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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Duraisamy, Premkumar Vidhya, V. Preetha, T. Niranjani, V. Kpr Institute Of Engineering And Technology Department Of Computer Science And Engineering Coimbatore India Sri Eshwar College Of Engineering Department Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350381665

This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random forest, and AdaBoost were assessed, with random forest notably achieving a remarkable 94.6% accuracy. These results emphasize machine learning's potential for precise, non-invasive personalized hair health predictions. Key factors such as genetics, diet, and stress were pinpointed, informing targeted care approaches. The research underscores machine learning's promise in accurately anticipating hair loss and suggests future avenues for expanding datasets, integrating diverse data sources, and developing user-friendly tools for improved hair care management. © 2024 IEEE.

关键词： Support vector machines

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Optimized Long Short-Term Memory Using Hybrid Salp Swarm Optimization and Tabu Search for Medical Text Classification 10

Optimized Long Short-Term Memory Using Hybrid Salp Swarm Opt...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Yogameera, T. Shanthi, D. Theni Kammavar Sangam College of Technology Department of Computer Science and Engineering Theni India Psna College of engineering and technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350384369

Recently, many patients have sought treatment ideas through social media. The medical texts include a wealth of information, including a huge number of medical musculatures and symptoms. Developing an intelligence model to classify patient-provided text content and recommend the appropriate department is critical. Hence, an efficient medical text classification method is proposed using long short-term memory (LSTM) optimized by hybrid salp swarm algorithm (SSA) and tabu search (TS) algorithms called SSA-TS. The hybrid SSA-TS approach is used to optimize the hyperparameters of LSTM for enhancing the accuracy and convergence rate. The developed optimized LSTM approach is used to classify the patient-provided text. The experimental results conducted over health news in Twitter datasets and the performance of developed optimized LSTM are compared with some variants of LSTM methods. The experimental results reveal good generalization performance when compared to the baseline models on average, demonstrating that our model performs better and is more robust © 2024 IEEE.

关键词： Tabu search

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Manhattan-based k-member clustering and enhanced rabbit optimization algorithm for k-anonymization in social network

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Multimedia Tools and Applications 2024年第30期83卷 74739-74756页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of computer science and engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of computing technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and Technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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Intent and URL based ML Classification for Phishing Detection

Intent and URL based ML Classification for Phishing Detectio...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Uloli, Thiruvaazhi Sadasivam, Sudha Kumaraguru College of Technology Department of Computer Science & Engineering Coimbatore India PSG College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350378177

Reports show that the number of phishing web sites is exponentially increasing and it is estimated that between 80% to 93 % of the data breaches are involving phishing attacks. With both probability of occurrence as well as impact high, it is a high risk calling for an effective solution. Linear approaches like multi-factor authentication currently practiced addressing an exponentially growing problem is inadequate. In this work, we present a solution using domain-based understanding of the attacker intent based on the attributes of information that the attacker ultimately seeks to know from the user and make it more effective by combining it with machine learning based solutions of phishing detection based on URL features. © 2024 IEEE.

关键词： Phishing

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Real-Time Lie-Speech Determination Using Voice-Stress Technology

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第2期5卷 81-93页

作者： Al-Dhaher, Fadi K. Mohammed, Duraid Y. Khalaf, Mohammed Al-Karawi, Khamis A. Sarfraz, Mohammad Al Maathidi, Muhammad Mazin Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq General Directorate of Education Anbar Ramadi31001 Iraq Computer Science Department Al-Maarif University College Ramadi31001 Iraq University of Diyala Iraq Electrical Engineering Department Aligarh Muslim University Aligarh India Computer Science Department University of Bahrain Bahrain

Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such as voice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, a historical and generally established approach, may be enhanced or replaced by these revolutionary real-time techniques. Traditional lie detection procedures, such as polygraph testing, have been challenged for their lack of reliability and validity. Newer techniques, such as brain imaging and machine learning, might offer better outcomes, although they are still in their early phases and require additional testing. This project intends to explore a deception-detection module based on sophisticated speech-stress analysis techniques that might be applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns, to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy of the system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems, by addressing the limitations of old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy, which uses random forest (RF) to select only the significant features for training when a real-life trial dataset consisting of audio files is employed. Next, utilizing the RF as a classifier, an accuracy of 88% is reached through comprehensive evaluation, thereby confirming its reliability and precision for lie-detection in real-time scena

关键词： National security

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Multimodal Deep Learning Framework for Non-Intrusive Autism Spectrum Disorder Detection 3

Multimodal Deep Learning Framework for Non-Intrusive Autism ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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TIKI-TAKA Optimized Algorithn Based on an Enhanced Security System for Teleradiology in DICOM using Two-Level DWT Watermarking

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International Journal of COMADEM 2023年第3期26卷 47-54页

作者： Karthiyayini, S. Muthumari, A. Department of Information Technology Mohamed Sathak Engineering College Tamilnadu Kilakarai India Department of Computer Science and Engineering Syed Ammal Engineering College Tamilnadu Ramanathapuram India

Tele radiology plays a significant role in medicine that allows secure transmission of medical imaging data over a wide range of communication network. It guarantees data reliability and enables convenient way of communication for diagnostic purposes and clinical interpretation. However it raises the issue of legal, privacy, ethical issue and other copyright authenticity. Hence security and privacy remains to be a predominant problem in the medical field. To address this issue the present study has been motivated by the concept of watermarking schemes. Improved tele radiology architecture was developed in this study with efficient watermarking scheme that incorporates the integrated activity of biometrics and cryptography. In accordance to the entropy model, the fingerprint feature obtained from DICOM image is retrieved for key generation. 2L-DWT and TT-OA algorithm is employed to obtain high efficiencywith respect to PSNR (77 dB), 121s of encryption, SSIM (0.99), and embedding efficiency (97%). © 2023 COMADEM International. All rights reserved.

关键词： Discrete wavelet transforms

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Safeguarding the Homeless Using ViTransformer in Violence Detection 3

Safeguarding the Homeless Using ViTransformer in Violence De...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Bhuvaneswari, R. Sai Lasya, M. Natarajan, B. Tamilselvi, M. Geetha, K. Velammal, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Karpagam College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350361186

In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges. © 2024 IEEE.

关键词： Object recognition

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Tech-Enabled Altruism: Harnessing Technology in Social Media for Food Waste Reduction 3

Tech-Enabled Altruism: Harnessing Technology in Social Media...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sujatha, E. Jayanthi, M. Sandeepa, N. Raju, D. Naveen Nagaraj, S. Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Business Systems Chennai India R. M. K. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

Altruistic Connections is a pioneering social media platform that amalgamates the functionalities of popular networking applications with a unique emphasis on fostering altruistic endeavors. Drawing inspiration from established platforms like Instagram, this project introduces a distinct dimension by enabling users not only to share their stories and experiences but also to engage in philanthropic activities seamlessly. The platform facilitates direct communication through messaging, creating a communitydriven space where users can connect over shared values and altruistic initiatives. Users can post, like, save, and comment on content while also exploring and participating in a marketplace specifically designed for charitablecontributions. The technical architecture of 'Altruistic Connections' ensures a secure and user-friendly experience. Robust messaging features, seamless posting mechanisms, and an integrated altruistic marketplace form the core of the platform. Emphasis is placed on privacy and data security, aligning with the trust users place in the community. Strategies for user acquisition involve collaborations with philanthropic influencers, engagement with altruistic communities, and partnerships to expand the platform's reach. The project's financial sustainability is achieved through potential collaborations with philanthropic organizations and businesses that align with the spirit of altruism. Addressing potential challenges related to the verification of charitable activities, transaction security, and content moderation, the platform implements rigorous measures to create a trustworthy and fulfilling environment. The abstract concludes by emphasizing the commitment of 'Altruistic Connections' to redefine social media engagement through technology, altruism, and the power of community. An altruistic marketplace forms the core of the platform. Emphasis is placed on privacy and data security, aligning with the trust users place in the community. © 2024

关键词： Marketplaces

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