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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priscila, S. Silvia Sakthivanitha, M. Yogeshwari, M. Devakirubai, T. Shirley Sirajudeen, M. Mohamed Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu 600126 India Department of Information Technology VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Data Analyst Saveetha Liberal Arts and Science College Tamil Nadu Chennai India Department of Commerce Madras Christian College Tamil Nadu Chennai India Thaloor Tamil Nadu The Nilgiris643239 India

ISBN: (纸本)9798331522667

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. This study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including data Mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. The purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques. © 2025 IEEE.

关键词： Decentralized finance

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An Overview of IoT-based ECG Wavelength Analysis for Remote Patient Monitoring

An Overview of IoT-based ECG Wavelength Analysis for Remote ...

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

作者： Kamrul Islam Najnin Sultana Nishu Md. Samiul Islam Md Tanvir Chowdhury Habibur Rahman Mahabubur Rahman Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Statistics and Data Science Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Adopting new IoT technologies holds great potential for changing the patient healthcare landscape. While IoT-enabled healthcare applications have become increasingly popular, it’s important to note that rural areas often face poor healthcare infrastructure and limited access to quality medical care. However, IoT is a valuable asset, especially in emergencies like Covid-19. To make IoT even more helpful in this area, we will explore the existing IoT systems for Remote Patient Monitoring (RPM), reveal their shortcomings, and provide systematic fixes. In this study, we look at the advantages of integrating IoT devices with sensors, cloud technologies, and ECG wavelength analysis, particularly in RPM. IoT-enabled devices are adept at collecting and sending real-time information, enabling healthcare professionals to remotely monitor vital signs, essential indicators, and health parameters. While there are challenges associated with empowering remote patient monitoring with IoT, it can also improve healthcare outcomes and reduce costs. This technology fusion provides a glimmer of hope during a pandemic, promoting not only better health but a more fulfilling life. To sum up, this inquiry prepares the ground for a future where healthcare is personalized and patient-centric, with a range of approaches, resilience in the face of failure, and triumphs in overcoming challenges.

关键词： Patient monitoring Cloud computing Medical services Electrocardiography Real-time systems Sensors Mobile applications Internet of Things Servers Faces

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Multi-Agent Deep Reinforcement Learning with Trajectory Prediction for Task Migration-Assisted Computation Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE All rights reserved.

关键词： Computation offloading

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning data science Feature extraction Convolutional neural networks data mining Remote sensing Information systems

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Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation

Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhou, Yuening Wang, Yulin Cui, Qian Guan, Xinyu Cisternas, Francisco Department of Marketing The Chinese University of Hong Kong Hong Kong School of Computer Science and Technology Dalian University of Technology Dalian China The Data Intelligence Team Maoyan Beijing China The Security Platform Department Tencent Shenzhen China

ISBN: (纸本)9798350368741

Next Basket Recommendation (NBR) is a new type of recommender system that predicts combinations of items users are likely to purchase together. Existing NBR models often overlook a crucial factor, which is price, and do not fully capture item-basket-user interactions. To address these limitations, we propose a novel method called Basket-augmented Dynamic Heterogeneous Hypergraph (BDHH). BDHH utilizes a heterogeneous multi-relational graph to capture the intricate relationships among item features, with price as a critical factor. Moreover, our approach includes a basket-guided dynamic augmentation network that could dynamically enhances item-basket-user interactions. Experiments on real-world datasets demonstrate that BDHH significantly improves recommendation accuracy, providing a more comprehensive understanding of user behavior. © 2025 IEEE.

关键词： basket-augmentation heterogeneous hypergraph Next basket recommendation price-aware

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TriBoost and Beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Katlariwala, Soham Biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, AdaBoost, Support Vector Machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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Peer-Guided Optimization: Incorporating Collaborative Learning into Stochastic Optimization in Machine Learning

Peer-Guided Optimization: Incorporating Collaborative Learni...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Joy Datta Rawhatur Rabbi Nafiz Imtiaz Rafin Md. Golam Rabiul Alam Department of Computer Science & Engineering School of Data & Sciences Brac University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A single optimizer is usually used to minimize the cost function in conventional machine learning. This paper introduces Peer-Guided Optimization (PGO), where two optimizers collaborate by sharing gradient information to help each other explore the cost surface and accelerate convergence. Unlike traditional optimization methods, PGO alternates steps between two optimizers across iterations. Based on the proximity to the optimal solution, each optimizer dynamically calculates a guidance parameter that influences the contribution of the peer optimizer during updates. PGO has two variants: Homogeneous PGO (using identical optimizers with different learning rates) and Heterogeneous PGO (combining different types of optimizers, such as Adam and SGD). Experimental results demonstrate that PGO consistently outperforms conventional single-optimizer methods in terms of accuracy and convergence speed, making it a promising strategy for optimizing deep learning models in various applications.

关键词： Electric potential Accuracy Costs Navigation Federated learning Stochastic processes Optimization methods Minimization Optimization Convergence

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A novel multi-method framework for 3D printed fiber-reinforced polymer concrete utilizing advance additive manufacturing techniques

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Asian Journal of Civil Engineering 2025年第4期26卷 1655-1668页

作者： Raut, Jayant M. Pimpalshende, Anjusha Chandak, Mayuri A. Patil, Tejas R. Pinjarkar, Latika Nair, Sruthi Department of Civil Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Computer Science and Engineering Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad 500090 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India Department of Civil Engineering Priyadarshini Bhagwati College of Engineering Maharashtra Nagpur 440024 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Computer Science (Data Science) Ramdeobaba College of Engineering and Management Maharashtra Nagpur 440013 India

Growing demands for customized, sustainable, and high-performance infrastructure urgently require innovative construction methodologies. Conventional methods for fiber-reinforced polymer concrete are not efficient in material usage, are inconsistent in their mechanical properties, and fail to satisfy the complex structural demands. The current methods of 3D printing are often affected by delamination of layers, bad alignment of fibers, and relatively high rates of defects, which adversely affect the structural integrity and efficiency of the printed components. To address these challenges, we propose a novel multi-method framework utilizing advance additive manufacturing techniques for 3D printed fiber-reinforced polymer concrete. Our research introduces four additional mechanisms: GCMME (Gradient-Controlled Deposition via Multi-Material Extrusion) for smooth material transitions with functional graded properties, DFAM (Directional Fiber Alignment Mechanism) for optimal reinforcement along stress trajectories, ARCS (Adaptive Rheology Control System) for viscosity modulation and self-healing capabilities, and AQA-PDM (AI-Based Quality Assurance and Predictive Defect Mitigation) for real-time defect detection and quality control. All the above-mentioned mechanisms can be used simultaneously to allow for the mass production of customised structural parts with outstanding mechanical properties. Significant results include tensile strength greater than 12 MPa, compressive strength greater than 50 MPa, enhanced flexural strength by about 35%, and the defects density of less than 0.5%. The material wastage is minimized by up to 25%. Moreover, self-healing efficiency in closure is more than 60% as well. This integrated method significantly enhances performance, accuracy, and sustainability in modular construction and thus provides a transforming solution for the infrastructure development process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 20

关键词： 3D printing Adaptive rheology Fiber-reinforced polymer concrete Gradient deposition Quality assurances

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Harnessing Medical Big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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An Enchanced Approach for Early Chronic Kidney Disease Prediction Using Advanced Machine Learning and data Processing Techniques

An Enchanced Approach for Early Chronic Kidney Disease Predi...

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International Conference on Inventive Computation Technologies (ICICT)

作者： S. Poongodi K. Sasirekha Sheela Rini Jeyapriya. S G.Dona Rashmi Department of Data Science PSGR Krishnammal College for Women Coimbatore India Department of Computer Science(Artificial Intelligence and Data Science) Dr.SNS Rajalakshmi College of Arts and Science Coimbatore India Deparrtment of Computer Science(PG) PSGR Krishnammal College for Women Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Artificial Intelligence and Machine Learning Kongunadu Arts and Science College Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic renal disease is a medical disorder in which the kidneys gradually slow down, reducing their ability to filter blood. Diabetes, high blood pressure, heart disease, and a family history of renal failure are the primary risk factors for this condition. The prognosis for patients with chronic kidney disease (CKD) varies according to the stage at which they are diagnosed. Stages 0 and 1 represent the early stages of the disease, whereas stage 4 represents the severe stages, where the kidneys are nearly at the point of failure. Early diagnosis is therefore essential for the patient's survival and the appropriate therapies. Blood tests, biopsies, urine tests, and imaging tests are among the medical diagnostic techniques that can be used to diagnose chronic kidney disease (CKD). However, these medical diagnostic techniques have certain drawbacks and restrictions. They are employed to identify diseases in their later stages and can occasionally yield erroneous results; they are also expensive. Consequently, the researcher has discovered a novel technique for the early identification of CKD that use machine learning algorithms to overcome these complications because machine learning algorithms are becoming increasingly significant in health care datasets. These methods also have issues with overfitting, class imbalance, biased results, problems with dataset interpretability, and high computing costs. Therefore, improved preprocessing techniques and machine learning algorithms are used in this research work to overcome the inadequacies of the existing approaches for CKD prediction. The model is tested using the Kaggle dataset on chronic renal disease. Class imbalance and missing attribute values are issues with this dataset. Additionally, this dataset has several categorical attribute values and additional variances in attribute values. A few preprocessing techniques are used to improve the data quality, including K-Nearest Neighbour for filling in missing attribute v

关键词： Adaptation models Accuracy Machine learning algorithms Computational modeling Machine learning Predictive models Chronic kidney disease Encoding Optimization Diseases

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