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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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Urban Vertical Farming with IoT and Precision Monitoring for a Sustainable Future

Urban Vertical Farming with IoT and Precision Monitoring for...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Unni Krishnn, A. Dutt, Amit Singh, Jay Bhavana, Munugapati Baswaraju, Swathi Mohammed, Baydaa Fathil Venkatraman, Akila Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science-Data Science Bangalore India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

The fast expansion of population worldwide and urbanisation had been estimated to have 80 % of the population in urban areas in the upcoming 50 years leads to an exponential rise in demand of food. These needs provide an urgent call for developing novel approaches to achieve global food demands amid sustainability problems. Urban Vertical Farming (UVF) offers an attractive solution for growing crops in the vertical dimension of structures, the urban skyscrapers, where land use is limited. However, it is fairly limited research which applies the current state of art in cutting edge technologies such as the Internet of Things, to try to automate and monitor farm processing. Thus, in this review paper, gaps in existing research, the potential for IoT enabled vertical farming, as well as challenges such as energy use, economic feasibility and urban policy integration are examined. Vertical farming can be enhanced through resource efficiency, scalability and level of sustainability through the use of IoT systems and AI driven data analytics which will also help promote urban food security. The research gives a summary of the current state, technological capabilities, and potential way forward in the process of successful vertical farming implementation in urban sets. © 2025 IEEE.

关键词： Food security

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Enhanced Handover Management in 5G Networks using Machine Learning in Fading Scenarios

Enhanced Handover Management in 5G Networks using Machine Le...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Deepak Upadhyay Sreekanth Rallapalli Zakir Hussain Nookala Venu Rashmi Sharma Shipra Shukla Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of MCA Nitte Meenakshi Institute of Technology Bengaluru India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of CSE (Data Science) G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This study explores the use of machine learning (ML) to improve initial handover performance for 5G networks by analysing fading-affected scenarios. In this study, we assess the ability of ML models to predict optimal handover decisions compared with conventional methods. In this study, simulation results show that our ML-based methods can reduce the handover inaccuracy by 50%, decrease latency up to 10 times and stabilize network performance. We accompany our assessment with a series of graphical analyses to show how ML algorithms change their behaviour dynamically in response to deteriorating link intensity due user mobility and increasing h/o frequency, thereby degrading the network performance. The results clearly illustrate that ML enabled handover optimization is significantly more effective than traditional methods in accommodating fading-channel dynamics, thereby improving both the network performance and user experience of future generation wireless networks.

关键词： Fading channels 5G mobile communication Wireless networks Simulation Wires Machine learning Handover User experience Stability analysis Reliability

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Multi-Agent Systems for Collaborative Financial Decision-Making Over Distributed Network Architectures

Multi-Agent Systems for Collaborative Financial Decision-Mak...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Himani Fnu Keshav Kaushik Priyanka Gaur Divya Valsala Saratchandran Anju Gairola Thapliyal Kawerinder Singh Sidhu Vineeta Singh Wipro Ltd Chicago IL USA Sharda School of Computer Science & Engineering Sharda University Greater Noida India Dept. of Computer Applications Chandigarh Group of Colleges Mohali India Bath and Body Works Inc. Ohio US Management and Commerce Department Institute of Technology and Management Dehradun Uttarakhand UIM Uttaranchal University Dehradun Department of Computer Science and Engineering School of Engineering and Technology (Formerly known as UIET Kanpur) Chhatrapati Shahu ji Maharaj University Kanpur Uttar Pradesh

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

In financial collaborative decision-making use case, we focus on Multi-Agent Systems (MAS) based financial decision making through distributed network architectures. The study analyses agent collaboration efficiency, system scalability, communication overhead, and decision-making accuracy through simulating different scenarios. These results show that the system adapts to varying dynamic conditions, since it continues producing high performance as the number of agents and connectivity of the network both increase. To illustrate, heatmaps and comparisons of overhead and decision texts show less communication overhead and better decisions than traditional centralized approaches can generate. The results confirm the suitability of MAS for complex financial target patterns, thus providing a scalable, efficient and robust solution for the next generation of financial decision support systems. These findings help extend MAS-inspired approaches to distributed, high-stakes decision making environments.

关键词： Heating systems Decision support systems Scalability Decision making Collaboration Network architecture Reliability Next generation networking Multi-agent systems Resilience

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Performance Enhancement and Secrecy Analysis of Hybrid RF/FSO Communication Links Under Combined κ-μ Fading and Exponential Turbulence Conditions

Performance Enhancement and Secrecy Analysis of Hybrid RF/FS...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Deepak Upadhyay Prakhar Prakhar Rashmy Moray Rashmi Sharma Nookala Venu Shipra Shukla Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Roorkee Institute of Technology Pune India Symbiosis Institute of Management Studies (SIMS) A Constituent of Symbiosis International Deemed University Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of CSE (Data Science) G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In this study, the secrecy performance evaluation of hybrid RF/FSO communication systems over κ-μ fading and exponential turbulence channels are conducted. We analyse the secure information rate and evaluate the throughput and secrecy capacity of the system through simulations. The results indicate that the proposed hybrid system improves reliability and security by combining adaptive modulation, beamforming, and encryption to combat interference and secure data transmission. Geographical heat maps help to visualize the influence of environmental factors on system performance. Federated learning in hybrid RF/FSO systems have been investigated in detail through simulations and numerical results to show the considerable improvements in communication reliability and performance that the increased redundancy and robustness of the hybrid scheme provides. This helps gain valuable insights into a better design of wireless networks in the future by highlighting the benefits of hybrid communication techniques and simulation-driven design to enhance performance and security across diverse complex environments and conditions.

关键词： Fading channels Analytical models Adaptation models Array signal processing Atmospheric modeling Computational modeling Modulation Throughput Encryption Security

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Constraint-Guided Learning of data-driven Health Indicator Models: An Application on the Pronostia Bearing dataset

arXiv

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arXiv 2025年

作者： Tefera, Yonas Van Baelen, Quinten Meire, Maarten Luca, Stijn Karsmakers, Peter KU Leuven Dept. of Computer Science ADVISE-DTAI Kleinhoefstraat 4 GeelB-2440 Belgium Leuven.AI - KU Leuven Institute for AI Belgium Flanders Make @ KU Leuven Belgium Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia Ghent University Department of Data Analysis and Mathematical Modelling Coupure Links 653 Gent9000 Belgium

This paper presents a constraint-guided deep learning framework to develop physically consistent health indicators in bearing prognostics and health management. Conventional data-driven approaches often lack physical plausibility, while physics-based models are limited by incomplete knowledge of complex systems. To address this, we integrate domain knowledge into deep learning models via constraints, ensuring monotonicity, bounding output ranges between 1 and 0 (representing healthy to failed states respectively), and maintaining consistency between signal energy trends and health indicator estimates. This eliminates the need for complex loss term balancing to incorporate domain knowledge. We implement a constraint-guided gradient descent optimization within an autoencoder architecture, creating a constrained autoencoder. However, the framework is flexible and can be applied to other architectures as well. Using time-frequency representations of accelerometer signals from the Pronostia dataset, the constrained model generates more accurate and reliable representations of bearing health compared to conventional methods. It produces smoother degradation profiles that align with the expected physical behavior. Model performance is assessed using three metrics: trendability, robustness, and consistency. When compared to a conventional baseline model, the constrained model shows significant improvement in all three metrics. Another baseline incorporated the monotonicity behavior directly into the loss function using a soft-ranking approach. While this approach outperforms the constrained model in trendability, due to its explicit monotonicity enforcement, the constrained model performed better in robustness and consistency, providing stable and interpretable health indicator estimates over time. The results of the ablation study confirm that the monotonicity constraint enhances trendability, the boundary constraint ensures consistency, and the energy-health indicator con

关键词： Electronic health record

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XANNLight: A Novel Hybrid Ensemble Model for Accurate Health Insurance Prediction and Trend Forecasting

XANNLight: A Novel Hybrid Ensemble Model for Accurate Health...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Kafila Sajja Suneel Jayendra Gopal Thatipudi M Jini Kumari K. Penyameen Anubhav Sharma School of Business SR University Warangal Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept. of Computer Science Jayendra Gopal Thatipudi Student University of North Texas USA Department of ECE Chennai Institute of Technology Velammal College Of Engineering And Technology Madurai Department of CSE IES institute of Technology and management IES University Bhopal Mp India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Health insurance is a critical industry that requires precise data analysis and predictive capabilities to ensure operational efficiency and cost management. In an era where large datasets are readily available, machine learning models have become indispensable for predicting claims and identifying trends in insurance data. The rapid growth of the health insurance industry has increased the need for accurate prediction models to optimize claim management and trend forecasting. This study presents XANNLight, a novel hybrid ensemble model that integrates Artificial Neural Networks (ANN) and LightGBM to predict health insurance claims with high precision. The proposed model handles complex, non-linear relationships using ANN while LightGBM provides fast and accurate predictions for structured data. The dataset used for model training and evaluation consists of 1338 records, with features including age, sex, BMI, number of children, smoker status, and region. The model's performance is evaluated based on accuracy, precision, recall, and F1 score. The XANNLight model achieves an accuracy of 98.12%, outperforming nine existing models, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, which achieved accuracy levels between 85% and 96%. Furthermore, XANNLight demonstrates superior precision (97.83%), recall (97.90 % ), and F1 score (97.86 % ). These results highlight the model's efficiency in processing large datasets and predicting insurance claims, making it an effective tool for health insurance companies aiming to enhance operational efficiency and cost management.

关键词： Industries Accuracy Costs Computational modeling Insurance Artificial neural networks Predictive models Market research data models Forecasting

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