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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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Optimizing Supply Chain Operations through Machine Learning: Opportunities and Challenges

Optimizing Supply Chain Operations through Machine Learning:...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： D. Santhakumar Gopalakrishnan Soundararajan Suman Chintala E. Bharath Krishna Murthy Inumula Kuldeep Chouhan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam – Chennai Tamil Nadu Muscat College Ruwi Sultanate of Oman Business Intelligence Architect USA Department of Artificial Intelligence & Data Science C K College of Engineering & Technology Anna University Cuddalore Tamil Nadu Symbiosis Institute of International Business (SIIB) Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial Networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality.

关键词： Supply chain management Costs Accuracy Supply chains Demand forecasting Machine learning Predictive models Generative adversarial networks Standards Genetic algorithms

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Retraction Note: A systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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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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Optimizing AI Algorithms for Accurate Remote Diagnosis in Cardiovascular Monitoring systems

Optimizing AI Algorithms for Accurate Remote Diagnosis in Ca...

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AI-Driven Smart Healthcare for Society 5.0

作者： Geetha Manoharan Lalit Mohan Trivedi S. Suma Christal Mary Divya Nimma Malik Bader Alazzam B Kiran Bala School of Business SR University Warangal Telangana India Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Information Technology Panimalar Engineering College Chennai PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

In recent years a new trend of architectures named the hybrid deep learning models have been derived as using several architectures jointly provides better performance for the problem. Out of these models, using CNN and GRU is therefore promising for upcoming tasks that need feature extraction and predicting sequential data. The primary contribution of the present work is an attempt to combine CNN for spatial feature extraction and GRU for temporal features, making the model efficient in handling sequential input data with lesser errors. The CNN-GRU structure based on this work combines CNN layer for feature extraction from raw input data and GRU layer to process features sequentially. This hybrid architecture is then fine-tuned by applying state of the art deep learning methods achieving higher classification accuracy. Accuracy of the proposed model was 98.9%, precision was 98.5%, recall was 99.1% and F1 score was 98.8%. An analysis of the results obtained shows that these values are markedly higher than when using only CNN or GRU, Random Forest, and SVM for standalone models. The proving work also confirms that the CNN-GRU hybrid model provides better solutions than the traditional model for various complicated classification problems in views of the better accuracy, precision, and recall. Nonetheless, it has the major drawbacks related to computational efficiency because it requires the input of large labeled data. Possible improvements for future work include further improvements to the efficiency of the algorithm as well as to investigate its effectiveness in practical domains such as healthcare, finance, and autonomous vehicles.

关键词： Deep learning Support vector machines Accuracy Computational modeling Computer architecture Medical services Feature extraction data models Classification algorithms Convolutional neural networks

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： P. Rajasekar Alok Kumar Prema Latha V Jayapal N Boddu Soundarya Santhoshi K. Karthik Mathiyalagan Sivaramkumar Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electrical and Electronics Engineering Guru Gobind Singh Educational Society Technical Campus Bokaro Jharkhand India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of Mechanical Engineering S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of BIOSCIENCES Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems.

关键词： Location awareness Deep learning Fault detection Feature extraction data augmentation Electrical fault detection data models Convolutional neural networks Integrated circuit modeling Long short term memory

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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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Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

arXiv

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arXiv 2025年

作者： Xia, Xiaobo Liu, Xiaofeng Liu, Jiale Fang, Kuai Lu, Lu Oymak, Samet Currie, William S. Liu, Tongliang School of Computer Science University of Sydney Australia Department of Statistics and Data Science Yale University United States Michigan Institute for Data and AI in Society University of Michigan United States School for Environment and Sustainability University of Michigan United States College of Information Science and Technology Penn State University United States Department of Earth System Science Stanford University United States Department of Electrical Engineering and Computer Science University of Michigan United States

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer trans-formative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Adaptive Computer Vision Solutions for Individuals with Special Needs

Adaptive Computer Vision Solutions for Individuals with Spec...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Issam Jebreen Shadi Nashwan Ahmad Al-Qerem Ali Mohd Ali Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarka University Jordan College of Engineering Technology Al-Balqa Applied University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

With the rapid evolution of artificial intelligence (AI) and computer vision, assistive technologies have become increasingly adept at supporting individuals with special needs. This paper provides a comprehensive examination of two critical areas: AI-powered visual assistance systems for the visually impaired and sign language recognition and generation frameworks for the Deaf and hard-of-hearing communities. We review the latest research trends, algorithms, and tools, highlighting emerging deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers in both domains. We then propose a unified framework that leverages edge computing, multimodal learning, and advanced human-computer interaction (HCI) techniques to address challenges such as real-time object recognition, complex gesture capture, data scarcity, and cultural variations in sign languages. We further detail an experimental design and set of results that demonstrate the feasibility and benefits of these approaches, followed by a discussion of ethical considerations, limitations, and future directions. Our goal is to illustrate a path toward more inclusive AI systems that offer robust solutions for accessibility, ultimately bridging communication gaps and fostering independence for individuals with special needs [1]–[11].

关键词： Sign language Computer vision Visualization Ethics Visual impairment Assistive technologies Transformers Real-time systems Object recognition Artificial intelligence

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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： Artificial intelligence breast cancer digital pathology whole slide images

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