List of Articles هوش مصنوعی

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  1 - An optimised hybrid method for ambulance dispatch based on complex networks and Artificial Intelligence
  Zainabolhoda Heshmati Mehdi Teimouri mehdi zarkeshzade Hadi Zare
  Nowadays, monitoring people’s health and helping those in need of medical attention is one of the most important responsibilities of governments. Emergency Medical Services (EMS) are therefore setup to provide timely care to victims of sudden and life-threatening injuri More

  Nowadays, monitoring people’s health and helping those in need of medical attention is one of the most important responsibilities of governments. Emergency Medical Services (EMS) are therefore setup to provide timely care to victims of sudden and life-threatening injuries or emergencies in order to prevent needless mortality or long-term morbidity. Providing rapid EMS with minimum response time will help improve survival rates. However, factors such as limited ambulance resources, large coverage area and high call-rates have caused delays in EMS, which have lead to lower survival rates. In this research the problem of ambulance dispatching with regards to lowering the response time is discussed. Response time is the most important factor in evaluating the performance of various EMS, which is directly proportional to mortality and survival rates. The most common method to reduce response time in ambulance dispatching is to choose the nearest available ambulance unit, i.e. the nearest neighbor (NN). Other methods use call-priority, first-in first-out (FIFO), preparedness and hybrid algorithms. One of the latest methods used in ambulance dispatching is based on complex networks. This method will apply a higher priority to the call that is more centrally located with respect to other calls. A hybrid algorithm has been proposed in this research which exceed the previous methods in terms of response time reduction.The proposed hybrid approach is based on complex network analysis and a search algorithm, which relies on the important parameters of dispatch, yet presents fewer constraints and overcomes some of the difficulties of the previous methods. In addition, prioritizing emergency calls is also considered. To evaluate these proposed algorithms, a combination of various parameters and operating environments has been simulated. The results of the simulations show improvements in response time reduction compared to previous methods. Manuscript profile
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  2 - Beautiful and Meaningful Iranian Names Production by Genetic Algorithm using Artificial Neural Network-Based Fitness Function
  Amir Shahab Shahmiri bahare zamani saeed shiry
  Beautiful and Meaningful Iranian Names Production by Genetic Algorithm using Artificial Neural Network-Based Fitness Function

  Beautiful and Meaningful Iranian Names Production by Genetic Algorithm using Artificial Neural Network-Based Fitness Function Manuscript profile
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  3 - Providing an optimal method for dispatching an ambulance based on complex networks and artificial intelligence
  مهدی زرکش زاده Zainabolhoda Heshmati Hadi Zare Mehdi Teimouri
  20.1001.1.27170411.1394.7.24.4.7
  The goal of emergency medical services is to reduce deaths and complications caused by diseases and injuries. Rapid dispatch of emergency services and reduced response time lead to increased survival rates. Response time is one of the important criteria for measuring th More

  The goal of emergency medical services is to reduce deaths and complications caused by diseases and injuries. Rapid dispatch of emergency services and reduced response time lead to increased survival rates. Response time is one of the important criteria for measuring the efficiency of emergency medical services. The usual method of sending ambulances is to send the nearest available unit, which pays attention to efficiency in the short term. One of the methods that has been recently mentioned in the field of ambulance dispatch is based on the analysis of complex networks. The purpose of this method is to send an ambulance to a call that is more central than other calls, which leads to better efficiency in the long run. Other methods in dispatching an ambulance are based on finding the best suitable route for service cars, and the time complexity of these methods is very high. In this article, using a hybrid approach and applying centrality criteria from complex network analysis and search methods based on artificial intelligence, an optimal and innovative method is presented to reduce the response time of emergency services. In addition, in the proposed method, the emergency priority of calls is also considered, which is an important variable in decisions. The proposed method has less limitations than the previous methods and the extensive simulation results confirm the significant improvement of this method compared to the previous methods such as the centrality method and the nearest neighbor method. Manuscript profile
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  4 - Artefacts and Producers Mapping of Iran's Artificial Intelligence Ecosystem based on Transformational Levels
  hamed ojaghi Iman Zohoorian Nadali Fatemeh Soleymani Roozbahani
  20.1001.1.27170411.1402.15.57.10.5
  As an emerging technological field, artificial intelligence has received increasing attention from companies and governments. The development of artificial intelligence both at business and country levels depends on knowing the current situation. This paper identifies t More

  As an emerging technological field, artificial intelligence has received increasing attention from companies and governments. The development of artificial intelligence both at business and country levels depends on knowing the current situation. This paper identifies the artifacts and producers presented in this field and maps them to transformational levels. Products/services and producers are achieved through capabilities provided by artificial intelligence. Then, based on the classification methodology and meta-characteristics, the transformational levels of the artifacts of Iran's artificial intelligence ecosystem have been extracted. 562 products/services were identified, which were offered by 112 companies. Machine vision and natural language processing have been at the top of the technologies used, with 44 and 27 percent of the products allocated to them, respectively. Artifacts and producers were classified into seven transformative levels: individual, organization, industry, electronic chip/hardware, society, platform, code/algorithm/library, and infrastructure. Iran's artificial intelligence productions have not grown in a balanced way. The three levels of platform, code/algorithm/library, and infrastructure as the main generator of other artificial intelligence products/services have had the lowest amount of production. It is suggested that a specialized marketplace for the supply of artificial intelligence application programming interfaces should be put on the agenda to stimulate the formation of the ecosystem. Manuscript profile