Pub Date : 2023-01-05DOI: 10.53759/181x/jcns202303002
A. Haldorai
With the use of Internet of Things (IoT), businesses can easily collect real-time information on all physical components in their operations. The use of Artificial Intelligence (AI) is growing in IoT applications and businesses, signaling a shift in how these businesses operate. Across the globe, businesses are rapidly adopting IoT technology to develop cutting-edge products and services, therefore creating a novel market niches and strategic directions. IoT and CPS (Cyber-Physical Systems) integrated with data science could potentially stimulate the next generation of "smart revolution." The problem that emerges then is how to effectively manage big data engendered with less current processing capacity. This paper reviews the elements of AI, IoT and CPS, including the components of IoT-CPS as well as defining the relationship between AI and IoT-CPS. In the review, it is noted that AI is vital in many application scenarios, but there are problems associated with this technology in the modern world. To deal with problem in an AI-enabled IoT environment, a more reliable AI system should be researched and integrated in real-life applications.
{"title":"A Review on Artificial Intelligence in Internet of Things and Cyber Physical Systems","authors":"A. Haldorai","doi":"10.53759/181x/jcns202303002","DOIUrl":"https://doi.org/10.53759/181x/jcns202303002","url":null,"abstract":"With the use of Internet of Things (IoT), businesses can easily collect real-time information on all physical components in their operations. The use of Artificial Intelligence (AI) is growing in IoT applications and businesses, signaling a shift in how these businesses operate. Across the globe, businesses are rapidly adopting IoT technology to develop cutting-edge products and services, therefore creating a novel market niches and strategic directions. IoT and CPS (Cyber-Physical Systems) integrated with data science could potentially stimulate the next generation of \"smart revolution.\" The problem that emerges then is how to effectively manage big data engendered with less current processing capacity. This paper reviews the elements of AI, IoT and CPS, including the components of IoT-CPS as well as defining the relationship between AI and IoT-CPS. In the review, it is noted that AI is vital in many application scenarios, but there are problems associated with this technology in the modern world. To deal with problem in an AI-enabled IoT environment, a more reliable AI system should be researched and integrated in real-life applications.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131194699","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}
Pub Date : 2022-10-05DOI: 10.53759/181x/jcns202202018
X. Hou, Donglai Lu
"Smart-Embedded Systems," or Cyber-Physical Systems (CPS), bring together physical, computational, and network resources into a single integrated system. These resources are put to work to provide a solid basis for a better quality of life and a more upscale standard of living. CPSs (Cyber-Physical Systems) are vital in supporting the need for smart products (e.g., airports, homes, cities, hospitals). Control the three sorts of resources Doi: physical, cognitive, and network. In order to uncover the essential knowledge in the various real-life segments, e.g., distribution, power transmission and telecommunication, CPSs stimulates human-to-human interaction via this regulatory measure Information security is a critical concern in today's technological world. Due to the complexity of the components and middleware in CPS, it is difficult to keep them safe from cyberattacks while yet allowing them to operate quickly and effectively. An in-depth explanation of CPS, their issues (including cyber threats), characteristics, and associated technology is provided in this paper Aside from security and performance, we also describe the prevalent Side Channel Attacks (SCA) e.g., Differential Power Analysis (DPA) and Simple Power Analysis (SPA), on cryptographic techniques (such as RSA and AES) and the countermeasures that may be used to protect against these attacks.
{"title":"Security Evaluation of Side Channel Attacks on CPS Architectures","authors":"X. Hou, Donglai Lu","doi":"10.53759/181x/jcns202202018","DOIUrl":"https://doi.org/10.53759/181x/jcns202202018","url":null,"abstract":"\"Smart-Embedded Systems,\" or Cyber-Physical Systems (CPS), bring together physical, computational, and network resources into a single integrated system. These resources are put to work to provide a solid basis for a better quality of life and a more upscale standard of living. CPSs (Cyber-Physical Systems) are vital in supporting the need for smart products (e.g., airports, homes, cities, hospitals). Control the three sorts of resources Doi: physical, cognitive, and network. In order to uncover the essential knowledge in the various real-life segments, e.g., distribution, power transmission and telecommunication, CPSs stimulates human-to-human interaction via this regulatory measure Information security is a critical concern in today's technological world. Due to the complexity of the components and middleware in CPS, it is difficult to keep them safe from cyberattacks while yet allowing them to operate quickly and effectively. An in-depth explanation of CPS, their issues (including cyber threats), characteristics, and associated technology is provided in this paper Aside from security and performance, we also describe the prevalent Side Channel Attacks (SCA) e.g., Differential Power Analysis (DPA) and Simple Power Analysis (SPA), on cryptographic techniques (such as RSA and AES) and the countermeasures that may be used to protect against these attacks.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124513279","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}
Pub Date : 2022-10-05DOI: 10.53759/181x/jcns202202016
Ivan Leonid
Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.
{"title":"Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms","authors":"Ivan Leonid","doi":"10.53759/181x/jcns202202016","DOIUrl":"https://doi.org/10.53759/181x/jcns202202016","url":null,"abstract":"Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, \"deep learning\" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"31 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120837835","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}
Pub Date : 2022-10-05DOI: 10.53759/181x/jcns202202019
Zhonghe Zuyan
Medical Cyber-Physical System (MCPS) is a system of diagnostic instruments that is vital to healthcare. Hospitals are increasingly using these technologies to ensure that patients get high-quality treatment at all times. To name just a few, the MCPS system software confronts high assurance requirements, inoperability, security and privacy, and a host of other issues. There is an assessment and discussion of the CPS infrastructure in the present work. The study into interconnected medical device systems (MDs) to improve healthcare safety and efficiency was improved by this article. In addition, experts in medical device design may benefit from this software, which can aid them in overcoming critical obstacles and challenges in the design of the healthcare device's networks. Wireless Sensor Networks (WSNs) and networking are both discussed, as well as their respective security. After that, the CPS technologies and frameworks were built, and greater attention was paid to CPS-based healthcare.
{"title":"An Review of Medical Cyber Physical System Architecture and STP Issues","authors":"Zhonghe Zuyan","doi":"10.53759/181x/jcns202202019","DOIUrl":"https://doi.org/10.53759/181x/jcns202202019","url":null,"abstract":"Medical Cyber-Physical System (MCPS) is a system of diagnostic instruments that is vital to healthcare. Hospitals are increasingly using these technologies to ensure that patients get high-quality treatment at all times. To name just a few, the MCPS system software confronts high assurance requirements, inoperability, security and privacy, and a host of other issues. There is an assessment and discussion of the CPS infrastructure in the present work. The study into interconnected medical device systems (MDs) to improve healthcare safety and efficiency was improved by this article. In addition, experts in medical device design may benefit from this software, which can aid them in overcoming critical obstacles and challenges in the design of the healthcare device's networks. Wireless Sensor Networks (WSNs) and networking are both discussed, as well as their respective security. After that, the CPS technologies and frameworks were built, and greater attention was paid to CPS-based healthcare.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116731437","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}
Pub Date : 2022-10-05DOI: 10.53759/181x/jcns202202020
A. Haldorai
A broad area of research, known as "Agent-Based Computing", focuses on developing "agent-based" intelligent software using agent-based techniques. However, there is a scarcity of research focusing on providing enough evidence of the superiority of agent-based techniques in creating complex software systems. This article has attempted to provide evidence for why agent-based techniques are superior to traditional methods for creating complex software systems, such as control systems. A case of a distinct agent-based control system (the power transportation management system used by Iber-26 drola) is used to illustrate these broader principles. This line of reasoning allows advocates of complex software engineering paradigms to accurately assert that their method can replicate the essential ideas behind agent-based computing. When broken down to their most basic components, agent-based systems are just computer programmes, and every programme has the same set of computable functionalities. The value of a paradigm lies in the way of thinking and the tools it teaches to programmers. As such, agent-based ideas and approaches are not just an extension of those now accessible within existing paradigms, but also well suited to the development of large, networked systems.
{"title":"Analysis on Intelligent Agent based Approach for Software Engineering","authors":"A. Haldorai","doi":"10.53759/181x/jcns202202020","DOIUrl":"https://doi.org/10.53759/181x/jcns202202020","url":null,"abstract":"A broad area of research, known as \"Agent-Based Computing\", focuses on developing \"agent-based\" intelligent software using agent-based techniques. However, there is a scarcity of research focusing on providing enough evidence of the superiority of agent-based techniques in creating complex software systems. This article has attempted to provide evidence for why agent-based techniques are superior to traditional methods for creating complex software systems, such as control systems. A case of a distinct agent-based control system (the power transportation management system used by Iber-26 drola) is used to illustrate these broader principles. This line of reasoning allows advocates of complex software engineering paradigms to accurately assert that their method can replicate the essential ideas behind agent-based computing. When broken down to their most basic components, agent-based systems are just computer programmes, and every programme has the same set of computable functionalities. The value of a paradigm lies in the way of thinking and the tools it teaches to programmers. As such, agent-based ideas and approaches are not just an extension of those now accessible within existing paradigms, but also well suited to the development of large, networked systems.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930744","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}
Pub Date : 2022-10-05DOI: 10.53759/181x/jcns202202017
Burova Margarita
In order to make physical systems run more efficiently and effectively at multiple levels of information processing, Cyber-Physical Systems (CPS) combine physical processes, Computing, Communication, and Control (the 3Cs) into a single system. There are many factors that make solving the problem of packet transmission in CPS difficult, including unforeseeable node mobility, low number of nodes, lack of global data, and intermittent network access. Currently, there is no good answer to this problem in the literature. The impact of the CPS on engineered systems is much greater in the present and future. Cyber-physical system integration often presents unique challenges in the fields of design, implementation, and application. One of the goals of this research is to better understand the various definitions of an integrated CPS, as well as the growth of new research areas in this field. The application of CPSs faces a number of difficulties, including those related to efficiency, reliability, controllability, and security. We are moving forward in technological innovation with the development of CPSs and the Internet of Things (IoT). It is possible for engineers to gain a better understanding of engineering systems and management modules by utilizing the CPS-IoT models.
{"title":"An Evaluation of Internet of Things and Cyber-Physical System Integration","authors":"Burova Margarita","doi":"10.53759/181x/jcns202202017","DOIUrl":"https://doi.org/10.53759/181x/jcns202202017","url":null,"abstract":"In order to make physical systems run more efficiently and effectively at multiple levels of information processing, Cyber-Physical Systems (CPS) combine physical processes, Computing, Communication, and Control (the 3Cs) into a single system. There are many factors that make solving the problem of packet transmission in CPS difficult, including unforeseeable node mobility, low number of nodes, lack of global data, and intermittent network access. Currently, there is no good answer to this problem in the literature. The impact of the CPS on engineered systems is much greater in the present and future. Cyber-physical system integration often presents unique challenges in the fields of design, implementation, and application. One of the goals of this research is to better understand the various definitions of an integrated CPS, as well as the growth of new research areas in this field. The application of CPSs faces a number of difficulties, including those related to efficiency, reliability, controllability, and security. We are moving forward in technological innovation with the development of CPSs and the Internet of Things (IoT). It is possible for engineers to gain a better understanding of engineering systems and management modules by utilizing the CPS-IoT models.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131210595","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}
Pub Date : 2022-07-05DOI: 10.53759/181x/jcns202202013
Sidny Chalhoub
In the process of analysing data, outlier detection (i.e., anomaly detection or novelty identification) is often misinterpreted to an identification of rare observations, occurrence or an item, which deviates highly from enormous data and never conforms to well- defined ideologies of a normal behaviour. The samples could stimulate more suspicion of being produced from various techniques, or appear unpredictable with the remaining portion of the specific dataset. Anomaly detection draws application in different domains such as neuroscience, statistics, machine vision, medicine, financial fraud, law enforcement and cyber security. The data that has been collected from real-life applications are rapidly increasing in dimension and size. As the aspect of dimensionality keeps increasing, data items become significantly sparse, amounting to an identification of variances becoming problematic. In addition, more conventional approaches for anomaly detection cannot function in a proper manner. In this paper, we have evaluated the applications and methods of anomaly detection.
{"title":"A Critical Review of the Applications and AI Techniques for Anomaly Detection","authors":"Sidny Chalhoub","doi":"10.53759/181x/jcns202202013","DOIUrl":"https://doi.org/10.53759/181x/jcns202202013","url":null,"abstract":"In the process of analysing data, outlier detection (i.e., anomaly detection or novelty identification) is often misinterpreted to an identification of rare observations, occurrence or an item, which deviates highly from enormous data and never conforms to well- defined ideologies of a normal behaviour. The samples could stimulate more suspicion of being produced from various techniques, or appear unpredictable with the remaining portion of the specific dataset. Anomaly detection draws application in different domains such as neuroscience, statistics, machine vision, medicine, financial fraud, law enforcement and cyber security. The data that has been collected from real-life applications are rapidly increasing in dimension and size. As the aspect of dimensionality keeps increasing, data items become significantly sparse, amounting to an identification of variances becoming problematic. In addition, more conventional approaches for anomaly detection cannot function in a proper manner. In this paper, we have evaluated the applications and methods of anomaly detection.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132620806","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}
Pub Date : 2022-07-05DOI: 10.53759/181x/jcns202202015
Christopher Neilson, Price Grigore
The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test the efficacy of the suggested method by implementing the improved design.
{"title":"Machine Learning and AI Application Behaviour Prediction for User Experience Modelling and Optimization","authors":"Christopher Neilson, Price Grigore","doi":"10.53759/181x/jcns202202015","DOIUrl":"https://doi.org/10.53759/181x/jcns202202015","url":null,"abstract":"The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test the efficacy of the suggested method by implementing the improved design.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"371 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116567016","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}
Pub Date : 2022-07-05DOI: 10.53759/181x/jcns202202014
A. Vilán, Pamela N. Walker
Cyber-Physical Systems (CPS) offer a wide array of applications. Integration of various heterogeneous infrastructures that generate data for intelligent analysis is discussed. The objective of this article is to review CPS and provide a discussion of the system's security flaws. Many earlier investigations have also been properly explained. Because of the increasing usage of CPS in sensitive organizations (e.g., healthcare and connected homes), the requirement for a risk assessment strategy is essential. The major concentration of risk evaluation has shifted from computer risk evaluation to network-based risk analysis as a result of our substantial dependence on the Internet. The goal of CPS vulnerability analysis is to develop a quantitative model for future system protection.
{"title":"A Review of CPS Design and Vulnerability Analysis","authors":"A. Vilán, Pamela N. Walker","doi":"10.53759/181x/jcns202202014","DOIUrl":"https://doi.org/10.53759/181x/jcns202202014","url":null,"abstract":"Cyber-Physical Systems (CPS) offer a wide array of applications. Integration of various heterogeneous infrastructures that generate data for intelligent analysis is discussed. The objective of this article is to review CPS and provide a discussion of the system's security flaws. Many earlier investigations have also been properly explained. Because of the increasing usage of CPS in sensitive organizations (e.g., healthcare and connected homes), the requirement for a risk assessment strategy is essential. The major concentration of risk evaluation has shifted from computer risk evaluation to network-based risk analysis as a result of our substantial dependence on the Internet. The goal of CPS vulnerability analysis is to develop a quantitative model for future system protection.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126224474","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}
Pub Date : 2022-07-05DOI: 10.53759/181x/jcns202202011
A. Haldorai, Shrinand Anandakumar
The term " Edge Artificial Intelligence (Edge AI)" refers to the part of a network where data is analysed and aggregated. Dispersed networks, such as those found in the Internet of Things (IoT), have enormous ramifications when it comes to "Edge AI," or "intelligence at the edge". Smartphone applications like real-time traffic data and facial recognition data, including semi-autonomous smart devices and automobiles are integrated in this class. Edge AI products include wearable health monitors, security cameras, drones, robots, smart speakers and video games. Edge AI was established due to the marriage of Artificial Intelligence with cutting Edge Computing (EC) systems. Edge Intelligence (EI) is a terminology utilized to define the model learning or the inference processes, which happen at the system edge by employing available computational resources and data from the edge nodes to the end devices under cloud computing paradigm. This paper provides a light on "Edge AI" and the elements that contribute to it. In this paper, Edge AI's motivation, definition, applications, and long-term prospects are examined.
{"title":"Motivation, Definition, Application and the Future of Edge Artificial Intelligence","authors":"A. Haldorai, Shrinand Anandakumar","doi":"10.53759/181x/jcns202202011","DOIUrl":"https://doi.org/10.53759/181x/jcns202202011","url":null,"abstract":"The term \" Edge Artificial Intelligence (Edge AI)\" refers to the part of a network where data is analysed and aggregated. Dispersed networks, such as those found in the Internet of Things (IoT), have enormous ramifications when it comes to \"Edge AI,\" or \"intelligence at the edge\". Smartphone applications like real-time traffic data and facial recognition data, including semi-autonomous smart devices and automobiles are integrated in this class. Edge AI products include wearable health monitors, security cameras, drones, robots, smart speakers and video games. Edge AI was established due to the marriage of Artificial Intelligence with cutting Edge Computing (EC) systems. Edge Intelligence (EI) is a terminology utilized to define the model learning or the inference processes, which happen at the system edge by employing available computational resources and data from the edge nodes to the end devices under cloud computing paradigm. This paper provides a light on \"Edge AI\" and the elements that contribute to it. In this paper, Edge AI's motivation, definition, applications, and long-term prospects are examined.","PeriodicalId":170349,"journal":{"name":"Journal of Computing and Natural Science","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131681551","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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