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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Santhakumar, D. Soundararajan, Gopalakrishnan Chintala, Suman Bharath, E. Murthy Inumula, Krishna Chouhan, Kuldeep Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai602105 India Muscat College Ruwi Oman Business Intelligence Architect United States C K College of Engineering & Technology Anna University Department of Artificial Intelligence & Data Science Tamil Nadu Cuddalore India Maharashtra Pune411057 India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

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. © 2025 IEEE.

关键词： Simulated annealing

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Multi-agent Scheduling for Shared Manufacturing Systems 6

Multi-agent Scheduling for Shared Manufacturing Systems

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 The Authors.

关键词： Mixed-integer linear programming

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Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention 3

Optimizing Load and Routes in Supply Chain Management with T...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Rajasekar, P. Begam, M. Shabeena Chattopadhyay, Shouvik Basha, S. Chand Chandre, Shanker Dwivedi, Vijay Kumar Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Sns College of Technology Department of Mba Coimbatore India Institute of Engineering & Management Department of Mba Kolkata India University of Engineering & Management Kolkata India St Ann's College of Engineering and Technology Department of Business Administration Chirala India School of Computer Science & Artificial Intelligence Sr University Department of Computer Science & Artificial Intelligence Warangal India Vishwavidyalaya Engineering College Department of Mathematics Ambikapur India

ISBN: (纸本)9798331508456

Ever since the industrial revolution, industries have partnered to solve marketing problems and create new channels. Because they don't understand their clients' needs, sales centers often pursue conflicting aims. Lack of information has extended delivery times. Due to supply time, quality, or bulk freight depreciation agreements, facility expenses rise to satisfy demand, resulting in excess inventory. Supply chain management, particularly regional inventory management, has considerably impacted beer manufacturing profitability. The recommended method involves training the model and extracting features. Dynamic and PCA feature extraction are different. While PCA's main purpose is to dissociate correlated from uncorrelated data, dynamic PCA considers auto- and cross-correlation in time-series observations. A revised CapsNet approach trained the models. The Improved CapsNet model outperformed the Attention and baseline models in every test. The model solved brewing supply chain issues with 94.68% accuracy. The most successful advanced machine learning approaches for improving SCM include augmented CapsNet, PCA, and dynamic PCA, according to research. These techniques boost industrial profitability when applied to major issues like excess stock and supply unpredictability. The suggested strategy is more reliable and superior than present solutions, according to results. © 2025 IEEE.

关键词： Supply chain management

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Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Kurma Mahesh Paladi Aishwarya Sabavath Nithin Shanmugasundaram Hariharan Bandar Vaishnavi Vasantha S V Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance.

关键词： Heart Machine learning algorithms Accuracy Computational modeling Medical services Predictive models Prediction algorithms Boosting Classification algorithms Diseases

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Interactive Physiotherapy Game Using React Three Fiber for Hand Motion Rehabilitation

Interactive Physiotherapy Game Using React Three Fiber for H...

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IEEE Students' Conference on Electrical, Electronics and computer Science (SCEECS)

作者： Ram Kumar Yadav Ashish Kumar Soni Apeksha Sakare Department of Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient’s hand. Through the "Create" function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere’s boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient’s growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber’s techniques, methods, and therapeutic potential for physical therapy.

关键词： Hands Visualization Three-dimensional displays Sensitivity Medical treatment Virtual environments Games Writing Stability analysis Optical fiber devices

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM

Hybrid Approach for Electricity Price Forecasting using Alex...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Bosubabu Sambana Kotamsetty Geethika Devi Bandi Rajeswara Reddy Galeti Mohammad Hussain Gownivalla Siddartha Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.

关键词： Temperature distribution Accuracy Recurrent neural networks Rain Electricity Predictive models Prediction algorithms data models Forecasting Long short term memory

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KNN-Powered Online Voting System to Improve Accuracy and Transparency of Election Process

KNN-Powered Online Voting System to Improve Accuracy and Tra...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jaswanth, Ullamparthy Muthukumar, D. Surendiran Abitha, R. Ganesh, Pamu Naveen, Nandyala Kavitha, G. Kalasalingam Academy of Research and Education Department Computer Science and Engineering Tamil Nadu Virudhunagar India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Artificial Intelligence and Data science Tamil Nadu Chennai India Dr.M.G.R Educational andResarch Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

The proposed system aims to create a safe online voting system. Nowadays voting remains a serious safety and security issue so this system provides high performance and security. The System seeks to develop a face recognition-based voting system that authenticates voters using machine learning, especially K-Nearest Neighbors (KNN). Entering the voter's unique username and Password is the first step in the online voting system. If the voter's Username and password are valid, the system proceeds to the next step, requesting permission to turn on the webcam and take a picture of them. Once the photo is taken, it verifies that the voter has only taken it once. The website allows the voter to cast his vote only once. Voter access is denied if the same voter picture is entered again. By logging into the website voter can cast their vote from any place. Every minute the website updates the results of the election. So, voters do not need to wait in long queues to cast their votes. To give voters real-time information on their voting progress. The system also features dynamic voice feedback to guide users through voting, ensuring clarity and engagement. This initiative uses facial recognition technology to enhance the security and efficiency of voting systems. © 2025 IEEE.

关键词： Face recognition

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Differentially Private Spiking Variational Autoencoder 27th

Differentially Private Spiking Variational Autoencoder

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yadav, Srishti Pundhir, Anshul Goyal, Tanish Raman, Balasubramanian Kumar, Sanjeev Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Mathematics Indian Institute of Technology Roorkee Uttarakhand Roorkee India

ISBN: (纸本)9783031783531

Spiking Neural Networks (SNNs) are poised to lead the next generation of artificial intelligence, offering energy efficiency and performance on par with traditional neural networks. With these advantages, SNNs are finding widespread applications across various domains. One significant area of interest is image generation using deep learning models like Variational Autoencoders (VAE). However, like other deep learning models, SNNs demand substantial training data to achieve desired outcomes, raising concerns about data privacy. Our pioneering contribution is the introduction of a Differentially Private Spiking Variational Autoencoder (DP-SVAE) for image generation and reconstruction. DP-SVAE employs standard Differentially Private Stochastic Gradient Descent (DP-SGD) to ensure privacy preservation. Additionally, we have evaluated the models against various adversarial attacks to highlight the importance of differential privacy. We comprehensively analyze the proposed model through extensive experimentation across publicly available benchmark datasets. This pioneering study marks the first exploration of privacy considerations in SNN-based VAEs and will catalyze further research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt19385 Jordan Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring 2

Wearable Biosensors: Transformative Innovations and Future P...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thute, Trupti Gudadhe, Amit Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Maharashtra Wardha India

ISBN: (纸本)9798331505745

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare. © 2025 IEEE.

关键词： mHealth

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