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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Saha, Srestha Pandey, Arvind Addya, Sourav Kanti Brata Nath, Shubha Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331531195

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.

关键词： Simulated annealing

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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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Optimizing medical image analysis through MViTX on multiple datasets with explainable AI

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Neural Computing and Applications 2025年第14期37卷 8479-8507页

作者： Sheakh, Md. Alif Tahosin, Mst. Sazia Alam, Mohammad Jahangir Begum, Mahbuba Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh

Cancer remains a leading cause of mortality worldwide, with early detection and accurate diagnosis critical to improving patient outcomes. While computer-aided diagnosis systems powered by deep learning have shown considerable promise, their widespread clinical adoption faces significant challenges in maintaining consistent performance across diverse imaging modalities and datasets. This research addresses the critical challenge of developing robust, generalizable deep learning models by proposing a comprehensive framework utilizing seven diverse medical imaging datasets encompassing fundus photography, histopathology, endoscopy, and MRI, covering diseases such as ocular toxoplasmosis, endometrial cancer, colorectal cancer, gastrointestinal disease, breast cancer, brain tumor, and tympanic membrane conditions. Our methodology combines customized data augmentation strategies (photometric, geometric, and elastic transformations) with an optimized vision transformer with external attention (MViTX) architecture. The MViTX model demonstrated exceptional performance with test accuracies ranging from 94.1 to 99.1% across all datasets, achieving superior metrics in accuracy, precision, recall, F1-score, and AUC compared to state-of-the-art CNNs. The model's effectiveness was further validated through ablation studies and explainable AI techniques, while its practical utility was demonstrated through deployment as a user-friendly web application. Our research establishes the effectiveness of combining tailored data augmentation with attention-based transformer architectures for medical image analysis, representing a significant step toward enhancing healthcare professionals' diagnostic capabilities and ultimately improving patient care outcomes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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Efficient Deployment Using CI/CD: Text Summarization Using NLP in Cloud 1

Efficient Deployment Using CI/CD: Text Summarization Using N...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Shingade, Bhuvan Hariharan, Siddharth Patole, Amol Singh, Kishan TPCT's Terna Engineering College Department of Computer Engineering Mumbai India Watumull Institute of Electronics Engineering and Computer Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350355611

The core task in natural language processing (NLP) is text summarization, which condenses important information from large volumes of text into brief summaries. This study reviews text summarization strategies using NLP principles, focusing on two main methods: extractive and abstractive summarization. Abstractive techniques generate summaries by interpreting and rephrasing information, while extractive methods highlight key sentences or paragraphs from the original text. Our study examines various algorithms and models within both paradigms, leading to the development of a novel summarization model. Inspired by advancements in deep learning and transformer architectures, our model integrates state-of-the-art techniques to produce high-quality summaries. We discuss the architecture, training, and evaluation of our model, demonstrating its effectiveness on benchmark datasets. Lastly, we explore potential applications and future directions, emphasizing the importance of custom-built solutions for modern information processing tasks. © 2025 IEEE.

关键词： Inverse problems

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DELTa: Dynamic Energy-and-Latency-aware Task scheduling for Fog-Cloud Paradigm

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IEEE Access 2025年 13卷 74617-74633页

作者： Mahapatra, Abhijeet Pradhan, Rosy Majhi, Santosh K. Mishra, Kaushik Department of Computer Science & Engineering Odisha Burla768018 India Sikkim Manipal University Sikkim Manipal Institute of Technology Department of Artificial Intelligence and Data Science Sikkim India Department of Electrical Engineering Odisha Burla768018 India Department of Computer Science and Information Technology Chhattisgarh Bilaspur495009 India Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India

The rapid proliferation of IoT devices like smartphones, smartwatches, etc. has significantly elevated the quantity of data requiring execution. It poses challenges for centralized Cloud computing servers, such as latency overhead and increased consumption of energy. To address this, Fog computing has been incorporated in this research as a complementary paradigm to the Cloud-based computing model. It thereby reduces the huge distance between end-user IoT gadgets and the Cloud computing servers. In order to improve the latency and waiting time this research employs a Multi-Level Queue (MLQ) for classifying tasks based on their priority. For parallel processing, a Self-adaptive Fuzzy C-means++ (SaFCm++) approach has been applied to cluster the Fog computing nodes depending on the heterogeneity of the computational nodes. For efficient scheduling, a Dynamic Energy-and-Latency-aware Task scheduling (DELTa) approach has been proposed to optimize latency, makespan, and utilization of resources. Extensive simulations are carried out for different test cases considering task and machine heterogeneity, followed by a statistical analysis. The overall improvements of the proposed method over other baselines are 9.75% for makespan, 64.59% for service latency, 13.90% for resource utilization, and 12.75% for energy consumption. The proposed energy-efficient scheduling method effectively manages diverse tasks and resources, enhancing task processing efficiency while minimizing energy consumption and maximizing resource utilization. Furthermore, the value of the Friedman statistics FF for the proposed model is estimated at 5.44, which shows superiority over the crucial value for four methodologies and four evaluation matrices. © 2013 IEEE.

关键词： Fog computing

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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An Integrated Agricultural Financial Inclusion System Leveraging Supervised Machine Learning & Data Analytics 6

An Integrated Agricultural Financial Inclusion System Levera...

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

作者： Rathi, Snehal Khedkar, Vijayshri Jadhav, Rutuja Kamate, Mugdha Potdar, Apurva Kamble, Prachi Computer Department Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798331522667

Agriculture is backbone of many economies but still farmers often face many challenges in accessing financial services or available resources for them, making informed crop choices and participating in government initiatives. Because of many farmers are not aware about all these things, to address these concerns, the proposed "Agricultural Financial Inclusion System" provides a comprehensive platform which integrates essential services with aim of improving farmers’ production and their financial awareness. Our system has a crop recommendation feature that helps farmers to choose the best suitable crop for their soil in the farm by taking climate and soil type into account. In order to make sure that farmers are aware about various available services/resources or any government schemes and able to take advantage of them, the platform also offers essential information on a variety of government initiatives, saving plans etc. By combining all these features, the Agricultural Financial Inclusion System encourage farmers to make smart choices which help them to enhance agricultural productivity and financial stability. In order to create more successful and sustainable agriculture sector, this integrated approach hopes to fill the gap between farmers and technology & resources they need. © 2025 IEEE.

关键词： Smart agriculture

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MedFILIP: Medical Fine-grained Language-Image Pre-training

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3587-3597页

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo The School of Computer Science and Technology Harbin Institute of Technology Harbin China The Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The College of computer and control engineering Northeast Forestry University Harbin China The Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia The Department of Biomedical Engineering Department of Computer Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestXray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. © 2013 IEEE.

关键词： Semantics

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A transfer learning approach for continuous speech recognition system in Indian language sadri

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International Journal of Information technology (Singapore) 2025年 1-10页

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur 713209 India

Sadri is the most widely used language of the Chotanagpur Plateau region of India. This is primarily a spoken language and developing an automatic speech recognition (ASR) system in Sadri is extremely important. When we searched the literature, we found no open ASR system or relevant resources in Sadri. So, we worked on developing an ASR system in Sadri. We created a Sadri speech corpus of around 20 h, employing 53 native speakers. Then, we implemented the baseline model using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). As the size of the training data was not sufficient, we investigated a transfer learning technique to improve performance. We found that ASR data are openly available in other related languages such as Hindi and Bengali. We used Bengali and Hindi data of 100 h each in a transfer learning framework along with the Sadri data. In our experiments, we found that the transfer learning-based model outperforms the baseline model. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： ASR Deep learning Indian language Sadri Speech recognition

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Marine Life Ecosystem Analysis Based on Climate Change Detection Using Deep Learning Algorithms

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 545-554页

作者： Rebecca, B. Sandhya, A. Pokkuluri, Kiran Sree Kanishka, Gunipati Shaik, Khasimbee Krishna, B. Murali Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities because of this reliance. The way society functions is being challenged by climate change and significant adaptation will probably be needed to deal with future changes in weather patterns. Machine learning (ML) methods have advanced to the point that they are now being offered as a tool to support climate studies, in addition to being the source of advances in other fields of study. This research proposes a novel technique in marine life ecosystem–based climate change detection using deep learning model. Input is collected as climate change detection–based marine life ecosystem and processed for noise removal with normalisation. Then this data has been segmented and classified using watershed semantic fuzzy component analysis with quantile shrinkage convolutional regression model. The experimental analysis has been carried out in terms of training accuracy, specificity, recall, AUC and NSE. Suggested technique achieved training accuracy 98%, specificity 96%, NSE 45%, recall 95% and AUC 97%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Convolutional regression Deep learning Fuzzy component analysis Marine life ecosystem

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