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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Chutrtong, Jaruwan Boonman, Narumon Sirichokworrakit, Supatchalee Kularbphettong, Kunyanuth Saun Sunandha Rajabhat University Faculty of Science and Technology Dept. of Industrail Microbiology Bangkok Thailand Saun Sunandha Rajabhat University Faculty of Science and Technology Dept. of Biology Bangkok Thailand Saun Sunandha Rajabhat University Faculty of Science and Technology Dept. of Foodscience and Technology Bangkok Thailand Suan Sunandha Rajabhat University Computer Science Program Bangkok Thailand

ISBN: (纸本)9798350320282

The objectives of this research paper are study online learning behaviors, the success factors of online learning and perspectives on online learning during the Covid-19 situation. It is quasi experimental research. The population is 127 students of the Faculty of science and technology, Suan Sunandha Rajabhat university. The sample group enrolled in microbiology courses, semesters 1 and 2 of 2020 academic year. Research tool is a questionnaire. This paper has two main objectives: to analyze the behavior of students and to predict learning behaviors through the online system by using SVM, and KNN. Overall academic success on study is also at moderate levels. The experiment shows that the support vector machine (SVM) model has the best and most stable effect. The average recall rate, precision rate, and accuracy rate reached 80.81%, 84.75%, and 81.83%, respectively. © 2022 IEEE.

关键词： COVID-19

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Light extraction improvement via ITO p-electrodes for InGaN red micro-LEDs emitting at 640 nm

Optics Continuum

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Optics Continuum 2025年第5期4卷 1040-1050页

作者： Cesur Altinkaya Mohammed A Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia cesur.altinkaya@kaust.edu.sa Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia daisuke.iida@kaust.edu.sa kazuhiro.ohkawa@kaust.edu.sa

We present light extraction efficiency (LEE) improvement for InGaN red micro-light emitting diodes (micro-LEDs) of various sizes operating at low current densities. We compared the characteristics of micro-LEDs with indium tin oxide (ITO) transparent p-electrodes with conventional opaque metal p-electrodes. 50 µm × 50 µm micro-LEDs with ITO p-electrodes achieved a peak on-wafer external quantum efficiency (EQE) of 2.54% with an emission wavelength of 640 nm at a current density as low as 0.4 A/cm 2 . This represents a 1.18-fold improvement in peak EQE compared to devices with metal p-electrodes. Light ray tracing simulation confirmed that the ITO p-electrodes exhibit 1.18 times higher light escape than metal-based micro-LEDs, validating the role of enhanced light extraction. These findings provide valuable insights for advancing high-definition display and VR applications.

关键词： Atomic layer deposition Indium tin oxide Light extraction Opacity Quantum efficiency Ray tracing

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Image Recognition by YoloV4 for Vehicle Type Distinction in Side of Road

Image Recognition by YoloV4 for Vehicle Type Distinction in ...

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2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022

作者： You, Ting-Hao Wu, Yi-Chao Fanjiang, Yong-Yi Fu Jen Catholic University Computer Science and Information Engineering New Taipei City Taiwan National Taitung University Interdisciplinary Program of Green and Information Technology Taitung Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

At present, most of the domestic and foreign research on vehicle recognition focuses on real-time camera recognition in front of the vehicle while driving and real-time camera recognition over the road for overhead camera angles. For roads in mountainous areas or special terrain, due to the small hinterland on the left and right side of the road, it is not easy to set up overhead camera systems for the road. Therefore, this thesis proposes 'Yolo_v4-based road vehicle recognition' to solve the above problem. Unlike the overhead camera system, this side camera detachable temporary road vehicle recognition system only needs a very small road hinterland and can reduce the cost of space construction. The Yolo_V4 deep learning algorithm can accurately identify different vehicle types to ensure road safety and traffic monitoring. In this study, the accuracy of the vehicle recognition model for the side camera angle is 96%, which is comparable to the accuracy of the vehicle recognition model for the top camera angle. © 2022 IEEE.

关键词： Object detection

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An Angular Position-Based Tracking Algorithm for Geolocation in Smart Cities Applications

An Angular Position-Based Tracking Algorithm for Geolocation...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jamerson A. Muniz Ricardo De Castro Paula Edson T. De Camargo Federal Technology University of Paraná (UTFPR) Toledo Brazil Graduate Program in Computer Science - State University of Western Paraná (UNIOESTE) Cascavel Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

Tracking a vehicle in real time usually requires the transmission of the geographical coordinates of its route to the Internet. As part of a partnership to develop smart cities applications, a prototype vehicle tracking system has been developed and evaluated. The algorithm records the geolocation of the vehicle every 30 seconds, which can lead to unnecessary coordinates being sent and the communication network being overloaded. The aim of this work is to develop an algorithm that is able to determine the location of a moving object based on its angular position. The proposed algorithm uses an inertial measurement unit sensor to determine the change of direction and is able to correctly determine the route even if a coordinate is lost. We conducted an experimental study comparing the periodic approach with angular position-based approach on a route of a public bus in the city of Toledo/PR. The results show that the angle-based algorithm reduces the number of transmitted coordinates by up to 30%.

关键词： Measurement units Machine learning algorithms Smart cities Geology Prototypes Inertial navigation Systems engineering and theory Real-time systems Internet Reliability

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Exploring the Relatedness of Educational technology in Enhancing Study Performance 8

Exploring the Relatedness of Educational Technology in Enhan...

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8th International Conference on Information Management and technology, ICIMTech 2023

作者： Othman, Norfaridatul Akmaliah Alamsyah, Doni Purnama Siswanto, Boby Morika, Doni Wijaya, Billiam Christofer Adinda, Putri Giyan Universiti Teknikal Malaysia Melaka Department of Management Technology Melaka76100 Malaysia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

The research design is to explore about support from perceived relatedness and students satisfied on the individual performance of online learning. The research spotlight on the behavior users regarding to e-learning, where the users is experienced student on e-learning. There were 663 users whose data was collected through online questionnaires. Data from users is processed using the Structural Equation Model with SmartPLS. The study results show that there is a relationship from perceived relatedness to increasing the satisfaction and performance of students learning. Student achievement is important because it can mediate and support study performance. Relatedness supports well the behavior of users related to adaptation to e-learning. Empathy, friendliness, affiliation and caring from students are considered in the evaluation of perceived relatedness. Studies on education technology reinforce the sustainable of academic performance through online learning concepts. © 2023 IEEE.

关键词： Students

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Logical Rules Enhanced Multimodal Reasoning Based on Biomedical Knowledge Graph

Logical Rules Enhanced Multimodal Reasoning Based on Biomedi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Peifu Han Tao Song Jianmin Wang College of Computer Science and Technology China University of Petroleum Qingdao China The Interdisciplinary Graduate Program in Integrative Yonsei University Incheon Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical knowledge graph reasoning is capable of discovering hidden new knowledge based on existing biomedical data, providing ideas and references for new drug discovery, disease research, and so on. The entire graph topology structure formed by triplets and the attribute descriptions for each entity are crucial information for discovering new knowledge. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, current multimodal reasoning techniques often rely solely on vector space distance inferences based on triplets themselves, making it difficult to capture more complex relationships and dependencies between facts. This work integrated triplet entity relations, graph topology structures, and attribute descriptions for each entity to incorporate richer information, and utilized logical rules as external knowledge for relational reasoning in biomedical knowledge graphs. We have evaluated our approach on PharmKG.

关键词： Accuracy Biological system modeling Semantics Knowledge based systems Knowledge graphs Cognition Topology Drug discovery Bioinformatics Diseases

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Joint Modeling of Multivariate Longitudinal and Survival Outcomes with the R package INLAjoint

arXiv

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arXiv 2024年

作者： Rustand, Denis van Niekerk, Janet Krainski, Elias Teixeira Rue, Håvard Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

This paper introduces the R package INLAjoint, designed as a toolbox for fitting a diverse range of regression models addressing both longitudinal and survival outcomes. INLAjoint relies on the computational efficiency of the integrated nested Laplace approximations methodology, an efficient alternative to Markov chain Monte Carlo for Bayesian inference, ensuring both speed and accuracy in parameter estimation and uncertainty quantification. The package facilitates the construction of complex joint models by treating individual regression models as building blocks, which can be assembled to address specific research questions. Joint models are relevant in biomedical studies where the collection of longitudinal markers alongside censored survival times is common. They have gained significant interest in recent literature, demonstrating the ability to rectify biases present in separate modeling approaches such as informative censoring by a survival event or confusion bias due to population heterogeneity. We provide a comprehensive overview of the joint modeling framework embedded in INLAjoint with illustrative examples. Through these examples, we demonstrate the practical utility of INLAjoint in handling complex data scenarios encountered in biomedical research. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Non-Human Primate Monitoring System: Early Alert System Against Crop Raids for Farms in Nepal

Non-Human Primate Monitoring System: Early Alert System Agai...

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IEEE Global Humanitarian technology Conference (GHTC)

作者： Daniil Slutskiy Zach Jacques Nuno Bazarian Saurav Basnet Amir Poudel Prakash Manandhar School of Engineering Wentworth Institute of Technology Boston Massachusetts USA Environmental Science and Policy Johns Hopkins University Baltimore MD USA Program of Computer Science Wentworth Institute of Technology Boston Massachusetts USA

ISBN: (数字)9798350377347

ISBN: (纸本)9798350377354

The ongoing shift of populations from rural villages to urban centers in Nepal has led to farmland abandonment, drawing wildlife, particularly rhesus macaques, closer to human settlements. This proximity has resulted in frequent crop raids, causing substantial agricultural damage, and affecting the wellbeing of farmers. Traditional non-lethal deterrent methods, including noisemakers and netting, have proven ineffective. To address this issue, the described project aimed to develop a surveillance system using AI technology and IoT modules to detect macaque raids and alert farmers promptly. The system comprises a Raspberry Pi equipped with a camera and microphone, designed to detect motion and process live video feeds for monkey detection. Audio data is also recorded for future model training. Unlike solutions requiring extensive infrastructure, this system is costeffective and deployable in remote areas without specialized transceivers. Initial tests indicate the system's potential to protect crops and enhance farmers’ quality of life, fostering a harmonious coexistence between humans and wildlife in Nepal.

关键词： Training Surveillance Wildlife Crops Cameras Transceivers Data models Feeds Artificial intelligence Microphones

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