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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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Ontologies in Design: How Imagining a Tree Reveals Possibilities and Assumptions in Large Language Models 25

Ontologies in Design: How Imagining a Tree Reveals Possibili...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Haghighi, Nava Yu, Sunny Landay, James A. Rosner, Daniela Computer Science Stanford University Stanford CA United States Symbolic Systems Program Stanford University Stanford CA United States Computer Science Department Stanford University Stanford CA United States Human Centered Design & Engineering University of Washington Seattle WA United States

ISBN: (纸本)9798400713941

Amid the recent uptake of Generative AI, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-based analyses are crucial, we argue that ontologies - concerning what we allow ourselves to think or talk about - is a vital but under-recognized dimension in analyzing these systems. Proposing a need for a practice-based engagement with ontologies, we offer four orientations for considering ontologies in design: pluralism, groundedness, liveliness, and enactment. We share examples of potentialities that are opened up through these orientations across the entire LLM development pipeline by conducting two ontological analyses: examining the responses of four LLM-based chatbots in a prompting exercise, and analyzing the architecture of an LLM-based agent simulation. We conclude by sharing opportunities and limitations of working with ontologies in the design and development of sociotechnical systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： foundation models generative AI large language models LLM agents ontological design ontologies

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and Electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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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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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment 8

Analyzing the Behavioral Aspects of Implementing Green IT in...

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

作者： Syamsuar, Dedy Triando, Fero Ulfa, Maria Antoni, Darius Mailangkay, Adele Witarsyah, Deden School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Bina Darma Postgradute Department Palembang Indonesia Universitas Bina Darma Information System Study Program Computer Science Faculty Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350326093

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy. © 2023 IEEE.

关键词： Decision making

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Data-Driven Incentive Mechanisms for Federated Learning in Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Fathi, Faeze Montazeri, Mina N.araabi, Babak Du, Hongyang Niyato, Dusit Kebriaei, Hamed University of Tehran School of Ece College of Engineering Tehran Iran Urban Energy Systems Laboratory Dübendorf8600 Switzerland Nanyang Technological University School of Computer Science and Engineering The Energy Research Institute @ Ntu Interdisciplinary Graduate Program Singapore Nanyang Technological University School of Computer Science and Engineering Singapore School of Computer Science Tehran Iran

Centralized machine learning algorithms in vehicular networks face privacy and resource constraints. Federated Learning (FL) addresses these by enabling collaborative model training without sharing raw data. To incentivize vehicle participation despite resource constraints, we propose a contract between Roadside Units (RSUs) and vehicles, specifying required computation resources and rewards based on local data quality. The contract is designed to maximize the utility of the RSU, subject to constraints that ensure the participation of the vehicles in the task and truthful reporting of their data quality parameter. However, designing an optimal contract with classical methods is limited to specific utility functions. To extend the design methodology of optimal contracts to a broader range of utility functions, our study proposes a deep learning-based solution. Specifically, we present a novel application of Deep Neural Networks (DNNs), marking the first instance of applying DNNs in contract theory-based incentive mechanisms to achieve nearoptimal contracts. We design the DNN's structure to inherently satisfy specific constraints of the optimization problem, enhancing its efficiency and accuracy in determining the optimal contract. We utilize an augmented Lagrangian method to approximate the optimal contract in federated learning tasks. A case study on traffic sign recognition demonstrates the applicability and effectiveness of our method. Comparing our approach with the well-known generative diffusion model shows that in the FL scenarios, the DNN results are closer to the optimal contract compared to those of the generative diffusion model. © 1967-2012 IEEE.

关键词： Traffic signs

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Physician Scheduling using Constraint Satisfaction programming

Physician Scheduling using Constraint Satisfaction Programmi...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Ahmed M. Salama Ahmed Fares Ahmed B. Zaky Biomedical Engineering Program Faculty of Engineering Cairo University Giza Egypt Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Banha Egypt Computer science and information technology Programs Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency.

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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