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6th IEEE International Symposium on Telecommunication Technologies, ISTT 2022

作者： Mostofa, Sadia Abdullah, Mardina Islam, Mohammad Tariqul Bahari, Siti Aminah Ahmmed, Mohammad Badal University Kebangsaan Malaysia Space Science Center Institute of Climate Change Dept. of Electrical Electronic and Systems Engineering Selangor Bangi43000 Malaysia University Malaysia Pahang Faculty of Computing Pekan26620 Malaysia

ISBN: (数字)9781665489423

ISBN: (纸本)9781665489423

Ionosphere behavior is characterized by geographical and temporal changes. It was possible to determine the mobility of the ionosphere by observing how the total electron content (TEC) changed over time. The study on TEC variation was conducted in 2008 and 2009 over Malaysia, where latitude ranged from 1° N to 8° E and longitude ranged from 95° to 120° E, with each year reflecting the same degree of solar activity. The hourly mean vertical TEC (VTEC) is used to monitor the difference of the TEC under various geomagnetic factors such as disturbance storm time (Dst), planetary 3-hour range index (Kp), and solar flux F10.7 variations. This paper considers four cases of geomagnetic disturbances were considered 9 March, 27 March, 4 September 2008, and 22 July 2009. A percentage deviation was calculated to further analyze the geomagnetic disturbances in the equatorial region on quiet days. Results showed the TEC during geomagnetic disturbances was lower than the quiet day, with the percentage deviations between -20% and 10%. An increment of TEC was shown after approximately 24 hours, which is in the storm recovery phase. Also found were that GPS-TEC reaches its highest around post noon and its lowest in the small hours of the morning. © 2022 IEEE.

关键词： Ionosphere

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Proactive Network Traffic Prediction using Generative Adversarial Network

Proactive Network Traffic Prediction using Generative Advers...

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International Conference on Information Networking

作者： Gyurin Byun Van-Vi Vo Syed M. Raza Duc-Tai Le Huigyu Yang Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of Superintelligence Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Proactive traffic management in 5G networks is crucial for optimizing network efficiency, ensuring quality of service, adapting to dynamic conditions, and enhancing the user experience. In this paper, we propose a Generative Adversarial Network (GAN) architecture that leverages spatiotemporal features in network traffic data to predict future traffic. Our approach incorporates a Convolutional Long Short-Term Memory (ConvLSTM) model within the generator of the GAN, which collaborates with the discriminator, utilizing a Convolutional Neural Network (CNN) model, to provide essential feedback for training the generator. This integration ensures that our model not only predicts future traffic with improved accuracy but also adapts to dynamic network conditions. Based on experimental results using network traces, our model significantly outperforms the baseline, reducing prediction error by 12% while forecasting network traffic for the next 1 minute. These findings represent a significant advancement in proactive network management, particularly in addressing the challenges posed by real-time streaming and other latency-sensitive applications in 5G networks.

关键词： Training Adaptation models 5G mobile communication Telecommunication traffic Predictive models Generative adversarial networks Generators

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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A Comparative Study of Hough Transform and PCA for Bolt Orientation Detection

A Comparative Study of Hough Transform and PCA for Bolt Orie...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Antonio Gambale Sonya Coleman Dermot Kerr Philip Vance Emmett Kerr Cornelia Fermuller Yiannis Aloimonos School of Computing Engineering & Intelligent Systems Ulster University Londonderry Northern Ireland Dept. of Electronic & Mechanical Engineering Atlantic Technological University Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland USA

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In the fields of manufacturing and robotics, accurately determining the orientation of manufacturing components, such as bolts, is a critical yet challenging problem due to the limitations of existing detection methods. This study introduces a novel methodology for addressing this issue, leveraging traditional computer vision techniques, by proposing a streamlined approach that exploits the inherent geometric properties of bolts for orientation detection. Two methods are presented to ascertain the initial axis angle of the bolt: the Progressive Probabilistic Hough Transform (PPHT) and Principal Component Analysis (PCA). These methods are used in conjunction with a novel tip direction detection approach. The results of the study demonstrate consistent accuracy in angle determination, with PPHT and PCA both achieving angular deviations below ±0.5° in the simple dataset, and PCA showing enhanced robustness in the dataset degraded by shadows, with a maximum error under ±1.5°. This research not only reaffirms the viability of fundamental computer vision techniques in modern robotic applications but also sets a precedent for simple, generalisable, and reliable orientation detection solutions. These methods effectively bridge the gap between highly specialised machine learning systems, which often require tailored, complex models and extensive training data, and more universally applicable, straightforward approaches.

关键词： Computer vision Training data Transforms Machine learning Fasteners Probabilistic logic Robustness Manufacturing Informatics Principal component analysis

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Transient stability improvement of Croatian power system using FACTS

Transient stability improvement of Croatian power system usi...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Anica Šešok Ivica Pavić Dept. of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (纸本)9798350399677

In the last twenty years, the share of renewable energy sources (RES) in total electricity production has increased significantly, which has led to certain problems in power system operation. In systems with a significant share of RES, connected to the transmission network via power electronics devices, the inertial response of the system is reduced. Due to the reduced inertia of the power system, synchronous generators are more sensitive to their disturbed states and as a result, can easily lose stability. Loss of stability of one generator can lead to failure of other system elements, which may ultimately result in partial or even complete breakdown of the power system. Recently, a large increase in production from renewable sources has been recorded in the Croatian power system, therefore this paper investigates their impact on the system's transient stability and the possibilities of its improvement using Flexible AC Transmission systems (FACTS).

关键词： Renewable energy sources Flexible AC transmission systems Asia Production Power system stability Stability analysis Synchronous generators

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Artificial Intelligence in Medicine: A review of challenges in implementation and disparity

Artificial Intelligence in Medicine: A review of challenges ...

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2021 IEEE International Conference on Health, Instrumentation and Measurement, and Natural Sciences, InHeNce 2021

作者： Rathinam, Alwin Kumar Lee, Yunli Chek Ling, David Ngo Singh, Rajesh Selvaratnam, Lakshmi Pamidi, Narendra Bandar Sunway Malaysia Sunway University School of Engineering and Technology Dept. of Computing and Information Systems Bandar Sunway Malaysia Putrajaya Malaysia

ISBN: (纸本)9781665441810

Artificial Intelligence (AI) has garnered wide interest both in the field of computer science and in the eyes of the general public. AI research has proven to be exciting and has huge potential to augment and improve many fields. The question remains though, whether AI can be implemented successfully in the fields of medicine and healthcare. Medical practice is governed by clinical guidelines, protocols and standards in order to ensure positive outcomes for the patient. Various disciplines within medicine have increasingly adopted the use of technology such as databases and communication systems to enhance healthcare services. Currently, AI has limited adoptability in clinical use, possibly due to challenges in implementation and existence of disparity in communication between AI scientists and medical personnel. In this review, we explore the key challenges that AI researchers face in terms of persuading medical practitioners to utilize AI systems for clinical practice. Industry best practices such as the development of specific quality standards and enhancing interdisciplinarity could help to bridge the gap between computer scientists, medical practitioners and medical administrators. © 2021 IEEE.

关键词： Medical imaging

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Experimental Analysis of U-Net and Mask R-CNN for Segmentation of Synthetic Liquid Spray

Experimental Analysis of U-Net and Mask R-CNN for Segmentati...

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2022 IEEE International Conference on computing, ICOCO 2022

作者： Pathan, Refat Khan Lim, Wei Lun Lau, Sian Lun Ho, Chiung Ching Khare, Prashant Koneru, Rahul Babu Sunway University School of Engineering and Technology Dept. of Computing and Information Systems Bandar Sunway Malaysia Sunway University School of Engineering and Technology Dept. of Engineering Bandar Sunway Malaysia Sunway University Sunway DataXSight Research Cluster Bandar Sunway Malaysia University of Cincinnati Department of Aerospace Engineering CincinnatiOH45221 United States University of Maryland Department of Aerospace Engineering College ParkMD20742 United States

ISBN: (纸本)9781665489966

In digital image processing, segmentation is a process by which we can partition an image based on some variables to extract necessary elements. Unlike typical objects, it is complicated to segment dynamic objects from a synthetic fluid dataset where properties like position and shape change over time. Experiments on image segmentation over this dataset are conducted using U-Net (semantic segmentation) and Mask R-CNN (instance segmentation) to compare their results. The training dataset is generated from seven labelled images through data augmentation. Training on 1000 and validating on 200 images, Mask R-CNN achieved more epochs quickly. Around 1000 epochs for Mask R-CNN and 500 epochs for U-Net, both models reached a similar result in terms of F1 score and can segment the object in the new images. © 2022 IEEE.

关键词： Semantic Segmentation

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Instructors' Perspectives on Capstone Courses in computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in Computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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SAI-ChileanDiet: A multi-label food dataset with self-acquired images of the Chilean diet

SAI-ChileanDiet: A multi-label food dataset with self-acquir...

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Pattern Recognition systems (ICPRS), International Conference on

作者： Roberto Morales Bastián Muñoz Angela Martínez-Arroyo Eduardo Aguilar Dept. of Computing and Systems Engineering Universidad Católica del Norte Antofagasta Chile Escuela Nutrición y Dietética Universidad de Valparaíso Valparaíso Chile

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

The lack of data representative of the Chilean diet precludes providing accurate applications based on real foods consumed by Chilean society. Moreover, for the multi-label food recognition problem, there are few datasets available, which is mandatory for food analysis considering the great variability of dishes that can be made by combining different foods, which do not have a particular name and therefore cannot be treated as a single-label recognition problem. In this paper, we introduce a new challenging multi-label food dataset, SAI-ChileanDiet, focused on the Chilean diet. The dataset includes foods of popular dishes from Chilean cuisine and others included in the Chilean diets. SAI-ChileanDiet considers different sources of data for training and test sets. The training set comprises images from the web and the test set comprises self-acquired images. This feature of the dataset allows us to assess the representativeness of public images in real-world Chilean foods and encourages the generation of advanced methods capable of dealing with the distribution shift of these sets. We evaluate on the proposed SAI-ChileanDiet dataset the performance of several state-of-the-art deep networks and AI platforms for multi-label food recognition. The experiments demonstrate how challenging the dataset is and the importance of validating self-acquired data to be aware of the performance it could offer in real-life conditions.

关键词： Training Image recognition Accuracy Pattern recognition Artificial intelligence

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