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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

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

ISBN: (纸本)9798350347319

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 (PM2.5 and PM10). 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 PM2.5 and PM10 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. © 2023 IEEE.

关键词： Classification (of information)

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A Note on Even-Sized Spectral Clustering

A Note on Even-Sized Spectral Clustering

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Hiroki MIYAKAWA Yasunori ENDO Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master's Program in Risk and Resilience Engineering University of Tsukuba Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Clustering is a typical unsupervised learning method for classifying unsupervised data. One of the clustering meth-ods, even-sized clustering based on optimization (ECBO), is a clustering algorithm that imposes a constraint to equalize the size of each cluster. ECBO has been suggested to be effective in delivery and other problems. However, it is limited to Euclidean space. On the other hand, spectral clustering with a wide range of applicability for partitioning graph data has been proposed. In this paper, we propose even-sized spectral clustering, which imposes a size-equal constraint on spectral clustering, and show that it is an extension of the graph partitioning problem. We also verify the validity of the results through numerical examples.

关键词： Clustering algorithms Partitioning algorithms Classification algorithms Optimization Unsupervised learning

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data

Optimizing Random Forest Classification Using Chi-Square and...

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International Conference on Informatics and Computing (ICIC)

作者： Andri Roni Yunis Tanti Undergraduate Program of Computer Science Department Universitas Mikroskil Medan Indonesia Undergraduate Program of Information Systems Department Universitas Mikroskil Medan Indonesia Undergraduate Program of Information Technology Department Universitas Mikroskil Medan Indonesia

Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model’s ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates.

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Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment

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Biomedical Materials and Devices 2024年第2期2卷 778-810页

作者： Bryan, Anastasia C. Heinz, Michael V. Salzhauer, Abigail J. Price, George D. Tlachac, M.L. Jacobson, Nicholas C. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Department of Psychiatry Geisel School of Medicine Dartmouth College Quantitative Biomedical Sciences Program Dartmouth College Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Department of Information Systems and Analytics Bryant University Center for Health and Behavioral Sciences Bryant University

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenges to treatment include financial, geographic, and stigmatic barriers, which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Considering advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers that prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. From a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real world clinical and general populations, a novelty specific to this review to the best of our knowledge. Toward this aim, we consider multiple study factors, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. We find that passive sensing features, such as GPS, heart rate, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. Despite this promise, however, our findings highlight important limitations in passive sensing research including (1) a trend toward smaller, specialized samples, (2) a predominance of data collection apps built on

关键词： Digital biomarkers Digital mental health Digital phenotyping Mental health assessment Passive sensing Ubiquitous sensing

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning 9

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Helmud, Ellya Widodo, Catur Edi Nurhayati, Oky Dwi Diponegoro University Semarang Indonesia Atma Luhur Institute of Science and Business Faculty of Information Technology Information Systems Study Program Pangkalpinang Indonesia Diponegoro University Faculty of Science and Mathematics Physics Department Semarang Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798331517601

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of 3010 fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of 95% with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of 94.56% with a loss value of 0.1794. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance. © 2024 IEEE.

关键词： Accuracy Convolutional Neural Network Data Augmentation Deep Learning Fish Species Classification

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Linkedin's Influence on User Success in The Professional World 9

Linkedin's Influence on User Success in The Professional Wor...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Oktavia, Tanty Roekman, Faza Fakhriansyah Putera, Muhammad Ikhsan Hidayah, Muhammad Alif Rafi, Muhammad Hosoda, Takaaki Gaol, Ford Lumban BINUS Graduate Program Information System Management Bina Nusantara University Information System Management Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan BINUS Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Social media has now become a place for others to exchange information in the form of writing, photos, videos, and audio. Social media has become a place for humans to interact from all over the world. In the use of social media, attractive professional information is conceptualized as access to relevant information and opening up better career opportunities. User content about work and choosing personal information compilation strategies consistently predict information usefulness. This study discusses the effect of using a linked-in application on the success of its users in the professional world. It is hoped that it can provide insight into the use of this recruitment application, making it easier for users to find work. The research model used in this research is quantitative. The survey was conducted on connected users in Indonesia using an online questionnaire, and the data collected was analyzed using PLS-SEM. Of the 68 respondents who took the survey, it can be said that all research models have a positive relationship with the successful use of the linked-in platform. © 2022 IEEE.

关键词： Surveys

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Innovative Solutions in Breath Alcohol Measurement for Improving Road Safety

Innovative Solutions in Breath Alcohol Measurement for Impro...

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2025 IEEE International Conference on Cybernetics and Innovations, ICCI 2025

作者： Pramkeaw, Patiyuth Suksomboon, Pimyada Leangruttananon, Warit Ganokratanaa, Thittaporn Makararpong, Davids Chumuang, Narumol Yimyam, Worawut Angsusingha, Chayawee Farooq, Adil Ketcham, Mahasak Media Technology Program King Mongkut's University of Technology Thonburi Bangkok10140 Thailand King Mongkut's University of Technology Thonburi Faculty of Science Bangkok10140 Thailand Thailand Business School The University of the Thai Chamber of Commerce Bangkok10800 Thailand Muban Chombueng Rajabhat University Digital Media Technology Ratchaburi70150 Thailand Business Information Management Phetchaburi Rajabhat University Phetchaburi76000 Thailand The National Broadcasting and Telecommunications Commission Bangkok Thailand Maynooth University Department of Electronic Engineering Ireland King Mongkut's University of Technology North Bangkok Management of Information Systems Bangkok10800 Thailand

ISBN: (纸本)9798331533267

This research presents the development and evaluation of a breath alcohol measurement device utilizing the MQ-3 alcohol sensor and an Arduino Nano-based control system. The primary objective was to design an accurate and reliable system for detecting breath alcohol concentration, with real-time feedback provided through an OLED display and an active buzzer for alerts. The device operates by measuring the alcohol concentration in the breath and converting the sensor data into parts per million (ppm). When the alcohol concentration exceeds the predefined threshold of 5% ABV, the system activates an audible alarm via the buzzer and displays the corresponding ppm value on the OLED screen. A series of tests were conducted with eight volunteers who consumed varying amounts of alcohol. The results demonstrated the system's high accuracy and responsiveness, with the ability to detect alcohol concentrations ranging from safe levels to dangerous thresholds. The device was tested multiple times to ensure consistency and minimize errors, with the final results confirming its effectiveness in providing reliable alcohol measurements. This system holds significant potential for applications in monitoring alcohol levels in drivers and individuals involved in safety-critical activities. The research underscores the importance of real-time alcohol detection systems in enhancing public safety and ensuring compliance with safety regulations. © 2025 IEEE.

关键词： Highway accidents

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Examining Gen-Z Intention to Participate in Deposit Refund systems: A Convergent Mixed-Method Study in Indonesia

Examining Gen-Z Intention to Participate in Deposit Refund S...

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Vocational Education and Electrical Engineering, International Conference on

作者： Muhamad Rachmat Gunawan Febriliyan Samopa Ahmad Mukhlason Doctoral Program in Technology Management Interdiciplinary School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

The rising environmental challenges in waste management have prompted an urgent need for effective recycling initiatives. This study investigates the intention of Generation Z (Gen-Z) in Indonesia to participate in a Deposit Refund System (DRS), a system aimed at promoting recycling behaviors. Using a convergent mixed-method approach, quantitative data from 228 respondents were analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess relationships between intention, distrust, environmental concern, and behavioral intention. Qualitative data analysis, employing vector embedding and k-means clustering, revealed key patterns and themes in Gen-Z’s attitudes. Integration of qualitative and quantitative results through PLS-SEM provided a comprehensive understanding of Gen-Z motivations towards environmental sustainability. The results show that environmental concern positively impacts behavioral intention, while distrust was found to be statistically insignificant. This study offers practical and academic insights for policymakers to design more effective DRS initiatives. Future research could explore the long-term behavioral shifts resulting from DRS participation and the role of digital platforms in enhancing engagement.

关键词： Waste management Electrical engineering Analytical models Data analysis Green products Education Mathematical models Vectors Data models Recycling

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The last course of coarse coding: Hemispheric similarities in associative and categorical semantic processing

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BRAIN AND LANGUAGE 2022年 229卷 105123页

作者： Mech, Emily N. Kandhadai, Padmapriya Federmeier, Kara D. Department of Psychology University of Illinois at Urbana-Champaign United States Department of Computing Studies and Information Systems Douglas College Canada Program in Neuroscience University of Illinois at Urbana-Champaign United States The Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign United States

To test theories that posit differences in how semantic information is represented in the cerebral hemispheres, we assessed semantic priming for associatively and categorically related prime-target pairs that were graded in relatedness strength. Visual half-field presentation was used to bias processing to the right or left hemisphere, and event-related potential (ERP) and behavioral responses were measured while participants completed a semantic relatedness judgement task. Contrary to theories positing representational differences across the cerebral hemispheres, in two experiments using (1) centralized prime presentation and lateralized targets and (2) lateralized primes and targets, we found similar priming patterns across the two hemispheres at the level of semantic access (N400), on later measures of explicit processing (late positive complex;LPC), and in behavioral response speeds and accuracy. We argue that hemispheric differences, when they arise, are more likely due to differences in task demands than in how the hemispheres fundamentally represent semantic information.

关键词： Hemispheric asymmetries Event-related potentials (ERPs) N400 LPC Associative priming Categorical priming Coarse coding hypothesis

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