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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy Research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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A Comparative Study of Energy Graph-Based Fault Detection and Isolation Techniques Applied to a Lignite Plant

SSRN

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SSRN 2023年

作者： Smith, JanHendrik van Schoor, George Uren, Kenneth Richard van Eldik, Martin Worlitz, Frank School of Electrical Electronic and Computer Engineering Faculty of Engineering North-West University South Africa Unit for Energy and Technology Systems Faculty of Engineering North-West University South Africa School of Mechanical Engineering Faculty of Engineering North-West University South Africa Faculty of Electrical Engineering and Computer Science University of Applied Sciences Zittau Görlitz Germany

Energy and exergy distribution in a system holds theoretical meaning across the physical domains of these systems and can therefore convey important information regarding its health. This energy information, however, requires careful analysis and representation to sufficiently convey its meaning. In this paper, a comparative study of three such analysis methods for the purpose of fault detection and fault isolation are discussed. These methods are designated as: 1) distance parameter method, 2) eigenvalue decomposition method and 3) residual method. The study utilises data from a physical lignite plant in Janschwalde, Germany, in combination with simulation data of specific faults in order to compare the sensitivity and robustness of the three methods. This is evaluated with respect to fault detection and isolation when noise and fluctuation in the system’s operating conditions are present at the full load operating point of the system. Each of these methods is an energy graph-based technique that relies on the energy information encapsulated within a node signature matrix, as defined by modern graph theory. The graphs capture the process topology and connectivity showing the causal relationship between the energy variables in a system, making it ideal for FDI applications. © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

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Ain Shams engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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Arabic stems are being built in a database utilising an automated process. 14

Arabic stems are being built in a database utilising an auto...

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14th International Conference on Intelligent systems: Theories and Applications, SITA 2023

作者： Laabidi, Abderrahman Yousfi, Abdellah Lrit, Said Iazzi Aouragh, Si Lhoussain Mohammed v University in Rabat Laboratory of Engineering Sciences Ices Team Ensias Rabat Morocco Mohammed v University in Rabat FSJES-Souissi Rabat Morocco Mohammed v University in Rabat Associated Unit to the CNRST-URAC29 Faculty of Sciences Rabat Morocco Mohammed v University in Robat High National School for Computer Science and Systems Analysis Morocco

ISBN: (纸本)9798350308211

This study explores the use of machine learning methods to improve morphological analysis of the Arabic language. In order to create a database of Arabic stems that academics can utilize, it examines the most well-known techniques and introduces a novel tool for finding Arabic stems from a corpus made up entirely of Arabic *** used a corpus of 30 million Arabic words to create this database. From this corpus of words, we were able to attain a precision of 94% using our stem detection method. The benchmarking strategy and these in-depth experimental findings offer a strong basis for assessing how well our novel strategy performs. © 2023 IEEE.

关键词： Database systems

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An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

arXiv

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arXiv 2025年

作者： Bocus, Mohammud J. Häkkinen, Juha Fontes, Hélder Drzewiecki, Marcin Qiu, Senhui Eder, Kerstin Piechocki, Robert School of Computer Science University of Bristol Bristol United Kingdom Circuits and Systems Research Unit University of Oulu Oulu Finland MpicoSys Embedded Pico Systems Sp. z o. o. Gdynia Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk Poland INESC TEC Porto Portugal Faculty of Engineering University of Porto Porto Portugal

his paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display. Extensive measurements were conducted to establish a detailed energy profile, which serves as a benchmark for evaluating the effectiveness of subsequent optimizations and future node iterations. To minimize the energy consumption, multiple optimizations were implemented at both the software and hardware levels, achieving a reduction of over 60% in total energy usage through software modifications alone. Further improvements were realized by optimizing the E-ink display driving waveform and implementing a very low-power mode for non-communication activities. Based on the measured data, three measurement-based energy consumption models were developed to characterize the energy behavior of the node under: (i) normal, unoptimized operation, (ii) low-power, software-optimized operation, and (iii) very low-power, hardware-optimized operation. These models, validated with new measurement data, achieved an accuracy exceeding 97%, confirming their reliability for predicting energy consumption in diverse configurations. © 2025, CC BY.

关键词： Digital storage

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6G Mobile Communications systems: Requirements, Specifications, Challenges, Applications, and Technologies 1

6G Mobile Communications Systems: Requirements, Specificatio...

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1st IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2021

作者： Yaklaf, Sliman Ka. A. Tarmissi, Khaled S. Shashoa, Nasar Aldian Ambark Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Umm Al-Qura University College of Computers and Information Systems Computer Science Department Makkah Saudi Arabia Libyan Academy for Postgraduate Studies Department of Electrical and Computer Eng. Tripoli Libya

ISBN: (纸本)9781665418560

The Fifth Generation (5G) Wireless networks were planned to be free from the limitations of previous generations. It has become necessary for society to move beyond 5G and design a new architecture that can incorporate the technological needs of both the individual and society. Therefore, if Sixth Generation (6G) mobile communications are to be developed, many aspects must be improved, the most evident being: latency, higher data rate, quality of service (QoS) and system capacity. When looking at the previously mentioned aspiration and requirements of future networks, the need for the development of 6G networks becomes clear. Nevertheless, many challenges which society must overcome for such networks to be implemented exist. The cost of solving such problems pales in comparison to the potential benefits of 6G networks. This paper discusses the challenges of implementing 6G networks. © 2021 IEEE.

关键词： Internet of Everything

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Academic challenges as part of capstone project resilience training

Academic challenges as part of capstone project resilience t...

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2021 IEEE AFRICON, AFRICON 2021

作者： Greeff, Jacob J. Nel, Andre Heymann, Reolyn Carroll, Johnson North-West University Optentia Research Unit School of Computer Science and Information Systems Faculty of Natural and Agricultural Sciences Vanderbijlpark South Africa University of Johannesburg Faculty of Engineering and the Built Environment Johannesburg South Africa

ISBN: (纸本)9781665419840

Capstone projects are the projects students undertake during undergraduate studies where they serve as the final gatekeeping process in engineering degrees to ensure students have the ability to leverage all of the knowledge they have gained in the solving of an open ended problem. As part of a larger project to improve the experience of students in a capstone project module, a challenge-based system has been implemented with inspiration taken from the Superbetter approach. This paper introduces the wider project context, presents the three design cycles the challenge system went through and presents results. In this paper it is shown that the implementation of the artefact was ultimately not a success, and the reasons for this are explored and recommendations for the implementation of similar systems in the future in other contexts are presented. © 2021 IEEE.

关键词： Students

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