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28th International Computer Science and engineering Conference, ICSEC 2024

作者： Wattanapornprom, Warin Innarong, Chatchaya Kerdpra, Isara Treesutrummas, Chisanupong Lertampaiporn, Supatcha Susutti, Wittawin King Mongkut's University of Technology Thonburi Faculty of Science Department of Mathematics Bangkok Thailand NSTDA King Mongkut's University of Technology Thonburi Biochemical Engineering and Systems Biology Research Group Bangkok Thailand

ISBN: (纸本)9798350366860

In today's digital era, fiction has become immensely popular and accessible through various channels, particularly online platforms where some content requires payment for access. Readers, therefore, seek fictions that align with their specific genre preferences to make informed reading choices. To assist in this process, we have developed a sophisticated genre-based fiction visualization testimonial system. This system leverages TF-IDF natural language processing algorithms for extracting key features and employs machine learning, specifically Naive Bayes algorithms, for accurate text classification. Implemented as a user-friendly sample website, the system visually represents fiction genres through pie charts and line charts. Our model achieved up to 91% accuracy in predicting the primary genre of fiction, thereby providing valuable support for readers in selecting fictions that best meet their preferences. © 2024 IEEE.

关键词： Visualization

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Air Pollutant Prediction Using Shallow Architecture Machine Learning Algorithms 8th

Air Pollutant Prediction Using Shallow Architecture Machine ...

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8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Olaifa, Moses Malele, Vusumuzi Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa Unit of Data Science and Computing Vaal Campus North-West University Vanderbijlpark South Africa

ISBN: (纸本)9789819786947

Over the past few decades, mortality rates associated with air quality pollution have risen in numerous countries around the globe. This pollution stems from different factors such as meteorological conditions, human activities from urban industrialization, vehicular emission, and so on. Air pollution constituents such as NO2, CO, SO, and coarse and fine particulate matter for different locations have been analyzed to learn both correlation and inter-dependency for long and short terms. However, many of these studies overlook datasets collected using low-cost tools and equipment. Most of the time, datasets of this nature are marred with inconsistencies and gaps at irregular intervals. This may render the air pollutant data unsuitable. In order to test the suitability of machine learning algorithms on such dataset, this study conducts a time-series forecast analysis using the prophet algorithm. Two additional models including linear regressor and random forest regressor are trained and tested on the same dataset and a comparative analysis of the three models is performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Investigating carbide characteristics effect on multiscale mechanical behavior of AISI 420 steel using crystal plasticity simulation

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Journal of Materials Research and technology 2025年 36卷 10487-10506页

作者： Kegu Lu Yadong Zhou Soheil Solhjoo Maysam Naghinejad Redmer van Tijum Yutao Pei Jan Post Advanced Production Engineering Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 3 9747 AG the Netherlands Engineering Systems and Design Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 9747 AG the Netherlands

Understanding the impact of precipitates on the mechanical behavior of material is crucial for effective material design and application. This research focuses on AISI 420 stainless steel, characterized by a ferritic matrix with dispersed carbides in its annealed state. This study employs a multiphase crystal plasticity (CP) model to systematically investigate the influence of variations in carbide characteristics, including their fraction at grain boundaries (GBs), number, average size, and volume fraction, on multiscale mechanical behavior. The workflow includes generating representative volume elements (RVEs) from the microstructure of the as-received AISI 420 steel, calibrating constitutive parameters within the CP model, and validating the CP-RVE model against experimental textures and mechanical properties. A novel method is introduced to capture anisotropic behavior by accurately replicating the experimental approach. Subsequently, diverse RVEs are generated by modifying carbide characteristics to evaluate their impact on stress-strain curves, anisotropic behavior, and micromechanical interactions, while preserving the characteristics of ferrites. The results are compared with experiments and theoretical insights. The findings reveal that an increase in either the fraction of carbides at GBs or the carbide volume fraction enhances the steel's strength. Empirical equations are derived to predict these effects on mechanical properties efficiently. Moreover, increasing the carbide volume fraction leads to more inhomogeneous strain and stress distributions in both carbides and ferrites, while reducing anisotropy (Lankford coefficient). This study highlights the potential of the CP-RVE model as a tool for exploring macroscale behavior and studying the influence of microstructural features.

关键词： Carbide AISI 420 steel Crystal plasticity Multiscale mechanical behavior

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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XgCPred: Cell type classification using XGBoost-CNN integration and exploiting gene expression imaging in single-cell RNAseq data

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Computers in Biology and Medicine 2024年 181卷 109066-109066页

作者： Abu-Doleh, Anas Al Fahoum, Amjed Hijjawi Faculty for Engineering Technology Biomedical Systems and Informatics Engineering Department Yarmouk University Irbid21163 Jordan

Background: The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research by enabling the exploration of gene expression at an individual cell level. This advancement sheds light on how cells differentiate and evolve over time. Effectively classification cell types within scRNA-seq datasets are essential for understanding the intricate cell compositions within tissues and elucidating the origins of various diseases. Challenges persist in the field, emphasizing the need for precise categorization across diverse datasets, addressing aggregated cell data, and managing the complexity of high-dimensional data spaces. Methodology: XgCPred is a novel approach combining XGBoost with convolutional neural networks (CNNs) to provide cell type classification with better accuracy in single-cell RNA-seq data. This combo reveals how well CNNs can detect spatial hierarchy in gene expression images and how XGBoost performs with large volumes of data. XgCPred utilizes an imaging representation of gene expression that is based on the hierarchical organization of genes found in the KEGG BRITE database. Results: Rigorous testing of XgCPred across multiple scRNA-seq datasets, each presenting unique challenges such as varying cell counts, gene expression diversity, and cellular heterogeneity, has demonstrated its superiority compared to earlier methods. The algorithm shows remarkable accuracy and precision in cell type annotation, achieving near-perfect classification scores in some cases. These results underscore its capability to effectively manage data variability. Conclusions: XgCPred distinguishes itself through its dependable and accurate cell type classification across a range of scRNA-seq datasets. Its effectiveness stems from sophisticated data handling and its ability to adapt to the complexities inherent in scRNA-seq data. XgCPred delivers reliable cell annotations essential for further biological analysis and research, marking a significant ad

关键词： scRNA-seq classification Gene expression profiling Machine learning in genomics Automated cell type annotation And high-dimensional data analysis

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A Comparison of Various Deep Learning Methods for Household Load Forecasting

A Comparison of Various Deep Learning Methods for Household ...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Deivamani, Karthikeyan Norouzi, Farshid Shekhar, Aditya Bauer, Pavol Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of DC Systems Energy Conversion and Storage Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350396782

Forecasting energy consumption is vital for smart grid operations to manage demand, plan loads, and optimize grid operations. This work aims at reviewing and experimentally evaluating six univariate deep learning architectures to forecast load for a single household using a real-world dataset. Multi-layer perceptron (MLP), Convolutional neural network (CNN) and recurrent neural networks (Simple RNN, Long Short Term Memory (LSTM)) were the neural network methods that were analysed along with robust LSTM architectures like Bidirectional LSTM and CNN-LSTM Hybrid. All the models were tuned using Bayesian optimization and evaluated using root mean squared error (RMSE) as the metric. In addition to neural network models, Seasonal ARIMA (SARIMA) a statistical model is also presented to observe the performance. As a result, Bi-directional LSTM was observed to have achieved the best performance with the smallest value of RMSE;however, it was also observed that differences in performances between other neural network models were quite low, especially between the RNN architectures. Additionally, although machine learning methods performed better than SARIMA the former model was more complex and computationally intensive. © 2023 IEEE.

关键词： deep learning electric load forecasting smart grid time-series forecasting univariate

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Generation Expansion Planning for the Kalimantan Interconnected System Considering the Renewable Energy Mix and Carbon Dioxide Emissions Targets 16

Generation Expansion Planning for the Kalimantan Interconnec...

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16th International Conference on Information technology and Electrical engineering, ICITEE 2024

作者： Alvin, Muhammad Putranto, Lesnanto Multa Sugiyantoro, Bambang Syukral Siregar, Ahmad Edy Universitas Gadjah Mada Faculty of Engineering Department of Electrical Engineering and Information Technology Grafika 2 Yogyakarta55281 Indonesia UIP3B Kalimantan Planning Analysis and Evaluation of Operational Systems Kalimantan Indonesia

ISBN: (纸本)9798350375817

The increasing annual demand for electricity requires an optimal generation expansion plan (GEP) while maintaining reliability levels, including renewable energy (RE) targets and carbon dioxide emission reductions to support global and national efforts against climate change. The optimization of GEP for the Kalimantan Interconnected System for the period 2026-2050 is carried out using OSeMOSYS software and the Linear Programming method. This optimization considers three scenarios: Business as Usual (BAU), RE Optimization, and Emission Optimization, which are analyzed in terms of installed capacity, renewable energy mix, carbon dioxide emissions, and levelized cost of electricity (LCOE). The Kalimantan Interconnected System in each scenario is dominated by coal and hydro power plants due to their economic advantages and abundant resources. The BAU scenario has the lowest LCOE of ¢8.20/kWh, but it does not support RE targets and results in high carbon dioxide emissions. The RE Optimization scenario meets the RE target with a LCOE of ¢8.49/kWh. Meanwhile, the Emission Optimization scenario meets both the RE target and emission reduction with the highest LCOE of ¢8.66/kWh, indicating that this scenario is the only one that can meet both targets, albeit with a high LCOE. © 2024 IEEE.

关键词： Low emission

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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