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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Muntasir, Fahim Al Wazed Wasi, Wasif Nafis, Mahdee Hossain, M. Mofazzal Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering and Technology Dept. of Electrical and Electronic Engineering Rajshahi Bangladesh Rajshahi University of Engineering and Technology Dept. of Electronics and Telecommunication Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350318739

High voltage alternating current (HVAC) transmission lines are the only way to transmit electrical energy from the power plant to the utilities. There are several types of transmission line faults that can interrupt the power supply, sometimes even resulting in national blackout. To ensure a highly reliable power supply system, detecting the faults in the quickest of time is crucial. This research used machine learning and neural network algorithm to find five different types of faults using data from a real power transmission line in Bangladesh. Apart from the accuracy of the methods, the focus was given on the response time in identifying the faults. The two boosting methods CatBoost and XGBoost stood out as the most precise among all the algorithms, achieving excellent accuracy and detection time in between 20 ms to 30 ms. The response time of all the methods was found to be in millisecond range, making them perfect for not only long term faults, but also to be implied in transient fault analysis. © 2023 IEEE.

关键词： Machine learning

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Comparison of Fast Frequency Response Methods in the Low-Inertia Grid of Cyprus 3

Comparison of Fast Frequency Response Methods in the Low-Ine...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Argyrou, Andreas Panagi, Savvas Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern electric power systems with high levels of penetration of renewable energy sources (RES) often present frequency security problems. The lack of inertia due to the reduced number of synchronous generators in these systems, in combination with the usual inability of RES to provide frequency support, leads to system operators having to curtail RES or rely heavily on Under Frequency Load Shedding schemes to ensure frequency security. Fast Frequency Reserves (FFR) have been proposed as a solution to strengthen the frequency support of the system and alleviate security problems. FFR allows mitigating Rate of Change of Frequency, Nadir, and post-fault frequency steady-state problems after an event. In this paper, we present, analyze, implement and compare five FFR controllers to alleviate frequency security problems in low-inertia grids. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and exhibit the impact on a real system. © 2024 IEEE.

关键词： Synchronous generators

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Investigation of Fuzzy and SOM based Segmentation of High-Resolution Satellite Images in HSL Color Space for Information Retrieval 9

Investigation of Fuzzy and SOM based Segmentation of High-Re...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Ganesan, P. Sathish, B.S. Leo Joseph, L.M.I. Murugesan, R. Sajiv, G. Akilandeswari, A. Gnanaprakash, M. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering AI Bengaluru India SR University Dept. of Electronics and Communication Engineering Telangana Warangal India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India SIMATS Saveetha School of Engineering Chennai India Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798350397376

In the proposed article, an innovative approach for the segmentation of region of interest (ROI) based on fuzzy and neural network (SOM-self organizing map) is investigated. In image analysis, clustering is an unique imperative practice wherein the complete data is separated as group of pixels and/or regions using its features. A satellite image is huge collection information. Many approaches and algorithms suggested for the dissection of satellite imagesto extract the indispensable and hidden information. In this work, fuzzy based FCM, PCM, PFCM, MFCM and SOM is applied to the satellite images to retrieve the information. The proposed method transforms the image in RG to HSL model prior to clustering. HSL model has some distinct characteristics such as perceptual, intuitive, polar coordinated, approximate the vision. The segmentation result is validated with image quality measures. © 2023 IEEE.

关键词： Color

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Big Data Analytics for Proactive Financial Fraud Detection and Prevention 2

Big Data Analytics for Proactive Financial Fraud Detection a...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Hasudungan, Yehezkeil Oganda, Fitra Putri Daeli, Ora Plane Maria Raihan Santoso, Nesti Anggraini University of Raharja Dept. of Informatics Engineering Tangerang Indonesia University of Raharja Dept. of Information System Tangerang Indonesia University of Raharja Dept. of Digital Business Tangerang Indonesia University of Raharja Dept. of Computer System Tangerang Indonesia

ISBN: (纸本)9798350351613

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics. © 2024 IEEE.

关键词： Data Analytics Financial Fraud Prevention Proactive Detection Risk Management SmartPLS

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COVID-19 Improved Diagnoses Based on the Open-morphology Filter and Deep-learning

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Recent Patents on engineering 2022年第3期16卷 122-130页

作者： Younus, Majid D. Zedan, Mohammad J. M. Malallah, Fahad L. Saeed, Mustafa G. Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer Science Dept. College of Science Cihan-Slemani University Slimani Iraq

Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus, there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying COVID-19-infected patients by computed tomography CT images. The contribution of this paper is that it proposes a pre-processing technique that increases the recognition rate compared to the techniques existing in the literature. Methods: The proposed pre-processing technique, which consists of both contrast enhancement and open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning for the training and testing operations. The dataset used in this research has been publically published, in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed of 2482 CT scans images, which include 1252 CT scans of SARS-CoV-2 infected patients and 1230 CT scans of non-infected SARS-CoV-2 patients. Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported accuracy of the dataset by 97.3%. Conclusion: The performance in terms of accuracy has been improved up to 0.5% by the proposed methodology over the published dataset and its method. © 2022 Bentham Science Publishers.

关键词： COVID-19

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COVID-19 Detection of CT Chest Images with Artificial Intelligence Algorithms 2

COVID-19 Detection of CT Chest Images with Artificial Intell...

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2nd International Conference on Advanced computer Applications, ACA 2023

作者： Lami, Lujain Qasim Naser Cevik, Mesut University College Altinbas University Dept. Electrical and Computer Engineering Al-Esraa Baghdad Iraq Altinbas University Dept. Electrical and Electronics Engineering Istanbul Turkey

ISBN: (纸本)9798350341829

Hundreds of thousands of people died as a result of the covid-19 sickness when it initially spread globally in 2019. Medical personnel is busier than ever as a result of this illness. Using machine learning and creating computer-aided diagnostic tools will help to some extent by reducing this pressure. The speedy elimination of this illness can be aided by any scientific investigation of it. In order, to extract Visual Geometry Group (VGG16) features, we used the publicly accessible Covid-19 dataset from the Kaggle website. Decision trees (DT), Random forest (RF), K-Nearest neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM) were some of the machine learning classification algorithms used to assess our work. In terms of classification accuracy, the Support vector machine(SVM)algorithm excels at 98% accuracy, and it also achieves impressively rapid results. © 2023 IEEE.

关键词： COVID-19

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A Comparative Study on the Effectiveness of Various Machine Learning Paradigms in Image Recognition and Predictive Modelling

A Comparative Study on the Effectiveness of Various Machine ...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Upadhyay, Deepak Gupta, Mridul Verma, Aditya Mittal, Saksham Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9781665474924

The efficiency of several machine learning algorithms in picture identification and predictive modelling is thoroughly compared in this research article. The necessity to find the best algorithms for certain use cases and the rising need for reliable and effective image recognition systems across a variety of areas are the driving forces behind the study. In order to assess how well these algorithms, perform in terms of accuracy, precision, and recall, the study uses a wide range of machine learning techniques, including classical and deep learning approaches. The study's findings show that convolutional neural networks, in particular, outperform conventional algorithms in image identification tests while standard and deep learning techniques perform similarly in tasks requiring predictive modelling. The study also emphasises the value of transfer learning and fine-tuning strategies in obtaining cutting-edge performance on picture datasets, and finally we will see results at the end. © 2023 IEEE.

关键词： Transfer learning

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Innovative Method for Enhancing E-Bikes Performance through riding mechanical force

Innovative Method for Enhancing E-Bikes Performance through ...

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2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023

作者： Yadav, Hritik Shrivastava, Neha Rathod, Devendra Singh Gupta, Tanisha Ies College of Technology Bhopal Dept. of Electronics and Communication Engineering Bhopal India Ies College of Technology Bhopal Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350311327

This paper presents a novel approach to charging E-bikes using their own mechanical energy generated during riding. Inspired by the method used in petrol vehicles where the battery charges itself while in use, we propose a similar system for E-vehicles, specifically E-bikes. Our method involves the addition of a secondary battery to the bike, which will charge itself while the vehicle is in motion, and when parked, it will charge the main battery. This innovative solution aims to improve the mileage of E-bikes, which is a significant drawback compared to petrol vehicles. © 2023 IEEE.

关键词： Vehicle performance

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Deep Predictive User Path-Inclusive Service Mobility in B5G/6G Edge Clouds 18

Deep Predictive User Path-Inclusive Service Mobility in B5G/...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Mwasinga, Lusungu J. Raza, Syed M. Le, Duc-Tai Kim, Moonseong Chung, Min Young Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Dept. of IT Convergence Seoul Theological University Bucheon Korea Republic of

ISBN: (纸本)9798350331011

Multi-access Edge Computing (MEC)-enabled Beyond 5G (B5G) or 6G networks require service migration to maintain service continuity and acceptable quality of experience for high-mobility User Equipment (UE). This involves migration of containerized applications to between Edge Hosts via the backhaul network as the associated UE moves away from its current gNodeB. Although crucial for both UEs and mobile network operators (MNOs), frequent migration not only prolongs service disruption time (downtime) for UEs but also increases operational expenses for the MNO. To address this problem, our study proposes a Deep Predictive User Path-Inclusive Service Mobility (DUPSM) approach for efficient management of the end-to-end migration process including deciding when and where to migrate the service. It features a deep Actor-Critic agent that learns suitable decision policy to balance cost-benefit trade-off interms of migration cost and quality of experience, respectively. As user mobility largely influences migration decisions, the proposed method incorporates user mobility path forecasting through a Generative Adversarial Network (GAN) based prediction technique to enhance ability for selecting suitable Edge Host while considering resource variations. The effectiveness of the proposed method is assessed through simulation experiments, showcasing an average reduction in service downtime of 22% when compared to a baseline scheme. © 2024 IEEE.

关键词： Reinforcement learning

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Summarizing Clustering Results using Sentence prototype based Language Models

Summarizing Clustering Results using Sentence prototype base...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Khare, Arpit Kumar, Dheeraj National Institute of Technology Jamshedpur Dept. of Computer Science and Engineering Jamshedpur India Indian Institute of Technology Roorkee Dept. of Electronics and Communications Engineering Roorkee India

ISBN: (纸本)9798350322972

Amount of experiment-related data in almost every domain has increased multifold. Hence, Understanding these novel datasets' essential characteristics is vital to developing complex statistical models and testing various hypotheses. Exploratory data analysis using unsupervised techniques such as data clustering is becoming popular among researchers. Although several clustering algorithms exist, little research has been done to develop strategies to help understand the characteristics and physical implications of generated clusters. This paper proposes to use natural language to describe the results generated by a clustering algorithm linguistically for straightforward interpretation and understanding. We discuss salient characteristics of clusters that one looks for while interpreting clustering results and develop several linguistic prototype summaries for each of them. These summaries are then coherently combined to form linguistic summarization of clustering results. Numerical experiments on real-life IRIS dataset generate highly readable and easy-to-understand descriptions of the clusters generated through three popular clustering algorithms - k means, single linkage, and DBSCAN. © 2023 IEEE.

关键词： K-means clustering

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