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

作者： Bokdia, Akshat Gunavathi, C. Vellore Institute of Technology Vellore School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Cancer gene expression data analysis using microarray technology is a critical domain in understanding the molecular intricacies of malignancy. With an emphasis on the incorporation of data augmentation techniques, this paper offers a thorough investigation into the categorization of binary class Lung cancer gene expression dataset. Effective dimensionality reduction is achieved by using univariate feature selection technique called SelectKBest method. Through the evaluation of performance indicators including accuracy and recall, the study offers detailed insights into the advantages and disadvantages of each augmentation strategy. The study's findings add to the current conversation in precision medicine and provide information for improving methods for analyzing gene expression data in relation to cancer classification. © 2024 IEEE.

关键词： Diseases

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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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Smart Surveillance Object Detection System 3

Smart Surveillance Object Detection System

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

作者： Chaudhary, Gaurav Pathak, Shreya Choudhary, Vikas Chauhan, Love Tripathi, Abhishek N School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350372120

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM's Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities. © 2024 IEEE.

关键词： computer vision

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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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The Threat of Quantum Computing to the Cryptographic Security of Blockchain 2

The Threat of Quantum Computing to the Cryptographic Securit...

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

作者： Modi, Pranit Asgola, Abhishek Mishra, Hiya Charanya, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350328202

Blockchain technology, born as the backbone of cryptocurrencies like Bitcoin, has rapidly expanded into a versatile platform spanning various industries. This review paper delves into the critical aspects of security and anonymity within blockchain applications. It simplifies complex technical concepts while exploring the foundation of blockchain security, including cryptography, decentralization, consensus mechanisms, and the challenges posed by smart contracts. Additionally, it examines the pseudonymous nature of blockchain addresses, privacy-enhancing cryptocurrencies, and the delicate balance between transparency and anonymity. As we navigate these intricacies, we address challenges such as scalability, regulatory concerns, and potential threats from quantum computing. This paper aims to provide a comprehensive yet easily digestible understanding of the security and anonymity dimensions in blockchain applications. © 2024 IEEE.

关键词： Blockchain

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of Systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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Sentiment Analysis of Financial News: Implications for Corporate Strategy in Risk Management, Mergers and Marketing

Sentiment Analysis of Financial News: Implications for Corpo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sendhil, R. Lijo, Joshua Abinaya, E. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331509828

This research study examines the ways to use sentiment analysis on financial news for corporate strategy making. We examine the impact of sentiment in financial news on corporate decisions (beyond technology) regarding risk management, mergers and marketing strategy using recent developments from research into sentiment analysis as input and contrasts it with our findings. This study presents unsupervised and supervised sentiment analysis methods, statistical methods and AI tools for sentiment analysis, and their contributions to identifying business needs. In addition, the paper underscores existing difficulties and received a number of limitations on current techniques in sentiment analysis that have to be tended for better supporting corporate strategy. © 2025 IEEE.

关键词： Decentralized finance

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Sentiment Analysis for Depression Detection Using Artificial Intelligence 3

Sentiment Analysis for Depression Detection Using Artificial...

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

作者： Choudhary, Dilip Kumar Gupta, Anisha Singh, Sriaum Abhinav, Tejashvi Agrawal, Tanvi Vellore Institute of Technology School of Electronics Engineering Vellore632014 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350372120

Depression, a leading mental health concern, affects millions globally. Early detection remains crucial for effective intervention. This study explores the potential of sentiment analysis, powered by Artificial Intelligence (AI), for depression detection. We analyse existing research, examining data sources, feature extraction techniques, and AI-based methodologies. We then conduct a preliminary review of existing AI-based depression detection systems, highlighting their strengths and limitations. Our findings indicate promising applications of sentiment analysis in identifying individuals at risk of depression. However, further research is necessary to address challenges related to data privacy and the need for robust and standardized methodologies. © 2024 IEEE.

关键词： Sentiment analysis

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Analyzing Sentiments: A Comprehensive Study of Roberta-Based Sentiment Analysis on Twitters 4

Analyzing Sentiments: A Comprehensive Study of Roberta-Based...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Krishnamoorthy, A. Sundhar, K.A. Naveen Kumar, V. Karthik, V. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu India Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

Social media platforms have become essential avenues for individuals to express opinions and emotions across diverse topics such as products, events, and policies, using both text and emojis. Understanding these sentiments is crucial, yet manually analyzing the vast data on platforms like Twitter is impractical. Automated sentiment analysis tools, exemplified by Roberta, offer a solution. Roberta, an evolving language model, employs advanced bidirectional learning to predict sentiment labels accurately. This study focuses on sentiment analysis on Twitter, leveraging Roberta to autonomously categorize tweets into positive, negative, or neutral sentiments. We utilizing natural language processing and machine learning algorithms for dataset preprocessing, incorporating sources like Kaggle and ensuring data integrity. Assessment metrics encompassing accuracy, precision, recall, and F1-score, in addition to confusion metrics, assess model performance. According to our findings, machine learning powered sentiment analysis models are dependable, even in the face of challenges and more so when processing Twitter data sets. The study gives direction on how this kind of model can be of help in analyzing sentiments expressed through social networking sites thus assisting scholars and professional Consultancy in the field. © 2024 IEEE.

关键词： Tweets

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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