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International Seminar on Application for technology of information and Communication (ISemantic)

作者： Irfan Mahendra Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Nusa Mandiri University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives; business architecture; data & information architecture; information system architecture; infrastructure and network architecture; security architecture; human resources architecture; governance, legal, and compliance; and standards & regulations.

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Multi-factor based face validation attendance system with contactless design in training event 18

Multi-factor based face validation attendance system with co...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON 2021

作者： Pichetjamroen, Sasakorn Rattanalerdnusorn, Ekkachan Vorakulpipat, Chalee Pichetjamroen, Achara National Electronics and Computer Technology Center Information Security Research Team Pathumthani Thailand Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9780738111278

Various methods for face validation-based authentication systems have been applied in a number of access control applications. However, using only one biometric factor such as facial data may limit accuracy and use, and is not practical in a real environment. This paper presents the implementation of a face time attendance system with an additional factor, a QR code to improve accuracy. This two- factor authentication system was developed in the form of a kiosk with a contactless process, which emerged due to the COVID-19 pandemic. The experiment was conducted at a well- known training event in Thailand. The proposed two-factor system was evaluated in terms of accuracy and satisfaction. Additionally, it was compared to a traditional single-factor system using only face recognition. The results confirm that the proposed two-factor scheme is more effective and did not incorrectly identify any users. © 2021 IEEE.

关键词： Face recognition

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A Low-profile Broadband Dual-polarized Magneto-electric Dipole Antenna for 5G Applications

A Low-profile Broadband Dual-polarized Magneto-electric Dipo...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Yihuang Yang Botao Feng Xiao Ding Li Deng Wenzhe Gu Kwok L. Chung College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China School of Intelligent Manufacturing and Electrical Engineering Guangzhou Institute of Science and Technology Guangdong China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This study proposes a compact wideband dual-polarized (DP) magneto-electric (ME) dipole antenna with cross-differential feeding, designed for 5G/6G millimeter-wave (mm-Wave) systems. The antenna features a geometrically symmetric configuration, integrating radiating elements and feeding networks into a single metallic layer, achieving a low-profile structure. By leveraging ME coupling mechanisms, the antenna supports dual-orthogonal polarization operation, offering a broad impedance bandwidth of 39.4% (26.3-39.1 GHz) under differential port excitation. Experimental results demonstrate excellent radiation performance, including highly symmetric radiation patterns, cross-polarization suppression below -30 dB, a gain range of 2.6-6.2 dBi, and an axial ratio (AR) below 2 dB across the operational band. The unified feed-radiator co-design effectively mitigates mm-Wave challenges such as narrow bandwidth and integration constraints commonly found in conventional dual-polarized antennas, which makes the proposed design a promising solution for compact MIMO arrays in the 28/39 GHz 5G NR bands.

关键词： Wireless communication Dipole antennas Magnetoelectric effects Microstrip antennas Directive antennas Antenna feeds Broadband communication Broadband antennas Wideband Gain

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Profit maximization of a wind-integrated deregulated system using V2G techniques and TCSC placement

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Frontiers in Energy Research 2024年 12卷

作者： Dawn, Subhojit Ramakrishna, A. Ramesh, M. Das, Shreya Shree Rao, K. Dhananjay Cali, Umit Ustun, Taha Selim Electrical and Electronics Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India Computer Science and Engineering Department Spoorthy Engineering College Hyderabad India School of Electronics Engineering VIT-AP University Amaravati India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway School of Physics Engineering and Technology University of York York United Kingdom Koriyama Japan

Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid’s ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy’s volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC’s involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer effic

关键词： Thyristors

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Long-term Water Quality Prediction based on Intelligent Optimization and Seasonal-trend Decomposition

Long-term Water Quality Prediction based on Intelligent Opti...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ziqi Wang Xiangxi Wu Jing Bi Haitao Yuan Jia Zhang MengChu Zhou Faculty of Information Technology Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the applications of water quality prediction in the field of regional water environment management are increasing. It refers to predicting the elemental values of the water environment in the future based on past monitoring data, which is essential to realize the real-time evaluation of water quality and dynamic control of pollution sources. However, the water environment indicators are affected by various elements, which have a large volatility and non-linear characteristics. In addition, most of the existing water quality predictions focus on single-step predictive modeling of single elements of the water environment and lack multi-step predictive analysis of multifactor data of the water environment. In this paper, a novel long-term prediction model based on genetic simulated annealing-based particle swarm optimization (GSPSO) with seasonal-trend decomposition using LOESS (STL) is proposed and named GSPSO-STL-Autoformer (GS-Autoformer). It realizes the multi-factor and long-term prediction of water quality time series data. Firstly, the Autoformer’s hyperparameters are optimized by the GSPSO to improve its convergence speed. Secondly, the multi-factor features are decomposed by the STL to make the model more focused on learning feature information of each component. Finally, the long-term prediction is realized by the Autoformer. Comparative experiments with state-of-the-art peers show that the GS-Autoformer can effectively improve the accuracy of multi-factor and long-term predictions.

关键词： Accuracy Time series analysis Water quality Simulated annealing Predictive models Water pollution Data models Predictive analytics Particle swarm optimization Water resources

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Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling

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computers in Biology and Medicine 2025年第PT B期192卷 110250-110250页

作者： Folorunso, Sakinat O. Abdulwarith, Akinshipo Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Artificial Intelligence Systems Research Group Department of Computer Science Olabisi Onabanjo University Ago-Iwoye Nigeria Department of Oral and Maxillofacial Pathology and Biology Faculty of Dentistry University of Lagos Lagos Nigeria Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MIVA Open University Abuja Nigeria Department of Information Technology Durban University of Technology KZ Durban South Africa Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Nigeria

Background: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection capabilities, imbalanced datasets, and computational inefficiencies. Methods: This study proposes a novel diagnostic framework TR-ROS-BAT-ML that integrates transfer learning, random sampling, and a BAT algorithm-based optimization strategy with ensemble machine learning classifiers. A dataset comprising 1224 hematoxylin and eosin (H&E)-stained histological images (at 100x and 400x magnifications) of normal oral epithelium and Oral Squamous Cell Carcinoma (OSCC) was collected from 230 patients using a Leica ICC50 HD microscopy camera. Pre-trained deep learning models (NANSNetLarge, EfficientNetB7, EfficientNetV2L, EfficientNetV2S, EfficientNetV2M) were employed for feature extraction. To address class imbalance, random oversampling techniques were applied. The BAT algorithm, inspired by bat echolocation behavior, was used for feature selection and hyperparameter tuning. Optimized features were classified using ensemble methods, including XGBoost, AdaBoost, Extra Trees (ET), Histogram-Based Gradient Boosting (HBGC), and MultiLayer Perceptron (MLP). Results: The proposed approach achieved high diagnostic performance across multiple model combinations. The best performance was recorded with the optimized ET model using random oversampling, achieving a recall of 0.992, demonstrating its efficacy in detecting oral lesions. In contrast, the combination of EfficientNetV2S + ROS + MLP yielded the lowest accuracy at 50.8 %. These results confirm the robustness of the TR-ROS-BAT-ML framework in handling imbalanced datasets and optimizing classification performance. Conclusions: This study demonstrates the effectiveness of combining nature-inspired optimization, transfer learning, and ensemble machine learning for enhanced detecti

关键词： Adaptive boosting

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Optimizing Monkeypox Lesions Detection with a Lightweight Hybrid Model

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IEEE Access 2025年 13卷 103709-103732页

作者： Al-Gaashani, Mehdhar S. A. M. Alkanhel, Reem Hassan, Dina S. M. Ba Mahel, Abduljabbar S. Aziz, Ahmed Khayyat, Mashael M. Muthanna, Ammar University of Electronic Science and Technology of China School of Resources and Environment 1st Ring Rd East 2 Section Sichuan Chengdu610056 China Princess Nourah bint Abdulrahman University Department of Information Technology College of Computer and Information Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia University of Electronic Science and Technology of China School of Life Science and Technology Chengdu610054 China Benha university Department of computer science Faculty of computer and Artificial intelligence Egypt Central Asian University Engineering school Tashkent Uzbekistan University of Jeddah Department of Information Systems and Technology Collage of Computer Science and Engineering Jeddah Saudi Arabia Peoples'Friendship University of Russia RUDN University Department of Applied Probability and Informatics 6 Miklukho-Maklaya St Moscow117198 Russia

The increasing global prevalence of monkeypox (mpox) has necessitated the development of accurate, efficient, and interpretable diagnostic models for timely disease identification. Although deep learning has advanced medical image analysis, diagnosing diseases with complex lesions, such as monkeypox, remains challenging in resource-limited settings. Existing methods often rely on large architectures that demand high computational resources and lack interpretability. To address these issues, we propose a lightweight, interpretable architecture that truncates MobileNetV2 for efficient feature extraction, incorporates Ghost Modules to reduce redundancy, and integrates a SA-MobileViT block to capture both local and global dependencies in lesion images. Grad-CAM visualizations enhance interpretability, and Optuna is used for hyperparameter optimization. Our model achieved an accuracy of 98.72%, precision of 98.76%, recall of 98.72%, and F1-score of 98.72%, outperforming existing approaches, especially in handling irregular lesion patterns. The model's design is ideal for deployment in resource-constrained settings, offering a robust solution for real-world healthcare applications. Grad-CAM, saliency maps, and other visualization techniques help improve clinical trust by highlighting relevant regions in input images, emphasizing key features of the lesions. The proposed model is lightweight with only 630,204 parameters, making it 92.3× smaller than ResNet152V2 (58,152,004 parameters) and 28.7× smaller than DenseNet201 (18,100,612 parameters). It requires 321.61M FLOPs, 35.8× fewer than ResNet152V2 (11.51G FLOPs) and 13.4× fewer than DenseNet201 (4.29G FLOPs). With a size of just 72.92 MB, it is 8.14× smaller than ResNet152V2 (594.08 MB) and 4.57× smaller than DenseNet201 (333.47 MB), making it an efficient solution for mobile and edge deployment in resource-constrained environments. © 2013 IEEE.

关键词： Deep learning

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Developing Healthcare Chatbot: Migrant Workers' Study With Design Implication

Developing Healthcare Chatbot: Migrant Workers' Study With D...

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International Conference on Business and Industrial Research (ICBIR)

作者： Weerachaya Jarupreechachan Thattapon Surasak Department of Computer Engineering Faculty of Engineering at Kamphaeng Saen Kasetsart University Kamphaeng Saen Campus Nakhon Pathom Thailand Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Migrant workers are often those with low income who are subject to a high risk of virus transmission due to environmental and occupational exposures. The COVID-19 pandemic is reported to exacerbate the existing vulnerabilities of these marginalized groups. This study aims to shed light on how the potential of a conversational agent (Chatbot) could help improve migrant workers' health information seeking and health literacy. The exploration extended to the existing barriers to seeking health information strategies to obtain the needed information during COVID-19. This study conducted an exploratory qualitative study using semi-structured interviews with a scenario-based approach to study the experiences of the migrant Thai low-income workers in Taiwan. A total of 12 Thai stakeholders and migrant workers participated in the study. Migrant Thai workers developed distinct health information behaviors and improved various types of health competencies amid the COVID-19 pandemic. However, the critical barriers to health literacy remain. As a result, this work found that the Chatbot posted several potential features that could improve health literacy on Access. Therefore, Apprise and Apply depend on the understanding, which can be enhanced if the understanding rate increases. This study addresses the need for the development of the potential of a conversational agent. The findings of this study would guide the authority and technological interventions to respond to migrant workers' health information needs with appropriate tools.

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Enhanced 5G Device-to-Device (D2D) Security in Spectrum Sharing System

Enhanced 5G Device-to-Device (D2D) Security in Spectrum Shar...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Ayat M. Alrjoob Ahmad A. Ababnah Mamoun F. Al-Mistarihi Khalid A. Darabkh Electrical and Communication Engineering Department School of Electrical Engineering and Information Technology German-Jordanian University Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology P.o. Box 3030 Irbid Jordan Computer Engineering Department School of Engineering The University of Jordan Amman Jordan

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

This paper presents a six-node system comprising two transmitter-receiver pairs, an eavesdropper, and a relay. It evaluates the security of the primary pair while the secondary uses jamming to protect against eavesdropping. We provide a closed-form formula for intercept probability and demonstrate improved security through theoretical analysis and simulations, emphasizing the system’s importance for secure communication in urban emergency scenarios involving first responders. In urban networks, D2D communication is crucial, especially in emergencies involving first responders. Our system ensures secure communication between command centers and field units, highlighting its importance in urban emergency scenarios.

关键词： Scalability Half-duplex system Interference Software Device-to-device communication Telecommunications Security Resource management Relays Jamming

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Student Experiences in a Global Software engineering Course Taught Jointly in Germany, Indonesia, and Japan

Student Experiences in a Global Software Engineering Course ...

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Conference on Software engineering Education & Training (CSEE&T)

作者： Felix Waigner Andreas Rossler Michael Evan Santoso Patricia Brockmann D. Moritz Marutschke Tri Astoto Kurniawan Victor Kryssanov Computer Science Department Nuremberg Institute of Technology Nuremberg Germany Faculty of Engineering Brawijaya University Malang Indonesia Faculty of Economics and Business Kyoto University of Advanced Science Kyoto Japan College of Information Science and Engineering Ritsumeikan University Biwako-Kusatsu Japan

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

This poster paper explores the dynamics of a col-laborative global software engineering (GSE) course, focusing on project management strategies in distributed settings involving teams from Germany, Indonesia, and Japan. An overview of the course organization and a real-world project to develop prototype solutions for smart tourism is given. This study highlights students' experiences working in multi-national cross-site teams. Difficulties encountered during the project phases, solutions devised and lessons learned by the students are presented.

关键词： Cloud computing Project management Collaboration Prototypes Focusing Organizations Synchronization

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