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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Kanaan, Anas Ghassan Pathmanathan, P. Ravindran Alawi, Nahg Abdulmajid Hatamleh, Amer Jusoh, Julaily Aida Aledinat, Lowai Saleh Aseh, Khairi Bamansoor, Samer UniSZA Faculty of Informatics and Computing Malaysia Petra University Faculty of Managment and Financial Sciences Business and E-Commerce Depaetment Amman Jordan International Institute of Applied Science Swiss School of Management Netherlands Geomatika University College Malaysia University of Aden Yemen Irbid National University Faculty of Financial and Business Science Head of Management Information System Department Irbid Jordan UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer and Information Technology MEDIU Malaysia University of Aden Aden Yemen

ISBN: (纸本)9781665432221

In Malaysia, an e-commerce company is still considered a new middleman. As well as the development of information and communication technology strongly affects business companies at the present time in terms of management, business strategies, production, sales and marketing, and other aspects that underpin business profitability in general. E-commerce, known as e-commerce, refers to a general term for a commercial activity using modern technologies, or a commercial transaction that includes the exchange of data via electronic frameworks, especially the Internet, as well as many modern systems used in personal computers, smart phones, etc. It is not just limited to doing business between companies but also related to online services between governments and individuals. Various investigations related to e-commerce have identified obstacles related to information technology (IT) organization, internet use, financial situation, government and administrative activities as important determinants of doing business online. Information and Communication technology (ICT) has revolutionized businesses around the world. In this paper, e-commerce practices in Malaysia are analyzed by their characteristics, drawbacks, openings and risks. This paper provides a general review of the current issues considered by e-commerce organizations today. It also suggests the challenges and problems facing e-commerce companies at the present time and how to solve them using e-commerce concepts. © 2021 IEEE.

关键词： Smartphones

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Mean-field Coherent Ising Machines with artificial Zeeman terms

arXiv

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arXiv 2023年

作者： Don, Sudeera Hasaranga Gunathilaka Mastiyage Inui, Yoshitaka Kako, Satoshi Yamamoto, Yoshihisa Aonishi, Toru School of Computing Tokyo Institute of Technology Tokyo Japan Physics and Informatics Laboratories NTT Research Inc. 940 Stewart Dr SunnyvaleCA94085 United States Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa Japan E. L. Ginzton Laboratory Stanford University StanfordCA94305 United States

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solves combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. In CIMs, a problem arises when attempting to realize the Zeeman term because of the mismatch in size between interaction and Zeeman terms due to the variable amplitude of the optical parametric oscillator pulses corresponding to spins. There have been three approaches proposed so far to address this problem for CIM, including the absolute mean amplitude method, the auxiliary spin method, and the chaotic amplitude control (CAC) method. This paper focuses on the efficient implementation of Zeeman terms within the mean-field CIM model, which is a physics-inspired heuristic solver without quantum noise. With the mean-field model, computation is easier than with more physically accurate models, which makes it suitable for implementation in field programmable gate arrays (FPGAs) and large-scale simulations. Firstly, we examined the performance of the mean-field CIM model for realizing the Zeeman term with the CAC method, as well as their performance when compared to a more physically accurate model. Next, we compared the CAC method to other Zeeman term realization techniques on the mean-field model and a more physically accurate model. In both models, the CAC method outperformed the other methods while retaining similar performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Ground state

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Visual-based Positioning and Pose Estimation

arXiv

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arXiv 2022年

作者： Phon-Amnuaisuk, Somnuk Murata, Ken T. Kovavisaruch, La-Or Lim, Tiong-Hoo Pavarangkoon, Praphan Mizuhara, Takamichi Media Informatics Special Interest Group Universiti Teknologi Brunei Brunei Brunei School of Computing and Information Technology Universiti Teknologi Brunei Brunei National Institute of Information and Communications Technology Tokyo Japan Thailand CLEALINKTECHNOLOGY Co. Ltd. Kyoto Japan

Recent advances in deep learning and computer vision offer an excellent opportunity to investigate high-level visual analysis tasks such as human localization and human pose estimation. Although the performance of human localization and human pose estimation have significantly improved in recent reports, they are not perfect and erroneous localization and pose estimation can be expected among video frames. Studies on the integration of these techniques into a generic pipeline that is robust to noise introduced from those errors are still lacking. This paper fills the missing study. We explored and developed two working pipelines that suited the visual-based positioning and pose estimation tasks. Analyses of the proposed pipelines were conducted on a badminton game. We showed that the concept of tracking by detection could work well, and errors in position and pose could be effectively handled by a linear interpolation technique using information from nearby frames. The results showed that the Visual-based Positioning and Pose Estimation could deliver position and pose estimations with good spatial and temporal resolutions. © 2022, CC BY.

关键词： Object detection

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A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works

A Recent Research on Malware Detection Using Machine Learnin...

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7th International Conference on Advances in Visual informatics, IVIC 2021

作者： Gorment, Nor Zakiah Selamat, Ali Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor43000 Malaysia Faculty of Informatics and Management Universiti Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9783030902346

Each year, malware issues remain one of the cybersecurity concerns since malware’s complexity is constantly changing as the innovation rapidly grows. As a result, malware attacks have affected everyday life from various mediums and ways. Therefore, a machine learning algorithm is one of the essential solutions in the security of computer systems to detect malware regarding the ability of machine learning algorithms to keep up with the evolution of malware. This paper is devoted to reviewing the most up-to-date research works from 2017 to 2021 on malware detection where machine learning algorithm including K-Means, Decision Tree, Meta-Heuristic, Naïve Bayes, Neuro-fuzzy, Bayesian, Gaussian, Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and n-Grams was discovered using a systematic literature review. This paper aims at the following: (1) it describes each machine learning algorithm, (2) for each algorithm;it shows the performance of malware detection, and (3) we present the challenges and limitations of the algorithm during research processes. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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Identifying Gamified Teaching Elements in Computer Science Course

Identifying Gamified Teaching Elements in Computer Science C...

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International Conference on Platform technology and Service (PlatCon)

作者： Hasnah Tanalol Hazmi Hashim Punitha Turumogan Noorsidi Aizuddin Mat Noor Aslina Baharum Farhana Diana Deris Preparatory Center for Science & Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computing and Informatics University Malaysia Sabah Kota Kinabalu Malaysia Department of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Penang Malaysia Real Estate Department Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Computing and Information System School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Universiti Malaysia Sabah Johor Bahru Johor Malaysia

Traditional education methods, having said, lack motivation and self-regulation. The 5G and 6G technology have improved education; one such is gamification. Gamification applies gaming mechanisms to non-gaming platforms. Sadly, the use of gamification is still limited, especially in Malaysia. Gamification affects user experience, so it is intriguing to see if it can solve education problems. A study was conducted to identify elements of gamification. A systematic literature review (SLR) determines the gamification elements. Results show the list of identified 30 elements as guidelines for developing gamified teaching in Computer Science Courses. Thus, it is hoped that the improved teaching and learning process can affect students' performance.

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Newton-SOR with Quadrature Scheme for Solving Nonlinear Fredholm Integral Equations 7th

Newton-SOR with Quadrature Scheme for Solving Nonlinear Fred...

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7th International Conference on Computational Science and technology, ICCST 2020

作者： Ali, L.H. Sulaiman, J. Saudi, A. Xu, M.M. Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia School of Mathematics and Information Technology Xingtai University 88 Quanbei East Street Xingtai City China

ISBN: (纸本)9789813340688

This paper presents a numerical method of Newton Successive Over-Relaxation (NSOR) iteration with quadrature scheme to approximate the solution of nonlinear Fredholm integral equations. Here, the quadrature scheme is used to derive the approximation equations of nonlinear Fredholm integral equations in order to develop a system of nonlinear equations. NSOR consists of two parts. In the first part, Newton’s method is used to linearize the developed system of nonlinear equations. Then, in the second part, SOR iteration is used to solve the corresponding system of linear equations to get the approximate solution. In order to validate the performance of the proposed method, Newton-Jacobi (NJacobi) and Newton-Gauss–Seidel (NGS) are used as the reference methods to perform the comparative analysis. Also, some numerical examples are presented to illustrate the validity of the NSOR. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Numerical methods

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RespBERT: A multi-site validation of a Natural Language Processing algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS)

TechRxiv

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TechRxiv 2023年

作者： Pathak, Ashwin Marshall, Curtis Davis, Carolyn Yang, Philip Kamaleswaran, Rishikesan School of Interactive Computing Georgia Institute of Technology AtlantaGA30308 United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA30322 United States Department of Surgery Emory University School of Medicine AtlantaGA30322 United States Division of Pulmonary Allergy Critical Care and Sleep Medicine AtlantaGA30322 United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30308 United States

Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction that is associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology associated with ARDS can be highly heterogeneous, with most cases being associated with infection or trauma. ARDS is often described as a clinical syndrome associated with poor oxygenation even in the presence of mechanical ventilation. The Berlin criteria of ARDS is the current gold standard for identifying whether patients had developed ARDS, however it often requires manual adjudication of the chest radiograph, resulting in limited tools to automate the process. Since the determination of ARDS is dependent on the presence of bilateral infiltrates on radiographic images, and this information is not typically available in Electronic Medical Record (EMR). Automated determination of the presence of radiological evidence would enable robust study of the syndrome by eliminating expensive individual inspection by physicians of the images. The text of radiological reports provides an opportunity for Natural Language Processing (NLP) to determine the status of the lungs for evaluating the imaging criterion. We developed a Natural Language Processing (NLP) pipeline to analyze radiology notes of 362 patients satisfying sepsis-3 criteria from the Electronic Medical Record (EMR) to determine possible ARDS diagnosis. The radiology notes were denoised and preprocessed. They were further vectorized through the word-embedding pipeline BERT and fitted to a classification layer using transfer learning. These classification models showed F1-score of 74.5% and 64.22% for Emory and Grady dataset respectively. © 2023, CC BY.

关键词： Radiology

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BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Hu, Kaimiao He, Yuan Wei, Jianguo Sun, Changming Geng, Jie Wei, Leyi Su, Ran Tianjin University College of Intelligence and Computing Tianjin30000 China CSIRO Data61 SydneyNSW1710 Australia Tianjin University Tianjin Key Laboratory of Cardiovascular Emergency and Critical Care Tianjin Municipal Science and Technology Bureau Department of Cardiology Chest Hospital Tianjin China Macao Polytechnic University Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science China Xiamen University School of Informatics Xiamen China

Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations. ©2013 IEEE.

关键词： Contrastive Learning

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Impact of Cardiac Dose on Overall Survival in Lung Stereotactic Body Radiotherapy (SBRT) Compared to Conventionally Fractionated Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer (LA-NSCLC)

引用

Journal of Cancer Therapy 2021年第7期12卷 409-423页

作者： Justin D. Anderson Jiuyun Hu Jing Li Steven E. Schild Mirek Fatyga Department of Radiation Oncology Mayo Clinic Phoenix Arizona USA School of Computing Informatics and Decision Systems Engineering Tempe Arizona USA School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta Georgia USA

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patie... 详细信息

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patient studies. Materials and Methods: 197 Patients treated with lung SBRT at Mayo Clinic Arizona were selected for this IRB-approved study. Multivariate Cox model with Akaike Information Criterion (AIC) was used to select patient specific covariates associated with OS. Heart dosimetry was represented by V_D indices, which is a percentage of volume exposed to dose D or greater. Multivariate Cox models with patient specific cova

关键词： Lung Cancer Lung SBRT Cardiac Toxicity Lung Radiation Therapy Non-Small Cell Lung Cancer

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After confronting one uncanny valley, another awaits

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第5期1卷 276-277页

作者： Jan-Philipp Stein Karl F. MacDorman Department of Media Psychology Chemnitz University of Technology Chemnitz Germany Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN USA

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