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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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Information Technology (ACIT)

作者： Raed Alazaidah Radwan Batyha Ghassan Samara Esraa Al Henawi Maram Y. Al-Safarini Ahmad A. Samhan Ali Elrashidi Mo’Ath Alluwaici Deana Al-Omari Department of Data Science and AI Faculty IT Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan

关键词： Measurement Machine learning Predictive models Diabetes Bayes methods Information technology Diseases

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A Systemic Big Data Framework for the Charging Pile Business 22

A Systemic Big Data Framework for the Charging Pile Business

引用

Proceedings of the 7th International Conference on Information Systems engineering

作者： Licheng Xu Shuai Xie Department of Software Engineering Universiti Malaya Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Malaysia

ISBN: (纸本)9781450397889

There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategies, which may elicit a negative response from EV users, cause colossal waste of resources and hinder the development of EVs. Based on the relevant charging pile data to be analyzed, this paper combines big data-related technologies to propose a big data framework for analyzing the charging pile data to solve the common charging service problems from a systemic perspective. The purpose of the framework is to provide decision-making reference information for charging pile operators, charging pile application operators and charging service marketers to improve the charging pile business more effectively. The framework is demonstrated by functional structure and technical structure.

关键词： Charging pile data Big Data framework Decision-making reference Charging business improvement

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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection

Enhancing Micro-Expression Recognition with Broadbent Attent...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Imran Ashraf Tanzila Kehkashan Muhammad Zaman Adnan Akhunzada Hafiz Khizer bin Talib Yanlong Cao Faculty of Computer Science University of Lahore Lahore Pakistan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis.

关键词： Emotion recognition Analytical models Accuracy Attention mechanisms Smart cities Feature extraction Robustness Convolutional neural networks Security Testing

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

Electrocoagulation-based AZO DYE (P4R) Removal Rate Predicti...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Akoulih, Meryem Tigani, Smail Byoud, Fouzia Rharib, Meryem El Saadane, Rachid Pierre, Samuel Chehri, Abdellah Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Computer and Software Engineering Polytechnique Montréal MontréalQC Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Azo dyes

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Dynamic Defense Method for Industrial Control Networks based on Shadow Honeynet

Dynamic Defense Method for Industrial Control Networks based...

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International Conference on computer and Communications (ICCC)

作者： Wei Yang XiaoLong Li Chao Xu Yu Yao Hai Yu XuanQi Jiao YiJia Shen JiaXuan Wang GeHao Liu Software College Northeastern University Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China Department of Cybersecurity Monitoring State Grid Liaoning Information and Communication Company

With the deep integration of Internet technology and industrial development, traditional closed industrial Internet faces an increasing array of dynamic attack vectors. To address these evolving threats, dynamic defense methods have become a crucial component of industrial control network security. This paper proposes a dynamic defense method based on a shadow honeynet, which involves a collaborative construction strategy with the industrial control network to share operational states and network structures. The method aims to collect targeted attack traffic and utilizes an efficient adaptive clustering technique to classify malicious traffic within the shadow honeynet. This enables real-time adjustments of defense strategies to detect new attacks. Furthermore, extensive experiments are conducted on a composite dataset comprising real-world data, SCADA data, and industrial control honeypot data. The results demonstrate significant performance improvements of the proposed method compared to existing approaches.

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The Importance of Performance Feedback in Virtual Reality (VR) Training: A Table Tennis Coach Perspective

The Importance of Performance Feedback in Virtual Reality (V...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Sunday, Kissinger Anvari, Soraya S. Li, Yiwei Sun, Junwei Wehbe, Rina R. Neyedli, Heather Batmaz, Anil Ufuk Machuca, Mayra Donaji Barrera Faculty of Computer Science VR Lab. Dalhousie University HalifaxNS Canada HCI4Good Dalhousie University HalifaxNS Canada University of Western Ontario TorontoON Canada School of Interactive Arts and Technology Simon Fraser University VancouverBC Canada HCI4Good Faculty of Computer Science Dalhousie University HalifaxNS Canada School Health & Human Performance Dalhousie University HalifaxNS Canada Department of Computer Science & Software Engineering Concordia University MontrealQC Canada Faculty of Computer Science Dalhousie University HalifaxNS Canada

ISBN: (纸本)9798400713958

Advances in Virtual Reality (VR) technology have redefined sports training, offering a new modality for athletes to prepare for competitions. This paper explores the coaching strategies used to train novice table tennis players in VR. Through interviews with the coach and participant feedback, we identified key strategies such as personalized feedback, table division, and a structured training regimen for skill development in VR. Challenges include limited facial awareness and the absence of virtual cheering, which impacted the coach’s ability to engage fully with participants. These findings offer valuable insights into coaching strategies in VR environments, contributing to developing more effective and engaging VR training systems. © 2025 Copyright held by the owner/author(s).

关键词： Virtualization

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Advanced Stock Market Prediction Using Unsupervised Federated Learning Techniques

Advanced Stock Market Prediction Using Unsupervised Federate...

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International CSI computer Conference, CSICC

作者： Mohsen Tajgardan Atena Shiranzaei Mahtab Jamali Reza Khoshkangini Mahdi Rabbani Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Computer Engineering Faculty of Industry and Mining (Khash) University of Sistan and Baluchestan Sistan and Baluchestan Iran Department of Computer Science and Media Technology Sustainable Digitalisation Research Centre Malmö University Malmö Sweden Canadian Institute for Cybersecurity University of New Brunswick NB Canada

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

In the realm of stock market prediction, traditional supervised learning approaches often struggle with the vast and diverse nature of financial data, coupled with privacy concerns. This paper explores a novel methodology that combines unsupervised learning techniques with federated learning system to enhance stock market prediction models. We present a comprehensive system where local models, trained using unsupervised methods, contribute to a global model through federated aggregation. By leveraging federated learning, our approach allows multiple financial institutions to collaboratively train models on their decentralized data while preserving data privacy. This approach addresses the challenges of data heterogeneity and communication efficiency, providing a robust and scalable solution for advanced stock market forecasting. Our experiments demonstrate that integrating unsupervised learning with federated learning not only improves predictive accuracy but also enhances the model's ability to identify emerging market trends and anomalies. Finally, we compare our distributed data model with other machine learning models that use local data.

关键词： Data privacy Federated learning Supervised learning Predictive models Parallel processing Data models Security Stock markets Forecasting Unsupervised learning

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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