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Next-Generation Artificial Intelligence Solutions for Comprehensive Analysis and Improvement of Heat Dissipation in High-Intensity Boiling Environments

Next-Generation Artificial Intelligence Solutions for Compre...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： H. Manoj T. Gadiyar S Vinod Kumar Vijilius Helena Raj Dinesh Kumar Yadav Mortada Kareem Sabi Hasoni Sorabh Lakhanpal Department of Computer Science and Business System Canara Engineering College India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh AlTalb Collage of Nursing National University of Science and Technology Dhi Qar Iraq Lovely Professional University Phagwara India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This research delves into the realm of AI-driven heat dissipation, offering a suite of algorithms to address the challenges posed by high-intensity boiling environments. The proposed predictive modeling algorithm, rooted in linear regression, provides a systematic and robust approach through 20 meticulously designed steps. This algorithm, along with neural networks, reinforcement learning, generative adversarial networks (GANs), and genetic algorithms, forms a versatile toolbox for thermal management. Linear regression, through its step-by-step predictive modeling process, ensures accurate heat dissipation predictions. Neural networks, employing backpropagation, showcase adaptability and complexity, excelling in real-time adaptation. Reinforcement learning, specifically Q-Learning, introduces dynamic decision-making strategies, vital for navigating complex thermal scenarios. GANs contribute by generating realistic synthetic data, an invaluable asset for simulating high intensity boiling environments. Genetic algorithms, resembling natural selection, prove versatile and scalable in evolving optimal solutions. The research evaluates these algorithms comprehensively, demonstrating the proposed method’s superiority in prediction accuracy, energy efficiency, adaptability, and overall performance. The proposed predictive modeling algorithm stands out as a benchmark, showcasing its potential for practical implementation and optimization in diverse scenarios. This comprehensive exploration sets the stage for innovative solutions to the intricate challenges of thermal management, as continuous adaptation and fine-tuning remain pivotal in this dynamic landscape.

关键词： Heating systems Adaptation models Accuracy Heuristic algorithms Neural networks Linear regression Predictive models Prediction algorithms Optimization Genetic algorithms

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Observation scheduling problem for AEOS with a comprehensive task clustering

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Journal of systems engineering and Electronics 2021年第2期32卷 347-364页

作者： CHANG Zhongxiang ZHOU Zhongbao YAO Feng LIU Xiaolu School of Business Administration Hunan UniversityChangsha 410082China Institute of Data Science and Decision Optimization Hunan UniversityChangsha 410082China Department of Mathematics Simon Fraser UniversitySurrey V3T 0A3Canada School of System Engineering National University of Defense TechnologyChangsha 410073China

Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization *** the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve *** on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.

关键词： observation scheduling comprehensive task clustering(CTC) bi-objective optimization image quality energy consumption(EC)

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

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A Machine Learning Algorithm For Developing A Smart Health system

A Machine Learning Algorithm For Developing A Smart Health S...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Shemily P John P Anju S. Barath Kumar P. Vijayaraghavan Nishesh Nigam G. Amirthayogam General Studies Unit University College of Bahrain Kingdom of Bahrain Department of Computer Science and Business System (csbs) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Biotechnology Karpagam Academy of Higher Education Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science & Engineering (cse) IES Institute of Technology and Management Bhopal M.P India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The merging of revolutionary technologies like deep learning has greatly affected the healthcare sector, changing diagnosis and decision-making processes. This study tries to explore the role of neural networks in Digital Health research by examining 192 records drawn from the database used by Scopus. Employing a carefully made search term, the study tries to identify nations that excel in book output, primary research subjects, top funding backers, and common research terms in this domain. Notably, the debut paper on AI in smart health appeared in 2011, signalling the origins of a sizeable surge of academic output. India has emerged as the leader in performing study in this area, showing a remarkable growth trend. Furthermore, the research shows that Access from the IEEE is the top magazine, having the largest number of papers within this area. This extensive review serves as an important tool for scholars, policy makers, and healthcare workers, giving deep insights into the development of artificial intelligence in Smart Health study. The results show a positive trend, demonstrating that artificial intelligence in Connected Health is set for substantial increase in the future. The creativity and sincerity of this study highlight its importance in developing knowledge and defining the future environment of healthcare innovation.

关键词： Technological innovation Ethics Machine learning algorithms databases Reviews Smart healthcare Neural networks

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks

Automated Defect Detection in Electronic Components using Co...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： A. Shobanadevi Sreekanth Kottu K R Senthil Kumar K. Amudha K. Praveena R. Venkatesh Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science & Engineering Mallareddy University Hyderabad Telangana India Department of Mechanical Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Science and Humanities-Physics R.M.D. Engineering College (An Autonomous Institution) Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Andhra Pradesh India Department of Physics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence.

关键词： Training Productivity Process control Quality control Streaming media Mathematics Manufacturing

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A Intelligent Approach for Fault Detection in Solar Photovoltaic systems based on BERT-BiGRU Network

A Intelligent Approach for Fault Detection in Solar Photovol...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Mohammed Hafeezuddin Bhagath Garapati T Vijayakumar Priyanka G Amit Chauhan A. Vidya Department of EEE Geethanjali College of Engineering and Technology Hyderabad India Department of EEE Geethanjali College of Engineering and Technology Cheeryal Telangana India Department of Artificial Intelligence and Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Life Sciences School of Sciences CHRIST (Deemed to be university) Bengaluru Karnataka India Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Trichy India

Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the ‘residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate.

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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A Lightweight NFC Authentication Algorithm Based on Modified Hash Function

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International Journal of Network Security 2022年第3期24卷 436-443页

作者： Cao, Fang-Ming Liu, Dao-Wei School of Data and Computer Science Guangdong Peizheng College Guangzhou510800 China Department of Computer Science and Engineering Guangzhou College of Technology and Business China

In the NFC application, the leakage of user privacy information is inevitable. A lightweight NFC authentication algorithm based on a modified hash function is proposed to ensure the security of user privacy information. In the proposed algorithm, the specific implementation process using the modified hash function is given, which can provide better security requirements. In implementing the modified hash function, the Hamming weight variable of the encryption parameter itself is skillfully used, which reduces the introduction of parameters and improves the security factor of the encryption function. From the perspective of formalization and security analysis, it can be shown that the proposed algorithm satisfies the security needs of users. Furthermore, the simulation result demonstrates that the cost of the proposed algorithm is better than other NFC algorithms. © 2022. International Journal of Network *** Rights Reserved

关键词： Hash functions

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： J Ruby Elizabeth Mathews Emmanuel E Aswini Shriram Pathak M. Bhuvaneswari Abhishek Choubey Department of Computer Science and Business System Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Sathyabhama university of science and technology chennai Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering IES Insititute of Technology and Management IES University Bhopal M.P. India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore Sreenidhi institute of science and technology Yamnampet ghatkesar Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both ‘weights’ while ‘bias’ factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application.

关键词： Accuracy Smart healthcare Merging Medical services data collection data models Diabetes

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Optimizing Cross-Border Supply Chains and Customer Satisfaction Using Conditional Decision Trees

Optimizing Cross-Border Supply Chains and Customer Satisfact...

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Automation and Computation (AUTOCOM), International Conference on

作者： S. Mohanasundaram Sharath Kumar Jagannathan Nagabalan U K. Manickam Sudeshna Seal N.Golden Stepha Department of Information Technology Government College of Engineering Erode Tamilnadu India Data Science Institute Frank J. Guarini School of Business Saint Peter's University New Jersery R.M.D. Engineering College India Department of Mathematics Sona College of Technology Salem Tamilnadu India OmDayal Group of Institutions Howrah West Bengal India Department of Mathematics R.M.K Engineering college India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Increasing customer satisfaction in a globalized economy depends much on the management of supply chains across national borders. Among the challenges escalating the complexity are differences in regulatory criteria, delays in logistics, and shifting customer expectations. This paper uses conditional decision trees (CDT) to help to simplify decision-making procedures and increase operational efficiency in supply chains spanning international borders. This model evaluates critical performance indicators (KPIs) including delivery time, cost efficiency, and customer satisfaction by means of transactional data from real-time operations. The model also factors customer comfort. All of which surpasses the traditional approaches, the proposed strategy results in a 15% improvement in delivery efficiency, a 12% reduction in logistical expenses, and a 9% increase in customer satisfaction ratings. Two baseline models were under comparison with CDT. These models stood for the Random Forest (RF) and the Decision Tree Regressor (DTR). The results show, based on supply chain criteria, CDT achieves an accuracy of 94.6% compared to DTR and RF, so offering a clear performance benefit. This relates to DTR's accuracy of 89.4% and RF's accuracy of 91.80%. This kind of approach not only improves operational efficiency but also offers data applicable regarding the factors affecting customer satisfaction. These findings imply that CDT might be a breakthrough instrument in the fields of worldwide supply chain management.

关键词： Radio frequency Accuracy Supply chain management Computational modeling Customer satisfaction Supply chains Predictive models data models Reliability Regression tree analysis

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