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检索条件"机构=Dept. of Applied Computer Engineering"

共 1563 条记录，以下是21-30 订阅

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Machine Learning Algorithms for Predicting Chronic Diseases 3

Machine Learning Algorithms for Predicting Chronic Diseases

引用

3rd IEEE World Conference on applied Intelligence and Computing, AIC 2024

作者： Bulus, Furkan Saoud, Bilal Shayea, Ibraheem Syzdykova, Zuleikha Faculty of Electrical and Electronics Engineering Dept. Electronics & Communications Engineering Istanbul34469 Turkey University of Bouira Faculty of Sciences and Applied Sciences Lisea Laboratory Dept. Electrical Engineering Bouira10000 Algeria Astana It University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350384598

Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management. © 2024 IEEE.

关键词： Adversarial machine learning

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EEG-Based Brain-computer Interface for Robotic System Control Using Right-Left Hand Movements with Ensemble Model 7

EEG-Based Brain-Computer Interface for Robotic System Contro...

引用

7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Paneru, Biplov Thapa, Bipul Paneru, Bishwash Shah, Krishna Bikram Dept. of Eleectronics and Communication Engineering Nepal Engineering College Pokhara University Nepal Dept. of Computer Science & Engineering Kathmandu University Nepal Dept. of Applied Sciences and Chemical Engineering Tribhuvan University Institiute of Engineering Nepal Dept. of Computer Science and Engineering Pokhara University Nepal

ISBN: (纸本)9798331515683

In this work, machine learning models are utilized to interpret EEG data for the development of a brain-computer interface (BCI) system that controls a robotic car. Voluntary hand motor actions are employed for interface controls, with each hand corresponding to a different direction. EEG data is recorded using a 14-channel EMOTIV EPOC+ device during motor activities such as moving the car left, right, or stopping. The raw EEG signals are preprocessed using Fast Fourier Transform (FFT) before training a range of models, including Random Forest, XGBoost, CatBoost, Bi-Directional GRU, Transformer-based, and a Stacking Ensemble Technique (SET). The highest accuracy is achieved by SET (87%), followed by XGBoost (82%). These results demonstrate the robustness of ensemble approach SET for EEG-based tasks. Although further advancements in real-time processing are necessary, the system's ability to successfully control the robotic car underscores the potential of BCIs in robotic control and assistive systems development. © 2025 IEEE.

关键词： Brain-computer Interface (BCI) Electroencephalography (EEG) Ensemble Model Machine Learning (ML) Robotic System Control SHAP

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Experimental Investigation of Torque Ripple Minimization in Three-Phase PMSMs Under Open-Circuit Fault Conditions Utilizing an Adaptive Proportional-Integral-Resonant Controller 50

Experimental Investigation of Torque Ripple Minimization in ...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ghanayem, Haneen Alathamneh, Mohammad Nelms, R.M. Dept. of Electrical and Electronics Engineering Al-Huson University College Al-Balqa Applied University Irbid Jordan Dept. of Electrical and Computer Engineering Auburn University AuburnAL United States

ISBN: (纸本)9781665464543

This study investigates the reduction of torque ripple in Permanent Magnet Synchronous Motors (PMSMs) during open-circuit fault (OCF) conditions using an adaptive Proportional-Integral-Resonant (PIR) control approach. Torque ripples, commonly exacerbated by faults in motor operation, result in undesirable vibrations, noise, and mechanical stress, thereby impacting the motor’s performance and longevity. The proposed PIR control method integrates a resonant controller with a conventional PI-current controller to enhance torque ripple suppression during OCF operation. Experimental validation, conducted using a dSPACE DS1104 platform, focuses on reducing torque ripple during OCF events. The results show that the adaptive PIR control method significantly reduces torque ripples, thus promoting smoother motor operation, increased efficiency, and increased reliability under fault conditions. © 2024 IEEE.

关键词： Permanent magnets

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Enhanced Power Control of Three-Phase Grid-Connected Inverters Using Proportional-Integral-Resonant Controller Under Unbalanced Conditions with a Comparative Analysis between PI and PR Controllers 50

Enhanced Power Control of Three-Phase Grid-Connected Inverte...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Alathamneh, Mohammad Ghanayem, Haneen Yang, Xingyu Nelms, R.M. Dept. of Electrical and Electronics Engineering Al-Huson University College Al-Balqa Applied University Irbid Jordan Dept. of Electrical and Computer Engineering Auburn University AuburnAL United States

ISBN: (纸本)9781665464543

This paper introduces an advanced approach to achieve real and reactive power control in grid-connected three-phase inverters under unbalanced grid conditions. A novel Proportional-Integral-Resonant (PIR) control strategy is proposed, aiming to address two primary objectives: maintaining balanced grid currents and point of common coupling voltages (PCC), while simultaneously injecting the grid with the desired real and reactive power. Comparative analyses with conventional Proportional-Integral (PI) and Proportional-Resonant (PR) controllers are conducted. The PI controller exhibits a better transient response and lower steady-state error compared to the PR controller. However, under unbalanced grid conditions, the PI controller exhibits oscillations in the real and reactive power output. In contrast, the PR controller effectively manages unbalanced situations and maintains power control. Thus, the proposed PIR method combines the advantages of both PI and PR controllers: superior transient response and lower steady-state error of PI, and effective handling of unbalanced conditions and power control of PR. The simulation and Hardwre’s experimental validation confirm the efficacy of the proposed method. © 2024 IEEE.

关键词： Transient analysis

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Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks

Deep Reinforcement Learning Optimized Intelligent Resource A...

引用

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Mohsin, Muhammad Ahmed Rizwan, Hassan Jazib, Muhammad Iqbal, Muhammad Bilal, Muhammad Ashraf, Tabinda Khan, Muhammad Farhan Pan, Jen-Yi Stanford University Dept. of Electrical Engineering United States University of California Dept. of Electrical Engineering Riverside United States Pakistan Institute of Engineering and Applied Sciences Dept. of Electrical Engineering Pakistan National Chung Cheng University Dept. of Communication Engineering Taiwan University of California Dept. of Electrical Engineering Irvine United States School of Computer Science and Information Technology University College Cork Ireland

ISBN: (纸本)9798350368369

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate. © 2025 IEEE.

关键词： Resource allocation

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Software Defect Predictor and Classifier Tool Using Machine Learning Techniques

Software Defect Predictor and Classifier Tool Using Machine ...

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Shankar, Sahana P. Pushp, Divyansh Makadia, Nemi Dubey, Raghav Nayak, Anusha Shashikant Chaudhari, Shilpa Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Karnataka Bangalore India M S Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The Software testing is critical for ensuring the quality of software, but it can consume more than half of a software project's total expenses. This research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. To improve the efficiency of software testing, this report presents research on software defect prediction using a hybrid machine learning algorithm that combines supervised and unsupervised learning techniques. The study used publicly available datasets to train and evaluate various machine learning models, including an ensemble learning model. A comparative analysis was conducted among KNN, Random Forest, Adaboost, Multilayer Perceptron, CNN, and Gaussian Naïve Bayes on different datasets. Feature selection was performed using various methods, including Recurrent Feature Elimination (RFE), SelectKBest, and Correlation, implemented by a tool created to select any feature selection algorithm and compare the results for various combinations with the above mentioned classifiers. The results achieved by the model are showed in detail for each of the algorithms on the selected datasets. The main objectives of the research are to create a tool to apply various machine learning techniques to the datasets and determine the most effective ones, as well as to explore different feature selection methods for improving the accuracy of defect prediction. © 2023 IEEE.

关键词： Software testing

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

引用

20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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An app-based Spaced Repetition Learning Environment with User Generated Video Content

An app-based Spaced Repetition Learning Environment with Use...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Schimanke, Florian Mertens, Robert Kolker, Lars Mertens, Robert Hsw University of Applied Sciences Dept. of Computer Science and Engineering Hameln Germany Bhh University of Applied Sciencees Hamburg Germany

ISBN: (纸本)9798350395761

Spaced repetition is a learning task that helps users repeat learning items at optimally computed intervals. It is best suited for small learning units such as learning cards. In a typical learning scenario these learning cards do not stand by themselves, rather they are the last step in the students' work process, summarizing the students' interaction with the learning material. In this paper a framework is developed that allows learning material curators and students to create spaced repetition learning units with back-references to multimedia learning content, i.e., learning videos. With the advent of video learning nuggets, a wealth of content was created that can now be used in spaced repetition learning. The approach also allows for linking to specific parts of those learning videos to enable students to quickly review learning content specific to the learning unit currently worked on. © 2023 IEEE.

关键词： Students

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Work in Progress: Adapting eduScrum to the Online Teaching Format 9

Work in Progress: Adapting eduScrum to the Online Teaching F...

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9th IEEE engineering Education World Conference, EDUNINE 2025

作者： Andersson, Christina Kroisandt, Gerald Frankfurt University of Applied Sciences Dept. of Computer Science and Engineering Germany Htw Saar School of Engineering Germany

ISBN: (纸本)9798331542788

The main idea of agile processes is that they focus on short loops with fast feedback, instead of lengthy projects only evaluated in the end. The agile principles show a lot of promising features, which could be used also in the teaching-learning process, e.g. in the engagement of students. Therefore, the agile manifesto has been successfully mapped to the educational context, giving rise to the pedagogical approach eduScrum. Early applications of eduScrum took place in schools, but it has also been adapted to suit tuition at university level as well. In this paper, we discuss the application of eduScrum in a data science course at university level, both with respect to the face-to-face setting and the current online tuition. We especially focus on challenges and opportunities with the transition of eduScrum to the online environment. Findings in the first in-class usage of the online-taught eduScrum approach are presented. © 2025 IEEE.

关键词： agile education eduScrum online tuition

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