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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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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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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Performance Analysis of CNN Models for Evaluating Biglycan as a Biomarker in Predicting Breast Cancer

Performance Analysis of CNN Models for Evaluating Biglycan a...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Krishnan, Sushil Sundaresan, Yokesh Babu Bachelor of Technology in Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781839539848

Breast cancer is found in the present generation at a higher level. Since the middle of the 20th century numerous unfavorable changes have been made to food, our way of life, obesity, the situation, and our microbiomes. Because of these variations, people have become more susceptible to cancer at an earlier age. The considered dataset is images of breast cancer-positive and cancer-negative histological sections utilizing the Biglycan biomarker. The dataset and models created from it can be used by pathologists and other healthcare practitioners to address probable disorders. Three widely used CNN models were examined for performance, and we discovered that MobileNet-V2 outperformed them all. Transfer learning-based breast cancer classification with the Biglycan biomarker dataset helps in tracking at the earlier stages. Additionally, our model outperformed earlier research using the same dataset in terms of accuracy. Our ability to create high-performance devices with lower power consumption and cheaper costs is made possible by the MobileNet-V2's ability to achieve greater precision while using a lighter model. In comparison with the existing work using LeNet, MobileNet-V2 outperforms. © The Institution of engineering & technology 2023.

关键词： Biomarkers

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The Role of Blockchain in Enhancing Digital Forensics: A Review of Challenges, Methodologies and Advantages

The Role of Blockchain in Enhancing Digital Forensics: A Rev...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Chauhan, Aviral Singh Menon, Rohit Singh, Ananya Srinivasan, Kathiravan School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331542344

As digital transformation accelerates world-wide, digital forensics has become increasingly crucial in storing and administering digital evidence during forensic investigations. Although digital forensics provides a swift and effective manner to handle digital records, it also faces threats in the form of cyber-attacks, tampering, and collusion, which can undermine the integrity of forensic procedures. These issues underpin a call for better evidence-related management, that is more secure and resilient. Decentralized solutions, particularly blockchain can be used to ensure integrity, confidentiality, and traceability properties necessary for effective evidence management. Transparency throughout the case is ensured by the distributed architecture of blockchain, which contributes to the authenticity of digital evidence. We conducted a qualitative review of research on decentralization in digital forensics, identifying key topics and challenges within this niche. While several articles explored specific areas of decentralized digital forensics, a unified perspective is missing. With this review, we wish to bridge this gap by providing a broader perspective on how blockchain can improve forensic procedures in various working areas by addressing vulnerabilities and building the foundation for more secure digital forensics. © 2024 IEEE.

关键词： Blockchain Chain of Custody Digital Forensics Hyperledger Information Security

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Rule and Encryption based Secure Routing for Internet of Vehicle Networks 2

Rule and Encryption based Secure Routing for Internet of Veh...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Anand, M. Muthurajkumar, S. Selvi, M. Thangaramya, K. Svn, Santhosh Kumar Kannan, A. Anna University Madras Institute of Technolog Department of Computer Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication devices and hence it helps in information exchange to vehicle drivers and the passengers. In IoV, Cybersecurity threats including Denial-of-Service (DoS) attacks are present and it compromises the security in communication among the vehicles and the passengers. Therefore, it is needed to develop a robust security mechanism that can protect the data communicated through routing in IoV. The inclusion of Encryption and Signature verification techniques in the routing algorithm will enhance the security. This paper proposes a Rule with Encryption-Oriented Secure Routing Protocol (REO-SRP) is proposed using clustering and RSA encryption with DSA Digital signature verification with rules for routing. Here, routing is performed after encryption of data and analysis of other vehicles using rules for reliable and secured data delivery. © 2024 IEEE.

关键词： Vehicles

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Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis

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IEEE Access 2025年 13卷 88917-88929页

作者： Walia, Jaskaran Singh Haridass, Kavietha Pavithra, L.K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Human Movement Analytics Chennai India

Underwater waste detection is a critical challenge for preserving aquatic ecosystems, particularly due to inherent underwater distortions such as light refraction, occlusion, and scattering. In this study, we present a novel deep learning framework for real-time underwater waste detection by evaluating state-of-the-art object detection algorithms on a manually annotated custom dataset comprising 10,000 curated augmented images across various water bodies to simulate real-world turbidity, illumination, and occlusion, consisting of three classes: Underwater Trash, Rover, and Biological Life. Our approach incorporates robust feature extraction and specialized data augmentation techniques that effectively mitigate the adverse effects of underwater distortions. We investigate multiple architectures, including YOLOv8n, YOLOv7, YOLOv6s, YOLOv5s, Faster R-CNN, and Mask R-CNN, analyzing key parameters such as mean average precision (mAP), inference speed, and computational efficiency. Our results demonstrate that YOLO-based models achieve faster & accurate performance, with YOLOv8n and YOLOv7 reaching a detection accuracy of 96%, compared to 81% and 83% for Faster R-CNN and Mask R-CNN, respectively. Computationally, YOLOv8n achieves 76 ms per frame, while YOLOv5s runs at 14 ms per frame, confirming real-time viability for AUV deployment. The proposed approach offers significant advantages over existing methods by enabling rapid, accurate detection with low computational overhead, thereby paving the way for integration with autonomous underwater vehicles (AUVs) for environmental monitoring and waste management. © 2013 IEEE.

关键词： Image segmentation

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Utilizing Deep Learning techniques for a multi-objective pollution forecasting model to enhance smart city sustainability

Utilizing Deep Learning techniques for a multi-objective pol...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Samal, K. Krishna Rani Vellore Institute of Technology University Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331508432

Accurate pollutant forecasting serves as a crucial component in air quality monitoring and control in a smart city. Traditional pollutant forecasting models such as statistical, and machine learning models follow a single-input, single-output learning approach, which fails to identify multi-output dependencies. In addition to that the traditional forecasting model ignores the data missing values and spatial dependencies of nearby locations, which can affect air pollutant concentration levels. The research work adopted a multiple imputation technique, which imputes the missing values using spatial attributes. A multi-objective air pollutant forecasting approach is developed, combining advanced data imputation with multi-input and multi-output (MIMO) multi-step forecasting capabilities. In addition to that the proposed multi-objective based Multi-output Bidirectional Long Short-Term Memory (MBLSTM) model used Bidirectional Long Short-Term Memory (BiLSTM) with the MIMO approach. Overall, the proposed MBLSTM model provides improved multiple pollutants forecasting results in a single run. © 2024 IEEE.

关键词： Adversarial machine learning

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Convolutional Neural Network (CNN) for Fake Logo Detection: A Deep Learning Approach Using TensorFlow Keras API and Data Augmentation 3

Convolutional Neural Network (CNN) for Fake Logo Detection: ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Murali, Saritha Gupta, Vaishnavi Saha, Soumyojyoti Sharan, Harsh Sinha, Prajwal Baibhav, Kumar Vellore Institute of Technology Department of Database Systems Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

In numerous applications, including image retrieval, brand monitoring, and counterfeit identification, the detection and categorization of logos play pivotal roles. This study proposes a novel methodology employing convolutional neural networks (CNNs) for discerning fraudulent logos. The CNN's structure involves several convolutional layers at its core, accompanied by max-pooling layers to extract features for classification. Training the CNN model involves a varied dataset containing both authentic and fake logo images, and its effectiveness is assessed using well-established metrics like accuracy, precision, recall, and F1-score. The results of the study indicate that the proposed model is proficient in distinguishing counterfeit logos accurately. Furthermore, the study delves into potential applications and future directions of using CNN for identifying counterfeit logos. This research presents a robust methodology for counterfeit logo detection, thereby supporting endeavors aimed at curtailing the proliferation of counterfeit goods and upholding brand integrity. © 2024 IEEE.

关键词： Image classification

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