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International Conference on Inventive Computation technologies (ICICT)

作者： Thanigaivelu P S Chitra Sabapathy Ranganathan S. Priya R M Asha S. Murugan T. R. GaneshBabu Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Electrical and Electronics Engineering Academy of Maritime Education and Training (AMET) Deemed to be University Chennai Tamil Nadu India Department of Civil Engineering Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Chemical processing plants are essential to many different sectors of the economy, but they also pose risks to people and the environment in the event of accidents like chemical spills. Using the Isolation Forest algorithm in conjunction with IoT sensors for leakage prevention, this study investigates a new way to improve chemical processing facility security. It uses the Isolation Forest method, which is well-known for its anomaly detection capabilities, to spot suspicious patterns that might indicate leaks in real-time. This technology, when combined with an Internet of Things (IoT) sensor network strategically installed across the facility, offers a thorough monitoring solution that can identify and prevent leaks before they become serious. It highlights the flexibility of the Isolation Forest algorithm by discussing its application to chemical processing facilities. The IoT sensors may be integrated, highlighting how user can improve the security architecture as a whole via data collecting, communication, and coordination. Quick detection and prevention of chemical leaks is validated by results from simulation studies or real-world deployments of the proposed system. To discuss potential challenges, such the scalability of the system and the dependability of the sensors, and offers methods to make sure the security architecture is strong. Measures to evaluate the system's efficiency, include reaction time, isolation score, and detection accuracy. The potential issues to be addressed are system's flexibility and the sensors' accuracy to provide solutions to enhance the proposed security architecture.

关键词： Chemical sensors System performance Forestry Sensor phenomena and characterization Sensor systems Real-time systems Safety

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Secure IoHT Data Sharing in the Cloud: Machine Learning with Logistic Regression for Privacy Protection

Secure IoHT Data Sharing in the Cloud: Machine Learning with...

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Intelligent Data Communication technologies and Internet of Things (IDCIoT), International Conference on

作者： Ramakrishnan Raman K. Saravanan Atul A Gokhale K. Anitha Ishwarya M.V S. Sujatha Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Healthcare delivery and patient monitoring have been transformed by the fast rise of the Internet of Healthcare Things (IoHT). Cloud storage of sensitive health data creates privacy issues. Logistic Regression (LR) is used to ensure IoHT data sharing in this investigation. The cloud architecture allows efficient data exchange while protecting health information. LR is used to predict data sharing risk and make privacy choices. A varied collection of health measures and demographic data trains the model to generalize well to numerous contexts. The suggested system is tested extensively with Logistic Regression, which reliably identifies and mitigates privacy threats. Data accessibility and privacy are balanced by the model's high sensitivity and specificity. It also examines the model's interpretability to provide privacy prediction factors. This openness improves user trust and regulatory compliance. This technique is scalable for large-scale IoHT installations. The cloud-based IoHT data sharing challenge has a unique approach. Machine learning, especially Logistic Regression, protects privacy without reducing health data usefulness. This research increases healthcare data security in the connected device age.

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Real-time Agrochemical Management Solutions using Cloud computing and K-Nearest Neighbors Algorithm

Real-time Agrochemical Management Solutions using Cloud Comp...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： T M Sivanesan N. Mohankumar N V Shibu V. Nandagopal B. Rajasekaran S. Srinivasan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharastra India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhra Pradesh India Department of Electronics and Communication Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm’s prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety.

关键词： Cloud computing Adaptation models Accuracy Weather forecasting Pesticides Nearest neighbor methods Prediction algorithms Real-time systems Sustainable development Crop yield

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Artificial Neural Networks for Enhancing Soccer Team Performance Through Tactical Data Analysis

Artificial Neural Networks for Enhancing Soccer Team Perform...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chitra Sabapathy Ranganathan Pramod Pandey M. Arulprakash Kanapathy Gopalakrishnan T. R. GaneshBabu S. Murugan Mphasis Corporation Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Emeritus Cambridge Institute of Technology Bengaluru India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This research investigates using artificial neural networks (ANNs) to improve football team performance via tactical data analysis. The growing amount of data produced during football matches poses challenges and possibilities for coaches and analysts. ANNs, as a category of machine learning, provide a strong foundation for recognizing patterns and extracting actionable insights from data. It created a prediction algorithm that evaluates team performance and recommends ideal tactics using player data, game video, and tactical formations. It indicates that ANNs can proficiently assess intricate relationships inside the game, enhancing coaches' decision-making. The model was also evaluated using past match data, demonstrating its capacity to forecast results and suggesting tactical modifications in real time. This research's ramifications beyond performance improvement players underscore the significance of data-driven methodologies in contemporary sports. This novel use of technology enhances player development and cultivates a profound comprehension of game dynamics, providing a competitive advantage in football.

关键词： Training Data analysis Accuracy Soft sensors Decision making Games Artificial neural networks Streaming media Real-time systems Sports

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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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Retraction Note: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

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Neural computing and Applications 2024年第16期36卷 1-2页

作者： Ghani, Mohd Khanapi Abd Mohammed, Mazin Abed Arunkumar, N. Mostafa, Salama A. Ibrahim, Dheyaa Ahmed Abdullah, Mohamad Khir Jaber, Mustafa Musa Abdulhay, Enas Ramirez-Gonzalez, Gustavo Burhanuddin, M. A. Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia Planning and Follow Up Department University Headquarter University of Anbar Anbar Iraq School of EEE Sastra University Thanjavur India Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Otorinolaringologi Hospital Pakar Sultanah Fatimah Muar Muar Malaysia Dijlah University College Baghdad Iraq Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Departamento de Telemática Universidad del Cauca Popayán Colombia

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Adaptive Silo Networks with Cloud computing and Reinforcement Learning for Responsive Grain Storage

Adaptive Silo Networks with Cloud Computing and Reinforcemen...

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International Conference on Inventive Computation technologies (ICICT)

作者： C. Jehan P. S. Kumaresh M. Raja Suguna R. Anto Arockia Rosaline Suguna M S. Murugan Department of Computer Science and Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sagunthala Engineering College Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering K.L.N.College of Engineering Sivagangai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This research study presents a novel method for managing grain storage facilities by combining adaptive silo networks, cloud computing, and reinforcement learning (RL). The suggested system’s goal is to improve grain storage efficiency by automatically modifying storage parameters in response to changes in the surrounding environment and real-time data. The adaptive silo network uses cloud computing capabilities to keep track of things like temperature, humidity, and grain quality in real-time. The proposed system is able to learn and adapt to new circumstances due to the use of RL algorithms. Interacting with environmental elements such as weather patterns, grain kinds, and demand changes, the model learns optimum storage solutions. The proposed approach improves storage efficiency with the use of advanced algorithms, adaptive control mechanisms, and real-time monitoring. Contributing to the long-term viability and financial success of agricultural supply chains, it optimizes grain storage systems to reduce losses and provide responsive management. With cloud computing, scalability and dispersed data processing, analysis, and decision-making are improved. The device enhances storage efficiency and gives advanced warning of impending problems, allowing for preventative maintenance and protection. To increase the responsiveness and efficiency of grain storage facilities, suggest using cloud computing and RL to create adaptive silo networks. By integrating advanced technology to overcome challenges in grain storage and management it advances the development of smart agriculture practices.

关键词： Smart agriculture Productivity Cloud computing Scalability Decision making Supply chains Storage management

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Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs

Deep Learning Approach to Modeling and Exploring Random Sour...

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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan Center for mmWave Smart Radar System and Technologies National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

In this paper, for the first time, deep learning (DL) based artificial neural network (ANN) is applied to model the effects of various random variations: work function fluctuation, random dopant fluctuation, and interface trap fluctuation, on gate-all-around silicon nanosheet MOSFETs. The number of fluctuations for each source variation is used as input features and their effects on devices of interest are studied qualitatively and quantitatively. The key figures of merit (FoM) are also extracted accurately from the transfer characteristics, which shows the competency of the ANN model in the domain of device modeling. © 2022 IEEE.

关键词： Nanosheets

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Personalized Compression Therapy Plans using Cloud computing and ML Analytics

Personalized Compression Therapy Plans using Cloud Computing...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Srinivasan Pramod Pandey M. Arulprakash K Umapathy R. Amutha M. Rajmohan Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communications Engineering Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University (Deemed University) Kanchipuram Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

For the treatment of diseases like lymphedema and chronic venous insufficiency, compression therapy is crucial, but conventional methods frequently produce less-than-ideal results because they are impersonal. This paper presents a novel approach that uses cloud computing and decision trees to create personalized compression therapy plans in healthcare settings. The proposed system uses cloud infrastructure's scalability and computational power to handle massive amounts of patient data collected in real-time from wearable Internet of Things (IoT) devices. Through device integration, pertinent physiological parameters can be continuously monitored, allowing prompt therapy plan modifications in response to changing patient requirements. Decision Trees, a machine learning (ML) algorithm selected for its capacity to comprehend complicated datasets and produce useful insights, are essential to the system's operation. Decision trees dynamically customize compression therapy regimens using patient data analysis to maximize patient comfort and treatment effectiveness. Early system trials have produced encouraging outcomes, including notable increases in patient satisfaction and treatment outcomes. This strategy prioritizes the delivery of personalized healthcare, which increases therapeutic efficacy and fosters a patient-centered treatment experience. The combination of cloud computing and Decision Trees is a progressive step forward in the healthcare industry, providing scalable solutions that can be tailored to the specific needs of each patient and method for future developments in personalized medical care.

关键词： Cloud computing Limbs Data analysis Medical treatment Real-time systems Decision trees Internet of Things biomedical monitoring Wearable devices Wearable sensors

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Robotic 3D Printing for Customized Industrial Components: IoT and AI-Enabled Innovation

Robotic 3D Printing for Customized Industrial Components: Io...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： T. Kanthimathi N. Rathika A. Jainul Fathima Rajesh K S S. Srinivasan Thamizhamuthu R Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Information Technology Francis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India

Manufacturing has been transformed by robotic 3D printing, the Internet of Things (IoT), and Artificial Intelligence (AI). The research discusses how these cuttingedge advancements may maximize industrial component customization efficiency and precision. 3D printing's fundamentals and advances are explained to emphasize its importance in the current industry. Robotics improves 3D printing speed and accuracy, demonstrating how automation enhances output in many industrial contexts. In this environment, the IoT enables real-time monitoring and management of 3D printing processes and predictive maintenance, assuring uninterrupted output. It discusses the IoT's potential to improve resource use and waste reduction with 3D printing. Intelligent algorithms for design optimization, material selection, and mistake detection are used in robotic 3D printing. Case studies show how AI-driven innovations improve product quality and design customization, meeting the rising need for individualized industrial components. These technologies work together to change production, according to our study. It demonstrates how this integration reduces costs and wastes and improves product performance. The problems and future of this technology fusion are explained, highlighting the necessity for strong cybersecurity and ethical issues.

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