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2021 IEEE Symposium on computers and Informatics, ISCI 2021

作者： Ismail, Waidah Grosan, Crina Abdullah, Zul Hilmi Aldailamy, Ali Y. Zainal, Nurezayana Hendradi, Rimuljo Faculty Science and Technology Universiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia International Halal and Fatwa CenterUniversiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia Faculty of Information Technology Inti International University Bandar Baru Nilai Negeri Sembilan Malaysia Department of Computer Science Brunel University Uxbridge United Kingdom Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Information System Study Program Universitas Airlangga Surabaya Indonesia

ISBN: (纸本)9781665402767

Stroke is one of the leading causes of death and long-term disability worldwide. The primary goal of post-stroke rehabilitation is to maximize the independence of the affected individuals by facilitating both neurological and compensatory functional recoveries in their daily lives. This paper presents an integrated study, using multiple linear regression (MLR) and whale optimization algorithm, for optimizing the post-stroke rehabilitation performance using Medical Interactive Rehabilitation Assistant (MIRA). MIRA is one of the serious games using virtual reality. The research objective finding the best games settings for MIRA. Analysis of variance and F-test were performed to justify the goodness of fit of the developed mathematical models, as well as the significance of the individual MIRA input settings of the games. Results demonstrated that the developed MLR models for average acceleration and distance were significant, with P=0.018 and P © 2021 IEEE.

关键词： Optimization

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Integration of Artificial Intelligence in Software Defined Networking technology through Developing Machine Learning Algorithms

Integration of Artificial Intelligence in Software Defined N...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Lamhot Naibaho Pulkit Raj Saxena Kuldeep Sharma Badria Sulaiman Alfurhood L. Pallavi Bhasker Pant English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia College of Computing Science and Information Technology Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India Department of Computer Sciences College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia CSE B V Raju Institute of Technology Narsapur Telangana India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN.

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer Science and information technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Implementation of Container Based Parallel System for Automation Software Testing

Implementation of Container Based Parallel System for Automa...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Yanapat Chuchuen Kritwara Rattanaopas Information Technology Program Faculty of Science and Technology Songkhla Rajabhat University Songkhla Thailand Computer Science Program Faculty of Science and Technology Songkhla Rajabhat University Songkhla Thailand

Software quality assurance (QA) is the most important parts of the software development life cycle. Currently, software testing is widely used for automation testing. Two of the popular tools are robot framework and selenium2. Furthermore, software testing has major methods based on Black-box (or functional) testing and White-box (or structural) testing. In order to decrease execution time and improve resource management, the parallel paradigm can be a key concept. Thus, to create the parallel paradigm, we propose the use of selenium-robot-framework Docker container with the parallelization agent. The results show that the 4 workers case of parallelization not only decreased to one-third of the execution time of 1 worker case but also used all physical CPU cores quite similar around 50%. Furthermore, we show how the parallel paradigm using containers and the software automation testing tool beneficial to the significance of resource management.

关键词： Software testing Automation Multicore processing Digital art Software quality Containers Tools

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Human-computer Interaction and Knowledge Transfer: Study of E-Learning Achievement

Human-Computer Interaction and Knowledge Transfer: Study of ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Doni Purnama Alamsyah Norfaridatul Ahkmaliah Othman Johan Muliadi Kerta Doni Morika Yudi Ramdhani Creativepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Department of Management Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Design Interior Department School of Design Bina Nusantara University Jakarta Indonesia Information System Program Satu University Bandung Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance.

关键词： Human computer interaction Analytical models Electronic learning Data models Informatics Knowledge transfer

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ROBUST FEATURE LEARNING AGAINST NOISY LABELS

arXiv

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arXiv 2023年

作者： Tai, Tsung-Ming Jhang, Yun-Jie Hwang, Wen-Jyi Free University of Bozen-Bolzano Italy NVIDIA AI Technology Center Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Taiwan Department of Computer Science and Information Engineering National Taiwan Normal University Taiwan

Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations. To this end, this paper proposes an efficient approach to tackle noisy labels by learning robust feature representation based on unsupervised augmentation restoration and cluster regularization. In addition, progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Our proposed design is generic and flexible in applying to existing classification architectures with minimal overheads. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier

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Procedia computer Science 2023年 227卷 482-489页

作者： Albert Verasius Dian Sano Adriel Anderson Stefanus Evaristus Didik Madyatmadja Hendro Nindito Agung Purnomo Corinthias P.M. Sianipar Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Department of Global Ecology Graduate School of Global Environmental Studies (GSGES) Division of Environmental Science and Technology Graduate School of Agriculture (GSA) Kyoto University Kyoto 606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis.

关键词： Comparative review analysis Naïve Bayes classifier tourism

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Personality Type Based on Myers-Briggs Type Indicator with Text Posting Style by using Traditional and Deep Learning

arXiv

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arXiv 2022年

作者： Ontoum, Sakdipat Chan, Jonathan H. Computer Science Program School of Information Technology King Mongkut's University of Technology Thonburi Bangmod Thung-Khru Bangkok Thailand Research Center King Mongkut's University of Technology Thonburi Bangmod Thung-Khru Bangkok Thailand

The term "personality" may be expressed in terms of the individual differences in characteristics pattern of thinking, feeling, and behavior. This work presents several machine learning techniques including "Naive Bayes", "Support Vector Machines", and "Recurrent Neural Networks" to predict people personality from text based on "Myers-Briggs Type Indicator (MBTI)". Furthermore, this project applies "CRISP-DM", which stands for "Cross-Industry Standard Process for Data Mining", to guide the learning process. Since, "CRISP-DM" is kind of iterative development, we have adopted it with agile methodology, which is a rapid iterative software development method, in order to reduce the development cycle to be minimal. © 2022, CC BY-SA.

关键词： Support vector machines

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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