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检索条件"机构=R&D Department of Security Application Technology"

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A Unified deep Learning Model for Fake Account Identification Using Transformer-Based NLP and Graph Neural Networks

A Unified Deep Learning Model for Fake Account Identificatio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： L. Selvam E.S. Vinothkumar r. Santhana Krishnan G. Vinoth rajkumar J. relin Francis raj P. Stella rose Malar Department of Computer Science and Engineering (Cyber Security) SRM Madurai College for Engineering and Technology Pottapalayam Sivaganga India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Fake accounts and bots on social media platforms pose significant threats, including misinformation dissemination, fraud, and manipulation of public opinion. To address these challenges, a hybrid deep learning model integrating Transformers and Graph Neural Networks (GNNs) is proposed. The model leverages BErT for textual analysis of user-generated content, including posts, comments, and profile descriptions, while GNNs analyze network-based features such as follower relationships, engagement behaviors, and interaction patterns. This combination ensures an enhanced understanding of both linguistic and structural characteristics, leading to improved detection accuracy. The dataset is curated from publicly available labeled social media account data, supplemented with API-based extractions to ensure diversity. Features include user metadata, textual content, and network structures. In the preprocessing phase, text is tokenized using a BErT tokenizer and converted into high-dimensional embeddings using BErT-based encoders. Simultaneously, a social graph is constructed, where users and interactions are represented as nodes and edges. These representations serve as input for the GNN model, learning patterns indicative of fraudulent activities. A dual-module architecture is implemented, comprising a BErT-based text classification model and a GNN-based network classification model. Outputs from both modules are fused in a shared feature space before classification. Model performance is evaluated using accuracy, F1-score, and AUC-rOC metrics. Experimental results demonstrate superior detection capabilities compared to singlemodality models. applications include social media security, fraud prevention, misinformation control, and e-commerce integrity. Future enhancements focus on real-time scalability and robustness against adversarial attacks.

关键词： deep learning Analytical models Adaptation models Social networking (online) Computational modeling Transformers Graph neural networks Fraud security Fake news

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Leveraging digital Twins and AI for Enhanced Clinical decision Support in Endometrial Cancer Treatment

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. r Cibi. A S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Data & Analytics Visualizaation – Delivery Virtusa Consulting Service Private Limited Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

digital twins are a relatively new concept that have lately garnered interest in the industrial sector. digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.

关键词： Accuracy Cancer treatment Medical services Machine learning digital twins Mirrors Artificial intelligence Wearable sensors Standards Cancer

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Gaurav Kumar G. devayani C Saraswathi r. Melba Kani Sandeep dongre M. S. Mohamed Mallick School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Master of Business Administration Mar Ephraem college of Engineering and Technology Kanyakumari India Symbiosis Institute of Business Management (SIBM) Constituent of Symbiosis International (Deemed University) Nagpur India Department of Computer Science Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences.

关键词： Consumer behavior Accuracy data preprocessing Transforms Market research Electronic commerce Convolutional neural networks Integrated circuit modeling Artificial intelligence Long short term memory

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Privacy-Preserving data Mining Process in Industry

Privacy-Preserving Data Mining Process in Industry

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： T.C. Subbu Lakshmi r. Anto Arockia rosaline r. Thanga Selvi d. Karunkuzhali S. Lavanya Department of Information Technology Francis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan DR. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

Protecting the privacy of users in web-scale data mining applications and systems like online searching, recommender systems, crowd-sourced platforms, and analytics apps has developed into an incredibly essential component in recent years. This is especially true in light of recent data breaches and new legislation such as GdPr. In this tutorial, we'll go over a history of privacy violations from the last two decades, along with the lessons we gained from them, as well as important rules and legislation and how privacy tactics have evolved into different privacy definitions and techniques. data mining raises unique privacy risks, despite being successful in many applications. The literature has looked at the trade-offs that the current system presents between utility and personal privacy. However, industrial deployment has its own unique challenges when it comes to the management of resource trade-offs between computation and communication. The industrial deployments that are taking place right now are rather scattered. Each device (such a mobile phone, for example) is responsible for storing a single data point, and it is the server's job to compute aggregates based on the data that is obtained from the distinct mix of public and private information that is associated with each device. In spite of the fact that computing on servers is quite inexpensive, computing on clients is somewhat pricey since clients are often comprised of low-power devices. The expectation maximization (EM) technique for distribution reconstruction is discussed in this study. It is superior to the existing approach regarding the degree of information loss. The percentage of information loss is only 4%. In particular, we show that the EM approach, when applied to data that has been subjected to a perturbation, eventually arrives at the maximum likelihood estimate of the distribution that was being modeled. We demonstrate that the EM algorithm produces reliable estimations of the original dist

关键词： Privacy Maximum likelihood estimation data privacy Legislation Tutorials data breach reliability theory

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Individual Intelligent recognition Method Based on Fingerprint Features of radar Emitter

Individual Intelligent Recognition Method Based on Fingerpri...

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IEEE International Conference on radar

作者： Zhongwei Liu Hongwei Gao Jie Chen dongming Zhou Yingchun Li Shuyan Sun rongrong Xiang School of Electronic and Information Engineering Beihang University Beijing China Radar R&D Center Beijing Institute of Radio Measurement Beijing China Department of Data Technology and Application Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665468893

Aiming at the problem of radar emitter individual recognition with unintentional modulation feature, a method of emitter fingerprint identification based on deep learning is proposed. In this method, the deep fingerprint features of radar emitter are extracted by deep Convolutional neural network, and the fingerprint feature library is constructed. Through similarity calculation, the individual recognition results of radar emitter signal are obtained. The results show that the new method can effectively identify both known and unknown emitter individuals.

关键词： deep learning Modulation radar Fingerprint recognition Feature extraction Libraries Convolutional neural networks

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Logistic regression for Personalized Hypertension risk Profiling in Cloud-Integrated Healthcare Systems

Logistic Regression for Personalized Hypertension Risk Profi...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： S. Saravanan S. ramya S K Monikapreethi Venkatesh Kavididevi rajesh Kambattan Kovarasan S. Sujatha Department of Electrical and Electronics Engineering Muthayammal Engineering College Namakkal Tamil Nadu India Department of Cyber Security Paavai Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Information Technology Vardhaman College of Engineering Hyderabad Telangana India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Advanced analytics for personalized chronic disease risk profiles are becoming possible as healthcare systems combine cloud-based technology. This work applies logistic regression (Lr) models to cloud-integrated healthcare systems for personalized hypertension risk profiling. A large dataset of heterogeneous patient records was used to train and evaluate the Lr model, including clinical characteristics and lifestyle variables. To assess the Lr model’s predictive accuracy in identifying high-risk hypertension patients and to provide a framework for personalized risk stratification that can be seamlessly integrated into cloud-based healthcare systems. The model’s performance was rigorously cross-validated and compared to risk assessment methods. The Lr approach predicted hypertension risk better than older techniques. Healthcare professionals and patients found the cloud integration of the personalized risk profiling system convenient, scalable, and accessible. It shows that cloud-based analytics may improve preventive healthcare by personalizing treatments to risk profiles. Lr techniques can optimize personalized hypertension risk management in cloud-integrated systems, advancing data-driven healthcare.

关键词： Hypertension Logistic regression Accuracy Precision medicine Medical services Predictive models data models risk management reliability diseases

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Analysis of Lung Cancer for developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mahaveerakannan r Murali dhar M S Sohan Goswami P. Gopala Krishna K. Jayanthi M. Sathish Kumar Department of Spatial Informatics The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana Department of Computer Science and Engineering Pollachi Institute of Engineering and Technology Coimbatore Pollachi Tamil Nadu Department of Cyber Security Karpagam college of Engineering Coimbatore Tamil Nadu

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method.

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Enabling Protection for Critical Infrastructure through security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

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International Conference on Communication and Electronics Systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption security Stakeholders Protection Computer crime Industrial Internet of Things

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Convolutional Neural Networks for Enhanced real-Time Traffic Incident detection and Image Classification

Convolutional Neural Networks for Enhanced Real-Time Traffic...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Monisha J L. ramalingam S. Mathankumar Narayanasamy P M. rajmohan S. Murugan Department of CSE (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Traffic incident detection and classification are essential aspects of enhancing road safety and efficiency. A new approach to enhancing real-time traffic incident detection and image classification with the help of Convolutional Neural Networks (CNNs), viz., Alex Net, is presented in this paper. Alex Net, a deep CNN architecture that is widely used for image recognition, is employed to classify traffic images and videos for incident identification. The system makes use of the ability of the model to learn robust features to identify images as different events, such as accidents, roadblocks, and vehicle breakdowns. Low latency and high accuracy incident identification is done by the system utilizing real-time traffic camera data and Alex Net. differentiation between different types of events. This is for the sake of enhancing reaction time, enabling effective traffic management, and differentiating between events. AlexNet enables 92% accuracy in real-time traffic detection and image classification, enhancing the efficiency of intelligent transportation systems. The results demonstrate how deep learning (dL) methods can revolutionise traffic monitoring systems and improve safety protocols for drivers.

关键词： deep learning Accuracy Electric breakdown real-time systems data models Safety Convolutional neural networks Image classification Accidents Videos

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Enhancement of security in IoT using Secure Hash Cryptography Algorithm with double Encryption (SHCA-dE)

Enhancement of Security in IoT using Secure Hash Cryptograph...

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International Conference on Communication and Electronics Systems (ICCES)

作者： G. Uganya d. rajalakshmi ramarao Vangapandu M. dinesh Jayant Giri Mahmoud Odeh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Vignan's Institute of Engineering for Women Vishakhapatnam Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

The “Internet of things” is the developing technology to transfer the world into smart innovation. “IoT” security is the most problematic issue in recent applications due to its properties including ‘centralized architecture’, ‘limited capacity devices’, and ‘ambiguity’. So, blockchain with “IoT” applications provides secure data communication compared to traditional “IoT” applications. Blockchain is a recent growing technology that has features including ‘decentralized structure’, ‘transparency’, ‘immutable records’, ‘increased capacity’, and ‘quick backtracks’. The ‘cryptography hash algorithm’ is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-dE) to protect the private key message. Then, this proposed algorithm has been analyzed with Md5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm.

关键词： Hash functions Technological innovation Accuracy Public key cryptography Blockchains Encryption Internet of Things data communication

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