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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, R. Dhanush, B. Jivitesh Kanna, S. Hari Rama Chandran, A. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

The advancements in modern computing technologies have significantly contributed to the development of advanced healthcare monitoring systems., enabling the early detection of critical conditions., such as falls. This review highlights key challenges and applications within healthcare monitoring and prediction models that consider relevant health parameters. In particular., this study discusses about the Random Forest algorithm., which is known for its capacity to manage complex datasets and generate precise patient health forecasts. By optimizing computational efficiency while enhancing predictive accuracy., this study demonstrates the efficacy of machine learning in healthcare. © 2025 IEEE.

关键词： Electronic health record

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

1st International Conference on Advances in Computer Science...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

ISBN: (纸本)9798331518578

The proceedings contain 260 papers. The topics discussed include: application of wide bandgap in power electronics: next-gen smart grids;flexible hybrid electronics for printing of nano and chip scale structures;reliability of electronic connections in fast chargers of electric vehicle;enhancing substation earthing system: a finite element method for minimizing ground resistance and analyzing electric potential and temperature distribution;design and analysis of thermal management systems for an electric vehicle battery;analysis of smart city solutions for sustainable urban growth in India;an approach for the reliability realization of dynamic wireless charging of electric vehicle system;analyzing the impact of blockchain technology on financial transactions and accounting practices in global trade;optimization of electric discharge machining process parameters using simulated annealing optimization algorithm;and machine learning for wireless network optimization: algorithms and applications for adaptive communication.

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Analysis of optimization Techniques for Dynamic Neural Networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

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3rd international conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 IEEE.

关键词： Neural network models

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Revolutionizing Road Safety and Optimization with AI: Insights from Enterprise Implementation

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international Journal of advanced computer science and applications 2025年第4期16卷 1008-1019页

作者： Ouahbi, Younesse Ziti, Soumia Intelligent Processing and Security of Systems Informatics Department Faculty of Science Mohammed V University Rabat Morocco

This study explores the key factors influencing the adoption of artificial intelligence (AI) in the logistics sector, with a particular emphasis on road logistics management. It examines the technological, organizational, and environmental contexts that shape AI integration, as well as the challenges faced by logistics managers, including the need for digital transformation, carbon emissions reduction, and advanced parcel tracking management. The objective is to identify technological and human-related barriers to AI adoption and to assess the level of interest and readiness among logistics companies, especially in the Moroccan context. A quantitative research approach was adopted, based on an online survey targeting logistics professionals and decision-makers, mainly from European and Moroccan small and medium-sized enterprises (SMEs). The collected data were analyzed using statistical methods, including linear regression and ANOVA, to evaluate the relationships between company characteristics, perceived complexity of AI tools, and the availability of qualified human resources. The findings indicate that perceived complexity and limited access to specialized skills significantly hinder AI adoption. Moreover, the perception of tangible performance benefits—such as increased operational efficiency and reduced CO2 emissions—emerges as a major driver for acceptance. These insights offer practical implications for logistics companies seeking to leverage AI technologies to optimize operations, reduce environmental impact, and enhance parcel tracking systems. A strategic roadmap is proposed to overcome the identified barriers and promote effective AI integration. © (2025), (science and Information Organization). All rights reserved.

关键词： Risk perception

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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility 3

MediHealth: An AI-Driven Mobile Solution for Enhanced Health...

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3rd international conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2025

作者： Raj, Sukrutha Shekhar, Shashank Baskaran, P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

ISBN: (纸本)9798331527549

Mobile health (mHealth) applications have revolutionized healthcare by enhancing patient engagement, providing tools for self-management, and supporting better health outcomes. This paper explores the development of an innovative mHealth application designed to empower patients in making informed healthcare decisions. The application leverages advanced features such as AI powered chatbots, personalized healthcare recommendations, and a report and prescription summarizer to simplify complex medical information. By allowing users to easily find appropriate healthcare professionals, access detailed prescription data, and receive symptom-based specialist recommendations, the app facilitates effective self-management of chronic conditions. Furthermore, it offers proactive care prompts, personalized advice, and health behavior reminders to promote better overall health outcomes. Despite challenges in data security, privacy, and equitable access, the study emphasizes the importance of a user-centered design approach in ensuring the widespread adoption and success of mHealth applications. Ultimately, the goal is to enhance healthcare delivery by improving patient engagement, facilitating seamless access to healthcare resources, and enabling well-informed decision-making. This application presents a holistic solution for advancing patient care across diverse healthcare settings. © 2025 IEEE.

关键词： Electronic health record

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InterACT: Enhancing Human-AI Interaction 2

InterACT: Enhancing Human-AI Interaction

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katta, Jithendra Kodumuru, Nikhil Reddy Katta, Swathi School of Engineering and Applied Sciences The George Washington University WashingtonDC United States Vignana Bharathi Institute of Technology Computer Science & Engineering dept. Hyderabad India

ISBN: (纸本)9798331505745

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools. © 2025 IEEE.

关键词： Object detection

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Medical Image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

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6th IEEE international conference on Image Processing, applications and Systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 IEEE.

关键词： Frequency domain analysis

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Resilience in Property Insurance: A Path to Sustainability 24

Resilience in Property Insurance: A Path to Sustainability

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5th international conference on computer science and Management Technology, ICCSMT 2024

作者： Xie, Chen Tu, Binghao Chen, Qihao Weng, Jidong College of Educational Science and Technology Zhejiang University of Technology Zhejiang Hangzhou China

ISBN: (纸本)9798400709999

This study addresses the challenge of extreme weather on property insurance, proposing a model that balances insurer profitability and affordability for owners. By integrating strategic underwriting, normalized data, and advanced forecasting, the model aims to enhance system resilience and close the Insurance Protection Gap, particularly in high-risk areas like the U.S. and Europe, which are increasingly affected by natural disasters. To achieve these goals, we employ state-of-the-art computer technologies, including 3D scene analysis for enhanced visualization and assessment of property risks, as well as machine learning algorithms for time series forecasting. These techniques, along with least squares optimization and robustness checks, allow for more accurate risk evaluation and support the development of sustainable underwriting practices. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Risk assessment

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