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AI and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing 4

AI and Machine Learning Techniques for Managing Complexity, ...

引用

4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： William, P. Nirsanametla, Yadaiah Al-Samalek, Ahmed Hussain, Ahmed Varshney, Neeraj Rao, A.L.N. Kopargaon Sppu Sanjivani College of Engineering Department of Information Technology MH Pune India Chitkara University Chitkara Center for Research and Development Himachal Pradesh India Al-Zahrawi University College Department of Medical Laboratory Technics Karbala Iraq Al-Farahidi University Medical Technical College Iraq GLA University Department of Computer Engineering and Applications Mathura India Lloyd Institute of Engineering& Technology Greater Noida India

ISBN: (纸本)9798350307757

Artificial neural networks (ANNs), fuzzy systems, expert systems, pattern recognition methods, and modern hybrid approaches to artificial intelligence (AI) may all be considered as phases of a progression that began more than 20 years ago. This trend began with the creation of artificial neural networks (ANNs). This research article includes a number of original discoveries and focuses on hybrid "artificial intelligence' (AI) and multi-strategy machine learning approaches. This new knowledge is presented as well as a discussion of the essential stages that comprise this process. One of the possible uses for agent-based holonic systems has been identified as being the management of complexity, changes, and interruptions in production systems. It is envisaged that more approaches would be incorporated together. In the event that one so chooses, the subject of defect detection might be rethought as one of binary categorization. Both the classification task Machine learning technique and the choice of the features that make up the data and are most significant to the process's quality were decided on the basis of the l1-regularized logistic regression. The establishment of a brand-new manufacturing industry that is being referred to as Smart Manufacturing. This was done in order to guarantee the highest possible level of quality throughout the whole of the process. This allowed for optimal efficiency in both areas. Because of this, it is feasible to combine the most relevant facts about the procedure. The suggested strategy is supported by a cutting-edge hybrid feature removal technique and the best classification threshold search algorithm currently available. The outcomes of the tests reveal that flaws can always be precisely detected without fail. © 2024 IEEE.

关键词： Expert systems

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Dynamic Energy-Aware Cloudlet-Based Mobile Cloud Computing Model for Green Computing 4

Dynamic Energy-Aware Cloudlet-Based Mobile Cloud Computing M...

引用

4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： William, P. Singh, Rahul Al-Samalek, Ahmed Hussain, Ahmed Badhoutiya, Arti Rao, A Kakoli Kopargaon Sppu Sanjivani College of Engineering Department of Information Technology MH Pune India Chitkara University Chitkara Center for Research and Development Himachal Pradesh India Al-Zahrawi University College Department of Medical Laboratory Technics Karbala Iraq Al-Farahidi University Medical Technical College Iraq GLA University Department of Electrical Engineering Mathura India Lloyd Institute of Engineering& Technology Greater Noida India

ISBN: (纸本)9798350307757

The usage of mobile cloud computing (MCC), which allows mobile users to access the benefits of cloud computing in a manner that is favourable to the environment, is an efficient strategy for addressing the present demands of the industrial sector. MCC is one of the acronyms for "mobile computing in the cloud."As a result of limitations imposed by wireless bandwidth and device capability, the installation of MCC has been met with a range of obstacles. These difficulties consist of, among other things, an increase in the amount of energy that is wasted and a delay in latency. To solve this issue, we have presented a dynamic cloudlet-based mobile cloud computing model (DECM), which makes use of dynamic cloudlets (DCL) to manage the additional energy that is necessary for wireless connections. As part of this investigation, we test our model by simulating an event that may take place in the actual world, and we also give data that can be relied upon for the evaluations. This research contributes significantly in two distinct ways. To begin, this study is the very first analysis of the most effective strategies for resolving concerns about energy loss within the framework of dynamic networking. It was carried out by a group of researchers from the United States and Canada. Second, the proposed model provides a path and theoretical foundations for more research to be conducted in the future. © 2024 IEEE.

关键词： Mobile cloud computing

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Security Enhancement In Iot Based Smart Grid System Using Cryptographic Techniques 4

Security Enhancement In Iot Based Smart Grid System Using Cr...

引用

4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： William, P. Chowdhury, Sohini Falah, Ali Hussain, Ahmed Kumar, Rakesh Rao, A.L.N. Kopargaon Sppu Sanjivani College of Engineering Department of Information Technology MH Pune India Chitkara University Chitkara Center for Research and Development Himachal Pradesh India Al-Zahrawi University College Department of Dentistry Karbala Iraq Al-Farahidi University Medical Technical College Iraq Applications GLA University Department of Computer Engineering Mathura India Lloyd Institute of Engineering& Technology Greater Noida India

ISBN: (纸本)9798350307757

The Smart Grid is a major technological advancement that has been made in the energy business. It enables better production, environmental sustainability, and responsiveness to renewable energy sources. The incorporation of Internet of Things (IoT) devices into the Smart Grid, on the other hand, introduces whole new security concerns that must be resolved immediately. The implementation of cryptographic protocols with the intention of bolstering the safety of internet-of-things (IoT)-based smart grid systems is the purpose of this project which can be patented. The research investigates particular safety concerns that are associated with Internet of Things-based smart grids. These concerns include data breaches, unauthorized access, and potential consequences. This work contributes to a secure and resilient IoT-based Smart Grid ecosystem, which provides reliable energy supply in the face of escalating digital threats. This is accomplished by strengthening the security posture of Smart Grids, which is the focus of this study. By examining a compiled table, stakeholders may determine which method will best meet their specific requirements for safety and then make their decision. © 2024 IEEE.

关键词： Smart power grids

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Security Enhancement In Iot Based Smart Grid System Using Cryptographic Techniques

Security Enhancement In Iot Based Smart Grid System Using Cr...

引用

Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： P. William Sohini Chowdhury Ali Falah Ahmed Hussain Rakesh Kumar A L N Rao Department of Information Technology Sanjivani College of Engineering Kopargaon SPPU Pune MH India Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Department of Dentistry Al-Zahrawi University College Karbala Iraq Medical Technical College Al-Farahidi University Iraq Department of Computer Engineering Applications GLA University Mathura Lloyd Institute of Engineering& Technology Greater Noida

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

关键词： Renewable energy sources Ecosystems Smart grids Safety Encryption Security Internet of Things

来源：评论

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Dynamic Energy-Aware Cloudlet-Based Mobile Cloud Computing Model for Green Computing

Dynamic Energy-Aware Cloudlet-Based Mobile Cloud Computing M...

引用

Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： P. William Rahul Singh Ahmed Al-Samalek Ahmed Hussain Arti Badhoutiya A Kakoli Rao Department of Information Technology Sanjivani College of Engineering Kopargaon SPPU Pune MH India Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Department of Medical Laboratory Technics Al-Zahrawi University College Karbala Iraq Medical Technical College Al-Farahidi University Iraq Department of Electrical Engineering GLA University Mathura Lloyd Institute of Engineering& Technology Greater Noida

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

The usage of mobile cloud computing (MCC), which allows mobile users to access the benefits of cloud computing in a manner that is favourable to the environment, is an efficient strategy for addressing the present demands of the industrial sector. MCC is one of the acronyms for "mobile computing in the cloud." As a result of limitations imposed by wireless bandwidth and device capability, the installation of MCC has been met with a range of obstacles. These difficulties consist of, among other things, an increase in the amount of energy that is wasted and a delay in latency. To solve this issue, we have presented a dynamic cloudlet-based mobile cloud computing model (DECM), which makes use of dynamic cloudlets (DCL) to manage the additional energy that is necessary for wireless connections. As part of this investigation, we test our model by simulating an event that may take place in the actual world, and we also give data that can be relied upon for the evaluations. This research contributes significantly in two distinct ways. To begin, this study is the very first analysis of the most effective strategies for resolving concerns about energy loss within the framework of dynamic networking. It was carried out by a group of researchers from the United States and Canada. Second, the proposed model provides a path and theoretical foundations for more research to be conducted in the future.

关键词： Wireless communication Cloud computing Terminology Computational modeling Green products Mobile handsets Servers

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AI and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing

AI and Machine Learning Techniques for Managing Complexity, ...

引用

Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： P. William Yadaiah Nirsanametla Ahmed Al-Samalek Ahmed Hussain Neeraj Varshney ALN Rao Department of Information Technology Sanjivani College of Engineering Kopargaon SPPU Pune MH India Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Department of Medical Laboratory Technics Al-Zahrawi University College Karbala Iraq Medical Technical College Al-Farahidi University Iraq Department of Computer Engineering and Applications GLA University Mathura Lloyd Institute of Engineering& Technology Greater Noida

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Artificial neural networks (ANNs), fuzzy systems, expert systems, pattern recognition methods, and modern hybrid approaches to artificial intelligence (AI) may all be considered as phases of a progression that began more than 20 years ago. This trend began with the creation of artificial neural networks (ANNs). This research article includes a number of original discoveries and focuses on hybrid "artificial intelligence’ (AI) and multi-strategy machine learning approaches. This new knowledge is presented as well as a discussion of the essential stages that comprise this process. One of the possible uses for agent-based holonic systems has been identified as being the management of complexity, changes, and interruptions in production systems. It is envisaged that more approaches would be incorporated together. In the event that one so chooses, the subject of defect detection might be rethought as one of binary categorization. Both the classification task Machine learning technique and the choice of the features that make up the data and are most significant to the process's quality were decided on the basis of the l1-regularized logistic regression. The establishment of a brand-new manufacturing industry that is being referred to as Smart Manufacturing. This was done in order to guarantee the highest possible level of quality throughout the whole of the process. This allowed for optimal efficiency in both areas. Because of this, it is feasible to combine the most relevant facts about the procedure. The suggested strategy is supported by a cutting-edge hybrid feature removal technique and the best classification threshold search algorithm currently available. The outcomes of the tests reveal that flaws can always be precisely detected without fail.

关键词： Production systems Uncertainty Machine learning Artificial neural networks Quality control Complexity theory Task analysis

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