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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Ashokkumar, S.R. 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 has become a significant problem in modern society, with the rapid dissemination of false information through social media and online platforms. This has led to an urgent need for effective methods to detect and combat fake news. In this paper, we propose a hierarchical framework for fake news detection using semantic analysis and source reliability. The framework comprises two main tiers: semantic analysis and source reliability. In the first stage, the text of the news article is analyzed using natural language processing techniques to extract features such as keywords, sentiment, and tone. Similarly, the credibility of the source is determined based on factors such as the website's domain authority and previous track record of spreading fake news. In the second tier, a hierarchical classifier combines the results from the semantic analysis and source reliability levels to make the final prediction. The hierarchical classifier initially employs a binary classifier to determine if the article is fake or not. If it is classified as fake, it is then passed on to another classifier to determine the severity of the fake news. Our framework aims to provide a more accurate and robust approach for detecting fake news by considering both the content of the news article and the credibility of its source. Experimental results on a real-world dataset demonstrate the effectiveness of our proposed framework compared to traditional fake news detection techniques. © 2024 IEEE.

关键词： Fake News Detection Machine Learning NLP

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Enhanced Bank Locker Security System Utilizing RFID for Dual-Layer Protection 5

Enhanced Bank Locker Security System Utilizing RFID for Dual...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Vadukanathan, Anusooya Duraikannu, Gnanakumar Jaganathan, Venkatamuthukumar Sridhar, Srividhya Suyambrakasam, Gobalakrishnan Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Kattankulathur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Tamilnadu Thandalam India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamilnadu Ponmar India

ISBN: (纸本)9798331504403

The proposed bank locker security system represents a comprehensive approach to enhancing security measures through the integration of RFID tags, biometric authentication, and GSM technology. Upon accessing the locker, users are required to present their RFID tag and undergo biometric verification, thereby ensuring a dual-layer authentication process. The use of RFID tags enables efficient identification of authorized users, while biometric authentication adds an additional layer of security by verifying unique biological traits, such as fingerprints or iris patterns. This combination significantly reduces the risk of unauthorized access, as individuals without proper authorization face considerable obstacles in gaining entry. In the event of an incorrect password input, the system activates a camera to capture the image of the unauthorized individual, promptly transmitting this image through a Telegram bot to notify bank staff of the attempted breach. Additionally, the system utilizes GSM technology to receive messages, enabling it to send alert messages via SMS to designated bank personnel in real-time. This proactive response mechanism ensures that any breaches are swiftly detected and addressed, thereby safeguarding the contents of the bank lockers effectively. By combining technology and human oversight, the system establishes a formidable barrier against unauthorized access attempts, providing comprehensive protection for the bank's assets and enhancing overall security measures. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Enhancing Spectrum Sensing in Cognitive Radio Networks using Deep Learning Models: A Solution for Low-SNR Challenges 3

Enhancing Spectrum Sensing in Cognitive Radio Networks using...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ramakrishnan, P. Priyanga, V. Sathiya, A. Sharmiladevi, D. Samundeeswari, A. Annapoorani, B.T. Department of Electronics and Communication Engineering M.Kumaarsamy College of Engineering Tamilnadu Karur639113 India Department of Computer science Engineering Shreenivasa Engineering College Tamilnadu Dharmapuri635301 India M.Kumaarsamy College of Engineering Tamilnadu Karur639113 India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem636005 India Department of Electronics and Communication Engineering Paavai Engineering College Namakkal637018 India Department of Electronics and Communication Engineering Kongu Engineering College Tamilnadu Erode638052 India

ISBN: (纸本)9798331532420

Spectrum sensing is a fundamental component of cognitive radio networks (CRNs), enabling efficient spectrum utilization by detecting and leveraging underutilized frequency bands. However, low Signal-to-Noise Ratio (SNR) environments present substantial challenges to reliable spectrum detection, leading to increased false alarms and missed detections. In this paper, we propose an enhanced deep learning approach utilizing advanced neural networks, including Generative Adversarial Networks (GANs), to improve spectrum sensing in low-SNR regimes. By generating high-fidelity, synthetic training data across varying SNR levels, our model gains robustness against noise interference, enabling superior feature extraction and detection accuracy. Comparative evaluations with other deep learning models demonstrate that the GAN-augmented spectrum sensing framework achieves significant performance improvements in low-SNR conditions, offering a promising solution to enhance spectrum reliability in CRNs. This work underscores the potential of GAN-based data augmentation and denoising in addressing noise-related challenges in spectrum sensing, with implications for the next generation of intelligent, adaptive CRN architectures. © 2024 IEEE.

关键词： Signal to noise ratio

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GPA: Uni-directional GRU-Based Traffic Prediction Model for Minimizing Air Pollution 4th

GPA: Uni-directional GRU-Based Traffic Prediction Model for...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Chatterjee, Kalyan Raju, M. Naveen Kumar, K. Praveen Kumar, R. Akshitha, Bhodigam Bandari, Sanjana Srishwan, Beecharaju Tarun, Battala Computer Science and Engineering Nalla Malla Reddy Engineering College Telangana Hyderabad500088 India Electronics and Communication Engineering Nalla Malla Reddy Engineering College Telangana Hyderabad500088 India

ISBN: (纸本)9789819726103

In today’s world, tackling air pollution is a significant challenge, and the transportation system stands out as one of the main culprits behind this issue. Consequently, this paper focuses on developing a future traffic flow forecasting system. To accomplish this, we used a Gated Recurrent Unit (GRU) and a large dataset of time-series data to construct the GPA model, which aims to predict future traffic flow. The GPA model effectively captures the long-term patterns within the sequential big dataset and incorporates traffic flow characteristics from various junctions. The general public needs an accurate and timely predictor of future traffic flow to help mitigate air pollution. To address this demand, this paper employs time-series analysis and explores the spatial and temporal correlations within traffic data for traffic prediction. The evaluation results demonstrate the performance of the GPA model, indicating that it outperforms the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models by achieving a 40% and 50% improvement in future traffic flow prediction, respectively, through network loss minimization. The network loss is measured using the root-mean-squared error, while model performance is assessed using the mean absolute error and mean squared error metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Time series analysis

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CDR-CNN: Convolutional Neural Networks for Alzheimer's Disease Severity Prediction with Clinical Dementia Rating 5

CDR-CNN: Convolutional Neural Networks for Alzheimer's Disea...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Venkatesan, Chandran Sumithra, M.G. Balan, Elakkiya Saravanan, S. Sri Krishna College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Venkateswara College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Dayananda Sagar Academy of Technology & Management Department of Computer Science Engineering in Data Science Bengaluru India

ISBN: (纸本)9798331504403

Precisely identifying the severity of Alzheimer's using MRI is crucial for enhancing diagnostic accuracy and improving patient care. This study presents a novel convolutional neural network designed to classify four classes of Alzheimer: none, very mild, mild, and moderate. The dataset, comprising 80,000 MRI scans from 461 participants, tackles the issue of substantial class skew, with a predominance of scans in the 'none' category. The proposed method deviates from conventional approaches by preserving ordinal information in predictions, as opposed to employing one-hot encoding for categorical classification. This study examines the effectiveness of ordinal classification relative to categorical classification, explores strategies for addressing class imbalance, and assesses the performance of various pre-trained model bases. This research aims to enhance diagnostic accuracy for AD, providing valuable insights for the medical, neuroscientific, and data science communities working with MRI and other medical imaging data. © 2024 IEEE.

关键词： Convolutional neural networks

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An ideal deeply contextual prediction model for an effective prediction and mitigation of cyberbullying in social networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1561-1579页

作者： Prathaban, Banu Priya Vaishnavi, T. Ramesh, Addanki Purna Sujaritha, M. Rama, R. Senthil Gracewell, J. Jeffin Department of Networking and Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai 603203 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Electronics and Communication Engineering DNR College of Engineering and Technology West Godavari Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India Department of Electrical and Electronics Engineering DMI College of Engineering Tamil Nadu 600123 Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Social media have evoked a host of problems, especially Twitter and Instagram, considering misconceived notions of free speech. Among the concerns that have been seen as crucial is the impact of cyberbullying;it is a global issue affecting an individual and society in general. Though various methods have been suggested for deterring cyberbullying, most of these depend on victim interaction and are hence ineffective in the task of independently identifying and preventing cyberbullying. Within this context, the research introduces a new, innovative framework that is ideal and deeply contextual in terms of prediction, thus creating an approach to detecting cyberbullying on social networks without directly engaging with any victim. Consequently, the key contribution of this work is developing the Embedded Residual Recurrent Convolution Network, a hybrid deep learning architecture for the detection of cyberbullying data with more precision and accuracy. Further, this is optimized by a Tenacious Projectile Optimizer for minimizing computational time and improving classifier performance. In this regard, the novelty of the proposed I-DCP framework is established in its superior performances on different social media datasets, with an average accuracy of over 99% and an execution time as low as 13 s, hence highly efficient for real-time detection of cyberbullying. These represent important advances compared to previous methodologies and constitute a strong and scalable response in the fight against cyberbullying. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And classification Artificial intelligence (AI) Cyberbullying detection Deep learning Instagram Optimization Social network Twitter

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From Attacks to Insights: XAI and Defense in Network Security 5

From Attacks to Insights: XAI and Defense in Network Securit...

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5th International Conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Kumar, Bharathi Ganesh Shanthini, E. PSG College of Technology Department of Computer Science and Engineering Tamil Nadu India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331530013

Network intrusion poses significant threats to cyber security, as malicious attacks can disrupt systems and compromise sensitive information. Real-world scenarios include unauthorized access to corporate networks, data breaches exposing sensitive customer information, and Distributed Denial of Service (DDoS) attacks that can impair critical infrastructure. This paper addresses these challenges using the UNSW-NB15 dataset, a comprehensive collection of real-world network traffic data. This dataset was chosen for its diversity in capturing contemporary cyber threats, making it ideal for evaluating model performance in detecting intrusions. While numerous classification models exist for this task, the Multi-Layer Perceptron (MLP) classifier is selected due to its ability to capture complex patterns and provide higher accuracy in non-linear datasets. To enhance the interpretability of the model, Explainable AI (XAI) techniques are employed, including SHAP, LIME, and permutation feature importance, which offer insights into model decisions by explaining feature contributions. However, adversarial machine learning introduces an additional layer of threat by exploiting model vulnerabilities. This paper focuses on the Zeroth Order Optimization (ZOO) attack, a black-box method that only requires access to the model's predictions, without needing knowledge of the architecture or gradients, to demonstrate how adversarial examples can drastically reduce model accuracy and compromise network security. Upon generating adversarial examples, a significant drop in accuracy is observed, identifying the features most susceptible to manipulation. Then XAI methods are applied again to analyze the features that contribute most to the misclassification, allowing us to understand the underlying vulnerabilities. Based on these insights, adversarial training is implemented as a defense mechanism, successfully restoring model accuracy and strengthening resilience against attacks. The results

关键词： Network intrusion

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Advanced Machine Learning Techniques for Early Diagnosis of Diabetic Macular Edema 8

Advanced Machine Learning Techniques for Early Diagnosis of ...

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8th International Conference on electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Muthuselvi, M. Duvvuri, Suneel Kumar Geetha Rani, K. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Applications Andhra Pradesh Rajahmundry India Department of Computer Science and Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

One consequence of diabetic retinopathy is diabetic macular edema (DME), a condition that effects the eyes of individuals with diabetes. More than 28 million people been affecting by diabetic macular edema due to diabetes, late diagnose of diabetic macular edema leads to vision loss. Available method with deep learning algorithms and artificial intelligence (AI) for prediction of diabetic macular edema is not effective way for diagnosing in early. Proposed a method with XGBoost algorithm for the classification, pre-processing, extraction and detection of a disease by fundus samples. This method resulted in 0.992, 0.989, 0.991 and 0.990 for accuracy, Recall, precision and F1-score respectively. © 2025 IEEE.

关键词： Deep learning

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Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

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computer Systems science & engineering 2023年第8期46卷 1707-1721页

作者： Alaknanda S.Patil G.Sundari Arun Kumar Sivaraman Department of Electronics and Communication Sathyabama Institute of Science and TechnologyChennaiTamil Nadu600119India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

The intact data transmission to the authentic user is becoming crucial at every moment in the current ***;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of *** resilience and imperceptibility are improved by choosing an appropriate embedding *** paper gives a novel system to immerse the secret information in different videos with different *** audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB *** Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD ***-audio is linked again with the stego-video in the third *** incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research *** spread spectrum approach is used to increase the complexity of secret data *** different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.

关键词： Audio steganography data hiding information security pixel value differencing

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A Deep Analysis on Security Aspects of Cloud Computing Ontologies 5

A Deep Analysis on Security Aspects of Cloud Computing Ontol...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 IEEE.

关键词： Cloud Computing Cloud service ontology systematic review

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