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43rd Chinese Control Conference, CCC 2024

作者： Huang, Xiangjie Lang, Xun Liu, Songhua Li, Peng Guo, Tao Yu, Li Yunnan University Department of Electronic Engineering Yunnan Kunming650091 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Honghe Power Supply Bureau Yunnan Power Grid Corporation Yunnan Honghe China

ISBN: (纸本)9789887581581

Electric transformers play a crucial role in power systems, and extensive research has been conducted on transformer state prediction. However, despite the importance of transformer bushing as a vital component of transformers, there has been limited research focused specifically on its state prediction. This work builds upon the state-of-the-art predictive network, the Autoformer, and introduces an enhanced functionality module (the Dilated Loss module) for efficient state prediction of transformer bushing. More specifically, the aim is to predict three essential parameters of the bushing: dielectric loss, end shield current, and equivalent capacitance. By forecasting future trends in these parameters, potential bushing or transformer faults can be detected at an early stage. To evaluate the effectiveness of the proposed method, we conducted comparative experiments with attention-based frameworks (Autoformer Pyrafomer, Informer), traditional deep learning algorithms such as Temporal Convolutional Network and Long Short-Term Memory, as well as the statistical methods (Moving Average). The results show that our Dilated Autoformer outperforms the aforementioned techniques in terms of four key metrics: mean square error, mean absolute error, root mean squared error, and coefficient of determination. Our proposed model reveals the potential of attention-based algorithms in predicting the status of transformer bushings. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dielectric losses

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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2023 Annual Intermountain engineering, Technology and computing, IETC 2023

作者： Sweeney, Nathaniel Xu, Caroline Shaw, Joseph A. Hocking, Toby D. Whitaker, Bradley M. Montana State University Electrical and Computer Engineering Department United States University of Michigan Electrical Engineering and Computer Science Department United States Montana State University Optical Technology Center United States School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (纸本)9798350335903

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training. © 2023 IEEE.

关键词： Anomaly detection

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Blockchain Integrated Federated Learning in Edge-Fog-Cloud systems for IoT based Healthcare Applications: A Survey

arXiv

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arXiv 2024年

作者： Rajagopal, Shinu M. Supriya, M. Buyya, Rajkumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India CLOUDS Lab School of Computing and Information Systems The University of Melbourne Australia

Modern Internet of Thing (IoT) applications generate enormous amount of data. To prototype the incoming data from such applications, data-driven machine learning has emerged as a viable method which can help to develop precise and reliable statistical models. Existing data is not effectively utilized by machine learning as it is stored in data silos and technology businesses are now required to treat user data carefully in accordance with user-privacy legislation in many regions of the world. It is self-evident that the samples in the typical machine learning centralized server paradigm have different probability distributions of data supplied. As a result, the common model fails to personalize. Without sufficient data, machine learning is unlikely to reach its full potential. Hence the new distributed paradigm, federated learning has gained popularity that supports collaborative learning while preserving privacy. IoT applications with cryptography characteristics will be capable of storing and transmitting data securely over networks by maintaining data consistency. Therefore, federated learning and blockchain integration are particularly beneficial for IoT applications that manage sensitive data such as healthcare. Although several studies have focused on various applications of blockchain technology and federated learning, a thorough examination of these technologies in edge-fog-cloud based IoT computing systems and healthcare applications is yet to be performed. The basic architecture, structure, types, functions, and characteristics of federated learning and blockchain are addressed at the outset of this survey article. This article also considers the wide range of applications to which federated learning and blockchain has been implemented, including edge, fog, cloud and IoT computing paradigms. Lastly, it evaluates the different implementations of federated learning in healthcare applications. © 2024, CC BY-NC-ND.

关键词： Blockchain

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Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach

Designing of a Voice-Based Programming IDE for Source Code G...

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2022 International Research Conference on Smart computing and systems engineering, SCSE 2022

作者： Nizzad, A.R.M. Thelijjagoda, Samantha British College of Applied Studies School of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model's training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only. © 2022 IEEE.

关键词： Python

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Integrating Function and Connection: Two Perspectives to Telepresence Robot Design for Classroom 25

Integrating Function and Connection: Two Perspectives to Tel...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Berhane, Roda Akiode, Deo Adejoke Joshi, Swapna Elmimouni, Houda School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Field Robotics Lab Northeastern University BostonMA United States Department of Computer Science University of Manitoba WinnipegMB Canada Information Systems and New Media University of Siegen Siegen Germany

ISBN: (纸本)9798400711879

This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 operators, 13 classmates), we identified differing priorities: operators of the robot emphasize the need for improved functional aspects like camera control, while co-present classmates value social presence, interaction and identification, advocating for features like personalization and effective communication channels. These insights underline the need for telepresence robots that balance functional efficacy with social integration to provide a holistic educational experience. This work, though incomplete, enriches Group and CSCW literature on telepresence robots to facilitate collaborative interactions by highlighting the distinct needs of remote and in-person students, proposing design recommendations for telepresence robots that are technically proficient and socially responsive. © 2025 Copyright held by the owner/author(s).

关键词： Students

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management systems

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Computer systems Science & engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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Modeling of IoT systems for the underground coal mining industry 26

Modeling of IoT systems for the underground coal mining indu...

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26es Jornadas de Ingenieria del Software y Bases de Datos, JISBD 2022 - 26th Conference on Software engineering and Databases, JISBD 2022

作者： Alfonso, Iván Garcés, Kelly Castro, Harold Cabot, Jordi Department of Systems and Computing Engineering Universidad de los Andes Bogotá Colombia IN3 Universitat Oberta de Catalunya Barcelona Spain ICREA

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Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

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Computer systems Science & engineering 2022年第10期43卷 397-412页

作者： Sunil Dhankhar Mukesh Kumar Gupta Fida Hussain Memon Surbhi Bhatia Pankaj Dadheech Arwa Mashat Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthan302017India Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Mechatronics Engineering Jeju National UniversityJeju63243Korea Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

In today’s digital era,the text may be in form of *** research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary *** rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords *** primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant *** second goal is to maintain or improve the classification system’s *** experiment included news articles from various genres,such as Bollywood,politics,and *** proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human *** evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.

关键词： Support vector machine(SVM) optimization precision Hindi character recognition optical character recognition(OCR) automatic summarization and compression ratio

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Open-Ended Coaxial Probe Based Chloride Concentration Prediction in Water Using Machine Learning Algorithms 7

Open-Ended Coaxial Probe Based Chloride Concentration Predic...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Venugopal, Veena Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Physical Science Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India School of Engineering and Applied Science Benette University UP Greater Noida India

ISBN: (纸本)9798331531836

Monitoring chloride levels in water is essential for environmental health, industrial processes, and safe drinking water, as both high and low concentrations can cause harm for living being. This paper presents a novel approach to detecting chloride concentrations using Open-Ended Coaxial Probe (OECP) sensing combined with machine learning algorithms. OECP sensing offers real-time, non-destructive, and precise monitoring, enabling early detection of contaminants. Scattering parameter (S11) data across a broad frequency range was collected with varying chloride concentrations in controlled laboratory experiments. Thirteen machine learning algorithms were trained to predict chloride concentrations, with different datasets for training and testing to ensure robustness. Among these, CatBoost demonstrated the best performance. After fine tuning and cross-validation, the CatBoost model achieved a test Mean Square Error (MSE) of 0.00627 and Coefficient of Determination (R2) of 0.9936, showcasing its accuracy for applications in water monitoring, corrosion prevention, and safeguarding public health. © 2025 IEEE.

关键词： Mean square error

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