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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Chetri, Chandan Huynh, Alvin Williamson, Sheldon S. Laboratory Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineering OshawaON L1G 0C5 Canada

ISBN: (纸本)9798350398069

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered. © 2022 IEEE.

关键词： Topology

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A-REA: Relation-Aware Graph Convolutional Network Entity Alignment Via a Differential Adjacency Matrix

A-REA: Relation-Aware Graph Convolutional Network Entity Ali...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Dang, Yuemei Qiu, Chen Gao, Feng Gu, Jinguang Zhang, Lihua Arman, Ghazaleh School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Software College East China Jiaotong University Jiangxi Nanchang China Urmia University of Technology Faculty of Information Technology and Computer Engineering Urmia Iran

ISBN: (纸本)9781665457033

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs. © 2023 IEEE.

关键词： Knowledge graph

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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A Multivocal Review of MLOps Practices, Challenges and Open Issues

arXiv

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arXiv 2024年

作者： Eken, Beyza Pallewatta, Samodha Tran, Nguyen Khoi Tosun, Ayse Babar, Muhammad Ali Faculty of Computer and Information Sciences Sakarya University Turkey CREST - The Centre for Research on Engineering Software Technology The University of Adelaide Australia Faculty of Computer and Informatics Engineering Istanbul Technical University Turkey

With the increasing trend of Machine Learning (ML) enabled software applications, the paradigm of ML Operations (MLOps) has gained tremendous attention of researchers and practitioners. MLOps encompasses the practices and technologies for streamlining the resources and monitoring needs of operationalizing ML models. software development practitioners need access to the detailed and easily understandable knowledge of MLOps workflows, practices, challenges and solutions to effectively and efficiently support the adoption of MLOps. Whilst the academic and industry literature on the MLOps has been growing rapidly, there have been relatively a few attempts at systematically synthesizing and analyzing the vast amount of existing literature of MLOps for improving ease of access and understanding. We conducted a Multivocal Literature Review (MLR) of 150 relevant academic studies and 48 gray literature to provide a comprehensive body of knowledge on MLOps. Through this MLR, we identified the emerging MLOps practices, adoption challenges and solutions related to various areas, including development and operation of complex pipelines, managing production at scale, managing artifacts, and ensuring quality, security, governance, and ethical aspects. We also report the socio-technical aspect of MLOps relating to diverse roles involved and collaboration practices across them through the MLOps lifecycle. We assert that this MLR provides valuable insights to researchers and practitioners seeking to navigate the rapidly evolving landscape of MLOps. We also identify the open issues that need to be addressed in order to advance the current state-of-the-art of MLOps. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Detection Algorithm for Safety Helmet Wearing of Chemical Plant Personnel Based on Improved YOLOv5m

Detection Algorithm for Safety Helmet Wearing of Chemical Pl...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Qiangqiang Li Kun Yu Haoran Wang Qianjun Guan Shihong Gao Jiamin Jiang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an Jiangsu China

Wearing a safety helmet at the work site of a chemical plant can effectively prevent safety accidents caused by head injuries, so it is very important to detect whether employees wear safety helmets. In order to solve problems such as serious information loss, large number of parameters, and weak detection ability of small targets in complex scenes in the sampling process of helmet detection algorithm. In this paper, an improved lightweight safety helmet detection algorithm LP- YOLOv5m (Lightweight And High Precision YOLOv5m) based on YOLOv5m is designed. LP-YOLOv5m incorporates the Wise-IoU (Wiou) loss function to enhance model generalization. Additionally, we add a small object detection layer to improve the feature extraction ability for small objects. Furthermore, upsampling was performed using CARAFE. Finally, a lightweight module named C3-F was proposed, which replaced the Bottleneck in the C3 module with FasterNet Block, which could effectively reduce the network model size. Comparative laboratory findings demonstrate that our improved model achieves a mAP50 of 92.1%, surpassing YOLOv5m by 3 percentage points while reducing the model size by 23.3%. The LP-YOLOv5m model can be better applied to practical scene requirements, and can effectively solve practical problems.

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A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

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computers, Materials & Continua 2022年第7期72卷 73-92页

作者： Farman Ali Sadia Khan Arbab Waseem Abbas Babar Shah Tariq Hussain Dongho Song Shaker EI-Sappagh Jaiteg Singh Department of Software Sejong UniversitySeoul05006Korea Institute of Computer Sciences&Information Technology The University of AgriculturePeshawar25130Pakistan College of Technological Innovation Zayed UniversityDubai19282UAE Department of Software Korea Aerospace UniversitySeoul10540Korea Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Chitkara University Institute of Engineering&Technology Chitkara UniversityRajpura140401India

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the ***,the existing systems may not efficiently classify the human brain tumors with significantly higher *** addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the *** this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second ***,in this paper,we introduce a well-annotated dataset comprised of tumor and normal *** experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

关键词： Tumor localization MRI Image classification GoogLeNet YOLOv3

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Lightweight Protective Clothing Detection Algorithm Based on Ghost Convolution and GSConv Convolution

Lightweight Protective Clothing Detection Algorithm Based on...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Qianjun Guan Kun Yu Haoran Wang Qiangqiang Li Shihong Gao Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an Jiangsu China

GhostNet is proposed by Huawei Noah’s Ark Laboratory in CVPR2020, which can be used with the same accuracy, with less speed and less computation than SOTA method. This convolution is a low-cost operation used to decrease the amount of computation and related parameters. Ghost convolution can be used as an alternative to traditional convolution operations because they have the same accuracy. However, utilize significantly less computation and a much smaller parameter count. GSConv(Group-Shared Convolution) is a convolution operation based on group shared weights. This operation achieves efficient parallel convolution operations by grouping convolution kernels. In this way, the parameters used and calculation complexity of the model can be reduced to some extent. This makes the network more computationally efficient and smaller in model size while maintaining accuracy. The algorithm can detect the location of protective clothing quickly and efficiently, and its accuracy and efficiency have been verified. Because the algorithm adopts lightweight network structure, real-time detection can be realized on lowpower devices efficiently. The model architecture enhances the performance based on the YOLOv5 target detection algorithm by incorporating additional features. So that the model can maintain a high precision, which is crucial for the protective clothing detection algorithm.

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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Aspect Level Songs Rating Based Upon Reviews in English

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computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Dysarthric Speech Recognition: A Comparative Study

Dysarthric Speech Recognition: A Comparative Study

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Conference on Speech Technology and Human-computer Dialogue, SpeD

作者： Dhvani Shah Vanshika Lal Zihan Zhong Qianli Wang Seyed Reza Shahamiri Department of Electrical Computer and Software Engineering Faculty of Engineering The University of Auckland Auckland New Zealand

Automatic Speech Recognition (ASR) systems are designed to convert spoken words into written text. These systems have the potential to greatly benefit individuals with speech impairments like dysarthria, improving their overall quality of life. However, there are significant challenges that researchers must address in order to develop an effective ASR system for atypical speech, specifically dysarthric speech. Among these challenges are the limited availability of dysarthric speech data and the need for an accurate interpretation model. In this study, we explored the development of dysarthric ASR systems, starting from traditional approaches that were specifically designed to recognize dysarthric speech. We then delve into deep-learning-based ASR systems and modern ASR engines that utilize transformers. Additionally, we highlighted the notable dysarthric speech collections and identified the key factors that impede the performance of dysarthric ASR systems. The findings indicate that although researchers have achieved gradual improvements in performance, further research is required to achieve practical performance levels. Likewise, it is important to establish guidelines and standards to accurately measure and benchmark the performance of dysarthric ASR systems in order to ensure their usefulness and effectiveness in real-world applications.

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