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Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software Informatics 1

Virtual Twin Analytics: Advancing IoT Security through AI-Po...

引用

1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Nanthini, S. Jain, Upma Arora, Renuka Bhatia, Rishu Sutaria, Kruti Patil, Harshal Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort's work represents an advance in that direction. Critical online security issues have been the focus of this proposed work's study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work's contributions to online security are as follows: The 'MalCrawler' web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler's ability to gather webpages - particularly dangerous ones - in order to provide datasets for ML-based an

关键词： Web crawler

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Smart Solar Powered System for Unauthorized Logging 6

Smart Solar Powered System for Unauthorized Logging

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Mathumitha Murali, S. Tharuna Shri, S. Roshini, P. Yesubai Rubavathi, C. Santhana Krishnan, R. Lakshmi Narayanan, K. Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India

ISBN: (纸本)9798350398496

The forest is crucial to maintaining the ecological system's balance. Cutting trees and removing them unlawfully from protected areas is known as illegal logging. This will have a significant impact on the ecological balance in turn. Due to illicit logging, the Reserve's forest cover has been reduced to less than 2511 square kilometers. Even some rare species' living conditions have been impacted by this. An IoT-based Smart System has been developed by us to monitor Illegal logging and to prevent these kinds of actions. In addition to identifying illegal logging, our technology learns about forest fires and alerts the appropriate authorities. This will stop illegal logging and forest fires from removing trees from the forest. With the aid of a solar power source, the complete process is observed. © 2023 IEEE.

关键词： Solar energy

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Leveraging Natural Language Processing for Detecting Fake News: A Comparative Analysis 2

Leveraging Natural Language Processing for Detecting Fake Ne...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Vivek Kumar, M. School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350371055

Fake news have become a number one situation within the contemporary digital world, with the appearance and vast use of social media and remarkable online systems. The capability to quickly and without trouble disseminate fake records has brought about wrong information and its potential to affect public opinion and decision-making. As a result, there has been a growing interest in automatic strategies for detecting and preventing fake news. Natural language processing (NLP) techniques have proven promising in this regard, as they may look at the language and content of document articles to identify patterns and characteristics related to fake news. This paper compares several NLP strategies for detecting bogus records, including machine-learning-based, linguistics-based, and hybrid strategies. It compares the strategies' overall performance with excellent datasets and assessment metrics. Our results show that machine learning-based strategies and linguistics-based approaches outperform other strategies for detecting fake information. © 2024 IEEE.

关键词： Fake News Detection Machine Learning NLP

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LeNet with Bidirectional Long Short-Term Memory for Signal Detection in Underwater Optical communication 1

LeNet with Bidirectional Long Short-Term Memory for Signal D...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Subhashini, S. Al-Farouni, Mohammed Kumar, B. Satish Sharmiladevi, D. Anita Sofia Liz, D.R. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Maisammaguda Department of Electronics and Communication Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. Due to complex underwater environment the high directionality of light beam and vibration transceiver incident optical power usually spans a very wide dynamic range. This paper, proposed LeNet with Bidirectional Long Short-Term Memory (BiLSTM) technique performed efficiently in the signal detection in under water optical communication by effectively extracting spatial features and capturing temporal dependencies. The data collected for this research includes data bits of 72 vectors of transmitted signals through the Gulf seawater. The LeNet approach process the received optical communication signals and extract spatial features from convolutional layer, converting them into relevant signal to enhance the communication of underwater. The combination of LeNet feature extraction capabilities and Bi-LSTM sequential learning the signal then enhances the model ability leading to achieve better performance in signal detection. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall, f1-score and Bit Error Rate (BER) metrics based on sea water. © 2024 IEEE.

关键词： Error statistics

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Smart Travel Solutions Harnessing Artificial Intelligence for Optimal Route Efficiency

Smart Travel Solutions Harnessing Artificial Intelligence fo...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Mohanapriya, D. Indumathi, R. Priyatharsini, S. Arounesh, A. Kumar, V.Kishore Kamalan, R. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Coimbatore India Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

This AI-based route optimizing system has put its focal point on only efficient travel routes based on real-time traffic. It focuses on calculating the shortest distance to source, along with destination and via points, distance, time-based traffic details, in order to arrive at the most optimal route for any journey. Advanced AI techniques in optimizing systems continuously adjust the route according to live traffic patterns so that users may experience the least delay possible. The system integrates with fuel consumption estimations provided the traveler through inputting vehicle mileage, enabling travelers to make better plans for their trips by considering time and fuel efficiency. This would save one time spent traveling, saves fuel, thus saving money while keeping the environment free from pollution. By such smart decisions on making routes, the system increases drivers' overall driving experience, especially in crowded areas or where there is heavy traffic. Since the system is inherently real-time, it's therefore very adaptive to the changing nature of road conditions, thus travel stream lined for the users. © 2024 IEEE.

关键词： Transportation routes

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Enhancing Sentiment Analysis on US-Based Twitter Data through Stemming in Long Short-Term Memory (LSTM) Networks 2

Enhancing Sentiment Analysis on US-Based Twitter Data throug...

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2nd IEEE International Conference on Innovations in High-Speed communication and Signal Processing, IHCSP 2024

作者： Sahu, Sourabh Patidar, Praveen Kumar Pandey, Ranu Verma, Richa Harne, Sachin Tiwari, Rovin Gyan Ganga Institute of Technology and Sciences Department of Electronics and Communication Engineering Jabalpur India Parul University Parul Institute of Technology Department of Computer Science and Engineering Vadodara India Shri Rawatpura Sarkar University Department of Computer Science and Engineering Raipur India Lalit Narayan Mishra College of Business Management Muzaffarpur India Gh Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India Rgpv Department of Electronics and Communication Engineering Bhopal India

ISBN: (纸本)9798350368949

Sentiment analysis is a branch of Natural Language Processing which has been utilized by the organization and industries to identify the sentiments for the services and products. Sentiment analysis processed the textual data and convert semantics to identify the sentiment by dividing into three classes which are positive, negative and neutral. Here in this work, sentiment analysis has been examined using Long Short-Term Memory (LSTM) on twitter dataset. The data is preprocessed to convert into root words through the process of stemming for linguistic normalization and then into one-hot encoding, and embedding. The sentiment has been predicted using LSTM and the architecture has been systematically outlined with the precise value of the dropout layers to prevent the overfitting. The dataset features US airline tweets and addresses the real-world challenge of class imbalance in sentiment analysis. The model provides the robust training metrics, achieving an accuracy of 97.47%. © 2024 IEEE.

关键词： Sentiment analysis

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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation Systems 2

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Arul Jose, R. Ebby Darney, P. Santhana Krishnan, R. Relin Francis Raj, J. Jansi Rani, D. Sundaravadivel, P. Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bengaluru India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency. © 2025 IEEE.

关键词： State of charge

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Building Safer Work Environments for Hazard Detection in Construction Sites with IoT and Cloud Server 10

Building Safer Work Environments for Hazard Detection in Con...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Azath, H. Sivanantham, E. Suganthi, B. Swarnalatha, E. Suresh, M. Elango, B. Vit Bhopal University School of Computing Science and Engineering Bhopal India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rvs College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Guru Nanak Institute of Technology Department of Electronics and Communication Engineering Hyderabad Telangana Ibrahimpatnam India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research explores real-time Internet of Things (IoT) methods to track and mitigate construction site hazards. The construction industry must innovate due to its high accident rate to protect workers. It uses IoT to create a system that watches the construction site and discovers real-time safety issues. The system uses sensors across the construction site to monitor worker location, equipment operation, ambient conditions, and structural integrity. In real-time, the IoT identifies potential threats from this data. Workers, supervisors, and other users receive timely alerts about dangers, enabling quick and focused mitigation. The system's proactive approach to building site safety reduces accidents, injuries, and deaths. With real-time IoT solutions, potential hazards can be discovered early and addressed quickly. Data collection and analysis allow the system to improve safety protocols and practices over time. It highlights the importance of real-time IoT technologies in construction site safety management. IoT devices improve hazard tracking and identification and safety culture by fostering proactive actions and informed decision-making. As buildings embrace digital transformation, this study enables safer and more efficient procedures. © 2024 IEEE.

关键词： Risk management

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity Threat Detection and Classification

A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity T...

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

作者： Akilandeswari, S. Soumya, T.R. Sumathi, S. Sushith, Mishmala Brinda, V. Rajan, S. E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Nagapattinam611002 India Prathyusha Engineering College Department of Computer Science Engineering Tiruvallur602025 India Velammal Engineering College Department of Electronics and Communication Engineering Chennai600066 India Adithya institute of Technology Department of Information Technology Kurumbapalayam India K.Ramakrishnan College Of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode638012 India

ISBN: (纸本)9798331509828

With the advancement of digital era comes an increased value for cybersecurity, and these need to be dealt with using advanced techniques. Deep learning - metaheuristic optimization hybrid model for advanced threat detection and mitigation is presented in this paper. With a Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory (LSTM) for sequence analysis, the proposed framework integrates all the modules in order to provide robust cyber-attack detection. In order to optimize the parameters of the model, a Particle Swarm Optimization (PSO) algorithm is used in order to achieve better classification accuracy and faster computation. Benchmark datasets are used for extensive experiments, where existing methods outperformed in terms of precision, recall, and Fl-score. The results demonstrate that with detected flagged FPs and the hybrid model, the increased detection rates are achieved without the reduction in true negatives, rendering it a practical solution for real world applications. This research adds to the field by providing a solution to cybersecurity problems that is scalable and adaptable to the present and beyond. © 2025 IEEE.

关键词： Convolutional neural networks

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