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International Conference on Sustainable engineering and Creative Computing (ICSECC)

作者： Rosalina Grace Selina Rila Mandala Faculty of Computing Information Technology Study Program President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175898

This research aims to help the user monitor their heart’s condition and notify other people in case of an emergency due to an abnormal heartbeat (normal heartbeat around 60-100 beats per minute). From the heart rate graph, the user could do a consultation with an expert in the nearest hospital using a map feature that will show the nearest hospital with a 1 KM radius from the user’s current location. The heart is undeniably an essential part of the human body system. The heart's missions are to pump blood to the lungs to expel the waste carbon dioxide that resulted from the respiration and pump blood that will deliver oxygen throughout the human body's circulation system. While the blood that carries oxygen being pumped throughout the human body, the heart will provide the oxygen to all cells. Body cells need to use oxygen to get the energy from food through a process called cellular respiration. Monitoring our heartbeat and pulse rate can increase people’s awareness about their heart’s health, motivating, and encouraging people to maintain a healthy eating plan and exercise. This research will be implemented ECG Sensor. The sensor will then send the data to the android application via HC-06 Bluetooth module. Monitoring the heart rate and analyzing the user’s heartbeat will be done in the android with a microcontroller device's help.

关键词： Bluetooth Heart beat Hospitals Microcontrollers Lung Electrocardiography Biomedical monitoring

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Fuzzy Time Series for Forecasting Railway Passengers in Indonesia

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Journal of Physics: Conference Series 2021年第1期2111卷

作者： Muhammad Fatih Rizqon Handaru Jati Department of Electronics and Informatics Engineering Education Postgraduate Program Yogyakarta State University Indonesia

Some fuzzy time series models have their own advantages and disadvantages. In addition, these models sometimes are complex and claimed to have better forecasting result than each other. The suitable model for forecasting depends on a wide variety of considerations. The models proposed by Chen (1996) applied simplified arithmetic operations and claimed more efficiency than before. The model proposed by Chen was introduced in 1996 and still exists in several previous studies. This research aims to forecast the number of railway passengers in Indonesia using the fuzzy time series. In addition, this research also evaluates the forecasting results based on mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed the forecasting results in this research has accuracy for 86.6%.

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Development of Training Kit Basic Aerial Robotics Using RC Plane Robot for Robotics Course

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Journal of Physics: Conference Series 2021年第1期2111卷

作者： F N Aziz Mashoedah Department of Electronics and Informatics Engineering Education Postgraduate Program Universitas Negeri Yogyakarta Indonesia

RC Plane Robot and basic aerial robotics are technologies that are widely used, so training kits are needed to learn them. This study aims to: (1) Design and build, (2) Test performance and (3) Determine the feasibility level of training kit basic aerial robotics using RC plane robot. The study used the Research and Development method with the following procedures: (1) Potential and problem, (2) Data collection, (3) Design, (4) Validation, (5) Revision and (6) Trial. The object of the research is the RC Plane Robot training kit and the practicum jobsheet. The research subjects were students of the Electronic engineering Education Study program, Faculty of engineering, Universitas Negeri Yogyakarta. The results of the research are in the form of hardware and practicum jobsheets. The hardware part consists of a robot plane, a work area (Box) and a remote control. The level of feasibility of training kits by material experts gets a percentage of 91%. By media experts get a percentage of 89.5%. By respondents (students) get a percentage of 86%. Based on these results, the training kit basic aerial robotics using RC plane robot is very feasible to be applied to the Robotics course.

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

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.

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Modified genetic algorithm for employee work shifts scheduling optimization

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Journal of Physics: Conference Series 2021年第1期1810卷

作者： N W S Saraswati I D M D Artakusuma I G A A D Indradewi Study Program of Informatics Engineering STMIK STIKOM Indonesia Denpasar-Bali 80225 Indonesia

Arranging an employee shift work's schedule requires high accuracy. It is because we have to pay attention to several constraints simultaneously. The genetic algorithm presents as a method which can automatize the process of arranging the schedule as well as optimizing the result of the schedule. The Shala Bali is a hospitality business whose scheduling was complicated because of the number of the employes. This research aimed at producing a shift work schedule of the employees in a week and to know the optimum genetic algorithm parameter in this case. The constraints that were taken into account in the arrangement of the schedule included the schedule conflict of the employees in one shift, schedule conflict of employees in 1 day, the same composition of employees per shift, employees should not get morning shifts after being in night shift the night before, each shift has at least 1 employee in the front office, and each employee is required to get 1 day off within 1 schedule period. This study was able to produce an optimal work schedule of employees with crossover probability (Pc) of 0.6, and mutation probability (Pm) of 0.3. The modification algorithm in chromosome generation and chromosome structure in this study results that changes in gene length (additional number of employees) do not have to be followed by an increase in the number of chromosome populations to get optimum results.

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A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms

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IISE Transactions on Healthcare Systems engineering 2019年第4期9卷 357-370页

作者： Wang, Kun Patel, Bhavika K. Wang, Lujia Wu, Teresa Zheng, Bin Li, Jing Industrial Engineering Program at School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States Department of Radiology Mayo Clinic in Arizona Scottsdale United States School of Electrical and Computer Engineering College of Engineering University of Oklahoma Norman United States

Full-field digital mammography (FFDM) and magnetic resonance imaging (MRI) are gold-standard techniques for breast cancer detection. The newly contrast-enhanced digital mammography (CEDM) integrates the complementary strengths of FFDM and MRI, and is being incorporated into the practice of leading institutions. The current clinical practice using CEDM is sub-optimal because it is primarily based on clinicians' trained eyes. Automated diagnostic systems under the conventional machine learning paradigm suffer from drawbacks, such as the requirement for precise segmentation, extraction of shallow features that do not suffice for diagnostic images, and adoption of a sequential design without a global objective. We propose a deep learning (DL)-empowered diagnostic system using CEDM, the core of which is a novel dual-mode deep transfer learning (D2TL) model. The proposed system is innovative in several aspects, including (1) a dual-mode deep architecture design;(2) use of transfer learning to facilitate robust model estimation under small sample-size;(3) development of visualization techniques to help interpret the model results and facilitate inter- and intra-tumor malignancy quantification;and (4) minimization of human bias. We apply D2TL to classify benign vs. malignant tumors using the CEDM data collected from the Mayo Clinic in Arizona. D2TL outperforms competing models and approaches. © 2019, © 2019 “IISE”.

关键词： breast cancer Deep learning imaging-based diagnosis transfer learning

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An indoor video-based for single face identification among people 3

An indoor video-based for single face identification among p...

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3rd International Conference on Science, ICOS 2019

作者： Indrabayu Areni, I.S. Arafah, M. Jusman Rachmat, M.P. Nurtanio, I. Informatics Department Hasanuddin University Makassar Indonesia Electrical Engineering Department Hasanuddin University Makassar Indonesia Informatics Study Program STMIK AKBA Makassar Indonesia Communication and Information Department Makassar City Official Makassar Indonesia

The purpose of this research is to build a system that can detect certain face among several people. The system works using face recognition in searching suspects from a database. 30 images and 9 videos are used for training and testing. In the training process carried out several stages: (1) pre-processing of face images, (2) extraction of facial image training features using the Histogram of Oriented Gradient (HOG) method, (3) face classification using the SVM method. There are several steps for testing process i.e.: (1) video frame extraction, (2) cascade classifier method with the Local Binary Pattern (LBP) feature descriptor, (3) HOG for extracting facial features detected in the frames, and (4) SVM as the face classifier. The processed video data is 1920x1080 pixel resolution which has been recorded using a CCTV camera which is mounted on a wall with height and slope angle of 2.5 meters and 60 degrees. There are two goals implemented in this research. The first goal is to find the highest accuracy from several testing of frame sampling by setting the best threshold value. The second goal is to find the lowest processing time. © Published under licence by IOP Publishing Ltd.

关键词： Local binary pattern

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Special issue on genome wide association study

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Quantitative Biology 2021年第2期9卷 105-106页

作者： Lin Hou Qiongshi Lu Can Yang Hongyu Zhao Center for Statistical Science Department of Industrial EngineeringMOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing 100084China Department of Biostatistics and Medical Informatics Department of StatisticsCenter for Demography of Health and AgingUniversity of Wisconsin-MadisonWl 53726USA Department of Mathematics the Hong Kong University of Science and TechnologyHong KongChina Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 *** is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through ***,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS *** rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and *** special issue of Quantitative Biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.

关键词： hundreds GWAS initiated

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Speech AI for All: Promoting Accessibility, Fairness, Inclusivity, and Equity

Speech AI for All: Promoting Accessibility, Fairness, Inclus...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Wu, Shaomei Wenzel, Kimi Li, Jingjin Li, Qisheng Pradhan, Alisha Kushalnagar, Raja Lea, Colin Koenecke, Allison Vogler, Christian Hasegawa-Johnson, Mark Su, Norman Makoto Ratner, Nan Bernstein *** Mountain ViewCA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States *** SeattleWA United States Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Gallaudet University WashingtonDC United States Apple CupertinoCA United States Cornell University IthacaNY United States Technology Access Program Gallaudet University WashingtonDC United States The Grainger College of Engineering University of Illinois Urbana-Champaign Urbana-ChampaignIL United States Department of Computational Media University of California Santa Cruz Santa CruzCA United States Department of Hearing and Speech Sciences University of Maryland College Park College ParkMD United States Neuroscience and Cognitive Science University of Maryland College Park College ParkMD United States

ISBN: (纸本)9798400713958

Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain. © 2025 Copyright held by the owner/author(s).

关键词： Speech enhancement

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

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