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Intelligent Power and Systems (ICIPS), International Conference on

作者： Chenyang Bing Tianyu Geng Siao Zhao Liwei Li CRRC Qingdao Sifang Locomotive&Rolling Stock Co. Ltd Qingdao China Faculty of Electronic and Information Engineering Liaoning University of Science and Technology Anshan China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

With the development of high-speed rail technology, the electrical system of the rolling stock, as a core component, is crucial for its normal operation. Once the electrical system fails, it may lead to safety accidents and property losses. Therefore, it is of great significance to study the identification and judgment of the false connection of the electrical circuit of the moving train set. The low resistance of the circuit is measured by injecting high current and voltammetry to establish the correlation between the resistance value and the false connection, so as to realize the accurate identification of the false connection abnormality. Aiming at the characteristics of vehicle electrical circuits with many complex structures, the magnetic retention relay array is used to realize multi-channel matrix testing, which improves the efficiency of circuit resistance testing. At the same time, the data is transmitted to the system platform through wireless communication. In the end, the development of "electrical connection reliability testing device" with the functions of high-current low-resistance testing, grounding resistance testing, automatic circuit testing, etc., to provide reliability assurance for the electrical system of the rolling stock.

关键词： Resistance Wireless communication Grounding Reliability engineering Rail transportation Safety Electrical resistance measurement Integrated circuit reliability Relays Magnetic circuits

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth Robertas Damaševičius David Taniar Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Faculty of Information Technolog Monash University Melbourne VIC Australia

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital.

关键词： Glaucoma Accuracy Hospitals Databases Retina Feature extraction Vectors Physiology Classification algorithms Optimization

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Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation with Heterogeneous Changes

arXiv

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arXiv 2024年

作者： Yazdani, Danial Branke, Juergen Khorshidi, Mohammad Sadegh Omidvar, Mohammad Nabi Li, Xiaodong Gandomi, Amir H. Yao, Xin Faculty of Engineering & Information Technology University of Technology Sydney Australia Warwick Business school University of Warwick United Kingdom School of Computing Leeds University Business School University of Leeds United Kingdom RMIT University Australia Faculty of Engineering & Information Technology University of Technology Sydney Australia University Research and Innovation Center [EKIK Obuda University Hungary Department of Computing and Decision Sciences Lingnan University China and CERCIA School of Computer Science University of Birmingham United Kingdom

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clustering over time in dynamic environments remains largely under-explored. This is partly due to a lack of dynamic datasets with diverse, controllable, and realistic dynamic characteristics, hindering systematic performance evaluations of clustering algorithms in various dynamic scenarios. This deficiency leads to a gap in our understanding and capability to effectively design algorithms for clustering in dynamic environments. To bridge this gap, this paper introduces the Dynamic Dataset Generator (DDG). DDG features multiple dynamic Gaussian components integrated with a range of heterogeneous, local, and global changes. These changes vary in spatial and temporal severity, patterns, and domain of influence, providing a comprehensive tool for simulating a wide range of dynamic scenarios. © 2024, CC BY-NC-SA.

关键词： Clustering algorithms

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Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

arXiv

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

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Comment Usefulness Classification on Youtube using Artificial Neural Networks

Comment Usefulness Classification on Youtube using Artificia...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Akkharawoot Takhom Pimmada Chirawat Prachya Boonkwan Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand School of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand

Social media represents a vast and constantly evolving data resource, utilized across various domains, including business. However, discerning valuable data for business purposes demands significant analysis and labor, leading to potential errors. To address this, we propose employing deep learning models for classifying useful data, focusing on a case study of quality comments on YouTube in the Thai language. Specifically, we experiment with four sequence-to-sequence models: Recurrent Neural (RNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU). The experimental results reveal that the Bi-LSTM model exhibits the most promising performance, achieving an accuracy of 94%. Furthermore, Bi-LSTM demonstrates remarkable precision of 0.94, recall of 0.95, and an F1-score of 0.95, underscoring its proficiency in the precise classification of quality comments.

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EVMAS: An energy-aware virtual machine allocation scheme in fog centers 2

EVMAS: An energy-aware virtual machine allocation scheme in ...

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2nd International Conference for Emerging technology, INCET 2021

作者： Naik, Madhusudan Barik, Lalbihari Kandpal, Meenakshi Patra, Sudhansu Shekhar Jena, Sudarson Barik, Rabindra K. Department of Computer Science Hakimpada Angul India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Sambalpur University SUIIT Department of Computer Science and Engineering Sambalpur India

ISBN: (纸本)9781728170299

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange's undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm. © 2021 IEEE.

关键词： Fog computing

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Beyond the hype: A TAM-based analysis of blockchain adoption drivers in construction industry

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Heliyon 2024年第19期10卷 e38522页

作者： Obidallah, Waeal J. Rashideh, Waleed Kamaruddeen, Ahmed Mohammed Alzahrani, Taher Alduraywish, Yousef Alsahli, Abdulaziz Alshuqayran, Nuha College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11673 Saudi Arabia Faculty of Engineering and Technology Department of Civil Engineering University of Botswana Gaborone Botswana

In complementing our existing knowledge of blockchain technology adoption in the construction industry, this research investigated how the technology Acceptance Model (TAM) applies to adopting blockchain technology among Saudi Arabian Construction companies. This study utilized cross-sectional data collection, causal research design, a quantitative research approach, and a simple random sampling technique to collect data from 248 Saudi Arabian construction companies. Partial least squares structural equation modelling (PLS-SEM) was utilized to analyze the data. The results showed that attitude toward adopting Blockchain (ATT) and perceived ease of use (PEU) are good predictors of behavioural intention to adopt Blockchain. The findings showed that when construction companies perceive the ease of use of Blockchain, they intend to adopt the technology. Attitude toward adopting Blockchain, directly and indirectly, influences behavioural intention to adopt the technology. However, top management support only directly leads to intention once the companies perceive the usefulness of blockchain technology. This paper complements the existing literature on adopting Blockchain in the Saudi Arabian construction industry. The study provides insight into the influence of top management support, attitude toward adoption, and perceived ease of use in the blockchain adoption process. © 2024 The Authors

关键词： Blockchain Construction industry PLS-SEM Saudi Arabia Smart contracts technology acceptance model

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Educational Data Mining: Inverted Classroom as a Teaching Strategy for Empowering Student Autonomy

Educational Data Mining: Inverted Classroom as a Teaching St...

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IEEE Colombian Communications Conference (COLCOM)

作者： Wilson Chango Chango Patricio Sailema Estefania jativa Hugo Sanchez Computer Science and Electronics Information Technology ESPOCH Riobamba Ecuador Department of Exact Sciences Universidad de las Fuerzas Armadas ESPE Sanlgolquı Ecuador Environmental engineering Faculty of Sciences ESPOCH Riobamba Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

This study sought to analyze the effectiveness of using the inverted classroom methodology as a teaching strategy to foster autonomy in high school students. The research was carried out with a non-probabilistic sample of 108 high school students, with the objective of obtaining an initial and exploratory knowledge about the topic addressed. The bibliographic review was the most relevant part of the project. The scientific methods applied were analysis, synthesis and experimentation. A questionnaire based on the Likert scale was used to collect data. To determine the internal consistency, Cronbach’s Alpha coefficient was used, obtaining a value of 0.89, which indicates high reliability and validity. To find the degree of statistical relationship between strategies, methodology and autonomy, the selection of the best characteristics was applied, which is based on the concept of information gain, observing a positive correlation between the strategy used and student autonomy. The results allowed concluding that the strategies are strongly correlated with autonomy, being particularly relevant in leadership, teamwork, self-management and learning orientation.

关键词： Education Online services Data mining Reliability Sampling methods Surveys Data collection Statistical analysis Educational programs Autonomous systems Land transportation Vehicles

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme.

关键词： Measurement Image segmentation Accuracy Image analysis Boosting Arteries Medical diagnostic imaging

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Digitalisation and Green Strategies: A Systematic Review of the Textile, Apparel and Fashion Industries

Circular Economy and Sustainability

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Circular Economy and Sustainability 2025年 1-53页

作者： Orisadare, Emmanuel Ayo Achukwu, Okechukwu Emmanuel Ogunyemi, Abiodun Afolayan Adedeji, Deborah Oluwayemisi Diyaolu, Idowu Jamiu Ugwu, Eunice Ifenyinwa Oluwatope, Ayodeji Oludola Bakare, Kudirat Oyebisi Awoyelu, Iyabo Olukemi Department of Information and Communication Technology University of Ilesa Osun State Ilesa Nigeria School of Computing Engineering and Technology Sir Ian Wood Building Robert Gordon University Garthdee Road Aberdeen AB10 7GJ United Kingdom School of Digital Technologies Tallinn University Narva mnt 29 Tallinn 10120 Estonia Department of Clothing and Textile Design Faculty of Sciences University of Ilesa Ilesa Nigeria Department of Family Nutrition and Consumer Sciences Obafemi Awolowo University Osun State Ile-Ife Nigeria Department of Home Economics and Hospitality Management Education Faculty of Vocational and Technical Education University of Nigeria Enugu State Nsukka Nigeria Department of Computer Science and Engineering Obafemi Awolowo University Osun State Ile-Ife Nigeria

The textile, apparel, and fashion (TAF) industries are vital to national economies, providing employment and fostering global economic development. On the other hand, TAF industries contribute to approximately 20% of global pollution. The rise of Industry 4.0 (I4.0) technologies—including the Internet of Things (IoT), Artificial Intelligence (AI), and robotics—aims to enhance production efficiency and resource management. However, this transition brings several challenges, such as potential disruptions from automation, concerns regarding energy efficiency, and escalating sustainability demands. In response, Industry 5.0 (I5.0) emphasises the integration of human-centric and sustainable practices, encouraging collaboration between humans and machines. While some existing studies highlight the potential of these technologies as enablers of the circular economy (CE), they often fall short of comprehensively linking these technologies to specific operational and strategic elements of CE. Furthermore, they do not sufficiently address how these digital tools fit within a broader sustainability framework in the context of CE principles. This review study aims to explore how digital transformation through I4.0 and I5.0 technologies can enhance circular economy practices in the TAF industries while alleviating the environmental and social challenges associated with industrial growth. The study examined 42 peer-reviewed studies from 2013 to 2023, employing a descriptive and theoretical literature review methodology to analyse the strategies, impacts, and challenges of digitalisation and green transition in the TAF industries. Findings reveal a significant disparity between firms in the Global North, which focus on technology advancement, and those in the Global South, facing implementation barriers. The study advocates for an approach that contextualises the integration of I4.0 and I5.0 technologies, ensuring that sustainable production practices align with regional needs. ©

关键词： Circular economy Digital transformation Green strategies Industry 4.0 Industry 5.0 Textile, apparel and fashion industries

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