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检索条件"机构=Computer Science And Engineering V.S.B Engineering College"

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An Efficient Framework for EEG signals Processing Using backpropagation Multilayer Convolutional Neural Network for brain Tumor Diagnosis 3

An Efficient Framework for EEG Signals Processing Using Back...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Dash, santamati Rayshan, Ahmed Raheem Hussain, Moiz Abdul Harika, P. Lakshmi Jawad, Israa Abed School of Sports and Yogic Sciences Kiit University Odisha India College of Veterinary Medicine University of Al-Qadisiyah Department of Physiology and Pharmacology Iraq Padmashri Dr. V.B. Kolte College of Engineering Electrical Engineering Department Malkapur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Bowrampet Hyderabad India AL-Nisour University College Department of Medical Laboratories Technology Baghdad Iraq

ISBN: (纸本)9798350354379

The average patient's life expectancy is shortened by a brain Tumour (bT), one of the most dangerous and aggressive diseases. The prognosis for patients with bTs is worse when they get inadequate medical therapy or a wrong diagnosis. An electroencephalogram (EEG) records electrical activity in the brain in a non-invasive manner. several modern imaging methods including MRI, sPECT, and PET, EEG offer superior tumor-specific diagnoses. This study offers a diagnostic method for identifying if an EEG signal is cancerous or normal. Initially, EEG signals were first used to capture brain data, which was then preprocessed using the stationary wavelet transform (sWT) method. The features of the brain data are then selected using a heuristic assessment. To extract features, a Grey-Level Co-Occurrence Matrix (GLCM) is utilized. Furthermore, we suggest a novel backPropagationMultilayer Convolutional Neural Network (bP-MCNN) approach for bT diagnostics for tumor categorization. A set of database samples was used to provide the neural network with the statistical information that had been extracted from the EEG signal during the training phase. After being trained, a neural network can accurately categorize a test set of samples into normal or cancerous. The suggested approach provides superior classification accuracy than the current methods, according to experimental data. © 2024 IEEE.

关键词： Electroencephalography

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Ai Machine Learning: Transformer Models For Enhanced Natural Language Processing - Techniques And Applications

Ai Machine Learning: Transformer Models For Enhanced Natural...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Pragith s G.M. Karthik T. sripriya E Rajasekaran s. Manthandi Periannasamy Mohamed Zubair Aslam M A Department of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India Department of ECE Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Science and Humanities V.S.B Engineering College Karur Tamil Nadu India Department of Electronics and Communication Engineering Mallareddy Engineering College for Women Maisammaguda Hyderabad Department of AI & DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Modern technologies are needed to strengthen network security defenses because of the increase in cyber attacks in recent years. because deep learning in particular allows for real-time threat identification and thorough performance evaluation, artificial intelligence (AI) has become a powerful tool for improving cybersecurity measures. This study examines how deep learning methods can be used in AI cybersecurity frameworks, with an emphasis on how well they can detect and counteract changing cyberthreats. The use of convolutional neural networks (CNNs) and deep neural networks (DNNs) for anomaly detection and pattern identification in network traffic is specifically discussed. The study also investigates the integration of AI-driven analytics to assess the performance and efficacy of security protocols, offering insights into optimizing defensive strategies against sophisticated cyber attacks. by leveraging AI capabilities, organizations can achieve proactive threat management, enhance incident response times, and fortify their networks against emerging cyber threats. This abstract sets the stage for a comprehensive exploration of AI's role in cybersecurity, highlighting its transformative impact on network security operations.

关键词： Deep learning Performance evaluation standards organizations Network security Natural language processing Real-time systems Time factors Convolutional neural networks Artificial intelligence Cyberattack

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Hand Talk: sign Language to Text Converter using CNN 8

Hand Talk: Sign Language to Text Converter using CNN

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8th International Conference on I-sMAC (IoT in social, Mobile, Analytics and Cloud), I-sMAC 2024

作者： shirisha, N. Jagannadham, D.b.v. Parshapu, Padma singu, sivanaga Malleswara Rao Kumari, v. sravani subhahan, D. Abdus Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India G.V.P College of Engineering Department of E.C.E Visakhapatnam India Hyderabad501510 India Vensai Technologies Inc. CummingGA30040 United States Godavari Institute of Engineering & Technology Department of Cse Rajahmundry India B V Raju Institute of Technology Department: Cse Narsapur502313 India

ISBN: (纸本)9798350376425

This research presents a novel system for converting sign language gestures into text, bridging the communication gap between non-sign language speakers and the deaf and dumb community. The system utilizes advanced image processing and machine learning techniques to accurately interpret and translate sign language. The proposed system employs a Convolutional Neural Network (CNN) classifier to process captured hand gestures and convert them into text. Through rigorous training and testing, the svM classifier achieved a 96.7% accuracy rate for one-minute intervals. This research contributes to the development of accessible and efficient sign language interpretation systems, facilitating communication and promoting inclusivity. by bridging the language barrier, this technology can enhance social interactions and improve the quality of life for individuals with hearing impairments. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning-Driven Hybrid Adaptive beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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2025 International Conference on visual Analytics and Data visualization, ICvADv 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive beamforming Algorithm (AbA) represents a promising approach to suppress interference in dense wireless sensor networks (WsNs), enhancing signal fidelity and network throughput. In dense WsNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array signal Processing (AAsP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (sINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant sINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WsNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(sINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： beamforming

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An Efficient Wireless sensor Network based Intrusion Detection system

An Efficient Wireless Sensor Network based Intrusion Detecti...

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Communication, security and Artificial Intelligence (ICCsAI), International Conference on

作者： Dinesh Rajassekharan v Ramachandran Dilip Kumar sharma Thirumoorthy Palanisamy Y. varatharaj Myilsam Ramesh Chandra Poonia Faculty of Computer Science Peninsula College Malaysia Department of Computer Science and Engineering GITAM School of Computing GITAM Deemed to be University Bengaluru India Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad Uttar Pradesh India

Wireless sensor Networks (WsNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WsNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WsNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping sensor Nodes (sN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated spectral Clustering based Intrusion Detection system and Responsive Mechanism (MMMMGPsC-IDs-RM) is compared with existing state-of-art techniques, and MMMMGPsC-IDs-RM outperforms with the highest detection rate of 96%.

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Permissioned blockchain security Pqfabric from Quantum and Classical Attacks

Permissioned Blockchain Security Pqfabric from Quantum and C...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： C. Tamilarasi K bhavadrarani Thiagarajan Kittappa N. subashini v. Anitha vimalraj E Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science & Technology Chennai Department of Computer Science and Engineering V.S.B. Engineering College Karur. Department of Mathematics Rajalakshmi Institute of Technology Chennai Department of Mathematics Sathyabama Institute of Science and Technology Chennai Department of CSE Panimalar Engineering College Poonamallee Chennai School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding byzantine Fault Tolerance (bFT) with quantum-safe encryption. The system gives protection against calculation based attacks by shor and Grover, tending to huge imperfections in existing blockchain structures. besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks.

关键词： Production systems Technological innovation Quantum computing supply chains standards organizations Oral communication Throughput Fabrics blockchains Encryption

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Apple Fruit leaf Disease Detection and Classification using Quantum behaved Particle swarm Optimization 2

Apple Fruit leaf Disease Detection and Classification using ...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACs 2024

作者： Math, Gayatri vishwanath, P. Adnan, Myasar Mundher Reddy, R Archana sundaram, s. Meenakshi Raichur Visvesvaraya Technological University H.K.E. Society's S.M.V. College of Engineering Department of ECE Karnataka Belgaum India H.K.E. Society's S.M.V. College of Engineering Department of ECE Karnataka Raichur India The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and DS Bengaluru India

ISBN: (纸本)9798350317558

Detecting and classifying apple fruit leaf diseases manually is a time-consuming and expensive task, requiring expertise. This process not only affects the quality of the leaf disease image but also demands expert intervention. In the dataset, there are 1615 healthy images distributed among four classes: apple scab, apple rust, apple black rot, and healthy apple. Data augmentation techniques were involved image inversion, noise injection, colour enhancement, rotation and scaling. Despite these efforts, the existing model faces challenges in an inefficient search space with high dimensions and local optima. To enhance the Quantum-behaved Particle swarm Optimization (QbPsO) based Long Term-short Memory (LsTM) dimension the search space is expanded globally, independently detecting the leaf disease. Feature extraction is performed using squeeze Net layers to analyse the leaf disease, reducing parameters to improve the detection process. The fire modulus in the analysis is involved in the process. The hyperparameter tuning process utilizes QbPsO to independently detect and classify the leaf disease, with the schrödinger equation playing a crucial role in the process. The obtained results demonstrate that the proposed method achiev.s.better accuracy of 98.76%, Precision of 98.72%, sensitivity of 98.82% and F1-score of 98.70%. on the plant dataset. These results ensure detection and classification performance compared to other existing methods, such as Discrete Wavelet Transformation (DWT), butterfly Optimization Algorithm (bOA) and Chaotic salp swarm Algorithm (CssA). © 2024 IEEE.

关键词： Apple Leaf Disease butterfly Optimization Algorithm Chaotic salp swarm Algorithm Long Term-shor Memory and Quantum-behaved Particle swarm Optimization

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computer vision Assisted Deep Learning Enabled Gas Pipeline Leak Detection Framework

Computer Vision Assisted Deep Learning Enabled Gas Pipeline ...

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smart Electronics and Communication (ICOsEC), International Conference on

作者： suri babu Nuthalapati M Arun C Prajitha s Rinesh K M Abubeker Cloudera Santa Clara California USA Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam India Department of ECE / Centre for Interdisciplinary Research Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India Department of Electronics and Communication Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

safe and economical infrastructure facilitates gas and oil transportation via pipeline networks. Leaks in natural gas pipelines, caused by ageing and other reasons, are common yet difficult to identify. Nevertheless, the most prevalent issue that impacts pipeline operation is leakage failure. To a large extent, the flow properties of the particular pipeline determine the accuracy with which a leaking location may be located. One helpful method for analyzing the many assumptions used to explain the fluid flow process and parameters is numerical modelling based on Artificial Intelligence (AI). Hence, this r4search suggests the Deep Learningassisted Gas Pipeline Leakage Detection system (DLGPLDs) using infrared cameras to accurately detect the leak location. A system of infrared cameras is suggested for use in gas plants, transportation, and manufacturing to track the undetectable leaks of methane gas in real-time. This research recommends using a histogram equalization procedure to remove the background noise effectively. The leaking of inv.s.ble gas may be successfully identified by this knowledge-based infrared camera system. This novel approach uses CNNs to categorize infrared camera images into groups based on whether or not they contain natural gas leaks. The research findings demonstrate that the suggested DLGPLDs model increases the gas leakage prediction ratio by 98.7%, classification accuracy ratio by $\mathbf{9 7. 4 \%}$, PsNR ratio by 96.5%, and error rate by 10.3% compared to other state-of-art models.

关键词： Accuracy Error analysis Computational modeling Pipelines Transportation Predictive models Cameras Prediction algorithms Numerical models Leak detection

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Cloud Computing-Enhanced and Raspberry Pi-powered solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics 5

Cloud Computing-Enhanced and Raspberry Pi-powered Solution f...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Giriprasad, s. Karpagam, T. Nalini, v. Mishra, Nilamadhab Mohankumar, N. Malathi, N. Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu Chennai India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Tamil Nadu Chennai India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350361155

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions. © 2024 IEEE.

关键词： Artificial intelligence

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Analyzing P300 signals to Assess Undergraduate Attention span Using Impulse Neiry bCI

Analyzing P300 Signals to Assess Undergraduate Attention Spa...

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brain computer Interface & Healthcare Technologies (iCon-bCIHT), International Conference on

作者： Kavitha sooda Indiramma M sanjeev Kubakaddi Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Department of AI and DS B.M.S. College of Engineering Bangalore India itie Knowledge Solutions Bangalore India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

The study on brain signals to understand working memory in undergraduate students has yielded promising results. However, the challenge remains in accurately assessing different brain activities. This work seeks to measure concentration in cognitive performance, offering a detailed dataset that tracks how individuals respond to cognitive tasks. A virtual reality-based solution has proven effective in capturing the P300 signal, providing a more focused and accurate measurement platform. The study aims to evaluate the efficacy of P300 signals as indicators of attention and cognitive engagement in educational settings. A brain-computer interface system, Impulse Neiry, captured and analyzed P300 signals from undergraduate students while performing various cognitive tasks. User attention span was evaluated by measuring parameters such as session duration and interaction frequency with the software, offering insights into the system's usability. The results indicated that participants experienced about 35% deeper relaxation, and their attention spans increased by 18.97% compared to baseline engagement levels.

关键词： Headphones Medical services Games Particle measurements software Electroencephalography Frequency measurement Cognitive science software measurement Usability

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