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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Hans, Arvind Vigenesh, M. Yuvaraj, S. Bhagwat, Suvarna R. Zaki, Hamza Yamang, Chiging Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India

ISBN: (纸本)9798350319125

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks. © 2023 IEEE.

关键词： destiny infrastructures networks packages wireless networks

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A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques 3

A Comprehensive Analysis of Hate Speech Detection and Recogn...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mannepalli, Praveen Kumar Sharma, Saurabh Rajput, Neha Kalamdhad, Sanjay Chandigarh University Department of Computer Science & Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Punjab India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science & Business System Nagpur India

ISBN: (纸本)9798331542375

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone. © 2025 IEEE.

关键词： Tweets

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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Deep Reinforcement Learning algorithms for Low Latency Edge Computing systems

Deep Reinforcement Learning algorithms for Low Latency Edge ...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： K. Kumaran E. Sasikala Department of Information Technology SRM Institute of Science and Technology Easwari Engineering College Chennai India Department of Data Science & Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems.

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A review of travel behavioural pattern using GPS dataset: A systematic literature review

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Measurement: Sensors 2024年 32卷

作者： Sivalingam, Prahaladhan Asirvatham, David Marjani, Mohsen Syed Masood, Jafar Ali Ibrahim Chakravarthy, N.S. Kalyan Veerisetty, Gopinath Lestari, Martha Tri Faculty of Innovation & Technology Taylor's University Subang Jaya Malaysia Department of Internet of Things School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India Center for Data Science School of Computer Science and Engineering QIS College of Engineering and Technology Andhra Pradesh Ongole523272 India Department of Mechanical Engineering QIS College of Engineering and Technology Andhra Pradesh Ongole523272 India Department of Business Communication Telkom University Indonesia

In the last few years, the Global positioning system (GPS) has been used to study travel patterns with the advancement of data collection techniques. A GPS device precisely records the time and the location data of an individual travel. The accuracy of travel studies is usually improved by integrating travel surveys or travel activity diaries with GPS data. GPS surveys are implemented to replace paper-based techniques and reduce human error on trip-misreporting issues. Several reviews have been performed previously on GPS travel studies. Still, a detailed systematic review on the comparison of different modes and methods on the entire procedure of GPS data processing has not been thoroughly studied. This review identified 116 peer-reviewed studies and journal papers from 2013 to 2020, and 53 papers were studied. The review focused on travel behavior pattern (TBP) analysis using GPS studies only and a combination of travel surveys. Our review reveals that 53 only 40 have quality assessment scores of >50 %, meaning the remaining TBP studies are conventional methods. The study also benefits data analysis and researchers to select the most appropriate data collection techniques for studying TBP and improvise them further for meta-analysis travel behavior studies. © 2024 The Authors

关键词： Global positioning system

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Securing IoT-Based Home Automation systems Through Blockchain technology: Implementation

Securing IoT-Based Home Automation Systems Through Blockchai...

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IEEE International Conference on system, Computation, Automation and Networking (ICSCAN)

作者： N. Palanivel K. Madhan C. Santhosh Kumar R. SarathKumar T. Ragupathi Deivanai S Department of CSE Manakula Vinayagar Institute of Technology Puducherry Department of CSE Mailam Engineering College Mailam Tamil Nadu India Depte of CSE Er.Perumal Manimekalai College of Engineering Hosur Department of ECE Sri Krishna College of Engineering and Technology Coimbatore Department of Data Science and Business System SRM Institute of Science and Technology Kattankulathur Tamilnadu India

Cybersecurity is the large subject matter that connects everything, processes, units and technologies. Network safety is a set of policies and settings. They are the integrity, accessibility and availability of facts and networks. Confidentiality of Software and Hardware Use. Today's community threats are continuously evolving and the surroundings is an increasing number of complex. Similarly, the clever domestic has emerge as a low-security threat. That's why it is so vital to remain secure online. Due to the vulnerability of the clever domestic network, there are purposes in many areas, such as users, locations, data, etc. Some clever units may additionally be lacking from your clever home. The machine has been hardened so that passwords can be scrambled. Unencrypted passwords can be searched on the machine or in the software. The safety of the clever domestic community is pretty high. Prioritize linked gadgets to forestall hackers from gaining access to them. Access to touchy or private data. Failure to do so should put your complete clever domestic community at risk. This learn about builds a mannequin to analyze specific protection problems in clever domestic conversation networks. This lookup wants to use qualitative methods, such as anecdotes. A lookup evaluation was once fundamental to raise out this study. and Other applicable records on the secondary facts series techniques used. Several research have been carried out on the protection threats associated to clever domestic networks and the use of applied sciences derived from block chain technology. Reduce protection troubles and defend your intelligence. Secure your domestic network.

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A Novel Proxy Based Reencryption Scheme for Secured data Communication by Using Internet of Things (IoT) and BlockChain Technologies

A Novel Proxy Based Reencryption Scheme for Secured Data Com...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Hasina Nasrin V. Chandramouli Balachandran. G Syed Zahada Ajay Bonde Nidal Abbas Department of Computer Science & Engineering (Data Science) MLR Institute of Technology Dundigal Hyderabad Telengana Department of S&H QIS College of Engineering and Technology AndhraPradesh Department of ECE Jeppiaar Engineering College Rajiv Gandhi salai Chennai Department of MCA QIS College of Engineering and Technology AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of Economic Faculty of Economics and Administrative Sciences Zarqa university Zarqa Jordan College of Business Administration University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This paper proposes a novel proxy-based re-encryption (PRE) scheme for secured data communication in the Internet of Things (IoT) environment, integrated with blockchain technology to enhance data privacy and security. The scheme employs identity-based encryption (IBE) to streamline key management and ensures efficient data sharing by leveraging blockchain for decentralized access control. Proxy servers, acting as intermediaries, handle resource-intensive computations, reducing the load on resource-constrained IoT devices. The system was evaluated across multiple parameters, including encryption, decryption, and re-encryption times, communication overhead, and computational cost. Results demonstrated that the system scales efficiently with data sizes, with encryption times increasing linearly from 5ms to 60ms for data sizes ranging from 10 KB to 100 KB. The re-encryption times followed a similar trend, ranging from 8ms to 60ms. Accuracy in maintaining secure data access control was recorded at 98.5%, ensuring that unauthorized access was effectively prevented. Communication overhead was minimal, with only a 3–12 KB increase across different data sizes due to the additional security features.

关键词： Access control Identity-based encryption Market research Distance measurement Blockchains Encryption Internet of Things data communication Servers Edge computing

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Intrusion detection based on phishing detection with machine learning

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Measurement: Sensors 2024年 31卷

作者： Jayaraj, R. Pushpalatha, A. Sangeetha, K. Kamaleshwar, T. Udhaya Shree, S. Damodaran, Deepa Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur TN Chennai India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology TN Coimbatore India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology TN Chennai India Department of Computer Science and Engineering Alpha College of Engineering and Technology Puducherry India VITBS Vellore Institute of Technology Chennai Campus TN India

Machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative Distribution Function gradient (CDF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method. © 2023

关键词： Intrusion detection

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya R. Ramya R Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： Resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

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Next-Gen Mental Health Detection through Deep Learning and NLP in Social Media

Next-Gen Mental Health Detection through Deep Learning and N...

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Innovations in Communications, Electrical and Computer engineering (ICICEC), International Conference on

作者： Jagan. A Senthil Kumar D S. Karunakaran Nageswari D Regina Shoba Dass J. Seetha Computer Science and Engineering Surya Group of Institutions Vikiravandi Villupuram Artificial Intelligence and Data Science St. Joseph's College of Engineering OMR Chennai Department of Computer Technology Kongu Engineering College Perundurai Erode Department of Science and Humanities R.M.K. College of Engineering and Technology Psychology CMR University Bangalore Department of Computer Science and Business Systems Panimalar Engineering College Chennai

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Social media proliferation promotes new modalities of understanding and intervening in mental health. This paper presents a new approach to mental health detection using some deep learning and natural language processing techniques over social media data. Enriched by extensive textual content shared across social media platforms such as Twitter, Reddit, and Facebook, we propose to design an automated system that can detect traces of mental health conditions like depression, anxiety, and stress. In line with the proposed methodology, Bi-LSTM networks and attention mechanisms shall be applied to exploit linguistic patterns and semantic features from user posts. Such techniques involve the integration of sentiment analysis and topic modelling, which increase the accuracy and contextual comprehension of detected signals. Either way, the system is trained against a curated dataset of annotated social media posts validated further against clinical assessments for strength and reliability, with quite promising preliminary results— Rubin et al.'s model achieves high precision and recall in mental health indicator detection. The results reinforce that AI-driven tools have an assay of potential in rendering early detection and intervention support, especially for mental illness, which has contributed to improved mental health outcomes globally.

关键词： Deep learning Technological innovation Sentiment analysis Accuracy Social networking (online) Blogs Semantics Mental health Web sites Synthetic data

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