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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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A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Insights on Improving Reasoning and Increasing Capabilities in Large Language Models 6th

Insights on Improving Reasoning and Increasing Capabilities ...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Krisshna, S. Varun Sendhil, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

Large Language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. These models have been reported to have "sparks" of artificial general intelligence owing to their very attractive properties such as high generalization and performance in unseen data. These models have demonstrated strong capabilities in generalization and performance on novel data, leading some to speculate they may contain early sparks of artificial general intelligence. However, true AGI remains an unachieved goal requiring breakthroughs beyond current techniques. LLMs have poor mathematical reasoning capabilities, inherit biases present in training data, and can hallucinate and deliver false information. In this chapter, we discuss several methods that improve the current state-of-the-art models through the use of external tools such as web browsing, better prompting techniques, scaling, using Reinforcement learning (RL) and tree-based search algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Enhancing Online Learning: A Pose and Emotion-Based Approach for Student Attention Monitoring 5th

Enhancing Online Learning: A Pose and Emotion-Based Approac...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Chingapurathu, Sarath Krishna Jeganathan, L. Janaki Meena, M. School of Computer Science and Engineering Vellore Institute of Technology TN Chennai600127 India

ISBN: (纸本)9789819747108

With the increasing popularity and preference of online classes, teachers are at a disadvantage of losing face-time with students and are faced with the challenge of monitoring their students’ attention levels remotely. This research, VisioFocus, presents a non-invasive student monitoring system using deep learning and computer vision to measure and analyze the attention levels of students. It has two main components namely Pose Estimation and Emotion Recognition which processes the captured facial images of the student through the webcam at 15 frames per second to measure the attention level. The Pose Estimation model uses pure computer vision and the Emotion Recognition model uses a modified ResNet deep convolutional network that could classify emotions portrayed into 6 different labels at about 70% accuracy. This research proposes a novel and reliable approach to measuring attention levels, which will have a significant impact on the education field. The analytical survey of attention levels after each lecture will enable educators to tailor their teaching approach to specific students, resulting in better academic development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Smart Classroom Power Automation: An Adaptive And Predictive Approach

Smart Classroom Power Automation: An Adaptive And Predictive...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Madhurima, Gangireddy Reddy, Panduga Venkata Jaya Srikanth Srinivasa Perumal, R. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529833

The growing demand for energy efficiency in educational institutions has led to the exploration of smart technologies for managing power consumption. This project introduces a "Power Automation for Classrooms"system that leverages long-range RFID (Radio Frequency Identification) technology to automate the activation of classroom lights and fans based on student attendance. The system operates by detecting student entries through RFID tags without the need for manual scanning, thus ensuring a seamless and instant process. The project aims to reduce energy wastage by activating the power in stages depending on the number of students present, with specific power allocation thresholds set according to the class strength. The data on students entered, as well as the power consumption for each classroom, is stored in the cloud, allowing for real-time monitoring, analysis and supports data-driven decision-making for future energy planning. © 2025 IEEE.

关键词： Decision making

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Exploring Independent Feature Extraction Techniques for Context-based Image Retrieval in Healthcare

Exploring Independent Feature Extraction Techniques for Cont...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Bokdia, Akshat Kulakarni, Siri R. Lodha, Nitin Vijayarajan, V. Surya Prasath, V.B. School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India Department of Computer Science University of Cincinnati Cincinnati United States

ISBN: (纸本)9798331536336

In medical informatics, developing efficient image retrieval methods is vital for the research and development of diagnosis and treatment processes. This study evaluates three different feature extraction methods that could be beneficial for enhancing context-based image retrieval (CBIR) in medical imaging applications. The techniques used are: Discrete Wavelet Transform (DWT) with Singular Value Decomposition (SVD), a combination of Sobel operators with DWT and SVD, and Autoencoders. Such hybrid feature extraction techniques are applied to gain salient characteristics and detect trends in the given image data. The BRATS dataset, which consists of various multi-modal MRI scans, is used in the study to validate the efficacy of each technique. To assess the performance of each technique, the InceptionNet V3 model is trained individually on each of these sets of feature-extracted images. Results show that, in terms of robustness and accuracy in classifying medical images, the DWT with SVD method outperforms the others. This research implicates choosing feature extraction techniques appropriately to improve CBIR systems in healthcare. © 2025 IEEE.

关键词： Content based retrieval

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Improving Cancer Classification Through an Deep Learning Framework Using Transfer Learning 2

Improving Cancer Classification Through an Deep Learning Fra...

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

作者： Subitha, D. Khalid, Fayyaz Kavitha, C. Abhinav, Gulisetty School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331505745

The primary objective of this work is to create an AI system that offers intelligible and transparent justifications for cancer classification. When it comes to medical imaging-based cancer detection, sophisticated image analysis techniques such as deep learning models are used to assess and interpret medical pictures while offering a transparent account of its reasoning. The performance of the three state-of-art pre-trained deep models: VGG-16, and EfficientNetB0 on a dataset of eight different cancer types obtained from Kaggle is analyzed in this work. Furthermore, a novel deep learning framework is proposed using the above mentioned three pre-trained models as the backbone network using transfer learning approach for each type of cancer. Each model's effectiveness is evaluated in classifying these cancers using metrics like accuracy, precision, recall, and F1-score. This work ultimately yields the best fit model that are tuned with the hyper parameters optimized using Powell's algorithm. © 2025 IEEE.

关键词： Deep learning

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