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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Identification and Detection of Behavior Based Malware using Machine Learning

Identification and Detection of Behavior Based Malware using...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Garg, Umang Sharma, Neeta Kumar, Manish Singh, Amar Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Noida International University Department of Computer Science Greater Noida India Cgc Landran Cec Department of Computer Science and Engineering Mohali India Graphic Era Hill University School of Commerce Dehradun India

ISBN: (纸本)9798350322309

Malware that exploits the Web on a regular basis becomes a real menace. The transmission of malwareis very rapid during the last two decades which needs to bedetected. One of the efficient approaches for the detection of malware is manual heuristics analysis. To recognize and identification of behavior -based malware detection, machine learning techniques are in the consideration which may provide the optimum solution. Every malware's activity in a mimicked scenario would be continuously assessed, as well as behavior evaluations would be generated. Therefore, a major challenge is to identify the real-time identification of malware using signature matching algorithms. The classification methods used for this article such as KNN, J48, Decision Tree, SVM, Naïve Bayes, Neural Network, and Multilayer perceptron. J48 decision trees, which focused on the results of the testing that consisted of all 5 classifications, had a recall of 95.7%, a specificity of 97.3 percent, a positive predictive rate 2.4%,and accuracy is 96.8%. This was their collective best performance. In conclusion, malware could be identified inan efficient and effective way using a proof-of-concept that focuses on autonomous behavior-based malware classification. © 2023 IEEE.

关键词： Malware

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Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

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Massive MIMO in 5G Networks: Trends and Challenges

Massive MIMO in 5G Networks: Trends and Challenges

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Manivasagam Garima Shukla, Meenu Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Massive Multiple-Input Multiple-Output (MIMO, which stands) has emerged as a key actor in the development of wireless communications technologies, which have been prompted by an increasing need for mobile communication and the concurrent worldwide bandwidth scarcity. Massive MIMO, positioned as a critical enabling technology in contemporary networks, combines antennas at both ends of the network and provides increased spectrum and energy efficiency with relatively simple processing techniques. A thorough understanding of the complexities of massive MIMO is necessary for the effective deployment of 5G and future generations of networks, which will enable a variety of uses for intelligent sensing systems. These developments improve performance and capacities while also contributing to the continuous growth of wireless communication technology. The discussion also identifies important outstanding research questions that will determine the direction of future studies in large MIMO systems, especially in the larger context of networks beyond 5G. With an eye on the future, this viewpoint seeks to direct and incentivize more research endeavors, promoting breakthroughs in the comprehension and use of massive MIMO technology for the next wave of wireless networks. © 2023 IEEE.

关键词： 5G Networks Beamforming Intelligent Sensing Systems Massive MIMO Millimeter Waves Small Cells and Terahertz Spectrum Efficiency Wireless Communication Technology

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Machine Learning Applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review 4

Machine Learning Applications for Online Partial Discharge D...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Tag, Ameera Refaat, Shady S. Kameli, Sayed Mohammad Saleh, Mohammad Alshaikh Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Texas A&M University Department of Electrical and Computer Engineering College Station United States

ISBN: (纸本)9798350306262

The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. The detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. Therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis. © 2024 IEEE.

关键词： Partial discharges

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks 4

Intelligent Kidney Abnormality Detection in CT Images Using ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Bajpai, Chhavi Anitha, S. Gaur, Manish Raghini, M. Kaliappan, S. Khan, Imran Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India Acs College of Engineering Department of CSE-Cyber Security Bangalore India Institute of Engineering and Technology Aktu Department of Computer Science & Engineering Lucknow India Velammal College of Engineering and Technology Department of Artificial Intell igence and Data Science Madurai India Lovely Professional University Department of Research and Development Jalandhar India Sharda School of Nursing Science and Research Sharda University Department of Medical Surgical Nursing Greater Noida India

ISBN: (纸本)9798350352931

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging. © 2024 IEEE.

关键词： computerized tomography

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Hyper Spectral Spatial Image Segmentation Using Machine Learning Algorithms 1

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Saravanan, G. Suresh, A. Jagannathan, J. Thanga Mariappan, L. Majeeth, Najeem Dheen Abdul Kumar, Nithish Veltech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamilnadu India

ISBN: (纸本)9798350371185

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active Learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy. © 2023 IEEE.

关键词： Image segmentation

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ETDA a tool for the development of e-Tongues

ETDA a tool for the development of e-Tongues

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2023 International Young Engineers Forum in Electrical and computer engineering, YEF-ECE 2023

作者： Reis, Tiago Fino, M. Helena Raposo, Maria Nova School of Science and Technology Department of Electrotechnical and Computer Engineering Caparica Portugal Department of Physics Caparica Portugal

ISBN: (纸本)9798350300420

This paper presents the Electronic Tongues Data Analytics (ETDA) tool for automatically extracting the relevant features from data obtained from an electronic tongue (e-Tongue) system based on impedance spectroscopy measurements. The tool was implemented in Python and was created to assist with the development of e-Tongues. This tool helps to quickly visualize the data from the experiments and can automatically create and calibrate the Principal Components Analysis (PCA) to fit the concentrations of a determined molecule present in the complex media. © 2023 IEEE.

关键词： Principal component analysis

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Unlocking The Power Of Deep Learning: A Guide To Automatic License Number Recognition 1

Unlocking The Power Of Deep Learning: A Guide To Automatic L...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Hemalatha, D. Begum, Almas Alex David, S. Rishitha, Gaddam Praharsha, Pothanaboyina Dimple Nandini, Chundi Vel Tech Rangarajan Dr Sagunathala R & D Institute of Science and Technology 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 Tamilnadu Chennai India

ISBN: (纸本)9798350328042

An image processing technique called Automatic Number Plate Recognition (ANPR) utilizes the vehicle's number plate to identify it. The goal is to create a successful automatic authorized car identification system that makes use of the license plate. In the majority of applications involving the movement of vehicles, the detection and recognition of a vehicle's license plate (LP) is a crucial method. Additionally, it is a hot research subject in the area of image processing. To find and identify LPs, numerous techniques, methods, and algorithms have been created. Prior to taking a picture of the car, the developed system first recognizes it. The region of an image that contains the car number plate is retrieved using image segmentation. An optical character recognition technique is used to recognize characters. However, due to the LP's characteristics which include the numbering system, colors, character languages, fonts, and size varying from one nation to the next. To make the detection and recognition procedure very effective, more research in this area is still required. Despite the fact that many scholars have studied this field, many current systems function in defined and regulated environments. For instance, some frameworks need sophisticated hardware in order to produce high-quality images or record video from cars moving slowly. Because of this, it is still challenging to successfully discover and identify LPs under various environmental circumstances and climatic variations. This article presents a deep learning-based automatic system for LP detection and recognition that is divided into three sections: character recognition, segmentation and detection. © 2023 IEEE.

关键词： Optical character recognition

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Microgrid digital twins: concepts and their controlling through multi-agent systems

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International Journal of High Performance Systems Architecture 2023年第4期11卷 225-236页

作者： Negi, Gaurav Singh Mishra, Anupama Gupta, Mukul Kumar Saxena, Nitin Kumar Saini, DilipKumar Jang Bahadur Joshi, Kapil Electrical and Electronics Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Uttarakhand Dehradun248140 India School of Engineering University of Petroleum and Energy Studies Dehradun248006 India Department of Electronics & Instrumentation Engineering MJP Rohilkhand University UP Bareilly243006 India Electrical and Electronics Engineering KIET Group of Institutions Ghaziabad201206 India Electrical and Computer Engineering University of Denver Denver80210 United States Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Pune411044 India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India

Real-time grid analysis is made possible by the electric digital twin grid, which combines history and present data to convey system status and project future circumstances. Cooperative smart agents that can solve problems bigger than one agent's scope make up a multi-agent system (MAS). A micro-grid digital twin (MGDT) uses real-time data interchange and high-fidelity models to simulate a microgrid’s operation. Hybrid energy storage systems are managed by agents who maximise renewable resources and minimise expenses. Microgrids combine sensing, control, and intelligence technology in utility networks. Plant management, communication protocols, and power system operations are all made easier with MAS-based control. For dependable microgrid operation, dispersed energy resources are coordinated by MAS. In order to handle complexity and improve resilience and control of the energy system, this study investigates the application of MAS in microgrid control. Energy management and agent communication in microgrids are the main topics of MAS research. Integrating microgrid digital twins with MAS holds promise for advancing energy systems. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Multi agent systems

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