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24th International Arab Conference on Information Technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

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Artificial Intelligence Identification Model for Chronic Kidney Disease 1

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5th International Conference on Innovative Computing, IC 2022

作者： Cheng, Ya-Fang Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Many people suffer from Chronic Kidney Disease (CKD). Nowadays, CKD is one of the top ten causes of death. CKD should via invasive examination to understand participants health status. If a non-invasive identification model can be established, it can provide users with self-assessment which let users quickly understand their physical condition. This study used machine learning method to establish an identification model of Chronic Kidney Disease. This study found the associated factors with kidney failure from the literature. Selected MJ database as information resources. Used two different factor selection methods to training model. Compared the performance with K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, eXtreme Gradient Boosting and Vote Algorithms, used the better one to establish the model. In this study, the best model used Vote algorithm to establish the model, and can only use 13 non-invasive factors. The accuracy is 88%, the precision is 73%, the sensitivity is 69%, the specificity is 93%, and the AUC is 0.92. The contribution of this study is to use non-invasive factors to identify Chronic Kidney Disease, but it is a preliminary evaluation and ultimately requires doctors to diagnose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Conceptual design of polygenic risk score web portal 6

Conceptual design of polygenic risk score web portal

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Rahutomo, Reza Budiarto, Arif Pardamean, Bens Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

The paradigm of polygenic risk score (PRS) has recently been getting much wider attention. PRS is a mathematical aggregate reflection of the risk conferred by deoxyribonucleic acid (DNA) variations to estimate the susceptibility of people to some certain onset of diseases. It is enumerated by summing risk alleles which are weighted by effect sizes in a large population derived from genome-wide association study (GWAS) where the gene variations are reproducible in different sets of GWAS. The emerging of direct-to-consumer genetics testing has tremendously increased access to genetics testing for broader users. The result of this genetics testing can be used as a disease predictive tool. One of the applications is PRS. PRS is a mathematical aggregate reflection of the risk conferred by DNA variations to estimate the susceptibility of people to some certain onset of diseases. However, the accessibility of these heavy statistics and mathematical models is limited to certain groups such as physicians and researchers. The objective of this study is to propose a conceptual design of web based PRS calculation available for public users. The users can maximize the utilization of their genotype data as a predictive tool for various disease risks. The design presented in this paper including the proposed class diagram, use case diagram, user journey diagram, and a series of user interface mock-ups. This result can be used as a prototype to gain users' feedback before implementing in production stage. © 2021 IEEE.

关键词： DNA

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An Architecture of Real-World Data Database 1

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5th International Conference on Innovative Computing, IC 2022

作者： Lee, Hsiu-An Hsu, Chien-Yeh Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

In the analysis of medical data, it is now important to use "Real-World Data (RWD)". In the past, clinical research in the medical field was often completed by clinical trials. Clinical trials are a kind of use of listed drugs and comfort according to research objectives. The process of comparing drugs or other medical treatments on subjects as a control group. The Real-world data research database is essential to precision health. In order to develop the value of clinical data, improve the medical care service, and promote precision medicine, the goal of this research is to design a Real-World Data Research Database, aiming at the wide range and standardization of data, the detail component including: *** standard Health Level 7-Fast Health Interoperability Resources (HL-7 FHIR).*** sorting and cleaning ***-based database research ***-based technology for identity verification and database authorization recognition mechanism. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clinical research

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Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

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International Automatic Control Conference

作者： Chun Ku Xiu-Zhi Chen Yen-Lin Chen Master Program in Artificial Intelligence Technology National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development.

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Monitoring Applications for Vehicle based on Internet of Things (IoT) using the MQTT Protocol

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Procedia computer science 2023年 227卷 73-82页

作者： Nurudin Arief Agus Sukmandhani Muhammad Zarlis Computer Science Department BinusOnline Learning Bina Nusantara University Jakarta Indonesia 11480 Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480

Monitoring of vehicle conditions is needed as one of the securities in driving because one of the accident rates is negligence in checking the condition of the vehicle. This study aims to help reduce the level of accidents and increase human awareness for vehicle conditions, especially minibus type vehicles. The system works to provide information on the condition of vehicles sourced from the Engine Control Unit (ECU). ECU data cars are obtained by SNIP traffic broadcast message and ask for data directly at the ECU. The car can provide vehicle information using a Canbus communication protocol through the OBD-II socket. Vehicle information can be seen by users through the website in real time. Based on the evaluation from the user side, this system provides ease of monitoring vehicle conditions because it can be accessed via the internet using various kinds of devices.

关键词： OBD-II Blackbox Sstem Logging ECU

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Design of Drone Remote-Control System using Human Motion Recognition 5

Design of Drone Remote-Control System using Human Motion Rec...

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5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Chen, Shyh-Wei Lai, Yu-Chun Liu, Chia-Hui Kuo, Chin-Guo Tunghai University Department of Computer Science Taichung Taiwan Tunghai University Master Program of Digital Innovation Taichung Taiwan Chinese Culture University Department of Applied Mathematics Taipei Taiwan National Taiwan Normal University Department of Industrial Education Taipei Taiwan

ISBN: (纸本)9781665433181

At present, the most common and low-cost method to control the drone is to use a mobile phone with an APP. The operator takes his mobile phone screen as a human-machine interface, and at the same time controls and receives the images returned by the drone. The control mode can be divided into two symmetry modes according to how the operator views and knows the location of the drone: exocentric viewing and egocentric viewing mode. In the exocentric viewing mode, the operator directly visually confirms the position of the drone, while in the egocentric viewing mode, the drone is equipped with a camera and transmits the flight image to the operator in real-time. Since the drone cannot be far away from the operator in the exocentric viewing mode, the relative egocentric mode allows the operator to feel immersed in the environment to control the drone more accurately. Therefore, by adapting the egocentric viewing and using human motion recognition, an unmanned vehicle remote-control system is designed. The low-speed, small-scaled, nature-stability UAVs are ideally suited for the proposed system. The system was designed with a PID controller to improve the UAV's handling. The experimental results verified the feasibility of the UVA control system and it could stabilize the posture of the drone. © 2022 IEEE.

关键词： Drones

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Using AI Algorithm to Establish the CVD Risk Assessment Model 1

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5th International Conference on Innovative Computing, IC 2022

作者： Chen, Yin-Chen Lee, Hsiu-An Hsu, Chien-Yeh Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

In Taiwan, diseases with cardiovascular include heart disease and cerebrovascular disease among the top ten causes of death. With the development of data mining in the medical field, it can be used to establish the risk prediction models of disease as a tool to assist physicians in decision-making in diagnosis. The purpose of this study is taking Taiwanese health examination data as an example, to identify the significant risk factors of cardiovascular disease and to establish the risk assessment model of cardiovascular disease using data mining. Using Chi-square test and Information Gain identify the correlation between various factors and cardiovascular diseases. Using eight algorithms such as decision tree, random forest, XGBoost, neural network, logistic regression, support vector machine, K nearest neighbor algorithm and voting algorithm to establish the risk assessment model of cardiovascular disease and using confusion matrix and AUC as model evaluation. Compare model performance with different factor combinations. Experiment result shows that it finds 22 questionnaires and biochemical variable risk factors affecting cardiovascular disease and 10 questionnaire factors. ANN and VOTE with 22 factors of Information Gain (threshold > 0.01) are the best models. Both models have the same accuracy (0.88) and AUC (0.90). The best model of questionnaire variable is ANN with the accuracy rate is 0.89, and the AUC is 0.91. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Database Design for Pre-Eclampsia Case Administration

Database Design for Pre-Eclampsia Case Administration

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Faisal Asadi Gregorius Natanael Elwirehardja Joko Pebrianto Trinugroho Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia.

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Recommendation News For Joumalist Based on Twitter Data Using CNN

Recommendation News For Joumalist Based on Twitter Data Usin...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ikrar Harvy Abba Suganda Girsang Melva Hermayanty Saragih Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods.

关键词： Training Convolution Social networking (online) Neural networks Blogs Training data Motion pictures

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