Abstract The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm. Our study proposes online and mini-batch Gaussian process regression algorithms that are easier to implement and faster to estimate for reinforcement learning. In our algorithm, the Gaussian process regression updates the value function through only the computation of two equations, which we then use to construct reinforcement learning algorithms. Our numerical experiments show that the proposed algorithm works as well as those from previous studies.
{"title":"Reinforcement learning with Gaussian process regression using variational free energy","authors":"Kiseki Kameda, F. Tanaka","doi":"10.1515/jisys-2022-0205","DOIUrl":"https://doi.org/10.1515/jisys-2022-0205","url":null,"abstract":"Abstract The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm. Our study proposes online and mini-batch Gaussian process regression algorithms that are easier to implement and faster to estimate for reinforcement learning. In our algorithm, the Gaussian process regression updates the value function through only the computation of two equations, which we then use to construct reinforcement learning algorithms. Our numerical experiments show that the proposed algorithm works as well as those from previous studies.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75118897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Knowledge reduction of information systems is one of the most important parts of rough set theory in real-world applications. Based on the connections between the rough set theory and the theory of topology, a kind of topological reduction of incomplete information systems is discussed. In this study, the topological reduction of incomplete information systems is characterized by belief and plausibility functions from evidence theory. First, we present that a topological space induced by a pair of approximation operators in an incomplete information system is pseudo-discrete, which deduces a partition. Then, the topological reduction is characterized by the belief and plausibility function values of the sets in the partition. A topological reduction algorithm for computing the topological reducts in incomplete information systems is also proposed based on evidence theory, and its efficiency is examined by an example. Moreover, relationships among the concepts of topological reduct, classical reduct, belief reduct, and plausibility reduct of an incomplete information system are presented.
{"title":"On numerical characterizations of the topological reduction of incomplete information systems based on evidence theory","authors":"Changqing Li, Yanlan Zhang","doi":"10.1515/jisys-2022-0214","DOIUrl":"https://doi.org/10.1515/jisys-2022-0214","url":null,"abstract":"Abstract Knowledge reduction of information systems is one of the most important parts of rough set theory in real-world applications. Based on the connections between the rough set theory and the theory of topology, a kind of topological reduction of incomplete information systems is discussed. In this study, the topological reduction of incomplete information systems is characterized by belief and plausibility functions from evidence theory. First, we present that a topological space induced by a pair of approximation operators in an incomplete information system is pseudo-discrete, which deduces a partition. Then, the topological reduction is characterized by the belief and plausibility function values of the sets in the partition. A topological reduction algorithm for computing the topological reducts in incomplete information systems is also proposed based on evidence theory, and its efficiency is examined by an example. Moreover, relationships among the concepts of topological reduct, classical reduct, belief reduct, and plausibility reduct of an incomplete information system are presented.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"106 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87091537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The existing predictive control strategy has comprehensive prior knowledge of the controlled process, requires weak continuity of the search space for parameter optimization, and its application is limited to some extent. Therefore, improved research on the fuzzy fractional proportional integral differential (PID) predictive control algorithm is proposed. First, the control principle of PID predictive control equipment is proposed. According to this principle, the structure of the PID predictive control equipment adaptive fuzzy PID energy-saving controller is constructed. Through the PID energy-saving control parameter setting principle and fuzzy control rules, the adaptive fuzzy PID energy-saving control of PID predictive control equipment is realized. Finally, the fractional order PID predictive transfer function model is constructed to improve the predictive control algorithm based on PID optimization technology. The experimental results show that the accuracy and efficiency of the designed algorithm can get the best performance index, and its stability, overshoot, time, and control accuracy are basically unchanged. In the small area temperature control, the disturbance interference is small, the anti-disturbance ability is good, and it has strong robustness.
{"title":"Improvement of predictive control algorithm based on fuzzy fractional order PID","authors":"Rongzhen Shi","doi":"10.1515/jisys-2022-0288","DOIUrl":"https://doi.org/10.1515/jisys-2022-0288","url":null,"abstract":"Abstract The existing predictive control strategy has comprehensive prior knowledge of the controlled process, requires weak continuity of the search space for parameter optimization, and its application is limited to some extent. Therefore, improved research on the fuzzy fractional proportional integral differential (PID) predictive control algorithm is proposed. First, the control principle of PID predictive control equipment is proposed. According to this principle, the structure of the PID predictive control equipment adaptive fuzzy PID energy-saving controller is constructed. Through the PID energy-saving control parameter setting principle and fuzzy control rules, the adaptive fuzzy PID energy-saving control of PID predictive control equipment is realized. Finally, the fractional order PID predictive transfer function model is constructed to improve the predictive control algorithm based on PID optimization technology. The experimental results show that the accuracy and efficiency of the designed algorithm can get the best performance index, and its stability, overshoot, time, and control accuracy are basically unchanged. In the small area temperature control, the disturbance interference is small, the anti-disturbance ability is good, and it has strong robustness.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134882972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.
{"title":"Application study of ant colony algorithm for network data transmission path scheduling optimization","authors":"Peng Xiao","doi":"10.1515/jisys-2022-0277","DOIUrl":"https://doi.org/10.1515/jisys-2022-0277","url":null,"abstract":"Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86173934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The objective of this study is to supply an overview of research work based on video-based networks and tiny object identification. The identification of tiny items and video objects, as well as research on current technologies, are discussed first. The detection, loss function, and optimization techniques are classified and described in the form of a comparison table. These comparison tables are designed to help you identify differences in research utility, accuracy, and calculations. Finally, it highlights some future trends in video and small object detection (people, cars, animals, etc.), loss functions, and optimization techniques for solving new problems.
{"title":"A review of small object and movement detection based loss function and optimized technique","authors":"R. Chaturvedi, Udayan Ghose","doi":"10.1515/jisys-2022-0324","DOIUrl":"https://doi.org/10.1515/jisys-2022-0324","url":null,"abstract":"Abstract The objective of this study is to supply an overview of research work based on video-based networks and tiny object identification. The identification of tiny items and video objects, as well as research on current technologies, are discussed first. The detection, loss function, and optimization techniques are classified and described in the form of a comparison table. These comparison tables are designed to help you identify differences in research utility, accuracy, and calculations. Finally, it highlights some future trends in video and small object detection (people, cars, animals, etc.), loss functions, and optimization techniques for solving new problems.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"27 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86483691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based on the percentage of an accuracy measure of the previous work, are surveyed and introduced, with the aim of producing a concise review of using these algorithms in crime prediction. It is expected that this review study will be helpful for presenting such techniques to crime researchers in addition to supporting future research to develop these techniques for crime analysis by presenting some crime definition, prediction systems challenges and classifications with a comparative study. It was proved though literature, that supervised learning approaches were used in more studies for crime prediction than other approaches, and Logistic Regression is the most powerful method in predicting crime.
{"title":"A study on predicting crime rates through machine learning and data mining using text","authors":"Ruaa Mohammed Saeed, Husam Ali Abdulmohsin","doi":"10.1515/jisys-2022-0223","DOIUrl":"https://doi.org/10.1515/jisys-2022-0223","url":null,"abstract":"Abstract Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based on the percentage of an accuracy measure of the previous work, are surveyed and introduced, with the aim of producing a concise review of using these algorithms in crime prediction. It is expected that this review study will be helpful for presenting such techniques to crime researchers in addition to supporting future research to develop these techniques for crime analysis by presenting some crime definition, prediction systems challenges and classifications with a comparative study. It was proved though literature, that supervised learning approaches were used in more studies for crime prediction than other approaches, and Logistic Regression is the most powerful method in predicting crime.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"80 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73027349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A good AI algorithm can make accurate predictions and provide reasonable explanations for the field in which it is applied. However, the application of deep models makes the black box problem, i.e., the lack of interpretability of a model, more prominent. In particular, when there are multiple features in an application domain and complex interactions between these features, it is difficult for a deep model to intuitively explain its prediction results. Moreover, in practical applications, multiorder feature interactions are ubiquitous. To break the interpretation limitations of deep models, we argue that a multiorder linearly separable deep model can be divided into different orders to explain its prediction results. Inspired by the interpretability advantage of tree models, we design a feature representation mechanism that can consistently represent the features of both trees and deep models. Based on the consistent representation, we propose a multiorder feature-tracking strategy to provide a prediction-oriented multiorder explanation for a linearly separable deep model. In experiments, we have empirically verified the effectiveness of our approach in two binary classification application scenarios: education and marketing. Experimental results show that our model can intuitively represent complex relationships between features through diversified multiorder explanations.
{"title":"A multiorder feature tracking and explanation strategy for explainable deep learning","authors":"Lin Zheng, Yixuan Lin","doi":"10.1515/jisys-2022-0212","DOIUrl":"https://doi.org/10.1515/jisys-2022-0212","url":null,"abstract":"Abstract A good AI algorithm can make accurate predictions and provide reasonable explanations for the field in which it is applied. However, the application of deep models makes the black box problem, i.e., the lack of interpretability of a model, more prominent. In particular, when there are multiple features in an application domain and complex interactions between these features, it is difficult for a deep model to intuitively explain its prediction results. Moreover, in practical applications, multiorder feature interactions are ubiquitous. To break the interpretation limitations of deep models, we argue that a multiorder linearly separable deep model can be divided into different orders to explain its prediction results. Inspired by the interpretability advantage of tree models, we design a feature representation mechanism that can consistently represent the features of both trees and deep models. Based on the consistent representation, we propose a multiorder feature-tracking strategy to provide a prediction-oriented multiorder explanation for a linearly separable deep model. In experiments, we have empirically verified the effectiveness of our approach in two binary classification application scenarios: education and marketing. Experimental results show that our model can intuitively represent complex relationships between features through diversified multiorder explanations.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"51 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73790806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Anti-jamming cyclic frequency feature extraction is an important link in identifying communication interference signals, which is of great significance for eliminating electronic communication interference factors and improving the security of electronic communication environment. However, when the traditional feature extraction technology faces a large number of data samples, the processing capacity is low, and it cannot solve the multi-classification problems. For this type of problem, a method of electronic communication signal anti-jamming cyclic frequency feature extraction based on particle swarm optimization-support vector machines (PSO-SVM) algorithm is proposed. First, the SVM signal feature extraction model is proposed, and then the particle swarm optimization (PSO) algorithm is used. Optimize the kernel function parameter settings of SVM to raise the classifying quality of the SVM model. Finally, the function of the PSO-SVM signal feature extraction model is tested. The results verify that the PSO-SVM model begins to converge after 60 iterations, and the loss value remains at about 0.2, which is 0.2 lower than that of the SVM technique. The exactitude of signal feature extraction is 90.4%, and the recognition effect of binary phase shift keying signal is the best. The complete rate of signal feature extraction is 85%. This shows that the PSO-SVM model enhances the sensitivity of the anti-jamming cyclic frequency feature, improves the accuracy of the anti-jamming cyclic frequency feature recognition, reduces the running process, reduces the time cost, and greatly increases the performance of the SVM method. The good model performance also improves the application value of the method in the field of electronic communication.
{"title":"Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal","authors":"Xuemei Yang","doi":"10.1515/jisys-2022-0295","DOIUrl":"https://doi.org/10.1515/jisys-2022-0295","url":null,"abstract":"Abstract Anti-jamming cyclic frequency feature extraction is an important link in identifying communication interference signals, which is of great significance for eliminating electronic communication interference factors and improving the security of electronic communication environment. However, when the traditional feature extraction technology faces a large number of data samples, the processing capacity is low, and it cannot solve the multi-classification problems. For this type of problem, a method of electronic communication signal anti-jamming cyclic frequency feature extraction based on particle swarm optimization-support vector machines (PSO-SVM) algorithm is proposed. First, the SVM signal feature extraction model is proposed, and then the particle swarm optimization (PSO) algorithm is used. Optimize the kernel function parameter settings of SVM to raise the classifying quality of the SVM model. Finally, the function of the PSO-SVM signal feature extraction model is tested. The results verify that the PSO-SVM model begins to converge after 60 iterations, and the loss value remains at about 0.2, which is 0.2 lower than that of the SVM technique. The exactitude of signal feature extraction is 90.4%, and the recognition effect of binary phase shift keying signal is the best. The complete rate of signal feature extraction is 85%. This shows that the PSO-SVM model enhances the sensitivity of the anti-jamming cyclic frequency feature, improves the accuracy of the anti-jamming cyclic frequency feature recognition, reduces the running process, reduces the time cost, and greatly increases the performance of the SVM method. The good model performance also improves the application value of the method in the field of electronic communication.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136305484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maha Mahmood, Farah Maath Jasem, Abdulrahman Abbas Mukhlif, Belal AL-Khateeb
Abstract Problem Recognizing written languages using symbols written in cuneiform is a tough endeavor due to the lack of information and the challenge of the process of tokenization. The Cuneiform Language Identification (CLI) dataset attempts to understand seven cuneiform languages and dialects, including Sumerian and six dialects of the Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. However, this dataset suffers from the problem of imbalanced categories. Aim Therefore, this article aims to build a system capable of distinguishing between several cuneiform languages and solving the problem of unbalanced categories in the CLI dataset. Methods Oversampling technique was used to balance the dataset, and the performance of machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and deep learning such as deep neural networks (DNNs) using the unigram feature extraction method was investigated. Results The proposed method using machine learning algorithms (SVM, KNN, DT, and RF) on a balanced dataset obtained an accuracy of 88.15, 88.14, 94.13, and 95.46%, respectively, while the DNN model got an accuracy of 93%. This proves improved performance compared to related works. Conclusion This proves the improvement of classifiers when working on a balanced dataset. The use of unigram features also showed an improvement in the performance of the classifier as it reduced the size of the data and accelerated the processing process.
{"title":"Classifying cuneiform symbols using machine learning algorithms with unigram features on a balanced dataset","authors":"Maha Mahmood, Farah Maath Jasem, Abdulrahman Abbas Mukhlif, Belal AL-Khateeb","doi":"10.1515/jisys-2023-0087","DOIUrl":"https://doi.org/10.1515/jisys-2023-0087","url":null,"abstract":"Abstract Problem Recognizing written languages using symbols written in cuneiform is a tough endeavor due to the lack of information and the challenge of the process of tokenization. The Cuneiform Language Identification (CLI) dataset attempts to understand seven cuneiform languages and dialects, including Sumerian and six dialects of the Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. However, this dataset suffers from the problem of imbalanced categories. Aim Therefore, this article aims to build a system capable of distinguishing between several cuneiform languages and solving the problem of unbalanced categories in the CLI dataset. Methods Oversampling technique was used to balance the dataset, and the performance of machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and deep learning such as deep neural networks (DNNs) using the unigram feature extraction method was investigated. Results The proposed method using machine learning algorithms (SVM, KNN, DT, and RF) on a balanced dataset obtained an accuracy of 88.15, 88.14, 94.13, and 95.46%, respectively, while the DNN model got an accuracy of 93%. This proves improved performance compared to related works. Conclusion This proves the improvement of classifiers when working on a balanced dataset. The use of unigram features also showed an improvement in the performance of the classifier as it reduced the size of the data and accelerated the processing process.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135699439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The Internet of medical things (IoMT) is a modern technology that is increasingly being used to provide good healthcare services. As IoMT devices are vulnerable to cyberattacks, healthcare centers and patients face privacy and security challenges. A safe IoMT environment has been used by combining blockchain (BC) technology with artificial intelligence (AI). However, the services of the systems are costly and suffer from security and privacy problems. This study aims to summarize previous research in the IoMT and discusses the roles of AI, BC, and cybersecurity in the IoMT, as well as the problems, opportunities, and directions of research in this field based on a comprehensive literature review. This review describes the integration schemes of AI, BC, and cybersecurity technologies, which can support the development of new systems based on a decentralized approach, especially in healthcare applications. This study also identifies the strengths and weaknesses of these technologies, as well as the datasets they use.
{"title":"Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions","authors":"Aya Hamid Ameen, M. A. Mohammed, A. N. Rashid","doi":"10.1515/jisys-2022-0267","DOIUrl":"https://doi.org/10.1515/jisys-2022-0267","url":null,"abstract":"Abstract The Internet of medical things (IoMT) is a modern technology that is increasingly being used to provide good healthcare services. As IoMT devices are vulnerable to cyberattacks, healthcare centers and patients face privacy and security challenges. A safe IoMT environment has been used by combining blockchain (BC) technology with artificial intelligence (AI). However, the services of the systems are costly and suffer from security and privacy problems. This study aims to summarize previous research in the IoMT and discusses the roles of AI, BC, and cybersecurity in the IoMT, as well as the problems, opportunities, and directions of research in this field based on a comprehensive literature review. This review describes the integration schemes of AI, BC, and cybersecurity technologies, which can support the development of new systems based on a decentralized approach, especially in healthcare applications. This study also identifies the strengths and weaknesses of these technologies, as well as the datasets they use.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"55 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83832482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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