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International Journal of Interactive Mobile Technologies 2025年第5期19卷 210-232页

作者： Hayadi, B. Herawan Hariguna, Taqwa Information Technology Education Faculty of Teacher Training and Education Bina Bangsa University Banten 42124 Indonesia Department of Information System and Magister Computer Sciences Universitas Amikom Purwokerto Purwokerto Utara 53127 Indonesia

This study aimed to compare the effectiveness of three predictive algorithms—logistic regression, random forest, and GBM—in predicting course completion using user engagement data from online learning platforms. By analyzing engagement metrics such as session duration, session frequency, and quiz scores, the study sought to identify the most effective model for forecasting course completion, providing insights into which aspects of student behavior were most predictive of success. Logistic regression emerged as the best overall performer, achieving the highest accuracy (52.13%) and F1-Score (56.17%), indicating its balanced approach to predicting course completion and non-completion. Random forest and gradient boosting machines (GBM) showed strengths in specific areas;random forest maintained a good balance between precision and recall, while GBM excelled in recall, identifying students likely to complete courses but with lower precision, leading to more false positives. The findings have practical implications for educational technology, particularly in designing personalized learning paths and targeted interventions to support at-risk students. The study also acknowledged limitations, including the dataset’s focus on engagement metrics without demographic context and the potential for model-specific biases. Future research should explore additional predictive features, larger datasets, and more advanced algorithms to enhance the robustness and applicability of predictive models in real-time educational settings. © 2025 by the authors of this article.

关键词： Prediction models

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Revolutionizing Digital Media Content using a Smart Drone and an Innovative ML Technique

Revolutionizing Digital Media Content using a Smart Drone an...

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Learning and technology Conference (L&T)

作者： Faris A. Almalki Rahaf A Almarzouki Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Management Information System Collage of Economics and Admissions King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

In the contemporary digital age, the fields of journalism and social media assume a pivotal role in disseminating information about current events and issues to the public. However, conventional techniques for gathering news and capturing images frequently encounter constraints and limitations. In recent years, technological advancements have transformed the creation and consumption of digital media content. This study presents an innovative solution that combines a smart drone with a reinforcement learning (RL) machine learning technique to enhance digital media content in a timely manner. The proposed framework has been subjected to validation and testing in a real-world scenario in Taif City through the transfer of rich digital media content. The results demonstrate promising outcomes, which support Saudi Arabia's efforts to enrich digital media content for tourism.

关键词： Technological innovation Social networking (online) Urban areas Reinforcement learning Media Journalism Information age Reliability Drones Testing

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp Pose Selection Skill

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Procedia computer Science 2025年 253卷 2776-2790页

作者： Sukhan Lee Byungwoo Jang Seokjong Hyeon Soojin Lee Jaesun Lee Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 Republic of Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of AI System Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Regional Industry Innovation Korea Institute of Industrial Technology Ulsan 44413 Republic of Korea

The edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate AI-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. The proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing.

关键词： Grasp Pose Selection Skill Edge Brain Framework Grasp Stability Object Manipulability Collision-Free Path Efficacy

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Trajectory Control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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A Machine Learning Model for Foreign Exchange Prediction 2nd

A Machine Learning Model for Foreign Exchange Prediction

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Ajibade, Temiloluwa Iyanuoluwa Jasser, Muhammed Basheer Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Adediran, Anthonia Oluwatosin Majeed, Anwar P. P. Abdul Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Department of Business Management Scholar School System Birmingham United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The rise of deep learning algorithms has sparked interest in the financial domain. The foreign exchange market (FOREX) is the globally leading financial market with millions of active stakeholders. Research in deep learning for forecasting price movements has shown its efficacy in financial markets. Notably, LSTM have empirically proven the best performance among other machine learning techniques. However, studies have shown the lack of deep learning hybrid comparisons to fortify empirical claims, especially in the FX market characterized for its high volatility and variance among currency pairs. This study develops a generalized LSTM model to forecast the major pairs and conducts a comparative analysis with a secondary hybrid deep learning model to assess and solidify the robustness of deep learning methods in forex forecasting. The empirical scenario simulates a classification problem employing cross-validation techniques to optimize the models’ generalization rigorously. Evaluation of the model utilized the accuracy, precision, recall, and f1-score metrics. Results indicated the performance of the models from best to worst were LSTM, SRNN and followed by CNN indicating the importance of testing deep learning approaches in different empirical setups. Conclusively, this study highlights the need challenges in forex forecasting and raises the need for more robust assessments on machine learning models forecasting capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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International Conference on Mechatronics (ICM)

作者： Gaku Takei Hidemine Obara Ryo Haneda Takahiro Nozaki Graduate School of Science and Technology Keio University Yokohama Japan Department of Physics Electrical and Computer Engineering Yokohama National University Yokohama Japan Department of System Design Engineering Keio University Yokohama Japan

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths.

关键词： Coils Low voltage Simulation Switches Multilevel inverters Motors Topology Sensors Voltage control Motion control

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In-House Developed Graphene-Based Leaf Wetness Sensor with Enhanced Stability

IEEE Sensors Letters

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IEEE Sensors Letters 2025年

作者： Patle, Kamlesh Yogi, Pooja Maru, Devkaran Agrawal, Yash Palaparthy, Vinay S. Moez, Kambiz DAIICT System Design Lab Department of Information and Communication Technology Gandhinagar382007 India University of Alberta iCAS Department of Electrical and Computer Engineering Faculty of Engineering EdmontonABT6G 2V4 Canada

Prolonged leaf wetness is a critical factor influencing the development and spread of plant diseases, particularly fungal pathogens, which thrive in moist environments. These pathogens negatively impact crop health, photosynthesis, nutrient absorption, and agricultural productivity. Accurately measuring leaf wetness duration (LWD) is essential for early disease detection and mitigation strategies. Leaf wetness sensors (LWS) are designed to detect wetness on leaf surfaces. Traditional LWS, fabricated using printed circuit boards (PCBs), have been extensively studied and are commercially available, such as the PHYTOS-31. However, flexible LWS are preferred due to their ability to conform to the natural shape of leaves, improving accuracy, better contact resistance, and durability under field conditions. However, these sensors exhibited limitations such as electrode oxidation and peeling, reducing stability and wetness sensitivity over time. To overcome these challenges, this study investigates replacing conventional metal-based interdigitated electrodes (IDEs) with graphene-based IDEs, leveraging graphene's superior electrical, mechanical, and thermal properties. The fabricated flexible graphene LWS has been evaluated for its sensitivity, response time, hysteresis, temperature response, and stability, which are about ≈26,000%, ≈ 35 seconds, ≈ 5%, ≈ 6.79%, and 5 months, respectively. Benchmarking against the commercially available PHYTOS-31 LWS demonstrated an absolute error of ±3%, confirming the reliability and accuracy. These results validate the potential of graphenebased flexible LWS for accurate and long-term monitoring of leaf wetness duration in agricultural and ecological applications. © 2017 IEEE.

关键词： Microbial electrodes

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A Switched Neural Process for Long-term Postprandial Glucose Trajectory Prediction based on Insulin Sensitivity Estimation

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Jing Shi, Dawei Cai, Deheng Liu, Wei Ji, Linong Shi, Ling Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing China Peking University People’s Hospital Department of Endocrine and Metabolism Beijing China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

Advanced sensing technology has enabled the real-time monitoring of blood glucose (BG), making it possible to explore the BG prediction problem based on continuous observations. In this work, we propose a novel switched neural process (NP) approach for long-term postprandial BG prediction, which has the probabilistic inference ability conditioned on preprandial BG measurements. Distinct representative NP models are constructed to capture the nonlinear BG dynamics based on a designed indicator. This indicator is developed to estimate insulin sensitivity (IS) at meal time by using physiological models, in which the effect of preprandial BG variation on IS estimation is evaluated. Besides, a robust model switching mechanism is designed to determine the appropriate NP model for prediction. The effectiveness of the proposed method is illustrated through the comparative results by using both the in silico data from the US Food and Drug Administration (FDA)-accepted UVA/Padova type 1 diabetes mellitus (T1DM) simulator and the clinical data from 47 T1DM subjects. The obtained results indicate that the proposed method outperforms existing methods especially for 3h and 4h prediction, which shows promising potential in predicting postprandial BG levels. © 1963-2012 IEEE.

关键词： Glucose

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