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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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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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APlus: An Intelligent Lecture Delivery System using Artificial Intelligence Learning Analytics 3

APlus: An Intelligent Lecture Delivery System using Artifici...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Kodagoda, Charith Madampe, Vihanga Ekanayake, Ridmi De Silva, Nirma Wijesundara, Malitha Atapattu, Pramadhi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka Ltd Colombo Sri Lanka

ISBN: (纸本)9781665408622

As technology advances, new ways of teaching and learning can be discovered. Online learning platforms are able to provide easy, informative, and effective methods of delivering information since people are getting more accustomed to the use of e-learning. With the help of Artificial Intelligence, these methods can be refined to understand the users on a deeper level and provide services that are helpful and are more tailored to each user. The purpose of this project is to design an e-learning platform equipped for both educators and students. The system will be able to allow users to record/upload lecture sessions and other related study materials to the service which in return, with the use of Artificial Intelligence, try to understand the context of each lesson, segment videos, generate questions and deliver the materials to the Learner. The system will use various inputs from the Learner including video observation time, watched duration, types of videos watched, answers for questions, etc. to build a comprehensive profile of Learners. The system will use the knowledge gathered to provide services such as recommendations to the Learner, track the progress of the Learner as well as provide useful statistics of lecture materials to the Content Providers. © 2021 IEEE.

关键词： Learning systems

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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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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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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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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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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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Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations

arXiv

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arXiv 2023年

作者： Duan, Qiqi Shao, Chang Zhou, Guochen Yang, Haobin Zhao, Qi Shi, Yuhui Harbin Institute of Technology Heilongjiang Harbin150001 China Faculty of Engineering and Information Technology University of Technology Sydney 15 Broadway Ultimo SydneyNSW2007 Australia Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions. First, we reveal empirical reasons of when decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous-game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition, there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the recent multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional test functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores. Copyright © 2023, The Authors. All rights reserved.

关键词： Nash equilibrium

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Domain-adaptive Person Re-identification without Cross-camera Paired Samples

arXiv

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arXiv 2023年

作者： Li, Huafeng Mao, Yanmei Zhang, Yafei Qi, Guanqiu Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Information Systems Department State University of New York at Buffalo State BuffaloNY14222 United States

Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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