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A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

Intelligent Automation & Soft Computing 2023年第1期35卷 537-552页

作者： Amishi Mahesh Kapadia P.Nithyanandam School of Computer Science and Engineering Vellore Institute of TechnologyChennai600012TamilnaduIndia

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret *** suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold *** notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in *** scrambling the cover image,Arnold transform adds security to the information that gets embedded and also allows embedding more information in each *** hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure *** proposed method employs a set of keys to ensure that information cannot be decoded by an *** experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats,including medical *** performance metrics proves that the retrieved cover image and hidden image are both *** System is proven to withstand rotation attack as well.

关键词： Reversible data hiding(RDH) integer wavelet transforms(IWT) arnold transform payload embedding and extraction

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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas Sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

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

作者： Harshavardhan, S. Upender, Patri Ganesh, N. Giftsy, Sharon Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798331529833

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology. © 2025 IEEE.

关键词： Chemical sensors

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Blockchain Powered Integrated Health Profile and Record Management System for Seamless Consultation Leveraging Unique Identifiers 2

Blockchain Powered Integrated Health Profile and Record Mana...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Khekare, Ganesh Ghugare, Soham Khatri, Rahul Majumder, Goutam Khekare, Urvashi School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798350328202

The Integrated Health Profile (IHP) leverages blockchain technology and smart contracts to establish a decentralized and tamper-resistant platform for the storage and sharing of decentralized health records (DHRs). Secured remote access to doctor-patient data is the need of the hour as the world moves towards home-based online services. In the IHP, each patient is assigned a unique identifier, and their health records are securely stored and linked to this identifier. The implementation of an individualized IHP card is the center of the IHP framework, which serves as a comprehensive repository for a patient's health records, including reports, prescriptions, medical bills, and insurance receipts. The IHP card is equipped with a unique QR code linked to the patient's unique identifier (UID). When scanned by a medical practitioner, the patient receives a one-time password (OTP) via a two-factor authentication process, providing an additional layer of consent and security. Upon successful verification, the doctor gains access to a specific subset of the patient's shared records, ensuring the patient's privacy is respected. The IHP framework significantly streamlines the customer intake process, eliminating the need for manual data entry at healthcare facility receptions. This reduction in administrative tasks minimizes waiting times and improves overall operational efficiency. Furthermore, by integrating the customer's medical history with the IHP card, medical service providers gain comprehensive insights into the customer's health status, facilitating accurate diagnoses and personalized treatment plans. To enable efficient healthcare service discovery, the IHP framework utilizes standardized application programming interfaces (APIs) to connect customer-centric applications (CCAs) with medical service provider applications (MSPs). Through these APIs, CCAs can access real-time information on available healthcare facilities, ensuring customers receive accurate and up-to

关键词： Authentication

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Review on distributed denial of service attack detection in software defined network

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 128-146页

作者： Karthika, P. Karmel, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

This paper has shown the enhancement of networks, and the ability to withstand DDoS attacks. Thus, this survey plans to make a review on 65 papers which concern DDoS attack detection in SDN. Therefore, the systematic analyses on the proposed method are analysed from each reviewed paper. In addition, the performance metrics and their best achievements in detecting the attack in each research paper are also analysed in this work. In addition, the mitigation technique used in each paper is examined in this work. Also, the chronological assessment and various tools utilised for implementing DDoS attack detection in SDN are considered and reviewed in this work. Finally, the survey depicts numerous research gaps and challenges that are more supportive for researchers to develop novel methods for detecting DDoS attacks in SDN. © 2023 Inderscience Enterprises Ltd.

关键词： Software defined networking

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Anomaly Detection in Medical Imaging: Bridging the Gap between 2D Processing and Interactive 3D Visualization

Anomaly Detection in Medical Imaging: Bridging the Gap betwe...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Abhisri, M. Sasidhar, Manikrishneshwar Subramanian, Aditya Sreeraghavan, Mirdhula Sivakumar, Surindernath Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350388602

This paper mainly aims to present the use of transform a 2D image into a 3D interactive model which will help us visualize the anomaly, allowing the medical sector a better understanding of anomalies in medical images, which may even unearth new findings. Using stable diffusion algorithms, the generative model and computer vision converts 2D images into interactive 3D models. Existing structures usually struggle with complex 2D images when the anomaly is detected, hence focus on building a 3D model for deeper understanding is necessary. In this model, the challenges and roadblocks in normal 2D images for anomaly detection and why a 3D model will provide a better insight into the anomaly from medical images is discussed. Next, it explores the technological benefits of having an advanced 3D model and how the 3D model will help in interacting with the anomaly by employing 3D models to show the expected progression in the anomaly extraction process. Finally outlining the previously implemented models to generate 3D interactive objects such as, generative adversarial networks (GANs) and convolutional neural networks (CNNs), as they have exhibited promising results in extracting meaningful features and detecting anomalies in medical images. It emphasizes the importance of bridging the gap between 2D image processing and interactive 3D visualization in anomaly by incorporating advanced techniques, leveraging 3D information, and enabling interactive exploration, that enhance the precision and efficacy of anomaly detection, aiding in achieving quicker diagnosis and treatment as outcomes in healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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BERT based Citation Recommender and Impactful Nodes Identifier in RDF Knowledge Graphs 4

BERT based Citation Recommender and Impactful Nodes Identifi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Vaidhyaraman, Raghavan Sharon Jessika, S. Sahaaya Arul Mary, S.A. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9798331515720

Knowledge graphs have emerged as powerful tools for representing and organizing information in a structured and interconnected manner. In this research, we aim to build a knowledge graph and a recommendation system for research papers. This research is divided into three parts: Charting the Research Progress (constructing the RDF knowledge graph and visualizing), The Search for Knowledge (making a research paper recommender system), and revealing the Trailblazers (finding the most impactful paper in the dataset). By leveraging state-of-The-Art tools and models, the knowledge graph was not only accurate but also visually intuitive, offering insights into the complex web of academic interrelations. The recommender system, built on this knowledge base, demonstrated high efficacy in suggesting relevant and significant research papers, with its recommendations being both contextually and academically pertinent. The impact analysis reveals the most influential papers, providing a clear view of research works that have shaped and directed academic discourse in their respective fields. © 2024 IEEE.

关键词： Knowledge graph

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques

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Geodesy and Geodynamics 2023年第3期14卷 275-291页

作者： Ajay Badugu K.S.Arunab Aneesh Mathew P.Sarwesh Department of Civil Engineering National Institute of TechnologyTiruchirappalli620015Tamil NaduIndia School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014Tamil NaduIndia

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island *** remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island *** present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter *** study also identifies hot spots and cold spots within the study *** this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at *** diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the *** trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than *** using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban *** spike in temperature for a particular region in the rural area is due to industry and the associated built-up *** study also identified cold spots with a 90 percent confidence level within the rural ***,cold spots with a 95 and 99 percent confidence level were not identified within the study area.

关键词： Urbanization Land surface temperature Urban heat island effect Hot spots Remote sensing

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