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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Dokania, Aryan Bamb, Anshul Dheeba, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819751990

When web file sharing is used, users can safely receive and share any files or documents over the web using any of their preferred web browsers. Though the existing cryptographic algorithms perform well in end-to-end encryption, they are not resistant to attacks that extract information from the computer system, such as passwords and encryption/decryption keys. In this paper, a hybrid method is proposed for secure and efficient end-to-end encryption. This paper focuses on secure key exchange, efficient data encryption, and strengthening of passwords, which is achieved by a combination of four cryptographic algorithms. The proposed hybrid cryptographic method is secure against several attacks, including man-in-the-middle attacks and side-channel attacks. The proposed method offers many advantages over traditional end-to-end encryption methods, including security, efficiency, and resistance to side-channel attacks. Extensive analytical and experimental results show that the proposed method is efficient enough to be used in a file sharing system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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An Intelligent Intrusion Detection using Rule-based CNN with PSO Optimizer 3

An Intelligent Intrusion Detection using Rule-based CNN with...

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

作者： Karpurasundharapondian, P. Selvi, M. Thangaramya, K. Santhosh Kumar, S.V.N. Kannan, A. 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)9798350372120

Security and secure routing are important design issues in the design of Wireless Sensor Networks (WSNs). Intrusion Detection Systems (IDSs) are useful for securing the communication in WSNs. An IDS can be developed by using machine learning based approach. On the other hand, it can be developed using a deep learning classification algorithm. Optimization techniques including Particle Swarm Optimization (PSO) are useful for choosing optimal features from given dataset. This paper proposes an extended CNN algorithm by extending the existing CNN classifier with rules, SVM and PSO named Rule Extended CNN with SVM (RE-SVM-CNN) for developing this IDS which reduces the false alarm rate by enhanced detection accuracy. © 2024 IEEE.

关键词： Wireless sensor networks

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Design of a Scoring System for National Fitness Volunteer Services under Deep Learning 4

Design of a Scoring System for National Fitness Volunteer Se...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Singh, Abhimeet Singla, Raghav Sodhi, Pulkit Pandian, M. Thurai School of Computer Science and Engineering Vellore Institute of Technology Vellore India

This work is developed to establish a comprehensive, scientific, and reasonable national fitness volunteer service scoring system under a long short-term memory (LSTM) recurrent neural network (RNN) algorithm. The LSTM RNN algorithm-based architecture of a three-layer information processing system for human motion recognition is proposed, which includes a data acquisition layer, a data calculation layer, and a data application layer. The LSTM RNN model is verified on the public dataset PAMAP2. Under the LSTM RNN-based information processing system for human motion recognition, the national fitness volunteer service scoring system is established by using the literature method, the survey method, the analytic hierarchy process (AHP) method, and the Delphi method. The candidate indicators are screened regarding the dimension of "service quality", the indicators are screened by the Delphi method, and the judgment matrix weights and consistency of each included indicator are analyzed. It is found that the information processing system for human motion recognition based on the LSTM RNN algorithm can effectively identify different motion states, such as sitting, lying down, running, walking, and cycling. A national fitness volunteer service scoring system based on the quality of communal sports facilities, services, fitness environment, affiliated sports facilities, and mass fitness service personnel is established under the LSTM RNN-based human motion recognition information processing system. The system includes one number of(#) first-level indicator, five number of(#) second-level indicators, and thirty two number of (#) third-level indicators. In summary, the LSTM RNN-based human motion recognition information processing system can correctly mine the types of fitness exercises, and the established national fitness volunteer service scoring system provides a scientific reference for building a perfect national fitness service system. © 2025 The Authors. Published by Els

关键词： Recurrent neural networks

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Comparative Prediction Analysis for Credit Scoring System 3

Comparative Prediction Analysis for Credit Scoring System

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rai, Anjali Mehfooza, M. Department of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The importance of predictive modelling for green selection-making in a variety of fields has expanded because of the fast development of information-driven technologies. The basic and primary goal of this research is to analyse and find an appropriate model which gives the best results in order to perform credit scoring on a given dataset. This study gives a thorough investigation of predictive modelling strategies, assessing their makes use of, benefits, and disadvantages. We discover the theoretical underpinnings and actual-international programs of famous predictive algorithms, including ensemble tactics, choice trees, neural networks, and regression-based totally fashions. This emphasises how vital variables like feature choice, model tuning, and facts exceptional have an effect on predictive accuracy. The revelation of every method’s overall performance in various datasets and eventualities through empirical analysis. By supplying insights that can assist practitioners pick appropriate fashions ideal to precise problem contexts, this studies adds to ongoing efforts to maximise predictive accuracy. The efforts highlight the opportunities of hybrid and ensemble techniques for complicated prediction tasks, suggesting avenues for future research to refine those methodologies. © 2025 IEEE.

关键词： Prediction models

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COVINet: A hybrid model for classification of COVID and Non-COVID pneumonia in CT and X-Ray imagery

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 149-159页

作者： Mittal, Vasu Kumar, Akhil School of Computer Science & Engineering Vellore Institute of Technology Chennai India

The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COVID-19 pneumonia but chest imaging, including CT scans and X-Ray imagery, can also be used as a secondary important tool for the diagnosis of pneumonia, including COVID pneumonia. However, the interpretation of chest imaging in COVID-19 pneumonia can be challenging, as the signs of the disease on imaging may be subtle and may overlap with normal pneumonia. In this paper, we propose a hybrid model with the name COVINet which uses ResNet-101 as the feature extractor and classical K-Nearest Neighbors as the classifier that led us to give automated results for detecting COVID pneumonia in X-Rays and CT imagery. The proposed hybrid model achieved a classification accuracy of 98.6%. The model's precision, recall, and F1-Score values were also impressive, ranging from 98-99%. To back and support the proposed model, several CNN-based feature extractors and classical machine learning classifiers have been exploited. The outcome with exploited combinations suggests that our model can significantly enhance the accuracy and precision of detecting COVID-19 pneumonia on chest imaging, and this holds the potential of being a valuable resource for early identification and diagnosis of the illness by radiologists and medical practitioners. © 2023

关键词： COVID-19

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Fake news detection on social media using Adaptive Optimization based Deep Learning Approach

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Multimedia Tools and Applications 2025年第9期84卷 6091-6111页

作者： Steni Mol, T.S. Sreeja, P.S. Department of Computer Science Hindustan Institute of Technology and Science Chennai India Department of Computer Applications Vellore Institute of Technology Chennai India

Various content-sharing platforms and social media are developed in recent times so that it is highly possible to spread fake news and misinformation. This kind of news may cause chaos and panic among people. The automatic and accurate detection of fake news is a complex task. To consider this issue, an automatic and effective fake news detection approach is implemented. The proposed methodology is named Adaptive Adam Adadelta Optimizer-based Deep Bi-directional Long Short Term Memory (Bi-LSTM), in which Adaptive Adam AO is the merging of Adadelta Optimizer (AO), Adaptive concept, and Adam Optimization. The Bidirectional Encoder Representations from Transformers (BERT) is used for the tokenization and then, feature mining is done. Also, the dimensionality of the features is reduced by Singular Value Decomposition (SVD) and Moving Principal Component Analysis (MPCA). From them, the top-N feature selection is performed using Joint Quantum Entropy. Finally, for fake news detection, Adaptive Adam AO trained Deep Bi-LSTM is used, which obtained maximum specificity, sensitivity, and accuracy of 0.928, 0.945, and 0.938. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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FedGB: Federated Gradient Boosting for Robust Autism Detection 15

FedGB: Federated Gradient Boosting for Robust Autism Detecti...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhar, Akanksha Gupta, Ishita Anuradha, J. Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

Autism Spectrum Disorder (ASD) presents substantial problems in cognitive, language, and social skills. Early detection is critical for effective intervention, spurring research into Artificial Intelligence (AI) approaches for ASD detection. Federated Learning (FL) is a promising way to improve accuracy and privacy by training models jointly across distributed datasets. This study presents 'FedGB,' a Federated Gradient Boosting (FedGB) strategy that addresses the drawbacks of existing methods and improves ASD identification. Using FL and Gradient Boosting methods, 'FedGB' combines GridSearchCV for hyperparameter tweaking to ensure optimal performance and secrecy. Extensive experimentation on varied ASD datasets demonstrates higher performance compared to existing approaches, demonstrating 'FedGB''s effectiveness and dependability in improving ASD detection while maintaining privacy. © 2024 IEEE.

关键词： Autism Spectrum Disorder Epsilon Federated Learning Gradient Boost Classifier Grid Search CV Machine Learning

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Click-Through Rate Analysis for Understanding User Behavior in Information Retrieval 15

Click-Through Rate Analysis for Understanding User Behavior ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Azad, Hiteshwar Kumar Sharma, Rohan Verma, Rohan Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

Improving user satisfaction and personalization in information retrieval is an ongoing challenge. This research delves into user behavior analysis using click-through data, providing a deeper understanding of user intent beyond initial interactions. This article utilizes click-through analysis to uncover user preferences, resulting in search results that align more closely with user intent. These insights contribute to more refined search experiences, enhancing user satisfaction and personalization across digital platforms. The experimental outcomes demonstrate the effectiveness of the proposed approach for capturing user preferences and tailoring search results accordingly. © 2024 IEEE.

关键词： Classification Analysis Click-Through Data Visualization Heatmap Visualization Machine Learning

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Enhancing Multiclass Pneumonia Classification with Machine Learning and Textural Features

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Machine Graphics and Vision 2023年第3-4期32卷 83-106页

作者： Godbin, A. Beena Jasmine, S. Graceline School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network’s performance is evaluated based on accuracy, precision, sensitivity and F1-score. © 2023 institute of Information technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： COVID-19

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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