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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Simulation and Emulation Tools for Fog Computing

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Recent Advances in computer science and Communications 2022年第3期15卷 315-322页

作者： Singh, Simar P. Kumar, Rajesh Sharma, Anju Reddy, S. Raji Vashisht, Priyanka Landran Mohali India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda India Department of Mathematics & Humanities Mahatma Gandhi Institute of Technology Hyderabad India Computer Science and Engineering Department The NorthCap University Gurugram India

Background: Fog computing paradigm has recently emerged and gained increasing attention in the present era of the Internet of Things. The growth of a large number of devices all around, leads to the situation of the flow of packets everywhere on the Internet. To overcome this situation and to provide computations at network edge, fog computing is the need of the present time that enhances traffic management and avoids critical situations of jam, congestion etc. Methods: For research purposes, there are many methods to implement the scenarios of fog computing i.e. real-time implementation, implementation using emulators, implementation using simulators etc. The present study aims to describe the various simulation and emulation tools for implementing fog computing scenarios. Results: The review shows that iFogSim is the simulator that most of the researchers use in their research work. Among emulators, EmuFog is being used at a higher pace than other available emulators. This might be due to ease of implementation and user-friendly nature of these tools and language these tools are based upon. The use of such tools enhance better research experience and leads to improved quality of service parameters (like bandwidth, network, security etc.). Conclusion: There are many fog computing simulators/emulators based on many different platforms that use different programming languages. The paper concludes that the two main simulation and emulation tools in the area of fog computing are iFogSim and EmuFog. Accessibility of these simulation/emulation tools enhance better research experience and leads to improved quality of service parameters along with the ease of their usage. © 2022 Bentham science Publishers.

关键词： Fog computing

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Rosacea Classification Using Deep Stacking Multi-Layer Model (DSMLM) 2

Rosacea Classification Using Deep Stacking Multi-Layer Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Antony Sophia, N. Saravanan, T.R. Vigneshwari, M. Deepika, N. Uma, K. Madhavan, P. Srm Institute of Science & Technology Department of Computational Intelligence Chennai Kattankulathur India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Rosacea, a widespread and complex skin disorder affecting most of the world population, is divided into various subtypes with unique clinical features the complexity of these subtypes makes accurate classification difficult. A unique technique called the Deep Stacking Multi-Layer Model (DSMLM) has been proposed as a solution for this complex classification. To extract microelements from clinical images and patient history data, DSMLM uses a multilayered neural network architecture. Using the stacking ensemble strategy to combine predictions from different base models makes it possible to improve the classification performance. The effectiveness of the DSMLM is evaluated using a large dataset of different rose cases, and it shows remarkable accuracy in distinguishing between different rose subtypes © 2024 IEEE.

关键词： Large datasets

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Integrating Deep Q-Networks and YOLO with Autonomous Robots for Efficient Warehouse Management 5

Integrating Deep Q-Networks and YOLO with Autonomous Robots ...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Vivek, N. Suriya, A. Kamala, V. Chinnasamy, N.V. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India KGiSL Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350351378

Autonomous robotic cars' rapid, accurate, and efficient product handling and navigation are changing warehouse inventory management. These 24/7 smart vehicles save labor costs, human error, and operational inefficiencies. Time-consuming and incorrect manual scanning and recording is used for inventory management. Traditional autonomous car Q-learning algorithms may suffer in dynamic inventory circumstances and lack the complexity needed for real-time decision-making. Our research creates novel algorithms for autonomous robotic cars. The Deep Q-Network (DQN) we utilize for navigation adapts better to changing circumstances than ordinary Q-learning. YOLO object detection replaces slower QR code recognition for inventory monitoring. We use Decision Trees to find QR code data insights for decision-making. Our research results are promising. A greater cumulative reward curve over 100 simulated episodes gives DQN a 40% learning efficiency advantage over Q-learning. With 95% accuracy, YOLO outperformed Haar Cascade classifiers in QR code detection by 40% speed and 15% accuracy. Decision Trees identified QR code data 5.9% better than Rule-Based Systems, indicating inventory dependability. Autonomous robots can simplify inventory management in warehouses. Algorithmic technology allows these ambulances to drive intelligently, detect warehouse things faster and more accurately, and make decisions based on complicated data. These advanced artificial intelligence algorithms may make robotic systems more responsive and customized, changing inventory management subfields. These algorithms must be updated so autonomous cars can drive to meet logistical needs. © 2024 IEEE.

关键词： Object detection

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Analysis on Detecting Cardiac Arrhythmia Using Advanced Technologies 8th

Analysis on Detecting Cardiac Arrhythmia Using Advanced Tech...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Radhika, N. Sujatha, D. School of Computer Science & Engineering MRUH Telangana Hyderabad India CSE-AI&ML Department MRCET Telangana Hyderabad India Department of Computational Intelligence MRCET Telangana Hyderabad India

ISBN: (纸本)9789819786657

Essential features of the various cardiac diseases that affect the human heart are provided by the electrocardiogram (ECG). A significant portion of the diagnosis of heart illness comes from the categorization of arrhythmias. An arrhythmia is any alteration to the regular electrical impulse sequence. Conventional techniques for analyzing digital signals, machine learning, and the offshoots, including deep learning approaches are widely used for analyzing and classification of ECG signals and more importantly, for the timely diagnosis of cardiac disorders and arrhythmias. An extensive review of the literature regarding ECG analysis is presented in this document. Also address the computational barriers of machine learning techniques to wearable devices. The study started with a search for pertinent papers and then the gathered data was examined. Examining the assessed ECG features and the machine learning technique for identifying arrhythmias was the second step. The analysis shows that even though the MIT-BIH database requires a large amount of preprocessing work, it chose a sizable proportion of studies. We examine the substantial corpus of literature on pre-recorded ECG data, signal collection, ECG signal processing, and noise reduction, identifying ECG spectrographic states through function technology, classification of ECG signals, and relative perspectives among the examined modules. We reviewed the ultra-modern machine learning strategies for automated arrhythmia detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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MOALF-UAV-MEC: Adaptive Multi-Objective Optimization for UAV-Assisted Mobile Edge Computing in Dynamic IoT Environments

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IEEE Internet of Things Journal 2025年第12期12卷 20736-20756页

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Al Bayda Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi China School of Software Technology Dalian University of Technology Nanchang Dalian China University of Science and Technology School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge Computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework's efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

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Recent Innovations and Improvements in Remote Heart Rate and Heart Disease Measuring Methods Using RGB Camera

Recent Innovations and Improvements in Remote Heart Rate and...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Balaraman, Kalaiselvi Claret, S. P. Angelin College of Science and Humanities Department of Computer Science SRM Institute of Science and Technology Chennai Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Chennai Kattankulathur India

ISBN: (纸本)9798350333244

The Heart Rate (HR) is the most significant preliminary health parameter and a vital indication of people's physiological condition. Thus, it's crucial to monitor periodically. During a given period, the heart beats at a specified rate. The rate of heartbeat is called Heart Rate. Accurate HR is essential to diagnosing and predicting heart failures and cardiovascular diseases. In recent years several innovative approaches for retrieving heart rate using low-cost digital cameras and image processing have been published by researchers. The ubiquitous availability of digital cameras paves the way for several innovative and low-cost vital sign monitoring applications. The review was conducted on a remote HR measuring method, such that continuous monitoring of HRV, ECG-based, and rPPG-based monitoring methods and working principles were analyzed. This study specifically focuses on the recent enhancement and advancement of non-contact-based heart rate estimation, classifies existing Photoplethysmography (PPG) techniques, and describes each step required in rPPG classification. This study identified many authors' most effective methods for a higher accuracy ratio. The professionals can apply the evaluated and categorized rPPG classification algorithms to enhance some aspects of a newly constructed rPPG algorithm to improve the heart rate accuracy most suited to their specific needs. The rPPG-based non-contact HR measurement will suit the current COVID-19 environment in a clinical trial. © 2023 IEEE.

关键词： Electrocardiograms

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