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检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology"

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AES Efficient Encryption of Multimedia Data using Threads for 5G System 2

AES Efficient Encryption of Multimedia Data using Threads fo...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Kumar, Nathi Kranthi Venkateswaramma, Jarabhala Nirosha, Chelumala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India St. Martin's Engineering College Dept. of Artificial Intelligence and Machine Learning Hyderabad India Hyderabad India Secunderabad India St. Martin's Engineering College Department of EEE Telangana Secunderabad500 100 India

ISBN: (纸本)9798331505745

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method. © 2025 IEEE.

关键词： Sensitive data

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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2023 International Conference on artificial intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Karman, K. Nattar Velmurugan, V. Raju, Kommisetti Murthy Sajana, T. Vijayalakshmi, V. Dhanraj, JoshuvaArockia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh West Godavari India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350334357

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective Data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management. © 2023 IEEE.

关键词： Cost effectiveness

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Edge Computing Devices Authentication using Quantum Computing

Edge Computing Devices Authentication using Quantum Computin...

引用

International Conference on Computing and Networking technology (ICCNT)

作者： Vinayak A. Telsang Mahabaleshwar S. Kakkasageri Anil D. Devangavi Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(V V), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices.

关键词： Privacy Quantum computing Costs Authentication Vehicular ad hoc networks Blockchains Registers Servers Industrial Internet of Things Edge computing

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Performance Analysis of Water Quality Monitoring System in IoT Using machine learning Techniques

Performance Analysis of Water Quality Monitoring System in I...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Rakesh, N. Kumaran, U. Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665420051

Nowadays, the Internet of things (IoT) and insights while sensing are being used in different areas of research to monitor, collect and separate knowledge from various sources of interest. As there is a huge increase in contamination of water from the industries in provincial to urban regions and over-use of land and ocean assets, the nature of water that each person can use has turned out to be more regrettable. High utilization of composts for crops and different synthetic concoctions in different segments like thermal and electrical power plants, leakages in the submarine pipeline, mining, and construction has significantly added to the general dip in water quality level as a whole. Water is the most significant need for human survival and thus there must be a framework made to test water characteristics and availability for drinking in urban communities and towns. The accessibility of good and better quality of water is most significant in counteracting outbreaks of waterborne diseases just as improving the quality of life. The point of the work proposed is to present a water quality observing system using machine learning techniques. © 2021 IEEE.

关键词： Internet of things

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Advanced Comparative Analysis of Time-Frequency Signal Processing Techniques and machine learning Models for Optimized Biomedical Classification

Advanced Comparative Analysis of Time-Frequency Signal Proce...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Reddy, Uma Lakhanpal, Sorabh Singh, Jay Meena, Talari Shakir, Sarah Abbas Sakthi, G. Mallireddy, Lohitha New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331527495

This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database. © 2025 IEEE.

关键词： Wavelet decomposition

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Smart Attendance Management using a Self-Supervised learning Approach

Smart Attendance Management using a Self-Supervised Learning...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： D. Vikram H Ajina S Gokilapriya M Malini Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Machine Learning Kalingarkarunanidhi Institute of Technology Coimbatore India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries.

关键词： Accuracy Soft sensors Self-supervised learning Transforms Medical services Feature extraction Real-time systems

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Adesh Pawar Palash Gourshettiwar Pradnyawant M. Gote Vishal Kumar Choudhary Prajyot Yesankar Shrikant Khadse Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U) Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration.

关键词： Neuroplasticity Transcranial magnetic stimulation Surgery Mental health machine learning Motors Market research Neurorehabilitation Transcranial direct current stimulation Neurofeedback

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A CNN Approach to Detect Vanishing Point by Considering Street Lane 5

A CNN Approach to Detect Vanishing Point by Considering Stre...

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5th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2021

作者： Shruthiba, A. Deepu, R. Dhomne, Amit Bangalore Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Computer Science and Engineering ATME College of Engineering Mysuru India NIT Rourkela CSE Odisha Rourkela India

ISBN: (纸本)9781665406109

Linear perspective is widely used in highway road images to create the impression of depth on 2D/3D photos and detect vanishing points with the help of parallel lanes lines on road images. Automated understanding of linear perspective in landscape, road, street photo collections video has a number of real-world applications. This problem of automated understanding of linear perspective in images is addressed with the help of hough transform and CNN framework architecture to detect vanishing points. However, images of the road taken from street and highway video pose a very great technical challenge, because an insufficient number of parallel edges or lines intersect and lead to false vanishing points. To solve this problem, a state of art vanishing point detection method is proposed that exploits the vanishing point and intersection of parallel lines with the help of possible parallel lines to lane lines available in the image, the center of origin, and quadrant. The proposed strategy essentially performs best in detecting vanishing points on a public road image data set. © 2021 IEEE.

关键词： Computer vision

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A Novel Python Based Fake Jobs Post Identification And Analysis Methodology Using Elevated learning Strategy 4

A Novel Python Based Fake Jobs Post Identification And Analy...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Vijayalakshmi, V. Anni Princy, B. Kandaswamy, Vijay Anand Sujatha, S. Kumar, Sarva Naveen Aruna, V. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India Sri Sairam College of Engineering Department of Eee Karnataka Bangalore India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India St.Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Online job boards and portals have grown in popularity in recent years, making it easier and more effective for people to look for work. False job ads have proliferated with the advent of digital transformation, which is both an opportunity and a threat. People looking for work may fall victim to these misleading listings, which can lead them astray, waste their time, and expose them to scams. The research presents Elevated learning based Fake Job Prediction (ELFJP), a claim that utilizes learning based classification algorithms to stop fake job advertisements on the internet. To assess the effectiveness of the suggested method, it is cross-validated using a traditional learning model known as Random Forest Classifier (RFC). We find the best job fraud detection model by comparing the results of different classifiers. Web fraud may be detected and prevented with the help of these classifiers. In a sea of seemingly legitimate job ads, it helps to single out the fraudulent ones. This system is able to identify fake job ads on the web by integrating classifiers with an ensemble learning model and then comparing and evaluating the results. In light of the ever-changing strategies used by con artists, the research stresses the significance of being proactive. To keep up with ever-changing fraud tactics, machine learning models are continuously improved and adjusted. The ultimate aim of this proposed approach is to help make a safer online job market by reducing the likelihood of fraudulent ads and increasing confidence among those looking for work. © 2024 IEEE.

关键词： Contrastive learning

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： M Revathi P Kalaiarasi S Jeevitha G Akilandasowmya Artificial Intelligence & Data Science St. Joseph's Institute of Technology Chennai India Department of Machine Learning Kalasalingam University Krishnankoil India Information Technology Jerusalem College of Engineering Chennai India Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy.

关键词： Support vector machines Image segmentation Accuracy machine learning algorithms Brain tumors Biological systems Intelligent systems Diseases

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