafiq, T. Hamza, M. F. Alrahmawy and R. El-Deeb, "Enhancing Arabic Aspect-Based Sentiment Analysis Using End-to-End Model," IEEE Access, vol. 11, Dec. 2023 [14] M. Aljebreen, B. Alabduallah, M. M. Asiri, A. S. Salama, M. Assiri and S. S. Ibrahim, "Moth Flame Optimization With Hybrid Deep Learning Based Sentiment Classification Toward ChatGPT on Twitter," IEEE Access, vol. 11, Sept. 2023 [15] I. Carvalho, H. G. Oliveira and C. Silva, "The Importance of Context for Sentiment Analysis in Dialogues," IEEE Access, vol. 11, Aug. 2023 [16] K. L. Tan, C. P. Lee, K. M. Lim and K. S. M. Anbananthen, "Sentiment Analysis With Ensemble Hybrid Deep Learning Model," IEEE Access, vol. 10, Sept. 2022 yosefAmiriAbdolrezashahmohammadi ahmadrezashekarchizadeh1017202515117010.66224/jict.48841.17.65.151http://jour.aicti.ir/fa/Article/48841http://jour.aicti.ir/fa/Article/Download/48841http://jour.aicti.ir/fa/Article/Download/48841http://jour.aicti.ir/fa/Article/Download/48841http://jour.aicti.ir/fa/Article/Download/48841http://jour.aicti.ir/fa/Article/Download/48841http://jour.aicti.ir/fa/Article/Download/48841http://jour.aicti.ir/fa/Article/Download/48841[1] م. ساکی و س. مهربان، "تاثیر بازاریابی رسانه‌های اجتماعی (وب‌سایت) بر روی وفاداری به برند با در نظر گرفتن نقش میانجیگرانه اعتماد به برند؛ (مطالعه موردی: شبکه اجتماعی اینستاگرام)،" *مطالعات مدیریت و کارآفرینی*، دوره ۴، شماره ۱/۱، صص. ۳۶-۴۵، بهار ۱۳۹۷.[2] X. Liu, H. Shin, and A. C. Burns, "Examining the impact of luxury brand's social media marketing on customer engagement: Using big data analytics and natural language processing," *Journal of Business Research*, vol. 125, pp. 815-826, 2021, doi: [10.1016/j.jbusres.2019.04.042](https://doi.org/10.1016/j.jbusres.2019.04.042).[3] S. Habachi, J. Matute, and R. Palau-Saumell, "Unlocking the effect of the gameful experience on brand loyalty and intention to use: The mediating role of customer brand engagement," *Journal of Marketing Management*, 2023.[4] R. Agus, D. Utama, and R. Novianty, "The impact of online customer reviews on purchase intention in online marketplace," *International Journal of Economics and Business Research*, vol. 10, no. 2, pp. 123-135, 2021[5] ر. امیری مقدم، س. ج. امینی، م. نوروزی، و د. فیاضی، "مقاله پژوهشی: سناریو‌های تهدیدات امنیتی عربستان علیه جمهوری اسلامی ایران در افق ۱۴۰۴،" *امنیت ملی*، جلد ۹، شماره ۳۲، صص. ۲۳-۴۹، ۱۳۹۸.[6] E. Brynjolfsson and A. McAfee, "The business of artificial intelligence," *MIT Sloan Management Review*, vol. 59, no. 1, pp. 1-10, 2017[7] X. Li and R. Du, "AI-powered branding: Opportunities and challenges," *Journal of Digital Marketing*, vol. 12, no. 3, pp. 45-60, 2020[8] A. Halinen and S. Nordberg-Davies, "Time to look forward: Supporting future orientation in business network research," *Journal of Business & Industrial Marketing*, vol. 38, no. 2, pp. 123-135, 2023[9] م. نوروزی، د. فیاض، و ح. سیف، "آینده‌پژوهی عوامل مؤثر بر محور مقاومت با تأکید بر ناآرامی‌های اجتماعی عراق و لبنان،" *فصلنامه علمی راهبرد دفاعی*، سال نوزدهم، شماره ۷۴، ۱۴۰۰.[10] ج. سی. گلن و ت. ج. گوردون، *دانشنامه بزرگ روش‌های آینده‌پژوهی*، ترجمه م. کیقبادی و ف. ملکی فر، تهران: انتشارات تیسا، ۱۳۹۳[11] ر. اسلاوتر و همکاران، *نواندیشی برای هزاره نوین*، ترجمه ع. ملکی فر و همکاران، تهران: مرکز آینده‌پژوهی علوم و فناوری دفاعی، ۱۳۸۶.[12] T. Jokinen et al., "Measurement report: Long-term measurements of aerosol precursor concentrations in the Finnish subarctic boreal forest," *Atmospheric Chemistry and Physics*, vol. 22, pp. 2237-2254, 2022, doi: [10.5194/acp-22-2237-2022](https://doi.org/10.5194/acp-22-2237-2022). [13] Heger. T, Boman. M (2015) - Technological Forecasting and Social Change, Zitiert von: 132 Ähnliche Artikel Alle 9 Versionen[14] P. A. Van der Duin, J. R. Ortt, and W. Aarts, "Contextual innovation management using a stage-gate platform: The case of Philips Shaving and Beauty," *Journal of Product Innovation Management*, vol. 31, no. 3, pp. 489-500, 2014[15]بیشاپ، پیتر؛ هاینز، اندی و کالینز، تری (1388) ترجمه مسعود منزوی، وضعیت کنونی سناریونویسی و تکنیک‌های اجمالی آن، تهران، انتشارات مرکز آینده‌پژوهی صنایع دفاعی[16] بل، وندل (1392) مبانی آینده‌پژوهی،ترجمه مصطفی تقوی، محسن محقق، تهران، انتشارات مرکز آینده‌پژوهی علوم و فناوری دفاعی[17] چرماک توماس جی،(1393)ترجمه سعید خزایی و مریم وکیل‌زاده، برنامه‌ریزی بر پایه سناریو، اصفهان، انتشارات شاخص پژوه[18] وندرهیدن، کیس و شارپ، بیل(2007) ترجمه مسعود منزوی، سناریو برای کسب موفقیت، تهران، مرکز آینده‌پژوهی علوم و فناوري دفاعی مؤسسه آموزشی و تحقیقاتی صنایع دفاعی[19] مایتزر، دانا و رگر،گویدو (1388)، ترجمه مسعود منزوی، رویکردها و دیدگاه‌های مختلف در سناریو،تاریخ، تفاوت‌ها، مزایا و کمبودها، تهران، مرکز آینده‌پژوهی علوم و فناوري دفاعی مؤسسه آموزشی و تحقیقاتی صنایع دفاعی[20] برادفیلد، رون،(1386)ترجمه مسعود منزوی، سناریو ها برای کسب موفقیت، تسهیل فرایند بسط سناریو: چند درس براي تسهیل گران، تهران، مرکز آینده‌پژوهی علوم و فناوري دفاعی مؤسسه آموزشی و تحقیقاتی صنایع دفاعی[21] A. Kaplan and M. Haenlein, "Rethinking AI: Learning from history," *Harvard Business Review*, vol. 97, no. 4, pp. 96-103, 2019[22] D. Chaffey, *Digital marketing: Strategy, implementation, and practice*, 7th ed. London, UK: Pearson Education, 2021.[23] O. Farshad et al., "Spermatotoxic effects of single-walled and multi-walled carbon nanotubes on male mice," *Frontiers in Veterinary Science*, vol. 7, Article 591558, 2020, doi: [10.3389/fvets.2020.591558](https://doi.org/10.3389/fvets.2020.591558)[24] M. H. Tito, "A comparative analysis of organizational data management practices: Insights from literature and AI perspectives for business efficiency and effectiveness," Master's thesis, University of Oulu, 2023.[25] A. Haleem, M. Javaid, and R. P. Singh, "An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges," *BenchCouncil Transactions on Benchmarks, Standards and Evaluations*, vol. 2, no. 4, 2023Volume 2, Issue 4,[26] K. Colombi, "Ethical implications in the process of AI-powered research platforms," *Polytechnic University of Milan*, Firenze University Press, 2023[27] B. C. O'Neill et al., "The scenario model intercomparison project (ScenarioMIP) for CMIP6," *Geoscientific Model Development*, vol. 9, no. 9, pp. 3461-3482, 2016[28] S. Khan, "Understanding the rise of artificial intelligence in the Norwegian banking sector: Reflection and settlement of trust, perceived behavior and consequences," *Abadar University Oslo*, 2023[29] ز. تخشید، "مقدمه‌ای بر چالش‌های هوش مصنوعی در حوزة مسئولیت مدنی،" *حقوق خصوصی*، دوره ۱۸، شماره ۱، ۱۴۰۰.[30] Artificial intelligence (AI) worldwide – Statistics & facts," Statista, 2023. [Online]. Available: [https://www.statista.com/topics/3104/artificial-intelligence-ai-worldwide/](https://www.statista.com/topics/3104/artificial-intelligence-ai-worldwide/).[31] The state of AI in 2022 and a half decade in review," McKinsey & Company, 2022. [Online]. Available: [https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review)[32] AI statistics," Exploding Topics, 2023. [Online]. Available: [https://explodingtopics.com/blog/ai-statistics](https://explodingtopics.com/blog/ai-statistics)[33] The legal challenges of artificial intelligence," IURICORN, 2023. [Online]. Available: [https://www.iuricorn.com/the-legal-challenges-of-artificial-intelligence](https://www.iuricorn.com/the-legal-challenges-of-artificial-intelligence).[34] Challenges of using artificial intelligence," Deloitte, 2023. [Online]. Available: [https://www2.deloitte.com/us/en/pages/consulting/articles/challenges-of-using-artificial-intelligence.html](https://www2.deloitte.com/us/en/pages/consulting/articles/challenges-of-using-artificial-intelligence.html).[35] https://www.apa.org/monitor/2021/11/cover-artificial-intelligence[36] س. خدامی، ب. اصانلو، و غ. سلیمانی، "شناسایی مزایای بازی‌وارسازی مشتریان فروشگاه‌های آنلاین مبتنی بر روش تحلیل تم،" *چشم‌انداز مدیریت بازرگانی*، جلد ۲۱، شماره ۵۱، صص. ۱۲۵-۱۵۲، ۱۴۰۱.doi: 10.52547/jbmp.2023.229954.1442[37] م. حسین‌زاده و همکاران، "ارائه چارچوب مفهومی استفاده از بازاریابی دیجیتال در بازار سرمایه بر اساس تئوری‌های رفتار برنامه‌ریزی شده و پذیرش فناوری مورد مطالعه: شرکت‌های کارگزاری بورس در تهران،" *فصلنامه اقتصاد مالی*، دوره ۱۶، شماره ۴، ۱۴۰۱.[38] م. شیرخدایی و همکاران، "شناسایی عوامل موثر بر هم‌آفرینی برند در جوامع برند رسانه‌های اجتماعی،" *مجله تحقیقات بازاریابی نوین*، سال دوازدهم، شماره ۴، ۱۴۰۲.[39] https://www.convinceandconvert.com/digital-marketing/artificial-intelligence-can-help-your-brand/.[40] https://www.analyticsinsight.net/what-are-the-important-factors-that-drive-artificial-intelligence/.[41] The present and future of AI," Harvard SEAS, 2021. [Online]. Available: [https://seas.harvard.edu/news/2021/10/present-and-future-ai](https://seas.harvard.edu/news/2021/10/present-and-future-ai)[42] https://www.forbes.com/sites/janakirammsv/2018/05/27/here-are-three-factors-that-[43] https://jdeiut.ir/the-effect-of-artificial-intelligence-on-the-branding-of-organizations/[44] https://1rose.ir[45] https://blog.kaprila.com[46] م. مشهدی‌زاده و ب. ساعدی، "بررسی روابط میان مشارکت مشتری، ایجاد ارزش مشتری و چسبندگی مشتری به شبکه‌های اجتماعی در نظام بانکی،" *فصلنامه علمی اقتصاد و بانکداری اسلامی*، شماره ۳۲، ۱۴۰۰.[47] How to build brand authenticity with artificial intelligence," Forbes, 2021. [Online]. Available: [https://www.forbes.com/sites/forbestechcouncil/2021/03/19/how-to-build-brand-authenticity-with-artificial-intelligence/](https://www.forbes.com/sites/forbestechcouncil/2021/03/19/how-to-build-brand-authenticity-with-artificial-intelligence/).[48] لیونبرگ و ایربای (۱۳۹۰). روش تحقیق و نگارش پایان نامه (ترجمه و. قربانی‌زاده و م. حسینی مرام) تهران: سازمان انتشارات جهاد دانشگاهی.[49] س. خزایی و ا. محمودزاده، *آینده‌پژوهی*، اصفهان: انتشارات پارس ضیاء شاخص پژوه، ۱۳۹۲.[50] ب. گیبسون، گ. مورگان، و ب. بوریل، *نظریه‌های کلان جامعه‌شناختی و تجزیه و تحلیل سازمان*، ترجمه م. ت. نوروزی، قم: انتشارات حوزه علمیه قم، ۱۳۹۱.Azam SadatMortazaviHassanYeganehAnitaHadizadeh10172025889210.66224/jict.49102.17.65.88http://jour.aicti.ir/fa/Article/49102http://jour.aicti.ir/fa/Article/Download/49102http://jour.aicti.ir/fa/Article/Download/49102http://jour.aicti.ir/fa/Article/Download/49102http://jour.aicti.ir/fa/Article/Download/49102http://jour.aicti.ir/fa/Article/Download/49102http://jour.aicti.ir/fa/Article/Download/49102http://jour.aicti.ir/fa/Article/Download/49102[1]https://www.gartner.com/en/newsroom/press-releases/2023-10-11-gartner-says-more-than-80-percent-of-enterprises-will-have-used-generative-ai-apis-or-deployed-generative-ai-enabled-applications-by-2026.[2]https://www.gartner.com/en/newsroom/press-releases/2017-12-13-gartner-says-by-2020-artificial-intelligence-will-create-more-jobs-than-it-eliminates.[3] Iftekhairul Islam, Fahad Shaon. (2020). If the Prospects of Some Occupations Are Stagnating with Technological Advancement? A Task Attribute Approach to Detect Employment Vulnerability. arXiv:2001.02783v1.[4] Dandan Qiao, Huaxia Rui, Qian Xiong. (2024). AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform.[5] International Monetary Fund, (2024). “Gen-AI: Artificial Intelligence and the Future of Work”.[6] Gmyrek, P., Berg, J., & Bescond, D. (۲۰۲۳). Generative AI and Jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper.[7] باشگاه اقتصاددانان، آثار هوش مصنوعی بر بازار کار، دنیای اقتصاد، شماره 5870، آبان 1402.[8] Braun, V. & Clarke, V. (2006). “Using thematic analysis in psychology”, Qualitative Research in Psychology, Vol. 3, No. 2, Pp. 77-101.[9] Holloway, I. & Todres, L. (2003). “The Status of Method: Flexibility, Consistency and Coherence”, Qualitative Research, Vol. 3, No. 3, Pp. 345-357.[10]https://www.europarl.europa.eu/at-your-service/en/stay-informed/research-and-analysis.[11] https://www.worldbank.org/en/access-to-information/ai-committee.[12] https://www.oecd.org/digital/artificial-intelligence/.[13]AI is likely to significantly impact jobs. https://www.oecd-ilibrary.org/science-and-technology/oecd-artificial-intelligence-papers_dee339a8-en.[ 4] Emilio Colombo, Fabio Mercorio, Mario Mezzanzanica, Antonio Serino. (2024). “Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs”, arXiv:2407.19204v1.[ 5] J. E. Triplett, (1999). "The Solow productivity paradox: What do computers do to productivity?" The Canadian Journal of Economics, vol. 32, no. 2, pp. 309-334.[ 6] D. H. Autor, (2015). "Why are there still so many jobs? The history and future of workplace automation," Journal of Economic Perspectives, vol. 29, no. 3, pp. 3-30.[ 7] EPRS, (2021). “Digital Automation and the Future of Work. Brussels,” European Union.[ 8] E. Brynjolfsson and A. McAfee, (2014). “The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies”, W. W. Norton & Company.[ 9] C. B. Frey and M. A. Osborne, (2017). "The future of employment: How susceptible are jobs to computerization?" Technological Forecasting and Social Change, vol. 114, pp. 254-280.[20] Sabina-Cristiana Necula. (2023). “Artificial Intelligence Impact on The Labour Force -- Searching for The Analytical Skills of The Future Software Engineers”, arXiv:2302.13229v1.[21] S. P. Mohanty, U. Choppali, and E. Kougianos, (2016). "Everything you wanted to know about smart cities: The Internet of things is the backbone," IEEE Consumer Electronics Magazine, vol. 5, no. 3, pp. 60-70.[22] World Bank, (2019). The Changing Nature of Work.[23] D. H. Autor, F. Levy, and R. J. Murnane, (2003). "The skill content of recent technological change: An empirical exploration," The Quarterly Journal of Economics, vol. 118, no. 4, pp. 1279-1333.[24] J. Manyika et al., (2017). "Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages," McKinsey Global Institute.[25] Yu Yang, (2020). “Analysis of the Impact of Artificial Intelligence Development on Employment”, IEEE.[26] صادقی فسایی، سهیلا؛ عرفان منش، ایمان. (1394). مبانی روش شناختی پژوهش اسنادی در علوم اجتماعی مورد مطالعه: تأثیرات مدرن شدن بر خانواده ایرانی. راهبرد فرهنگ، شماره بیست و نهم، بهار.[27] House of Commons Science, Innovation and Technology Committee, (2023). “The governance of artificial intelligence: interim report”, Ninth Report of Session 2022–23.[28] V. Dignum, (2019). “Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way”, Springer.[29] ۴۰ درصد از مشاغل جهان تحت تاثیر هوش مصنوعی – ITIRAN | آی تی ایران[30] OECD, (2021). “Artificial Intelligence and Employment: New Evidence from Occupations Most Exposed to AI”.AlirezaMansouri101720259312410.66224/jict.49814.17.65.93http://jour.aicti.ir/fa/Article/49814http://jour.aicti.ir/fa/Article/Download/49814http://jour.aicti.ir/fa/Article/Download/49814http://jour.aicti.ir/fa/Article/Download/49814http://jour.aicti.ir/fa/Article/Download/49814http://jour.aicti.ir/fa/Article/Download/49814http://jour.aicti.ir/fa/Article/Download/49814http://jour.aicti.ir/fa/Article/Download/49814[1] B. Liu and L. Zhang, "A survey of opinion mining and sentiment analysis," in Mining text data, ed: Springer, 2012, pp. 415-463.[2] M. Jalili, "Social power and opinion formation in complex networks," Physica A: Statistical Mechanics and its Applications, vol. 392, pp. 959-966, 2013.[3] I. N. Lymperopoulos and G. D. Ioannou, "Understanding and modeling the complex dynamics of the online social networks: a scalable conceptual approach," Evolving Systems, vol. 7, pp. 207-232, 2016.[4] M. S. Granovetter, "The strength of weak ties," American journal of sociology, vol. 78, pp. 1360-1380, 1973.[5] J. A. Holyst, K. Kacperski, and F. Schweitzer, "Social impact models of opinion dynamics," Annual reviews of computational physics, vol. 9, pp. 253-273, 2002.[6] Y. Dong, M. Zhan, G. Kou, Z. Ding, and H. Liang, "A survey on the fusion process in opinion dynamics," Information Fusion, vol. 43, pp. 57-65, 2018.[7] H. Noorazar, "Recent advances in opinion propagation dynamics: A 2020 survey," The European Physical Journal Plus, vol. 135, pp. 1-20, 2020.[8] D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabási, D. Brewer, et al., "Computational social science," Science, vol. 323, pp. 721-723, 2009.[9] H. Lietz, A. Schmitz, and J. Schaible, "Social Network Analysis with Digital Behavioral Data," easy_social_sciences, pp. 41-48, 2021.[10] R. Urena, G. Kou, Y. Dong, F. Chiclana, and E. Herrera-Viedma, "A review on trust propagation and opinion dynamics in social networks and group decision making frameworks," Information sciences, vol. 478, pp. 461-475, 2019.[11] K. Lichtenegger and T. Hadzibeganovic, "The interplay of self-reflection, social interaction and random events in the dynamics of opinion flow in two-party democracies," International Journal of Modern Physics C, vol. 27, p. 1650065, 2016.[12] A. Hanifa, C. Debora, M. F. Hasani, and P. Wicaksono, "Analyzing Views on Presidential Candidates for Election 2024 Based on the Instagram and X Platforms with Text Clustering," Procedia Computer Science, vol. 245, pp. 730-739, 2024.[13] S. Hong and S. H. Kim, "Political polarization on twitter: Implications for the use of social media in digital governments," Government Information Quarterly, vol. 33, pp. 777-782, 2016.[14] A. Mansouri, M. Mahmoudi, M. Farhoodi, and S. M. Mirsarraf, "Persian Rumor Detection Using a MultiClassifier Fusion Approach," International Journal of Information and Communication Technology Research, vol. 16, pp. 33-43, 2024.[15] A. Bondielli and F. Marcelloni, "A survey on fake news and rumour detection techniques," Information sciences, vol. 497, pp. 38-55, 2019.[16] K. Sharma, F. Qian, H. Jiang, N. Ruchansky, M. Zhang, and Y. Liu, "Combating fake news: A survey on identification and mitigation techniques," ACM transactions on intelligent systems and technology (TIST), vol. 10, pp. 1-42, 2019.[17] X. Zhou and R. Zafarani, "A survey of fake news: Fundamental theories, detection methods, and opportunities," ACM Computing Surveys (CSUR), vol. 53, pp. 1-40, 2020.[18] C. Castellano, S. Fortunato, and V. Loreto, "Statistical physics of social dynamics," Reviews of modern physics, vol. 81, p. 591, 2009.[19] P. Sobkowicz, "Modelling opinion formation with physics tools: Call for closer link with reality," Journal of Artificial Societies and Social Simulation, vol. 12, p. 11, 2009.[20] F. Klügl and A. L. Bazzan, "Agent-based modeling and simulation," AI Magazine, vol. 33, p. 29, 2012.[21] C. M. Macal and M. J. North, "Tutorial on agent-based modelling and simulation," Journal of simulation, vol. 4, pp. 151-162, 2010.[22] C. Macal and M. North, "Introductory tutorial: Agent-based modeling and simulation," in Proceedings of the 2014 Winter Simulation Conference, 2014, pp. 6-20.[23] F. Schweitzer, T. Krivachy, and D. Garcia, "How emotions drive opinion polarization: An agent-based model," arXiv preprint arXiv:1908.11623, 2019.[24] M. Tomaiuolo, G. Lombardo, M. Mordonini, S. Cagnoni, and A. Poggi, "A Survey on Troll Detection," Future Internet, vol. 12, p. 31, 2020.[25] L. Mastroeni, P. Vellucci, and M. Naldi, "Agent-based models for opinion formation: A bibliographic survey," IEEE Access, vol. 7, pp. 58836-58848, 2019.[26] O. Abid, S. Jamoussi, and Y. B. Ayed, "Deterministic models for opinion formation through communication: A survey," Online Social Networks and Media, vol. 6, pp. 1-17, 2018.[27] J. Lorenz, "Continuous opinion dynamics under bounded confidence: A survey," International Journal of Modern Physics C, vol. 18, pp. 1819-1838, 2007.[28] C. A. Devia and G. Giordano, "A framework to analyze opinion formation models," Scientific Reports, vol. 12, p. 13441, 2022.[29] C. A. Devia and G. Giordano, "Probabilistic analysis of agent-based opinion formation models," Scientific Reports, vol. 13, p. 20152, 2023.[30] I. V. Kozitsin, "A general framework to link theory and empirics in opinion formation models," Scientific reports, vol. 12, p. 5543, 2022.[31] N. E. Friedkin, "A formal theory of social power," Journal of Mathematical Sociology, vol. 12, pp. 103-126, 1986.[32] R. P. Abelson, "Mathematical models of the distribution of attitudes under controversy," Contributions to mathematical psychology, vol. 14, pp. 1-160, 1964.[33] R. Hegselmann and U. Krause, "Opinion dynamics and bounded confidence models, analysis, and simulation," Journal of Artificial Societies and Social Simulation, vol. 5, 2002.[34] M. Taylor, "towards a mathematical theory of Influence and attitude change," Human Relations, vol. 21, pp. 121-139, 1968.[35] M. H. DeGroot, "Reaching a consensus," Journal of the American Statistical Association, vol. 69, pp. 118-121, 1974.[36] R. A. Holley and T. M. Liggett, "Ergodic theorems for weakly interacting infinite systems and the voter model," The annals of probability, pp. 643-663, 1975.[37] N. E. Friedkin and E. C. Johnsen, "Social influence and opinions," Journal of Mathematical Sociology, vol. 15, pp. 193-206, 1990.[38] N. E. Friedkin and E. C. Johnsen, "Social influence networks and opinion change," Advances in Group Processes, vol. 16, pp. 1-29, 1999.[39] K. Sznajd-Weron and J. Sznajd, "Opinion evolution in closed community," International Journal of Modern Physics C, vol. 11, pp. 1157-1165, 2000.[40] K. Sznajd-Weron, "Sznajd model and its applications," arXiv preprint physics/0503239, 2005.[41] D. Stauffer, A. O. Sousa, and S. M. De Oliveira, "Generalization to square lattice of Sznajd sociophysics model," International Journal of Modern Physics C, vol. 11, pp. 1239-1245, 2000.[42] G. Deffuant, D. Neau, F. Amblard, and G. Weisbuch, "Mixing beliefs among interacting agents," Advances in Complex Systems, vol. 3, pp. 87-98, 2000.[43] G. Deffuant, F. Amblard, G. Weisbuch, and T. Faure, "How can extremism prevail? A study based on the relative agreement interaction model," Journal of artificial societies and social simulation, vol. 5, 2002.[44] G. Deffuant, F. Amblard, and G. Weisbuch, "Modelling group opinion shift to extreme: the smooth bounded confidence model," arXiv preprint cond-mat/0410199, 2004.[45] A. Nowak, J. Szamrej, and B. Latané, "From private attitude to public opinion: A dynamic theory of social impact," Psychological review, vol. 97, p. 362, 1990.[46] S. E. Asch, "Opinions and social pressure," Scientific american, vol. 193, pp. 31-35, 1955.[47] S. E. Asch, "Studies of independence and conformity: I. A minority of one against a unanimous majority," Psychological monographs: General and applied, vol. 70, p. 1, 1956.[48] S. Galam, "Minority opinion spreading in random geometry," The European Physical Journal B-Condensed Matter and Complex Systems, vol. 25, pp. 403-406, 2002.[49] C. Altafini, "Dynamics of opinion forming in structurally balanced social networks," PloS one, vol. 7, p. e38135, 2012.[50] C. Altafini, "Consensus problems on networks with antagonistic interactions," IEEE transactions on automatic control, vol. 58, pp. 935-946, 2013.[51] C. Altafini and G. Lini, "Predictable dynamics of opinion forming for networks with antagonistic interactions," IEEE Transactions on Automatic Control, vol. 60, pp. 342-357, 2015.[52] X. Chen, P. Tsaparas, J. Lijffijt, and T. De Bie, "Opinion dynamics with backfire effect and biased assimilation," PloS one, vol. 16, p. e0256922, 2021.[53] P. Erdős and A. Rényi, "On random graphs I," Publ. Math. Debrecen, vol. 6, pp. 290-297, 1959.[54] D. J. Watts and S. H. Strogatz, "Collective dynamics of ‘small-world’networks," nature, vol. 393, pp. 440-442, 1998.[55] A.-L. Barabási and R. Albert, "Emergence of scaling in random networks," science, vol. 286, pp. 509-512, 1999.[56] U. Krause, "A discrete nonlinear and non-autonomous model of consensus formation," Communications in difference equations, pp. 227-236, 2000.[57] D. N. Sotiropoulos, C. Bilanakos, and G. M. Giaglis, "Opinion formation in social networks: a time-variant and non-linear model," Complex & Intelligent Systems, vol. 2, pp. 269-284, 2016.[58] E. Kurmyshev and H. A. Juárez, "What is a leader of opinion formation in bounded confidence models?," arXiv preprint arXiv:1305.4677, 2013.[59] S. Wongkaew, M. Caponigro, and A. Borzi, "On the control through leadership of the Hegselmann–Krause opinion formation model," Mathematical Models and Methods in Applied Sciences, vol. 25, pp. 565-585, 2015.[60] J. Lorenz, "Continuous opinion dynamics of multidimensional allocation problems under bounded confidence: More dimensions lead to better chances for consensus," arXiv preprint arXiv:0708.2923, 2007.[61] P. Liu and X. Chen, "An overview on opinion spreading model," Journal of Applied Mathematics and Physics, vol. 3, p. 449, 2015.[62] E. Alraddadi, "Opinion formation among mobile agents," Cardiff University, 2021.[63] R. Chhimpa and A. C. Yadav, "$1/f $ noise in the Ising model," arXiv preprint arXiv:2503.04105, 2025.[64] A. Mansouri and F. Taghiyareh, "Phase Transition in the Social Impact Model of Opinion Formation in Scale-Free Networks: The Social Power Effect," Journal of Artificial Societies and Social Simulation, vol. 23, p. 3, 2020.[65] A. Mansouri and F. Taghiyareh, "Phase transition in the social impact model of opinion formation in log-normal networks," Journal of Information Systems and Telecommunication (JIST), vol. 1, pp. 1-14, 2021.[66] A. Mansouri and F. Taghiyareh, "Effect of segregation on the dynamics of noise-free social impact model of opinion formation through agent-based modeling," International Journal of Web Research, vol. 2, pp. 36-44, 2019.[67] J. A. Hołyst, K. Kacperski, and F. Schweitzer, "Phase transitions in social impact models of opinion formation," Physica A: Statistical Mechanics and its Applications, vol. 285, pp. 199-210, 2000.