Search

Objective In recent years, advancements in information technology, such as wireless EMG, high-resolution cameras, programs like OpenSim, innovations in textile sensors, and the emergence of artificial intelligence and smart mobile devices, have provided biomechanists with new tools and approaches. This study aims to investigate emerging trends in sports biomechanics, summarizing and providing practical insights from research conducted between 2015 and 2023. Methods A systematic search of research articles on new biomechanics approaches published between 2015 and 2023 was conducted. Specialized databases were queried, and 47 articles meeting the inclusion criteria were selected for analysis. Results Analysis of the selected studies revealed that the integration of information technology, artificial intelligence, smartphones, software, and wearable medical sensors in sports biomechanics has shown promising results in enhancing performance and reducing injury risks. Conclusion The findings of this study suggest that advancements in sports biomechanics technologies are pushing the boundaries of current research. Continued exploration and application of these technologies will likely shape the future of sports science and performance.

The aim of this paper is to explore the ethical implications of artificial intelligence (AI) in the realm of art. Drawing on the philosophy and ethics of AI, this study examines the ethical challenges that may arise in the art domain. Given the increasing use and penetration of AI in the field of art, there is a need for more careful attention to the ethical dimensions of this phenomenon by scholars of art and philosophy. To achieve the objective of the paper, concepts such as AI, some of its techniques, and the ethical issues of AI in art are explained and analyzed using the analytical-descriptive method. The findings of the study reveal that ethical issues such as privacy and surveillance, manipulation of behavior, opacity and transparency, bias in decision systems, and automation and employment can be investigated in the field of art. These ethical issues are sometimes in conflict and sometimes in direct relation with each other. This study suggests that addressing the ethical questions of AI in the field of art requires addressing the ethical questions of AI in general. [ABSTRACT FROM AUTHOR]

The term algorithm in various disciplines has its roots in algebra which is one important part of mathematics. Algebra in the course of time has contributed to many developments and achievements in various disciplines such as economics, informatics science, and computer science; and through this process, algorithm transactions have emerged. With the widespread use of artificial intelligence algorithms in various aspects of human life, individuals' transactions in various markets have also taken on a fresh newlook. Nowadays, what is considered as algorithmic transactions in the securities markets has some practical demonstrations. The use of algorithms of artificial intelligence in technical analysis of the market data, the proposal for entering into an optimal transaction with a trader, and then a decision to make a transaction in a specific time and within a specific bid and ask scope which is contemplated by the trader is an example of such innovation. The use of algorithms in market data analysis, identifying appropriate investment opportunities, selecting optimal stock portfolios, placing orders, and formation and execution of contracts, are just a few aspects of the applications of these thoughtful and mysterious brains in the legal trading arena. Due to the unique features of algorithms in terms of speed, accuracy and intelligence, they have been very effective in the financial efficiency of the market players and have been very popular among retail consumers, institutional investors and issuers of securities. Given the market's compelling need for auxiliary tools, the explosive volume of traders' transaction information, issuers, and publicly traded stocks, algorithmic trading in the capital market has flourished; an area that is not only managed and controlled by the regulations governing private contracts but also by the requirements of the government's regulatory role in these markets. However, despite the above-mentioned advantages, there have been challenges associated with this process. Such challenges include unfilled expectations of the counterparties in the transactions, the ambiguities and vagueness of the decision, and the way the algorithm functions. Also the competitive fractions among the traders and market players, lack of legal, regulatory, and technical transparency in the process of algorithmic transactions, undermining of principles of equity and good faith, and weakness in the protection of the consumers are problems that follow the association of the artificial intelligence in capital markets transactions. The adaptability of trading algorithms to legal rules has raised new questions, each of which opens up new horizons in contract law, competition law, civil liability, etc. In practice, with the expansion of the use of this kind of transaction, legal experts and legislators could have a better mental adjustment and awareness in facing the consequences and corollaries of algorithmic transactions in the securities markets and this readiness and awareness shall ensue many advantages. This study, while examining the legal dimensions of algorithmic trading in the capital market, addresses economic and legal challenges and strives to pave the way for interested parties in this field with several proposed solutions for future studies. This paper, therefore, through giving a picture of such algorithmic transactions in the securities markets and capital markets seeks to highlight and demonstrate the intersection between law and artificial intelligence with a focus on the legal challenges associated with the capital market. The paper also seeks to attract the attention of the readers and experts in this field to set the necessary guidelines and encourage them to adopt a scientific approach in dealing with such a phenomenon. For this purpose in this paper, we shall first discuss the concepts and species of algorithmic transactions and then by positing the legal challenges associated with such transactions, shall seek to come up with solutions to tackle such legal challenges. The contribution of this work is offering some framework and safe legal infrastructure for the promotion of the use of algorithmic transactions in the securities market. [ABSTRACT FROM AUTHOR]

Objective: The objective of the present study is to identify and categorize the dimensions and applications of artificial intelligence in library services. Methodology: The present research method is the meta-synthesis applied using the seven-stage Sandelowski & Barroso to obtain a comprehensive combination of the research subject based on qualitative studies. These steps include the clear problem statement, systematic literature review, search of selected texts, extraction of information provided in texts, analysis and combination of findings, quality control, and presentation of results. The English papers related to the research field from 2010 to 2020 in the Web of Science database and the Persian articles relevant to the research field from 2001 to December 2020 in the Magiran, Noormags, SID, IranDoc, Civilica, Ensani.ir, and ISC databases have been investigated. After concordance of article acceptance criteria, 35 papers out of 1116 retrieved articles were selected for review by the meta-synthesis method. Findings: According to the evaluations performed by the content analysis approach, 35 articles identified with the most overlapping in three topics, eight concepts, and 36 codes for the dimensions of artificial intelligence were labeled in libraries. The research findings indicate that artificial intelligence will play a role in acquisitions, organization, search on databases, reference, book circulation, information retrieval, planning and design and development of the library, and more extensively employed in public services and technical services compared to the library management services. Moreover, by comparing the frequency of application of artificial intelligence in the concepts extracted from the articles, it is concluded that the functions of artificial intelligence mostly affect the circulation department of the library; the reference, information retrieval, and information organization are in the next places, respectively. According to studies, management services are at the lowest level in terms of being affected by the advantages of artificial intelligence. In the indicators section, "diagnosis and understanding of user information need", "selecting the appropriate strategy for search and responding users," and "analysis of question and search strategy" are the most affected indicators, and "knowledge management in the library" and "digital library design" are the least affected indicators by the functions of artificial intelligence. Conclusion: The results of the present study indicate that artificial intelligence can make extensive developments in all sections of libraries and will drastically change the library. Identifying the components of artificial intelligence in libraries and adding knowledge in this field makes librarians and information center managers better understand artificial intelligence technology and its potentials for application in different sectors of libraries. [ABSTRACT FROM AUTHOR]

Electronic government refers to the provision of continuous government information and services to people through the Internet or other digital methods. The electronic government revolution has the ability to transform the public sector and re-establish the relationship between the government and citizens, a revolution that would have been almost ineffective without the element called artificial intelligence. One of the biggest developments of the last few years can be called the launch of e-government by artificial intelligence all over the world. In this paper, we address the challenges of e-government systems and propose a framework that uses artificial intelligence. In the next step, we present an intelligent e-government database architecture that supports the development and implementation of e-government artificial intelligence applications. The main goal is to use reliable artificial intelligence techniques in advancing the current state of e-government. Services to minimize processing time, reduce costs and improve citizen satisfaction. [ABSTRACT FROM AUTHOR]

Purpose Due to the importance of artificial intelligence, the increasing growth, and the high penetration rate of this technology in other sciences, the necessity of conducting scientometric studies is fully felt. The literature review on the research topic shows that very few studies have been conducted in this case or with not enough comprehensiveness. Considering rapid and significant progress in this field, it needs to be clarified to reveal the current situation and future trends based on scientometric techniques. To fill this existing gap, the present study seeks to answer the question that how is the scientific map of the published studies in the field of artificial intelligence by Iranian researchers in the Scopus database, and based on this map, what are the noteworthy fields of researchers? Methodology The current research is a quantitative and practical study using scientific methods, including co-word analysis and social network analysis. The research population includes all documents in the field of artificial intelligence with at least one author who is affiliated with an Iranian organization and indexed in the Scopus database from the beginning to January 2022. VOSviewer and Gephi are the software used to analyze data and extract maps. The reason for choosing the Scopus database is that this database contains 70 million research articles reviewed by more than 5000 international publishers (Zhang et. al, 2021), and also the database has relative uniformity in all fields (Batooli et al. 2019). Findings The results of the analysis show that Iranian researchers have published the most documents in 2021, and also the best position of Iran in the field of artificial intelligence was the 20th rank, which was achieved in 2021. Overall, Iran ranks 31 in this database and third among Middle Eastern countries after Israel and Turkey. Among the types of published documents, articles with number of 3124 are in the first place and notes are in the last place with a number of two published items. 19.9% of the published documents of Iranian papers have not received any citations. The greatest number of published documents have between 1 and 5 citations. The highest number of citations belongs to an article with 824 citations. Also, findings show the most frequent keywords are "artificial intelligence", "algorithm" and "optimization". The network structure of high-frequency keywords with a repetition threshold of at least 15, consists of five clusters. In the first cluster, the keyword "neural network" has the highest link weight. In the next "artificial intelligence", "machine learning" and "learning systems" have the highest link weights and the only word in the fifth cluster is "mathematical model" and keywords are placed in five clusters: “inference and prediction”, “intelligent algorithms”, “diagnosis and learning”, “data analysis”, and “mathematical models”. In the coauthor network of 80 countries collaborating with Iranian writers, the most collaboration is between Iranian and American researchers. This network has classified collaborating countries into eight clusters based on direct relations. Conclusion The findings showed that the most frequent keywords are artificial intelligence, algorithm, optimization, and neural network. Optimizing is common between this study and GhaviDel, Nezamdost, & Riahinia's (2020). Also, the neural network is common between the present study and the study of Feng & Law (2021) and the study of Darko et al. (2020). The comparison of the most frequent keywords in Iranian and non-Iranian documents shows that Iranian researchers in choosing their research topics were aligned to the general trend of scientific development in most years, and in some years, they have progressed with a step delay. The results of the cluster analysis show that the studies were divided into five thematic clusters. This is even though the study of GhaviDel, Nezamdost, & Riahinia (2020) identified 12 clusters. Also, the difference between the results of the clustering of the present study and the study of Taherian & Assareh (2012) is that in the present study, clustering was done on the documents published by Iranian researchers, but in that study, the clustering was done on all the published documents in the field of artificial intelligence. According to the findings of the research, at the beginning of 2022, Iran was in the 31st rank. Meanwhile, Iran's rank in the study of FarzinYazdi and RezaeiSharifabadi (2017) in the same Scopus database was 17. Iranian researchers do not observe standard affiliations of education institutes and it leads to inaccuracy in the search results. Also, the results show that the process of Iranian documents has been generally upward. However, this growth rate is lower compared to other countries. In the following research topics, Iranian researchers have been in line with the general direction for most of the years, and in some years they have been one step behind. [ABSTRACT FROM AUTHOR]

Purpose: Emerging technologies have become one of the main concerns of societies of today. Governments and societies are well aware that their economic future and survival in today's changing world are tied to new technologies, especially artificial intelligence technology. This paper aims to help managers, practitioners, and policymakers in prioritizing the implementation of artificial intelligence technology in applications such as transportation, health, security, education, and research. Design/methodology/approach: After a relatively complete follow-up in the field of artificial intelligence research, the criteria for prioritizing emerging technologies have been calculated and weighted by the Friedman method. Then, the application areas have been identified by the thematic analysis method. Finally, using the COPRAS multi-criteria decision-making technique, the application areas of artificial intelligence have been ranked. Findings: This research resulted in a model and indicated that the three areas of economics, transportation, and governance were given more priority for the application of artificial intelligence technology, respectively. Research limitations/implications: Designing mechanisms to monitor advances in emergent technology and artificial intelligence to improve the proposed model and update the model were the most important suggestions. Another suggestion was to identify the gap between the theoretically developed model and practice. Practical implications: This paper provides a reliable prioritization of the applied areas using scientific criteria appropriate to the country. This provides a scientific-operational package, and policy-making by creating a logical relationship between theory and practice. If such a document is presented in policy and legislative circles, this prioritization can be one of the most important tools for decision-making and rescuing from political and strategic dilemmas. Social implications: Improving the quality of human life by implementing artificial intelligence in various usages of daily life, and facilitating social availability to the important types of possibilities are the most important achievement of this research. Originality/value: Since no model has been found to prioritize the implementation of artificial intelligence technology in applications, the proposed model closes such a gap. The paper provides appropriate tools both to practitioners and policymakers who intend to decide and deploy artificial intelligence. [ABSTRACT FROM AUTHOR]

The emergence of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) in China on December 2019 has led to a global outbreak of COVID-19, which spread worldwide and became an international public health issue. People all over the world must fight in this unexpected battle and the part each individual plays, is important. The health care system has done an excellent job, and the government has done various actions to help the community control the virus spreads as well. On the other hand, in most cases people help to improve the situation alongside the policies. However, the role of information technology in helping various social institutions to fight COVID-19 is hidden and is not well appreciated. The purpose of this study is to discover the hidden role of information technology (IT) that ultimately helps to control the epidemic. Research has shown that strategies for using potential technologies can be beneficial. These IT strategies can also be tailored either to control the epidemic or to support community exclusion during the epidemic which in turn, helps control the spread of infection. This article sheds light on the impact of various technologies that help health care systems, government and public in different aspects to fight COVID-19. In addition to the technologies implemented, this paper also deals with the potential unexpected technologies that can be effective in controlling epidemic conditions for future applications. It has also tried to provide IT-based solutions to deal with the disease epidemic as well. [ABSTRACT FROM AUTHOR]

New performance-based design approaches in earthquake engineering aim to accurately and transparently assess the risk of loss of life and structural damage. Advanced analytical models are used to determine the performance of structures, with one of the key components being the deterioration of structural members under seismic loads. Multilinear backbone curves are commonly used in regulations and software to simplify the behavior of members subjected to seismic loads, including the deterioration components. This paper proposes using machine learning models to predict the deterioration components of reinforced concrete (RC) columns. A dataset of 255 experimental data from 1973 to 2002 is used to predict the deterioration components using different machine learning methods. The RC columns have three failure modes: bending, shearing, and bending-shearing. The deterioration components predicted by the analytical relationships are compared with the results obtained from machine learning methods. The dataset includes 14 features as model inputs and 3 features as outputs. The paper examines three algorithms for predictions: AdaBoost, artificial neural network (ANN), and random forest (RF). The analysis is conducted using Python software. The results show that the random forest model has an accuracy rate of 91% for the Plastic chord rotations from yield to cap , 81% for Post-capping plastic-rotation capacity from the cap to point of zero strength , and 88% for normalized energy dissipation capacity compared to other algorithms. Also, the results obtained from the predicting models have considerable accuracy compared to analytical relationships. Compared to analytical models, the random forest model has significantly been improved in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient determination (R2). These improvement are 79% , 75.3% and 46.5% in (R2), 63.7% , 48.5% and 86.7 in (RMSE), 64% , 92% and 89.4% in (MAE).The results showed that the random forest model has been significantly improved the accuracy of determination of deterioration components compared with analytical models.

In this paper, a fuzzy clustering system is presented to display the flow field changes in the rotor outlet of centrifugal turbomachinery. What is important in the research done in the field of turbomachinery is the need for all the fields to properly understand the phenomena of flow inside the turbomachines, which has complexities. For this reason, the most advanced laboratory equipment is used in this regard, which is associated with issues such as time consuming, high costs, and a large number of required tests, and doubles the importance of simulating and observing current phenomena through artificial intelligence algorithms. The present system operates on the basis of fuzzy clustering so that the spatial data (from the PIV measurement system) by the number of specific clusters to the field display in the initial time; then, by applying changes to the cluster related to the time series (from the system measurement of LDA) that contains the recorded changes of the current during the time of the data mining, the new field data are obtained at a new time step and the clustering of the data shows the variation of the flow field in the fuzzy environment. In this paper, the flow field was investigated for 6 successive steps, and the results of the system output showed the variation of the flow field from the rotor rotation at different angles. [ABSTRACT FROM AUTHOR]

Purpose: Emerging technologies have become one of the main concerns of societies of today. Governments and societies are well aware that their economic future and survival in today's changing world are tied to new technologies, especially artificial intelligence technology. This paper aims to help managers, practitioners, and policymakers in prioritizing the implementation of artificial intelligence technology in applications such as transportation, health, security, education, and research.Design/methodology/approach: After a relatively complete follow-up in the field of artificial intelligence research, the criteria for prioritizing emerging technologies have been calculated and weighted by the Friedman method. Then, the application areas have been identified by the thematic analysis method. Finally, using the COPRAS multi-criteria decision-making technique, the application areas of artificial intelligence have been ranked.Findings: This research resulted in a model and indicated that the three areas of economics, transportation, and governance were given more priority for the application of artificial intelligence technology, respectively.Research limitations/implications: Designing mechanisms to monitor advances in emergent technology and artificial intelligence to improve the proposed model and update the model were the most important suggestions. Another suggestion was to identify the gap between the theoretically developed model and practice.Practical implications: This paper provides a reliable prioritization of the applied areas using scientific criteria appropriate to the country. This provides a scientific-operational package, and policy-making by creating a logical relationship between theory and practice. If such a document is presented in policy and legislative circles, this prioritization can be one of the most important tools for decision-making and rescuing from political and strategic dilemmas.Social implications: Improving the quality of human life by implementing artificial intelligence in various usages of daily life, and facilitating social availability to the important types of possibilities are the most important achievement of this research.Originality/value: Since no model has been found to prioritize the implementation of artificial intelligence technology in applications, the proposed model closes such a gap. The paper provides appropriate tools both to practitioners and policymakers who intend to decide and deploy artificial intelligence.

Objective: The objective of the present study is to identify and categorize the dimensions and applications of artificial intelligence in library services.Methodology: The present research method is the meta-synthesis applied using the seven-stage Sandelowski & Barroso to obtain a comprehensive combination of the research subject based on qualitative studies. These steps include the clear problem statement, systematic literature review, search of selected texts, extraction of information provided in texts, analysis and combination of findings, quality control, and presentation of results. The English papers related to the research field from 2010 to 2020 in the Web of Science database and the Persian articles relevant to the research field from 2001 to December 2020 in the Magiran, Noormags, SID, IranDoc, Civilica, Ensani.ir, and ISC databases have been investigated. After concordance of article acceptance criteria, 35 papers out of 1116 retrieved articles were selected for review by the meta-synthesis method.Findings: According to the evaluations performed by the content analysis approach, 35 articles identified with the most overlapping in three topics, eight concepts, and 36 codes for the dimensions of artificial intelligence were labeled in libraries. The research findings indicate that artificial intelligence will play a role in acquisitions, organization, search on databases, reference, book circulation, information retrieval, planning and design and development of the library, and more extensively employed in public services and technical services compared to the library management services. Moreover, by comparing the frequency of application of artificial intelligence in the concepts extracted from the articles, it is concluded that the functions of artificial intelligence mostly affect the circulation department of the library; the reference, information retrieval, and information organization are in the next places, respectively. According to studies, management services are at the lowest level in terms of being affected by the advantages of artificial intelligence. In the indicators section, "diagnosis and understanding of user information need", "selecting the appropriate strategy for search and responding users," and "analysis of question and search strategy" are the most affected indicators, and "knowledge management in the library" and "digital library design" are the least affected indicators by the functions of artificial intelligence.Conclusion: The results of the present study indicate that artificial intelligence can make extensive developments in all sections of libraries and will drastically change the library. Identifying the components of artificial intelligence in libraries and adding knowledge in this field makes librarians and information center managers better understand artificial intelligence technology and its potentials for application in different sectors of libraries.

The prediction of local scouring as a dynamic and nonlinear phenomenon using methods of acceptable predictive capability has always been of interest to researchers. The shape of the bridge pier is one of the important factors in the formation and magnitude of the scour hole. In this paper, the scour depth of three bridge piers with cylindrical, sharp nose and rectangular shapes was predicted in two scenarios using the support vector machine algorithm with 395 field data obtained from the US Geological Survey and Froehlich (1988), based on different combinations of dimensionless parameters as the water attack angle (α), Froud number (Fr), the ration of pier length to width (l/b), and the ratio of mean sediment size to pier width (D50/b). The results of the study, while confirming the acceptable performance of the SVM algorithm for all piers in both scenarios, showed that in the first and second scenarios, the most optimal performance was related to the rectangular pier shape with correlation coefficient of 0.8702 and 0.8838, with and maximum Ds (DDR) values of 0.854 and 1.229 respectively, during the testing phase. The positive effect of increasing the number of data on the performance of the SVM algorithm was also confirmed by further probing the evaluation indicators. The results of the comparison pointed out the overestimation of the predicted scour depth values of absolute error between 11% to 35%. [ABSTRACT FROM AUTHOR]

Objectives: By identifying the scientific researches of artificial intelligence carried out by universities, industry and the government, this paper seeks to outline the research situation of Iran in the field of this technology in comparison with China and the United States as the two leading countries in artificial intelligence. Based on this perception, the main goal of the current research is to extract the interactive model of university, industry, and government as the main actors in the scientific research collaboration of artificial intelligence in the three studied countries. Methods: This research was conducted based on the scientometric method and the use of the Scopus database in the time domain of the last two decades. Countries were ranked based on the number of documents and citations to the top 1000. Then the documents were separated based on organizational affiliations into academic, industrial, government, and international groups. On this basis, the co-authoring network of Iran's documents was drawn based on organizational collaboration. Through mutual information analysis, the quality of cooperation in Iran was compared with the other two countries. For this purpose, a meaningful examination of the arrangement of categories was done through Shannon's entropy calculations. Results: With a small difference, China and America had the most documents and Iran was in the 30th position. In the citation, America, with a significant difference, was first, China was second, and Iran was 17th. By normalizing the number of studies in terms of population and GDP, the United States was placed in a much higher position than China, and the distance between Iran and China in the normalized results was also much smaller. In the uncertainty and synergy of interactions, the same order was maintained. Among the players in Iran, Tehran University, followed by the Amirkabir University of Technology the and Sharif University of Technology, have had the most scientific productions. University, industry, and government data in Iran were experimentally manipulated. The highest output based on the least input change was achieved through increased industry research. Conclusions: The most important difference in the cooperation model of Iranian actors with the other two countries is the level of industry participation. Among the three countries, America shows the most industrial research collaborations. In the case of Iran, it was also concluded that the policy of encouraging the industry to participate in artificial intelligence research will be most effective. This result is also supported by experimental manipulation of values. [ABSTRACT FROM AUTHOR]

Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]

Sustainable innovation is necessary for organizational survival as well as success in the turbulent environment of digital era, particularly in the currentCOVID-19 epidemic crisis. Many of today’s innovations employ a combination of digital technologies while relying on telecommunications infrastructure besides Internet access. The COVID-19 crisis one-year experience has revealed that to what extent the innovations formed in the digital context have succeeded to enhance society’s resilience to this crisis. In this regard, the present study was aimed at providing a comprehensive model of innovations in the digital context and their role and effect on the COVID-19 crisis. Qualitative meta-synthesis was the main method of this research, in which the kappa coefficient was calculated to assess the model and Shannon entropy was calculated to specify the significance coefficient of the indices. To this end, the previous studies on digital innovations over the COVID-19 crisis were first extracted through searching for relevant keywords and 634 papers were found that 78 of which were finalized at four consecutive screening stages. Subsequently, the identified indices in different papers were coded and the main themes were identified through categorizing the codes. The results revealed that technologies based on artificial intelligence, Internet of Things, cloud technologies, and blockchain are the fundamental areas of digital innovation during the COVID-19 pandemic. Moreover, health, education, and business were identified as the key areas for applying digital innovations during the pandemic.

The focus of this study was to evaluate the quality of water resources, specifically for agricultural purposes. The researchers aimed to optimize the evaluation process by reducing the number and types of parameters used in water quality indicators, thereby reducing measurement costs. To achieve this, Shannon's entropy method was employed to determine the weight of each parameter in the water quality index. The study utilized artificial intelligence algorithms, individually and in combination, to predict the weighted entropy water quality index. The goal was to assess the suitability of water for agricultural purposes. The results indicated that the sub-index of bicarbonate had the highest weight (0.24) among the parameters, while sodium absorption had the lowest weight (0.18). The combined decision tree and neural network algorithm compared to the simple decision tree and neural network algorithm with higher R² (0.999 compared to 0.996 and 0.998) and lower RMSE (0.331 compared to 0.331 and 1.081). It showed a higher accuracy in predicting the weighted entropy water quality index. Furthermore, by analyzing the importance of parameters using the Pearson correlation matrix, it was discovered that removing bicarbonate and reducing the number of inputs to 4 allowed the combined decision tree and neural network algorithm to maintain high accuracy in predicting the index. Overall, this study demonstrated the effectiveness of Shannon's entropy method and artificial intelligence algorithms in evaluating the quality of water resources for agricultural purposes. The optimized approach can assist in planning and ensuring the proper use of water resources, contributing to the continuity of farming activities. [ABSTRACT FROM AUTHOR]

Introduction: Most heart diseases show symptoms on ECG, but diagnosing heart disease with ECG requires the knowledge and experience of medical specialized. Because these specialists may not always be available, it is necessary to design tools to diagnose heart disease in these situations. Objective: In this paper, a two-stage approach based on artificial neural networks is designed to diagnose heart disease using ECG information. Method: To design the proposed approach, first ECG information for 861 refers to a number of medical centers in Arak city is collected and and data consulted is proccesed by specialists. Then 154 features from ECG as input variables in proposed approach has been specified. In the first stage of approach, an artificial neural network is designed to detect the status of the ECG in two situation as usable and unusable. Then, in the second stage, using the usable ECG information, an artificial neural network is designed to diagnose the presence or absence of heart disease. Finally, the performance of the two-stage approach is evaluated and its accuracy and precision in determining the status of the ECG as well as the disease status is determined. Results In the proposed approach, the neural network for the determining of ECG status has an precision of 97.1% and an accuracy of 97.3%, and also the neural network for the diagnosis of heart disease has an precision of 95.8% and an accuracy of 95.4%. Conclusion: Considering the high effeciency of the proposed approach in the determining of ECG status and also diagnosing heart disease, it is possible to use this approach as a reliable assistant to assist the treatment staff. [ABSTRACT FROM AUTHOR]

Background and Objective: Water distribution networks are prone to terrorist attacks by injecting toxic substances, due to their vastness and availability. The main objective of this paper was detecting the extent of intentional pollution in the urban water distribution network by self-organizing map (SOM). Materials and Methods: The existing hydraulic condition of the water distribution network covered by reservoir No. 4 in Tehran was modeled as a pilot. Possible injection scenarios of contamination in different parts of the water distribution network were performed using qualitative analysis of the water distribution network, using the EPANET analyzer engine and coding in R software environment. Artificial neural network of SOM was used to find the contamination range for the injection of arsenic at different times and places in the distribution network. Results: The concentration of contamination at a certain point decreased over time and a high correlation was observed between time and concentration. The extent of contamination depended on the consumption of subscribers and consequently, the time of contaminat injection. The results of the artificial neural network model showed that the method developed in this research was 91% accurate and was able to determine the extent of contamination in the water distribution network at high speed. Conclusion: SOM can be used as a complement to the water quality monitoring and pollution detection system in the urban water distribution network to determine the extent of pollution when detecting potential pollution in the shortest possible time, and as an alternative to quantitative-qualitative modeling of the water network. [ABSTRACT FROM AUTHOR]

n GEO communicational satellites, thrusts are being used for many missions such as station keeping, longitude change maneuver and actuators desaturation. These types of actuators need fuel and its estimation requires many complicated calculations. In this paper, a novel method based on availed data for previous satellites is proposed for estimation of satellite fuel mass and it does not need mathematical modeling of satellite dynamics. Two methods, least square method and artificial intelligence, are used to this aim and two mathematical model are proposed for satellite fuel mass estimation. By applying the models to several previous satellites, it is shown that the models have lower than 5% average error. The proposed method in this paper is quick and accurate and can be utilized for GEO satellites feasibility study and conceptual design

Objective: Data generation in biology and biotechnology has greatly increased in recent years due to the very rapid development of high-performance technologies. These data are obtained from studying biological molecules, such as metabolites, proteins, RNA, and DNA, to understand the role of these molecules in determining the structure, function, and dynamics of living systems. Functional genomics is a field of research that aims to characterize the function and interaction of all the major components (DNA, RNA, proteins, and metabolites, along with their modifications) that contribute to the set of observable characteristics of a cell or individual (i.e., phenotype). Furthermore, in a breeding program, genetic improvement can be maximized through accurate identification of superior animals that are selected as parents of the next generation, thereby achieving breeding goals. Artificial neural networks have been proposed to alleviate this limitation of traditional regression methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. Omics data can be too large and complex to handle through visual analysis or statistical correlations. This has encouraged the use of machine intelligence or artificial intelligence. The objectives of this study was to review the main applications of artificial intelligence methods in functional genomics, cancer, agriculture, domestic animals and its intertwined fields, i.e. epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics, discuss important aspects of data management, such as data integration, cleaning, noise removal, balancing and ratio of missing data, functional genomicssystem modeling, artificial intelligence and systems biology, addressing legal, ethical and economic issues related to the application of artificial intelligence methods in the field of genomics and presenting a view of possible scenarios in the future. Materials and methods: In this review, all researches conducted in the field of artificial intelligence application in functional genomics, cancer, agriculture, domestic animals, and its intertwined fields, i.e. epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics, were tried, focusing on the applications of recent years after Increase production of big data to be studied and used. Results: The studies showed that the application of artificial intelligence in all fields, including functional genomics, cancer, agriculture, domestic animals, and its intertwined fields, i.e., epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics, is increasing rapidly and has many benefits. Conclusions: Considering the vital applications that are often addressed by biology and especially functional genomics, it is better to deal with artificial intelligence tools that are able to help mechanistic understanding of biological processes. In other words, enabling systems biology is important to reap the benefits of AI results in genomics. Interpretability can certainly help AI to be more easily adopted in practical applications such as medicine. In our opinion, increasing the volume and diversity of reliable big data and integrating it with theoretical modeling will help increase human trust in AI-based predictions and decisions in the future. On the one hand, model-based approaches can provide knowledge-based constraints. On the other hand, the results of artificial intelligence can help to create the parameters of models of biological systems. Functional genomics, as well as all fields of medicine, biology, agriculture, animal science, and other sciences that involve both individual and collective rights, is a complex field of research. Those who want to use AI in this field will find it difficult to navigate, as the field is sensitive to legal, ethical, and spiritual aspects. Artificial intelligence opens many opportunities that we should not refuse for fear of not understanding all the steps. The path that artificial intelligence will follow is just beginning to unfold. It has many promises and many potential dangers ahead. This path will probably be long and irreversible. Artificial intelligence will change our lives and we need to change our minds as soon as possible to adapt, accept, and manage the resulting changes in the best possible way, to ensure that they will bring as many benefits as possible and will cause the least possible negative consequences for us. [ABSTRACT FROM AUTHOR]

Artificial intelligence provides unique opportunities to improve the performance of various industries, including the automotive industry. The present study seeks to identify the applications and requirements of using artificial intelligence in new automotive products such as self-driving cars by obtaining opinions from managers and employees of domestic automotive companies through semi-structured interviews and thematic analysis. The interviewees included 11 managers and 17 employees, of which 15 had a bachelor's degree, 11 had a master's degree, and 2 had a doctorate degree. 21 codes were identified in the applications section and 26 codes were identified in the requirements section. After conducting 28 interviews, theoretical saturation was achieved. From the codes identified in the applications section, self-driving cars and voice assistants, shared transportation, and resource allocation, expert staff, and team formation can be mentioned in the requirements section. Considering the variety of artificial intelligence applications in new car products and according to the specified requirements according to the opinions of experts, the development of a suitable platform for hard and soft technologies in an integrated manner; And government support regarding the creation of legal infrastructure can improve the development path of the current technology. Of course, in order to create a context for the successful operation of artificial intelligence in the automotive industry, all the effects of its application from different cultural and social aspects should be considered with a systematic perspective. [ABSTRACT FROM AUTHOR]

Introduction: Early detection of coronary artery disease is critical. Previous studies show that new methods of artificial intelligence for prediction and diagnosis of diseases have been widely considered by the researchers. The purpose of this paper is the classification of angiography to normal and abnormal by using Artificial Intelligent methods. Methods: In this diagnostic study, the datasets were collected from 152 patients who had been undergoing the coronary angiography, and for classification of the angiography results, a combination of fuzzy neural network and genetic algorithm is used. The proposed system was implemented and evaluated by MATLAB software. Results: In implementation of the proposed system, 85% of the data was used for the training phase and the remaining 15 percent was used for the test phase. The results of the simulation in accuracy, sensitivity and specificity indicators on average state were 0.9496, 0.9253, 0.9435 and 0.9569, respectively. Under optimum conditions, an estimate of 1 was obtained for each indicator. Conclusion: The use of genetic algorithms in ANFIS training phase led to improved speed of the simulation. Also the high performance indicators prove that the proposed system is effective for classifying and diagnosing patients with coronary heart disease. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer in women and one of the leading of death among them. The high and increasing incidence of the disease and its difficult treatment specifically in advanced stages, imposes hard situations for different countries' health systems. Body temperature is a natural criteria for the diagnosis of diseases. In recent decades extensive research has been conducted to increase the use of thermal cameras and obtain a close relationship between heat and temperature of the skin's physiology. Thermal imaging (thermography) applies infrared method which is fast, non-invasive, non-contact and flexibile to monitor the temperature of the human body. This paper investigates highly diversified studies implemented before and after the year 2000. And it emphasizes mostly on the newely published articles including: performance and evaluation of thermal imaging, the various aspects of imaging as well as The available technology in this field and its disadvantages in the diagnosis of breast cancer. Thermal imaging has been adopted by researchers in the fields of medicine and biomedical engineering for the diagnosis of breast cancer. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field. Thermography does not provide information on the structures of the breast morphology, but it provides performance information of temperature and breast tissue vessels. It is assumed that the functional changes occured before the start of the structural changes which is the result of disease or cancer. These days, thermal imaging method has not been established as an applicative method for screening or diagnosing purposes in academic centers. But there are different centers that adopt this method for the diognosis and examining purposes. Thermal imaging is an effective method which is highly facilitative for breast cancer screening (due to the low cost and without harms), also, its impact will increase by combining other methods such as a mammogram and sonography. However, it has not been widely recognizesd as an accepted method for determineing the types of tumors (benign and malignant) and diseases of breast tissue. [ABSTRACT FROM AUTHOR]

Because of the very rapid growth of knowledge in both meaning and volume, knowledge management is a necessity, and not doing it reduces usefulness. This applies to all sciences, including architecture. One of the signs of the rapid growth of architectural knowledge in the last decade is the increase of software with various functions to solve architectural problems which shows the increase in data volume and the multi-factor nature of design processes. Therefore, the purpose of this study is to achieve a theoretical model and provide a proposal for the implementation of knowledge management using artificial intelligence in the architectural design process. This paper uses a qualitative method that is the basis of conceptual models of the knowledge discovery data (KDD) process and is an exploratory study to highlight the functions associated with applying the knowgedge management (KM) process in architecture. The result of this research is to present a simulated model of knowledge management in architecture and modeling of the process of building an automated design intelligence. According to the obtained results, the introduction of artificial intelligence to design the facade of buildings, can provide the process of data management, information and knowledge in architecture to achieve multifactorial design. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

Introduction and Objective Rivers serve as crucial sources of freshwater on Earth, and precise prediction of river flows plays a vital role in effective water resource management. To address this need, researchers have proposed various models. These models can be categorized into two types: (1) hydrological/hydraulic physically based models and (2) data-driven models. The cited papers provide evidence of the significance of employing data-driven models for hydrological issues, particularly in predicting river discharge.Materials and Methods In this study, the researchers utilized a gene expression programming model (GEP) and support vector machine (SVM) to predict the discharge of the Ferizi River. The dataset used in the study covered a period of ten years (2011-2020) and consisted of daily Ferizi river discharge readings obtained from the Moushang hydrometry station. The study area had an average altitude of 2171 meters above sea level, with a longitude range of 58° 49' 30" to 59° 4' 30" E and a latitude range of 36° 20' 1" to 36° 32' 1". For the prediction models, daily river discharge data from 1 to 5 days ahead were utilized as input variables for both the GEP and SVM models. To ensure data homogeneity and address any deficiencies, the researchers employed the run test and calculated the correlation coefficient between neighboring stations. Subsequently, the dataset was randomly divided into two groups: 80% for model training and 20% for model testing, as well as evaluating the modeling error. The performance of the models was assessed during the training and validation stages using various evaluation metrics, including root mean square error (RMSE), coefficient of correlation (R), bias, Kling Gupta (KGE), and Nash–Sutcliffe model efficiency (NSE). Additionally, in order to estimate the inflow to the Ferizi River using the SVM model, three common types of kernel functions in hydrology were examined, namely linear kernel, polynomial, and radial basis functions.Results and Discussion Among the different functions considered, the radial basis function was selected due to its lower error compared to other functions when applied to the variables. The best input model, Model No. 5, incorporated previous flow variables with a delay of five time steps. In the training phase, both the GEP and SVM models exhibited the highest performance in forecasting the daily flow of Moshang station. The performance of the applied models suggests that SVM (RSME = 1.15, R = 0.985, NSE = 0.85, and KGE = 0.79) demonstrates higher precision in the validation stage for river discharge prediction compared to GEP (RSME = 1.65, R = 0.964, NSE = 0.78, and KGE = 0.69).Conclusion and Suggestions This study has demonstrated that the utilization of soft computing techniques, such as SVM and GEP, is a powerful tool in predicting river flow. These techniques are capable of establishing a favorable relationship between input and output parameters, enabling accurate simulation of average and maximum daily flow.

Introduction: The advent of ChatGPT as an artificial intelligence (AI) tool has changed the classroom atmosphere among medical students leading to controversial issues in education, particularly in writing. Although some medical students exhibit strong inclination to use ChatGPT in their writing classes, most professors are still uncertain about the outcome. Methods: This study tries to assess medical students’ attitudes toward using ChatGPT in their academics writing course held in the Fall semester, 2023. An intact class of medical students from Shiraz University of Medical Sciences (SUMS) took part in this qualitative research. Two semi structured interviews were conducted at the beginning of the course, prior to the implication of ChatGPT in revising their essay writing, and after completing the course. Braun and Clark thematic analysis was applied to analyze the interview data. Results: Six main codes were identified about using ChatGPT (i.e., familiarity, speed, vocabulary range, structure, teacher’s role, plagiarism). At first, most students, whether those who were familiar with ChatGPT or those who had only heard of it, agreed with its positive effect. But after using ChatGPT, their opinions about using ChatGPT in class were changed. Conclusion: This study may provide an insight for educators who are interested in using AI in a learning environment. However, without professors’ management, using ChatGPT in higher education cannot enhance students’ writing skill efficiently. To this end, both students and their professors should increase their language knowledge and general knowledge to use it in the best and most effective way in education. [ABSTRACT FROM AUTHOR]

In maritime law, the duty of the carrier to make the vessel seaworthy is one of the fundamental obligations in the contracts of carriage of goods by sea, so that the non-performance of this obligation may lead to the contractual liability of the carrier towards the owner of the goods. The aforementioned obligation is considered to be a multifaceted obligation, one of the important aspects of which is the hiring of a competent crew to sail the vessel to its intended destination. Therefore, if there is no crew on board or the crew recruited does not have the necessary qualifications, there is no doubt that the vessel is unseaworthy. The recent emergence and expansion of artificial intelligence has impacted nearly all industries, including shipping. Large companies in the shipping industry are currently focused on constructing autonomous vessels using this technology. The vessels are constructed with the main approach of relying solely on artificial intelligence for navigation at sea, without the intervention of a crew. This replacement of the crew with artificial intelligence raises fundamental legal questions. One such question is how the carrier can fulfill its seaworthiness obligation without the presence of a crew. In this regard, it is important to consider whether existing international rules can be applied to autonomous vessels or if new rules should be created to account for the unique nature of this vessel class. From a technical perspective, autonomous vessels can have varying degrees of autonomy. However, current research primarily focuses on fullyautonomous vessels. The full degree of autonomy in this category of vessels means that the vessel is able to make decisions and determine the necessary actions without the intervention of humans and solely by relying on its operating system. In response to the above-mentioned questions, different approaches have been proposed. While emphasizing the effectiveness of existing traditional rules, some believe that based on the concept of “functional equivalence,” existing international rules can also be applied to this category of vessels. In fact, according to this approach, artificial intelligence in fully autonomous vessels is supposed to do the same work that crews do in conventional vessels; therefore, based on the functional equivalence, the existing rules can be applied to artificial intelligence. On the other side, another group, referring to the “black box” problem of artificial intelligence, believes that the reformulation of the doctrine of seaworthiness should be put on the agenda in such a way that the seaworthiness of the vessel can be assessed by evaluating the algorithms used in artificial intelligence. The said problem can be explained in this way: although the input data and the output result of artificial intelligence are comprehensible by humans, humans cannot understand the decision process taken by artificial intelligence and its reason. There is, therefore, no way to evaluate whether the algorithms used in artificial intelligence are suitable for the intended sea voyage or not, and inevitably, the concept of seaworthiness must be reformulated. Finally, the third approach finds the solution in revising the existing rules because the nature of artificial intelligence requires that appropriate rules be imposed to assess the seaworthiness of autonomous vessels, and the existing rules cannot be interpreted in such a way to be applied to the mentioned vessels. [ABSTRACT FROM AUTHOR]

Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. Gait (i.e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i.e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance.This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i.e., probability of the match being positive) between two gait profiles. This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. The proposed model extracts gait profiles (from low-resolution camera feeds) by breaking down the complete gait cycle into four quarter-cycles using the variations in the width of the region-of-interest and then saves the gait profile in the form of four distinct projections (i.e., vectors) of length 20 units each, thus, summing up to 80 features for each individual’s gait profile. The focus of this study revolved around the speed-accuracy tradeoff of the proposed model where, with a limited dataset and training, the model runs at a speed of 30Hz and yields 85% accurate results on average. A Receiver Operating Characteristic Curve (ROC) is obtained for comparison of the proposed model with other machine learning models to better understand the efficiency of the system

In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research

Introduction: Cancer is one of the most important health issues in the current and next centuries. Understanding the mechanism of interaction between antibody-protein residues is essential for designing targeted anticancer drugs based on monoclonal antibodies. Prediction of the effective structure is the first step for production of monoclonal antibodies. Methods: This paper is a systematic review of the state-of-the-art researches on prediction of interaction sites and specification of antibody structures. Artificial neural networks or web servers are frequently used for evaluation of interaction sites while some researchers have employed evolutionary algorithms for prediction of the effective structure of antibodies. Accordingly, 14 methods based on the protein spatial structure, 28 researches based on the molecular amino-acide sequence (without usage of the spatial structure), and 18 antigen/antibody structure prediction techniques were reviewed. Results: We demonstrated that the accuracy of structure-based methods can be increased up to 80% while the acuracy of sequence-based methods was rarely better than 75%. Since the spatial structure of many antibodies is unknown, some researchers raised the accuracy (even to 96%) by only antibody sequences able to interact with some similar antigens in training neural networks. Therefore, we suggest this approach for structure prediction of monoclonal antibodies because of its adequate high accuracy. Conclusion: In this paper, after reviewing available methods for prediction of antibody-protein interaction sites, some suggestions were made for effective prediction of structure of monoclonal antibodies.

Artificial intelligence tools such as GBT Chat have sparked many discussions among academics; While some see them as a useful tool to speed up research and others see it as a threat to the integrity of authorship. Like it or not, large language models (LLM) are taking over the scientific literature in both good and bad ways. These irreplaceable free tools to use are and will be a reality, and it's up to us how we accept them and work with them. The biggest concern today in using artificial intelligence tools like ChatGPT is the lack of accuracy and the presentation of false information in such a way that the text may seem acceptable at first, but upon closer examination, it can be found that it contains false information; In addition, when the article is sent to a journal, the editors and reviewers will not have time to check the correctness of the article phrase by phrase and reference by reference. If scientific articles written with artificial intelligence tools are not properly reviewed, there is a high risk of providing false information; Especially among writers who do not specialize in this topic. Misuse of AI is a problem that can be solved by adjusting training methods. It is very important to develop curricula and educate university students about the ethical concepts of artificial intelligence, as well as train experts in the field of ethical issues of artificial intelligence to solve this problem. With the advancement of artificial intelligence, educational methods should be adjusted to combat abuse, and ethical considerations and responsibility in the use of this tool should be prioritized. We need to educate researchers about the pros and cons of this growing technology and focus on writing scientific papers that show the pros and cons of using them. Also, in order to combat the misuse of artificial intelligence, we need to involve all policymakers and users in a joint effort to create ethical frameworks and guidelines. In this context, we must create a multidisciplinary approach that involves experts in the fields of health information management, health policymaking, medical journalism, medical education, and ethicists in the development of curricula. Only through a joint effort can we ensure the responsible use of AI technology. By educating university students on ethical considerations and responsibility in the use of artificial intelligence tools such as ChatGPT, developing relevant curricula and providing guidelines on the correct use approach, we hope to provide an environment so that such tools do not become a tool of false information and with Human expertise and judgment should be used.

Purpose: Delisted companies, despite their importance in the economic and social issues of society, is less considered in the financial literature. This issue is important because for each country, one of the criteria for economic measurement is the size of the capital market. Therefore, the delisted companies not only destroys the company's reputation, its stock price and the market for the sale of its shares, but also affects the growth of the market and the economy of each country. The present study seeks to review the financial statements and audit reports of active companies and compare it with delisted companies to design a model for forecasting delisted companies in the Tehran Stock Exchange with the help of artificial intelligence modeling techniques. Methodology: In this study, which was conducted on companies of the Tehran Stock Exchange, data related to three years before the delisting of 73 companies removed from the stock exchange from 2003 to 2019 in the first group and data of 148 active companies that are continuously. They were present in the stock market in the second group and were selected by systematic elimination method. Then, with data mining techniques, which are among the most efficient and up-to-date models of artificial intelligence, and with the help of multi-layered perceptron neural network classifiers, decision tree, and Bayesian theory classifiers, stock delisted companies have been predicted. Findings: The findings show that the Bayesian classifier had the best performance and the multilayer perceptron neural network was in the second place and the decision tree classifier was in the third place. Originality/Value: Little research has been done in the field of predicting delisted companies from the Iran capital market. This study by filling this gap, suggests to researchers to use other classifiers, combine several classifiers together to better cover the errors of each, combine classifiers with each other and weigh in a way that is more accurate, add other variables influential in the dismissal of companies, including the ownership structure and shareholder composition can have other results. [ABSTRACT FROM AUTHOR]

Objective This systematic study aims to analyze qualitative research related to the use of Artificial Intelligence (AI) systems in urban management. The study seeks to achieve a comprehensive understanding of the current state of AI policy experiences in cities. By systematically reviewing and synthesizing prior findings in this domain, this research aims to contribute to the development of more effective policies for the responsible and efficient use of AI in urban management. Methods In this study, a meta synthesis approach was employed, relying on the seven-step framework proposed by Sandelowski and Barroso (2007). Thematic analysis was used as the tool for qualitative data analysis. To assess research quality, inter-coder agreement, provision of coded text samples, reporting of research process steps, and iterative reviewer discussions were utilized. Results Relevant articles were identified and analyzed. The findings from the coding process were categorized twice, first within a contextual framework following PESTLE’s analysis, and secondly into 27 proposed thematic categories. Furthermore, three dimensions of sustainability (data-driven, algorithmic, and ethical) in AI systems relevant to urban management were identified. Conclusion As a final outcome of this research, we propose a metaphorical framework called the "AI Policy Staircase for Cities." This framework comprises five steps: World view, Sociey, Legal system, Urban Governance, and Technology Management. Researchers of this metaphorical framework take a cross-disciplinary approach to prioritize among these five high-level concepts and introduce key dimensions within each, providing guidance to stakeholders involved in AI policy-making for urban management. [ABSTRACT FROM AUTHOR]

Ensemble modelling is expanding in several areas of engineering, especially different aspects of water engineering. Accurate estimation of saffron water requirement (SWR), an essential strategic production of the agriculture sector, is a crucial and influencing act in local water planning of this region. Hence, this study aimed to check the applicability of ensemble modelling in enhancing SWR at Birjand, Southern Khorasan, Iran. The actual water requirement of saffron was recorded in the field lysimetric laboratory at the University of Birjand. The simulation of water requirement was conducted utilizing Decision Tree Regression (DTR) with input climate features. Additionally, Boosting and Bagging methods were employed to establish and enhance the ensemble process of soil water requirement (SWR) simulations. To track the effectiveness of any method, some comparative tests were designed, such as statistical criteria (RMSE and MAE) detection, Violin plot analysis, over/underestimation, times series comparison, and error improvement test. Results indicated that although the acceptable performance of DTR in simulating SWR, the probable improvement was potentially felt. Derived results confirmed that supervised ensemble modelling (Boosting) could enhance the accuracy of DTR by more than 30 percent (reducing absolute error from 36 mm to 23.65 mm), resulting in declining RMSE from 0.44 mm to 0.07 mm. Further, different experiment outcomes revealed that the Boosting algorithm quality is more appealing than DTR and Bagging outputs. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is an emerging technology that simulate human intelligence in machines and systems for their application business. Development of artificial intelligence requires extensive R&D activities. Management of R&D in field of AI needs to deploy novel knowledge to formulate and implementation of strategy, assign resources, organize and use of special tools. This paper aims, to identify and prioritize the components of data driven management of R&D in artificial intelligence technology. A hybrid technique was employed to perform the research. In Qualitative part, the literature of topic is reviewed, and 12 experts are interreviewed. Their opinions are analyzed based on grounded theory and 8 axial components were identified. In Quantitative part, by a questionnaire, the opinions of 85 experts of R&D and artificial intelligence were gathered through a questionaire and analyized based on structural equations model. The relvance and validity of the components were confirmed. The found components were weighted and proiritized through SWARA method as: systematic management, resources supplying, capability of big data analytics, supportive policies, infrastructures, data science development, organizational factors and business advantages.

Background & Aims: In December 2019, the corona virus in China's Wuhan city, the center of Hubei province, and spread very quickly and completely involved society members and government bodies in all countries. On the other hand, today, with the existence of a new interface called social media, most of the natural conversations and expressions of people with each other and in small and large groups are recorded, and therefore, with the help of data mining and text mining tools, it is possible to analyze big data. Very close to the real estimates of what is going on in the public mind. The use of these emerging methods of computational analysis can provide an objective picture of what is happening in society in front of the eyes of the managers, and in this way, increase the quality of the analysis of managers and decision-makers lead to better policies and planning, and so on. These models can provide a basis for evaluating the effectiveness of adopted policies and programs. But in general, it can be said that at a time when there is still no clear understanding of the help that intelligent data mining can have by using cognitive approaches, among society members and managers of the country, the existence of such research can fulfill this understanding, expectation a, and demand. It created among society and its managers that, in principle, perceptions, and decisions should be made using methods that rely on objective big data. From this point of view, the importance of the current research is doubled in terms of the impact it will have in clarifying and introducing such methods and its macro decision makers. It is obvious that the error in perception is caused by unconscious perceptual biases, generalizing parts of society to the whole society, not being up-to-date with the changes and developments of social beliefs and ruling based on what was perceived in the past, and many other perceptual errors of this kind., leads to decisions, criticisms, and, wrong behaviors that both lose resources and opportunities and waste the capital and public trust of the society finally, leads to decisions and policies that are not only ineffective but sometimes leave destructive and harmful effects on society and solve the crisis. Theoretically, this type of analysis is new in the space of media analysis, and the aspects that this research deals with the natural crisis of Corona are new and, of course, very practical. The combined use of artificial intelligence and cognitive science in analysis has a lot of room for growth and this research can encourage interdisciplinary research and theoretical activities in this field. From a practical point of view, the results of this research will be very useful and practical for all those who are involved in the field of macro policies, media people, also related business owners. The presentation of the results and achievements of this research can lead to the demands of the stakeholders, but during the time of the research and when its achievements were revealed, based on the request of the National Corona Headquarters and relevant decision-making institutions in the country, consultations were given based on the achievements. It was their request. Therefore, the key issue in the current research is to find a model to explain the dynamics of changing feelings and attitudes of Athenian society in the natural crisis of Corona. This research is conducted on Persian-speaking social media users and for Iranian society. Methods: Since in order to achieve this goal, it is necessary to carry out a lot of research and examine the problem from various dimensions, the method of Meta synthesis was chosen, and for this purpose, a call was made in the last months of 2018 and with the peak of the Corona wave. And is affected by various aspects of people's lives, was held at the headquarters of the development of cognitive sciences and technologies. For this purpose, more than 25 million Persian language posts from social media were collected, preprocessed, and then made available to interested teams between February 1, 1998, and June 11, 1999. Then, the results of these plans have been extracted in the form of 148 propositions. Results: The results of the analysis of propositions, during qualitative research, led to the identification of 11 main categories, which were placed in 6 different dimensions of a comprehensive model. In this way, various aspects of the cognitive approach of Iranian social media users in this natural crisis led us to a comprehensive model that we called "Samee". Conclusion: In general, it can be stated that this model specifies that the influencers are effective on the mentalities, emotions, and perceptual attitudes and behavior of users in cyberspace, and again, the behaviors of users in cyberspace are effective on their mentalities, emotions, and attitudes. It is impressive. When the data of this research was extracted and analyzed, it was related to the first months of the corona outbreak. Obviously, similar research will be needed to carry out this study until the end of the Corona era and even months after that and to examine the next events and the cognitive approaches of social network users. In addition, other types of crises occur in societies, which are not necessarily natural crises, but this method of analysis can be done on those crises as well, and its results can be examined and applied in society. Using the methods introduced in this research can provide very valuable achievements for the managers and policymakers of the country. The research that this research is based on, points to the limitations of any computational work in the social and cultural field. We will eat them. One of the very key tasks to pave the way for such analyzes is to conduct preliminary research, which will generally be either in the direction of creating the necessary infrastructure for computational social sciences or in the direction of the necessary infrastructure for linguistic analysis and natural language processing in Persian. Since the theories and algorithms used in natural language processing for the Persian language are generally derived from the same theories and algorithms of natural language processing, researchers that deal with natural language processing with a cognitive approach can be very innovative and solve many problems that in these fields, it should be resolved. [ABSTRACT FROM AUTHOR]

Background and Objective: Water distribution networks are prone to terrorist attacks by injecting toxic substances, due to their vastness and availability. The main objective of this paper was detecting the extent of intentional pollution in the urban water distribution network by self-organizing map (SOM). Materials and Methods: The existing hydraulic condition of the water distribution network covered by reservoir No. 4 in Tehran was modeled as a pilot. Possible injection scenarios of contamination in different parts of the water distribution network were performed using qualitative analysis of the water distribution network, using the EPANET analyzer engine and coding in R software environment. Artificial neural network of SOM was used to find the contamination range for the injection of arsenic at different times and places in the distribution network. Results: The concentration of contamination at a certain point decreased over time and a high correlation was observed between time and concentration. The extent of contamination depended on the consumption of subscribers and consequently, the time of contaminat injection. The results of the artificial neural network model showed that the method developed in this research was 91% accurate and was able to determine the extent of contamination in the water distribution network at high speed. Conclusion: SOM can be used as a complement to the water quality monitoring and pollution detection system in the urban water distribution network to determine the extent of pollution when detecting potential pollution in the shortest possible time, and as an alternative to quantitative-qualitative modeling of the water network.

Detecting occluded faces is a non-trivial problem for face detection in computer vision. This challenge becomes more difficult when the occlusion covers majority of the face. Despite the high performance of current state-of-the-art face detection algorithms, the detection of occluded and covered faces is an unsolved problem and is still worthy of study. In this paper, a deep-learning-face-detection model Niqab-Face-Detector is proposed along with context-based labeling technique for detecting unconstrained veiled faces such as faces covered with niqab. An experimental test was conducted to evaluate the performances of the proposed model using the Niqab-Face dataset. The experiment showed encouraging results and improved accuracy compared with state-of-the-art face detection algorithms

One of the most significant issues and challenges of human existence since the emergence and advancement of technology is "being human." The meaning of "being human" (in this research), as opposed to machines, is existence with passion. According to the Danish philosopher "Søren Kierkegaard," passion is the most critical driver of human life. There is a belief that with the advancement of technology, human beings are getting closer to machines. Humans are entering the era of artificial intelligence and "transhumanism," which "Max Tegmark" calls "Life 3", not only the survival but also the existence of humans is endangered.. About two centuries ago, Kierkegaard, in his diaries and the book "Two Ages: A Literary review" pointed out that man is moving away from the passion in the modern age. The current research seeks to find the relationship between passion as the driving force of human life and the position of humans with machines and living in the era of transhumanism and artificial intelligence by examining Kierkegaard's opinions about the present age and human passion. As a result, by referring to Kierkegaard's theories, it is possible to understand that the best way to escape passivity for humans in the age of technology is to choose the authentic self and prove the spiritual existence of humans in the moral and religious stages, provided that such an idea is converted into action. [ABSTRACT FROM AUTHOR]

Evaluating the ultimate bearing capacity of piles has been always an important concern for geotechnical engineers. Pile setup is a term which refers to an increase in bearing capacity of pile after a specific time. This increase is mainly considered relevant to the dissipation of excess pore water pressure created as a result of disturbance of the soil around the pile. Many researches have been centered on the investigation of pile setup and the factors influencing that. Results indicate that soil and pile properties can affect the occurrence and intensity of this phenomenon. The application of artificial intelligence such as artificial neural networks and evolutionary algorithms are considered as efficient and powerful methods for prediction and function finding purposes. Group Method of Data Handling is an intelligent approach that operates in a similar pattern to artificial neural networks. In this system, dual combinations of input variables are created in the form of Kolmogorov-Gabor polynomials. Based on the evaluation criteria such as Root Mean Squared Error (RMSE) and determination coefficient (R^2), the polynomials with higher accuracy are selected and introduced to the next layer as inputs. This repetitive approach is used to reach the best polynomials predicting the target variable of the project. GMDH is a self-organized system in which the number of required layers and neurons are determined during the running process. In this paper, a dataset obtained from the literature review and contains information about 170 test piles derived in clay and mixed soil, is used in which the ultimate bearing capacity is considered as a target, while the other ones are independent variables. It is noticeable that to evaluate the efficiency of the ultimate model, data is randomly divided into training and testing data which the former includes 118 and the latter has used 52 data. The results of this study indicate that the initial bearing capacity of pile (End Of Driving) and undrained shear strength ( S_u) have a significant effect on the time-dependent increase in bearing capacity of pile. The ultimate model obtained from the GMDH system with the RMSE of 742.85 and the (R^2) of 0.768 is an acceptable equation that can be used in the process of pile design.

Artificial intelligence-based innovative technologies have advanced at an unprecedented rate in recent years. We can examine the legal implications of these technologies from a variety of angles. The focus of this study is on search engines' civil liability. It seeks to answer the question of how browsers should be held liable in the event of data misuse, based on general civil liability rules. This article employs a descriptive-analytical approach to investigate the Iranian civil liability system and its gaps in the face of artificial intelligence-based technologies, particularly search engines. The principles and pillars of civil liability of search engines in the misuse of user data are investigated in this regard. The findings of this study point to flaws in the Iranian civil liability system, as well as its lack of modernity and fault-oriented orientation. Using various legal tools, we must defend users' rights and prevent corporate abuse in the digital domain. Among the recommendations of this paper are the recognition of browsers' legal personality, the use of various mechanisms such as mandatory civil liability insurance, the acceptance of strict liability, and the adoption of special laws governing artificial intelligence.

Aim: The rapid acceleration of computer-based technologies is not only aimed at overshadowing the personal lives of people, but also people in line with new technologies, understand well its place and importance in various organizations, and want to use it to improve various functions. Libraries and librarians are expected to use the latest technologies as leaders in written records classification and presentation in science and technology. Considering the growing capabilities of technologies based on artificial intelligence, the main purpose of this study was to study the usability of Iranian products/software based on artificial intelligence in libraries and library software. Methodology: This research is applied in terms of purpose and analytical and descriptive survey in terms of quantitative method. The study population was all officially registered companies in Iran. By referring to the site and a large number of these companies, in the first stage, they were limited to companies that were registered in the field of computers, and so 780 companies were identified. In the second step, 92 companies that are active in the fields of software, especially artificial intelligence, and deep learning, were selected, and only companies that were active in the field of services and software of artificial intelligence and have a web were selected for review. The sites were active and according to these criteria, the research sample includes 21 companies active in the field of artificial intelligence software products. The data collection tool was a researcher-made checklist that included company demographic information and research questions, and its reliability was confirmed with Cronbach's alpha of 0.84. Findings: The research findings showed that most fields of activity of the mentioned companies were text, image, and audio processing, and the products of these companies can be used in library software. Asr Goyesh Pardaz and Sarzamin Hooshmand companies are among the oldest companies in this field that have been operating since 2005. Amer Andish Hooshmand Company with 30 employees has the largest number of specialized staff. Conclusion: The utilization of artificial intelligence in the field of library software in Iran is a nascent and growing science and companies related to this field to design and produce native software compatible with the official language of the country are on the path of progressive development. it can be said that libraries and librarians are also interested in using new technologies in libraries, although they need the training to use them. [ABSTRACT FROM AUTHOR]

Digital space is increasingly associated with the tourism industry, and content production as an important part of this space is seeking to attract customers. Content is the core of digital marketing which is in charge of educating, raising awareness, informing and creating added value for customers. The purpose of this study is to present a content marketing model based on artificial intelligence in the tourism industry of Fars province. This paper presents a content theory model based on data based theory. Genetic algorithm is used as one of the artificial intelligence algorithms for content selection, content format and content distribution channel. The results show that artificial intelligence has the ability to select the content that is more applicable and useful than the experts and human resources. Finally, this model was able to be efficient and effective in spreading content in Fars province tourism industry.

Objectives: Blockchain was originally a mechanism for providing bitcoin cryptocurrencies, but it soon deviated from its original purpose and now spans a variety of industries. This work provides a systematic literature review of multiple domains of blockchain. In this regard, the purpose of the article is to identify the areas of application, advantages, challenges, and related technologies of blockchain. Methods: This research is a systematic review. All articles were collected from PubMed, Google Scholar, ACM, ScienceDirect, IEE, emerald, Elsevier, Applied sciences, and ResearchGate databases between 2014 and 2020. Based on the search conducted in the mentioned databases, 295 sources were collected as databases for review. Then, in four stages and by removing unrelated sources, 90 valid sources were analyzed and the axes related to blockchain were extracted. Results: Based on the finding, the areas of blockchain applications were classified into 26 axes, with the highest frequency in the areas of Smart contract (22 sources), supply chain management (21 sources), health (21 sources), governance requirements (18 sources), finance (18 sources), energy (14 sources), education (13 sources), smart city (11 sources), cryptocurrencies (8 sources), public transportation (8 sources), agriculture and food (7 sources), Insurance(7 sources) and banking(7 sources). Blockchain-related technologies are based on the frequency of resources reviewed including the internet of things (22 sources), cloud computing (10 sources), artificial intelligence (8 sources), big data (7 sources), and edge computing (4 sources). The benefits of blockchain were identified in 24 areas that the most frequently of which were Tracking and transparency (27 sources), Information security (24 sources), Privacy and confidentiality (14 sources), building trust (11 sources), immutability (11 sources), decentralization (10 sources), cost savings (7 sources), Manage and store data (7 sources) and Digital asset maintenance (7 sources). Also, the challenges of using blockchain were identified in 18 cases scalability (14 sources), lack of regulations (10 sources), energy consumption (9 sources), complexity (5 sources), and unrecoverable (4 sources) in this area are the most common. At the end of the article, while referring to blockchain research in countries, the research gap and its future directions are presented. Conclusions: Blockchain is considered one of the main technologies of the fourth industrial revolution. Our Findings show that blockchain is a multidimensional, complex, and widely used technology. Due to the radical nature of blockchain, conceptual and theoretical discussions continue in this direction. Our initial classification of the four blockchain dimensions can help determine the architecture of this technology in organizations and communities. Our study can serve as a basis or inspiration for future work. [ABSTRACT FROM AUTHOR]

Background: The fourth industrial revolution consists of combining network devices with cloud computing methods and analyzing large data and artificial intelligence, which makes it possible to call such an infrastructure smart. In a Smart Hospital, all things and devices are designed to be connected and integrated, thus achieving better patient care, increasing efficiency and reducing time waste. Therefore, the aim of this paper was to recognize the components of smart hospital based on disruptive technologies of industry 4.0. Materials and Methods: This applied research has been done in two phases using qualitative approach in winter 2019. In the first step, the components of smart hospital were recognized from previous studies. In the second step, research experts evaluate conceptual model by Delphi method. The expert panel consists of 15 individuals active in information technology in healthcare according to targeted sampling. Results: According to research results the main components of smart hospital are eight technologies: Internet of things technology, robotic, blockchain technology, cloud computing, big data, augmented and virtual reality technology, additive manufacturing and artificial intelligence. Conclusion: According to components of smart hospital, Hospitals managers should equip their organization and adopt process and equipment by disruptive technologies. Due to sanctions, investment in Iranian knowledge-based companies active in new technologies field and Joint venture with them can be a suitable solution for healthcare policymakers.

Artificial intelligence (AI) is a discipline interested in the processes and methods that allow a machine to perform tasks related to human intelligence. It offers many opportunities related to problem solving, quick decision-making, increasing efficiency and reducing costs. Because of its so various fields of application, artificial intelligence is at the heart of the new industrial revolution. Algeria aims to present its AI strategy by 2020. In this paper, we are interested in defining AI, its potential fields of application, and in particular, its influence in the customer journey and position of RFID (Radio-Frequency Identification) in the chain; application in the aviation sector and its relationship to the Internet of Things are also described through examples.

Received: 31/10/2018 | Accepted: 28/02/2019 There are many new philosophical queries about the moral status and rights of artificial intelligences; questions such as whether such entities can be considered as morally responsible entities and as having special rights. Recently, the contemporary philosophy of mind philosopher, Eric Schwitzgebel, has tried to defend the possibility of equal rights of AIs and human beings (in an imaginary future), by designing a new argument (2015). In this paper, after an introduction, the author reviews and analyzes the main argument and then deals with four criticisms witch Schwitzgebel himself has presented and rejected and ultimately, the author presents and explains two new critiques against the argument. According to the writer, though the argument regarding his new criticisms and some other probable critiques is not convincing, or at least needs to be corrected, however, it holds appeal and raises questions and may open a new horizon for future questions and researches.[1] Keywords [1]. I consider it my duty to thank Tyler Burge (UCLA), Eric Schwitzgebel (University of California, Riverside), Sam Cumming (UCLA) and David Chalmers (New York University) for their beneficial conversations and emails regarding Schwitzgebel’s argument and the two final critiques. Also I am very grateful to the reviewers for the Journal of Philosophical Theological Research for their precise comments. 🞕 Mazarian, A. (2019). Critical Analysis of the “No Relevant Difference” Argument in Defense of the Rights of Artificial Intelligences, The Journal of Philosophical-Theological Research, 21(79), 165۔ 190. https://doi.org/ 10.22091/jptr-pfk.2019.3925.2023.

The deregulation in power market is lead to competition among market participant to increase efficiency. In electricity market generation is the best candidate for iterance in competition to improve productivity and efficiency in resource allocation and offer lowest price by highest quality will be yielded. In the pool-based electricity market, every Genco submits a bidding price in ten step offer to the Independent System Operator (ISO) for every hour of the next day. One of the most important parameters affecting the profitability of Genco’s and their bidding pattern is the cost of energy generation. Therefore, in this paper, taking into account the costs of fuel consumption, depreciation, maintenance, operation and utilization and other costs as factors affecting the production cost. In this article artificial intelligence algorithms applied to calculate the energy generation cost function at different levels of production (base load, partial load, and full load), broken down by fuel type. According to the results, the average cost per kilowatt-hour of energy with gas fuel is 398 Rials and diesel fuel is 500 Rials