Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.Machine learning (ML)

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Now showing 1 - 10 of 13
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    Future of connectivity: a comprehensive review of innovations and challenges in 7G smart networks
    (IEEE, 2025-04) Chamola, Vinay
    The evolution from 1G to 6G networks has transformed global communication, progressing from basic voice calls in 1G to the immersive, AI-enabled experiences of 6G. As emerging AI-driven applications like autonomous systems, the Internet of Everything (IoE), and immersive technologies demand unprecedented capabilities, 7G networks are set to redefine connectivity by overcoming the limitations of earlier generations. This paper comprehensively reviews the innovations and challenges in 7G networks, focusing on integrating advanced AI and machine learning paradigms such as meta-learning, incremental learning, distributed intelligence, and reinforcement learning to enhance adaptability, resource allocation, and edge performance. The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. The paper highlights the use of technologies, including blockchain for decentralized security, quantum computing for robust encryption, terahertz communication for ultra-fast data transfer, zero-energy solutions for sustainability, and generative AI for intelligent network optimization and automation. By addressing these challenges and exploring cutting-edge strategies, this paper envisions 7G networks as the foundation for a secure, intelligent, and sustainable digital future, equipped to combat emerging cyber warfare threats, enhance resilience against technological disruptions, and support innovations across smart cities, autonomous systems, healthcare, and industrial IoT.
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    Unveiling the future: A comprehensive analysis of 6G technology and its transformative potential
    (Springer, 2025-06) Chamola, Vinay
    Sixth-generation (6G) technology signifies a major leap in mobile communications, offering ultra-reliable, low-latency, and high-throughput connectivity. This review investigates the foundational technologies underpinning 6G, including Terahertz (THz) communication and ultra-massive multiple input, multiple output (MIMO), and explores their capabilities in enabling high-speed, consistent, and scalable communication infrastructures. A key focus of this study is the application of machine learning (ML) and deep learning (DL) in optimizing network slicing and addressing security challenges within 6G networks. While network slicing allows for flexible, service-specific logical network partitions, it also introduces technical challenges such as dynamic resource allocation, secure slice isolation, and real-time threat detection. To mitigate these, we assess ML-driven approaches—including reinforcement learning (RL), federated learning, and anomaly detection models—that facilitate intelligent orchestration and adaptive security. Furthermore, we highlight practical deployment barriers such as data privacy concerns, computational limitations at the edge, and the need for interpretable models and standardization. This comprehensive review provides insights into the current state, challenges, and potential solutions for integrating ML-based mechanisms to enhance the efficiency, scalability, and security of next-generation communication systems.
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    A comprehensive survey on data converters for IOT applications: scope, issues, and future directions
    (IEEE, 2025-03) Gupta, Anu; Shekhar, Chandra; Chamola, Vinay
    Data converters significantly contribute to efficient and accurate data processing in Internet of Things (IoT) systems. As IoT expands into agriculture, industrial automation, and healthcare (AIH), precise and low-power data conversion has become crucial to support longer battery life and reliable performance in IoT devices. Efficient data converters are key to reducing energy use, especially in components like comparator circuits, which consume significant energy in successive approximation register analog-to-digital converters (SAR ADCs). This survey provides an in-depth review of recent developments in low-power data converter design, examining techniques that help reduce power consumption at various stages. It emphasizes advancements, such as energy scaling, dynamic voltage references, and architectural optimizations that enhance efficiency without compromising performance. A specific analysis of emerging technology trends, such as the application of machine learning in data converter design, is explored to stimulate further innovation. Machine learning (ML)-based optimization, including adaptive calibration, noise reduction, and real-time performance optimization, presents new opportunities for enhancing efficiency and accuracy while addressing critical design constraints in IoT applications. While quantum encryption offers promising advancements in securing IoT data transmission, a broader security perspective beyond encryption is necessary, including concerns, such as attack detection and data integrity, ensuring the robustness of IoT systems. This review also examines latency, signal integrity, and accuracy issues, offering a roadmap for next-generation converter designs and reducing power consumption in data converters, which are fundamental to enhancing the performance and lifespan of IoT devices.
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    A detailed comparative analysis of automatic neural metrics for machine translation: bleurt & bertscore
    (IEEE, 2025-04) Chamola, Vinay; Gupta, Karunesh Kumar
    Bleurt a recently introduced metric that employs Bert, a potent pre-trained language model to assess how well candidate translations compare to a reference translation in the context of machine translation outputs. While traditional metrics like Bleu rely on lexical similarities, Bleurt leverages Bert's semantic and syntactic capabilities to provide more robust evaluation through complex text representations. However, studies have shown that Bert, despite its impressive performance in natural language processing tasks can sometimes deviate from human judgment, particularly in specific syntactic and semantic scenarios. Through systematic experimental analysis at the word level, including categorization of errors such as lexical mismatches, untranslated terms, and structural inconsistencies, we investigate how Bleurt handles various translation challenges. Our study addresses three central questions: What are the strengths and weaknesses of Bleurt, how do they align with Bert's known limitations, and how does it compare with the similar automatic neural metric for machine translation, BERTScore? Using manually annotated datasets that emphasize different error types and linguistic phenomena, we find that Bleurt excels at identifying nuanced differences between sentences with high overlap, an area where BERTScore shows limitations. Our systematic experiments, provide insights for their effective application in machine translation evaluation.
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    Advancements in Yoga Pose Estimation Using Artificial Intelligence: A Survey
    (Bentham Science, 2024) Chamola, Vinay; Rout, Bijay Kumar
    Human pose estimation has been a prevalent field of computer vision and sensing study. In recent years, it has made many advances that have helped humanity in the fields of sports, surveillance, healthcare, etc. Yoga is an ancient science intended to improve physical, mental and spiritual wellbeing. It involves many kinds of asanas or postures that a practitioner can perform. Thus, the benefits of pose estimation can also be used for Yoga to help users assume Yoga postures with better accuracy. The Yoga practitioner can detect their own current posture in real-time, and the pose estimation method can provide them with corrective feedback if they commit mistakes. Yoga pose estimation can also help with remote Yoga instruction by the expert teacher, which can be a boon during a pandemic. This paper reviews various Machine Learning, Artificial Intelligence-enabled techniques available for real-time pose estimation and research pursued recently. We classify them based on the input they use for estimating the individual's pose. We also discuss multiple Yoga posture estimation systems in detail. We discuss the most commonly used keypoint estimation techniques in the existing literature. In addition to this, we discuss the real-time performance of the presented works. The paper further discusses the datasets and evaluation metrics available for pose estimation.
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    Artificial Intelligence Empowered Digital Twin and NFT-Based Patient Monitoring and Assisting Framework for Chronic Disease Patients
    (IEEE, 2024-03) Chamola, Vinay
    People suffering from chronic diseases require continuous support in monitoring their nutrition, diagnostic tests, medication, and daily activity tracking. Given the low ratio of patients to healthcare providers, it becomes infeasible to provide one-to-one support to patients. Furthermore the existing online medical consultation platforms are costly for regular approach. Addressing these issues, we propose to monitor and assist patients suffering from chronic disease using a novel Al-based and IoT-supported digital twin platform. A digital twin of a patient grows with the patient, and it helps in continuous and remote patient monitoring. Further, the digital twin enables the creation of patient-specific personalized treatment models, enabling doctors to conduct virtual simulations of the suitability of certain drugs and procedures. The data collected from the digital twin is fed to machine learning models for intelligent analysis, feedback, and support. The proposed solution incorporates five essential machine learning models using novel algorithms for drug recommendation, chronic disease stage detection, nutrition tracking and recommendation, patient activity tracking, and patient data anonymization. Addressing patients' lack of motivation to participate in emerging patient monitoring frameworks, we incorporate an incentive mechanism rooted in Non-Fungible Tokens (NFTs) to encourage active participation in patients, which also has the added benefit of helping patients to store their historical medical data securely.
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    Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space
    (IEEE, 2024-04) Chamola, Vinay
    This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.
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    Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework
    (Elsevier, 2024-09) Chamola, Vinay
    Millions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that can help break down the barriers that prevent individuals from seeking the support they need for their mental well-being. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.
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    Decentralized Renewable Resource Redistribution and Optimization for Beyond 5G Small Cell Base Stations: A Machine Learning Approach
    (IEEE, 2023-03) Chamola, Vinay
    Optimal resource provisioning and management of the next generation communication networks are crucial for attaining a seamless quality of service with reduced environmental impact. Considering the ecological assessment, urban and rural telecommunication infrastructure is moving toward deploying green cellular base stations to cater to the needs of the ever-growing traffic demands of heterogeneous networks. In such scenarios, the existing learning-based renewable resource provisioning methods lack intelligent and optimal resource management at the small cell base stations (SCBS). Therefore, in this article, we present a novel machine learning-based framework for intelligent resource provisioning mechanisms for micro-grid connected green SCBSs with a completely modified ring parametric distribution method. In addition, an algorithmic implementation is proposed for prediction-based renewable resource re-distribution with energy flow control unit mechanism for grid-connected SCBS, eliminating the need for centralized hardware. Moreover, this modeling enables the prediction mechanism to estimate the future on-demand traffic provisioning capability of SCBS. Furthermore, we present the numerical analysis of the proposed framework showcasing the systems’ ability to attain a balanced energy convergence level of all the SCBS at the end of the periodic cycle, signifying our model’s merits.
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    Next generation stock exchange: Recurrent neural learning model for distributed ledger transactions
    (Elsevier, 2021-07) Chamola, Vinay
    A distributed stock exchange system encompasses multiple network hosts that participate in the sharing and exchange of resources. In such exchanges, the mediator or stock exchange must manage and delineate all operations in a cohesive manner. Stock exchange (SE) also acts as the transaction manager to provide consistent, isolated, durable, and atomic transactions for participating entities. However, the work for the stock exchange is not so straightforward as it may sound. With multiple transactions happening per second, the global serializability and concurrency control becomes an issue resulting in multiple threats and vulnerabilities. We propose a novel stock exchange that integrates time series prediction to distributed transactions and understanding the rapid global transactions and limitations of resources at the stock exchange. We use distributed acyclic graph (DAG) based distributed ledger technology IOTA to provide security and consensus for independent users. The paper proposes a time-variant model that adjusts its predictions based on transactions, moments of observations, participating entities, and history. We show that our model outcasts other state-of-art schemes in terms of prediction accuracy. Also, the model is fair, fast, and scalable to handle millions of transactions per second.