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2024 5th International Artificial Intelligence and Blockchain Conference, AIBC 2024

作者： Godson, Sam Santhosh, V.V. Dhushyanth, S.B. Anandakumaran, Kirtanaa Prem Sankar, N. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu Vellore India

ISBN: (纸本)9798400710780

Predicting Bitcoin prices remains a challenging task due to the inherent volatility and non-linearity of cryptocurrency markets. This paper investigates the effectiveness of Self-Organizing Maps (SOMs) implemented through the SuSi package for Bitcoin price prediction. We compare SuSi's performance with established deep learning methods, including Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNNs), Deep Neural Networks (DNNs) and N-BEATS. We evaluate these approaches using real-world Bitcoin price data and analyze their performance based on accuracy, interpretability, and training efficiency. Our findings suggest that SuSi offers a competitive alternative to deep learning methods, particularly when considering training efficiency. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Contrastive Learning

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Leveraging Convolutional Neural Network and Explainable AI for Enhanced Pneumonia Detection 1

Leveraging Convolutional Neural Network and Explainable AI f...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Murugan, Ramalingam Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331520540

Advances in machine learning and computer vision have significantly improved the diagnostic capabilities of medical imaging. Convolutional Neural Networks (CNNs) have emerged as a crucial tool for image classification, particularly in detecting pneumonia from chest X-ray images. Despite their high accuracy, CNNs face challenges in clinical adoption due to their lack of interpretability. Medical practitioners require a clear understanding of the decision-making process to trust AI-generated diagnoses. To address the aforementioned challenge, we propose pneumonia classification model incorporating CNN with a combination of the model and Grad-CAM, for enhanced interpretability. Grad-CAM visually outlines critical regions, influencing the prediction, so the clinician can easily confirm AI-based diagnoses with a more confident and credible approach. It is exclusively for pediatric pneumonia, an accurate and explainable machine for early interventions that can also work in resource-scarce regions. Our research uses a set of 5,863 pediatric chest X-ray images, divided into Pneumonia and Normal classes. The dataset was acquired from Guangzhou Women and Children's Medical Center and divided into three subsets: training, validation, and test. By improving the accuracy and transparency of the diagnosis, this model can help bridge the gap between AI and the real world. This can help facilitate quicker and more reliable pneumonia diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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Congestion Based Adaptive Association Mechanism for IEEE 802.11ah Based Large IoT Network 5th

Congestion Based Adaptive Association Mechanism for IEEE 80...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： R, Nandhini R, Radha School of Computer Science and Engineering Vellore Institute of Technology–Chennai Chennai India

ISBN: (纸本)9783031770807

The IEEE 802.11 ah standard is the prominent protocol for medium range communication in the domain of Internet of Things (IoT), facilitating interaction between wireless sensor nodes and access points. Supporting up to approximately 8192 nodes connected to a single access point through single hop strategies, IEEE 802.11 ah employs CSMA/CA for the authentication and association process of sensor nodes with access point. However, the standard encounters challenges such as significant collisions and prolonged network setup times when attempting to associate an excessive number of wireless sensor nodes with a single access point, primarily due to authentication thresholds set at intervals. In this research, we propose a dynamic adjustment of the authentication threshold based on network congestion status. Learning parameters including the number of requests served successfully by the access point in the past, present and a congestion threshold are leveraged to adapt to current network congestion levels. Through evaluation using the LILD method in the ns-3 simulator, our approach demonstrates considerable enhancements in terms of collision reduction and network setup efficiency. This research endeavours to promote responsible network management practices for improving the performance and reliability of IEEE 802.11 ah based IoT deployments. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Internet of things

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A case study of Nifty 50 Index

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IEEE Access 2025年 13卷 87036-87047页

作者： Singh, Priya Jha, Manoj Patel, Harshita Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics & Computer Applications Bhopal 462003 India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study. © 2013 IEEE.

关键词： CNN Ensemble learning LSTM Random Forest Statistical Analysis Stock Market TCN

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Classification

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2025年第2期12卷 1-13页

作者： Annadurai, Amrutha Prusty, Manas Ranjan Pandey, Trilok Nath Patra, Subhra Rani School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Information Systems and Operations Management University of Texas at Arlington Texas 76019 United States

INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network. RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model. © 2025 A. Annadurai et al.

关键词： Convolutional neural networks

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Leveraging RAG for Effective Prompt engineering in Job Portals 2

Leveraging RAG for Effective Prompt Engineering in Job Porta...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Haneef, Farhan Varalakshmi, M. Peer Mohamed, P.U. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India CapitalOne United States

ISBN: (纸本)9798331530389

Traditional recruitment methods are time-consuming and heavily reliant on manual processes, leading to inefficiencies. This paper explores the integration of AI technologies into a job portal to streamline the hiring process, focusing on AI's ability to automatically parse and extract information from resumes and job descriptions to match candidates with relevant job opportunities. The platform leverages Large Language Models (LLMs) to extract key information, such as skills, education, and work experience, from both resumes and job descriptions. Fine-tuning through prompt engineering ensures accurate extraction, even when data fields are incomplete or missing. To further enhance matching accuracy, Retrieval-Augmented Generation (RAG) techniques are employed. These mechanisms retrieve relevant information from a structured skills database to provide context, which, combined with the generative capabilities of LLMs, enables more contextually accurate matches. Candidates receive personalized job recommendations based on the information extracted from their resumes, while employers can post job descriptions and use AI-driven tools to match candidates. This approach of using contextual prompts not only improves matching accuracy but also reduces computational time, eliminating the need for custom models tailored specifically to resumes or job descriptions. © 2025 IEEE.

关键词： Employment

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