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International Conference on Computing and Networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

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Extending Cislunar Communication Network Reach Using Reconfigurable Intelligent Surfaces

arXiv

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arXiv 2024年

作者： Huroon, Aamer Mohamed Donmez, Baris Huang, Yu-Chih Kurt, Gunes Karabulut Wang, Li-Chun EECS International Graduate Program National Yang Ming Chiao Tung University Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Dept. of Electrical Engineering Polytechnique Montréal Montréal Canada

This study introduces a novel approach to enhance communication networks in the cislunar space by leveraging Reconfigurable Intelligent Surfaces (RIS). Using the ability of RIS to dynamically control electromagnetic waves, this paper tackles the challenges of signal attenuation, directivity, and divergence in cislunar missions, primarily caused by immense distances and that Earth-based station transmitters do not always face the Moon. A new optimization problem is formulated, whose objective is to maximize the received signal-to-noise ratio (SNR) for Earth-to-Moon communications. We derive a closed-form solution to the problem of determining the optimal RIS phase shift configuration based on the effective area of the RIS. Through extensive simulations, this paper demonstrates how optimal adjustments in RIS phase shifts can significantly enhance signal integrity, hinting at the substantial potential of RIS technology to revolutionize long-distance cislunar communication. Copyright © 2024, The Authors. All rights reserved.

关键词： Moon

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Merging UAV-Derived Metrics with Crop Physiology to Estimate Sunflower Yield

Merging UAV-Derived Metrics with Crop Physiology to Estimate...

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Frontiers of Information Technology (FIT)

作者： Usama Athar Muhammad Ali Zuhair Zafar Haris Khurshid Karsten Berns Muhammad Moazam Fraz School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Oilseeds Research Program National Agricultural Research Center (NARC) Islamabad Pakistan Department of Computer Science Rheinland-Pfälzische Technische Universität Kaiserslautern Germany

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need for precise sunflower seed yield predictions. This study proposes a combined approach that integrates 12 remotely sensed vegetation indices (VIs) with 3 key physiological traits - fresh weight, dry weight, and moisture factor - to capture the complex dynamics affecting sunflower crop. Predictive performance of VIs alone and in combination with physiological traits is evaluated using Root Mean Squared Error (RMSE). Our results show that incorporating these two sets of features, along with their temporal, spectral, and statistical characteristics, leads to consistent improvements in sunflower seed yield estimation that can reduce the root mean squared error to as low as 0.4075 ( $\text{kg} / \text{plot}$ ).

关键词： Vegetation mapping Moisture Autonomous aerial vehicles Physiology Seeds (agriculture) Sensors Yield estimation Reliability Vehicle dynamics Remote sensing

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NIR-Responsive Methotrexate-Modified Iron Selenide Nanorods for Synergistic Magnetic Hyperthermic, Photothermal, and Chemodynamic Therapy

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ACS Applied Materials and Interfaces 2024年第20期16卷 25622-25636页

作者： Thirumurugan, Senthilkumar Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Dhawan, Udesh Liu, Wai-Ching Wang, An-Ni Liu, Xinke Hsiao, Michael Tseng, Ching-Li Chung, Ren-Jei No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong Hong Kong New Territories999077 Hong Kong Scrona AG Grubenstrasse 9 Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei10608 Taiwan

Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) were designed for use in magnetic hyperthermic, photothermal, and chemodynamic therapy (MHT/PTT/CDT) for breast cancer. To illustrate their efficacy, FeSe2 NRs were modified with the chemotherapeutic agent methotrexate (MTX). MTX-modified FeSe2 (FeSe2-MTX) exhibited excellent controlled drug release properties. Fe2+ released from FeSe2 NRs induced the release of •OH from H2O2 via a Fenton/Fenton-like reaction, enhancing the efficacy of CDT. Under alternating magnetic field (AMF) stimulation and 808 nm laser irradiation, FeSe2-MTX exerted potent hyperthermic and photothermal effects by suppressing tumor growth in a breast cancer nude mouse model. In addition, FeSe2 NRs can be used for magnetic resonance imaging in vivo by incorporating their superparamagnetic characteristics into a single nanomaterial. Overall, we presented a novel technique for the precise delivery of functional nanosystems to tumors that can enhance the efficacy of breast cancer treatment. © 2024 The Authors. Published by American Chemical Society

关键词： Magnetic resonance imaging

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Wahyu Haris Kusuma Atmaja Yaya Heryadi Ilvico Sonata Lukas Wayan Suparta Computer Science Department Binus Graduate Program - Computer Science Bina Nusantara University West Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy.

关键词： Support vector machines Java Accuracy Web and internet services Artificial neural networks Predictive models Internet Broadband communication Optical fiber subscriber loops Robots

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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Networking, electrical engineering, computer Science, and Technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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The Pairing Analysis Improvement of Magnetized Structure in Electromagnetic Bulging Process in Case of Tube with Field Shaper

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Journal of Electromagnetic Analysis and Applications 2024年第4期16卷 43-67页

作者： Blaise Kevine Lenz Soronga Zhanqian Zhan Jing Liu Jonas Asamoah Mohamed Bangali Bangura Matala Makiese N’kodia Kambani Chrionie Md Jobayer Hossain Yongdong Chen Program Debugging and Design Department Guangdong Lyric Robot Automation Co. Ltd Huizhou China Electrical and Automation Department China Three Gorges University Yichang China Electronic Department Power Electronic Research Center University of Kent Giles Ln Canterbury UK Program Debugging Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Electrical and Computer Engineering Department South China University of Technology Guangzhou China World Standardization Certification and Testing Group (Shenzhen) Co. Ltd. Shenzhen China Electrical Engineering-Power System Mapon University Kindu Democratic Republic of Congo Department of Economy Managment School of Management Computer and Telecomunication Rabbat Marocco Materials Visual Calibration Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Program Design Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China

In the current practical science, the accuracy in the formability of metal alloys being the goal when using electromagnetic forming (EMF) technology, which is a high-speed processing technology that uses Lorentz forces to achieve plastic deformation of sheet metal;according to the previous analysis, the results have shown that in most cases, the Lorentz force acting on the workpiece (metal) is not uniform, there are uneven axial deformations of the metal plates which prevent the rapid advancement of today’s technology. In this article, we presented some advanced analyzes which will lead us to improve the technical solution for the problems of non-uniform axial deformations of the metals in the traditional tube electromagnetic forming technology (EMF). A field shaper is used as a practical forming tool to influence the magnetic field and magnetic pressure distribution, thereby improving the forming ability and result during the electromagnetic forming (EMF) process and we see that induced eddy current control is realized by changing the structural parameters of the magnetic field shaper;which improves the strength and controllability of the magnetic force that acts on the workpiece;thereby a greater radial magnetic pressure can be achieved with field shaper than the case without it;the field shaper regulates the electromagnetic force, the distribution of the magnetic pressure decreases, and the uniform force area of the tube increases which effectively enhances the uniform range of the pipe electromagnetic bulging and the electromagnetic induction coupling between the coil and the metallic workpiece is generally required to produce the Lorentz forces. Using COMSOL Multiphysics® simulation software helped us to accurately represent the real world, simulating multiple physical effects that happened in this model during the process.

关键词： Electromagnetic Forming Field Shaper Lorentz Force Magnetic Pressure Distribution

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia computer Science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

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Processes 2023年第8期11卷

作者： Sajid, Injila Gautam, Ayushi Sarwar, Adil Tariq, Mohd Liu, Hwa-Dong Ahmad, Shafiq Lin, Chang-Hua Sayed, Abdelaty Edrees Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions. © 2023 by the authors.

关键词： Efficiency

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