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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device

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Procedia Computer Science 2023年 227卷 1153-1162页

作者： Jason A. Wijaya Pauw D. Andersen Nico Surantha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

关键词： sleep sleep quality actigraphy artificial intelligences fuzzy logic

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Low-Resolution Face Recognition for CCTV and Edge-Powered Smart Attendance Systems

Low-Resolution Face Recognition for CCTV and Edge-Powered Sm...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Nico Surantha Edward Yose Sani Muhamad Isa Department of Electrical Electronics and Communication Engineering Faculty of Science and Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Attendance systems have become more modern, and one of the biometric systems without physical contact is face recognition. However, many face-based attendance systems still carry out attendance individually and cannot detect multiple faces simultaneously. In addition, capturing facial data in real-time is still a challenge because the relatively large distance between the camera and the individual reduces the ability to recognize faces. The general solution is to use super-resolution to generate better-quality faces while maintaining the main facial recognition features. One technique still being researched is super-resolution generative adversarial networks (SRGAN). SRGAN can enlarge the resolution of captured images and maintain image quality sufficient for face recognition. The attendance system can be easily integrated into edge devices such as the Jetson Nano. This paper proposes automatic and effective attendance systems with the super-resolution technique to detect and recognize faces in low-resolution input. The experimental results show that using face data capture with a resolution of 40 × 40 pixels and a four-fold magnification results in a resolution of 160 × 160 pixels. Combining Face SRGAN with FaceNet architecture as the basis of face recognition can achieve an accuracy rate of 78.19% and an F1-Score of 81.13% with an average processing time of 1.61 seconds per frame on a PC and 14.55 seconds per frame on a Jetson Nano at an average of face recognition per frame of as many as up to 8 faces simultaneously.

关键词： Support vector machines Image quality Accuracy Face recognition Image edge detection Superresolution Generative adversarial networks

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Sleep Monitoring Systems based on Edge Computing and Microservices Caching

Sleep Monitoring Systems based on Edge Computing and Microse...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Nico Surantha David Jayaatmaja Sani Muhamad Isa Faculty of Science and Engineering Department of Electrical Electronics and Communication Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

The rapid development of Internet of Things (IoT) technology has enabled the widespread deployment of health monitoring systems. Traditionally, the health monitoring system has been limited by centralized processing and storage in the cloud, leading to latency issues and potential data loss. This paper introduces a smart sleep monitoring system based on edge computing, utilizing microservices architecture and caching techniques. The proposed system employs edge computing to enable data processing closer to the source, reducing latency and improving real-time monitoring capabilities. Caching is employed to reduce database load and optimize random access memory (RAM) usage. This research addresses latency and response time challenges on IoT health monitoring platforms in environments with poor network quality while optimizing database load and resource usage on Jetson Nano as the edge computing device. Using Electrocardiogram (ECG) data as input, the proposed system yields impressive performance metrics. The research results indicate that the proposed system can increase throughput by 26.92 KB/s, reduce response time by 18.8 ms, and decrease latency by 20.86 ms compared to the previous work. Message Queuing Telemetry Transport (MQTT) integration reduces CPU usage by approximately 40% and RAM usage by about 81.24%.

关键词： Cloud computing Databases Random access memory Microservice architectures Real-time systems Nanoscale devices Internet of Things

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Gamification in the Marketplace for Gen Z: Determinants Using Gamified Learning Theory

Gamification in the Marketplace for Gen Z: Determinants Usin...

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International Seminar on Application for Technology of Information and communication (ISemantic)

作者： Michael Christian Kurnadi Gularso Henilia Yulita Oktafalia Marisa Muzammil Suryo Wibowo Sunarno Sunarno Management Universitas Bunda Mulia Jakarta Indonesia Master of Management Universitas Bunda Mulia Jakarta Indonesia Master of Communication Studies Universitas Bunda Mulia Jakarta Indonesia Business Management School Business Undergraduate Program Bina Nusantara University Bekasi Indonesia Medical Profession Study Program Krida Wacana Christian University Jakarta Indonesia Mechanical Engineering Universitas Tama Jagakarsa Jakarta Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Gamification is the process of incorporating game design aspects into non-gaming products or services to enhance usage and provide more long-term effects. The implementation of gamification in diverse practical focus areas necessitates the application and adaptation of the gamification theoretical idea to technological breakthroughs and their application. The goal of this study is to examine the determinants of gamification in the marketplace using gamified learning theory. This research takes a quantitative approach, utilizing SEM-PLS modeling and the SmartPLS 4.0 analytic tool. Participants from Generation Z were employed for this study. This group is recognized for its tendency to become readily bored and to move around while utilizing a product. According to the findings of this study, varying the intensity of recommendations to other people can demonstrate that the output does not only stop at the outcomes of playing, such as the acquisition of coins or other rewards, but also extends to the act of recommending to others. This is consistent with the game's features, which include the ability to invite friends to help finish objectives. This study has theoretical significance for enhancing differentiated learning theory. In practice, the findings of this study highlight the importance of consistently designing instructional features and content to keep students' attention. The research's flaws stem from the usage of limited game elements and the participants' background profile, which was ignored in favor of ignoring their game experience.

关键词： Seminars Analytical models Adaptation models Games

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Nico Surantha Vito Vincentdo BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Department of Electrical Faculty of Scienc and EngineeringElectronics and Communication Engineering Tokyo City University Japan

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined.

关键词： Fuzzy logic Training Actuators Adaptive systems Hydroponics Media Control systems

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Michael Christian Ratlan Pardede Henilia Yulita Yuli Kartika Dewi Suryo Wibowo Sunarno Sunarno Management Universitas Bunda Mulia Jakarta Indonesia Master of Management Universitas Bunda Mulia Jakarta Indonesia Master of Communication Studies Universitas Bunda Mulia Jakarta Indonesia Graduate School of Business Universiti Sains Malaysia Penang Malaysia Medical Profession Study Program Krida Wacana Christian University Jakarta Indonesia Mechanical Engineering Universitas Tama Jagakarsa Jakarta Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Aside from providing considerable benefits to individuals, AI can also generate anxiety, especially given its sophistication and the level of intimidation that exists in everyday life. The aim of the paper is to assess the usage behavior of AI, using anxiety-related AI configuration as an exogenous variable and gender as a moderating factor. This study was conducted using a quantitative method with empirical approach, utilizing data from 81 participating Indonesian academics. We used SmartPLS 4.0 and SEM-PLS to conduct this research. The study's findings effectively show that anxiety-related AI configurations significantly influence AI usage behavior in negative interactions (P value $=\mathbf{0. 024}$ ; original sample $=-0.51$ ). This study also explained that as worry levels increase, women's efficiency decreases, resulting in a lower aptitude for AI use when compared to men. As their anxiety levels increase, more women consider using artificial intelligence. This study benefits both scholars and the industry by deepening our understanding of behavioral attitudes towards AI use, with a particular focus on gender-based AI users.

关键词： Industries Correlation Automation Anxiety disorders Internet of Things Artificial intelligence Robots

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Understanding ADC variation by fat content effect using a dual-function MRI phantom

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European Radiology Experimental 2024年第1期8卷 19页

作者： Liu, Yi-Jui Tsai, Tung-Sheng Li, Ya-Hui Peng, Jo-Hua Chang, Hing-Chiu Peng, Hsu-Hsia Lee, Ying-Chieh Lee, Tung-Yang Liou, Chang-Hsien Lin, Tz-Feng Chew, Fatt-Yang Chou, Ruey-Hwang Juan, Chun-Jung Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital 199 Sec. 1 Xinglong Rd. Hsinchu County Zhubei City 302 Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering Chinese University of Hong Kong Hong Kong Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Institute of Nuclear Engineering and Science National Tsing Hua University Hsinchu Taiwan Department of Fiber and Composite Materials Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan Department of Radiology School of Medicine China Medical University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung 406 Taiwan Center for Molecular Medicine China Medical University Hospital Taichung Taiwan Department of Medical Laboratory and Biotechnology Asia University Taichung Taiwan

Background: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. Methods: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the “iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ,” IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. Results: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R² = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. Conclusions: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. Relevance statement: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. Key points: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values

关键词： Adipose tissue Diffusion magnetic resonance imaging Phantoms (imaging) Quality assurance (healthcare) Quality control

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Utilizing Graph Neural Networks for Breast Cancer Prognosis Prediction with High-dimensional Genomic Data

Utilizing Graph Neural Networks for Breast Cancer Prognosis ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tzu-Chen Huang Te-Cheng Hsu Yi-Hsien Hsieh Che-Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communications Engineering National Tsing-Hua University Hsinchu Taiwan Department of Electrical Engineering NTU Taipei Taiwan Master’s Program in Smart Medicine and Health Informatics NTU Taipei Taiwan Center for Computational and Systems Biology NTU Taipei Taiwan

An accurate prediction of breast cancer is essential to help physicians make appropriate treatment recommendations to reduce the chance of excessive treatment, avoiding unnecessary anxiety for patients. Cancer prognosis is highly related to patients’ genomic features, which are high-dimensional in nature. In this study, we utilize a systems biology feature selector for dimension reduction to select 20 prognostic biomarkers that are considered closely related to breast cancer prognosis from the high dimensional RNA Sequencing (RNA-Seq) data. Furthermore, we establish a graph neural network (GNN) and a multi-layer perception (MLP) graph-level readout method to better extract the underlying gene interactions from the corresponding gene interaction network (GIN). With the help of GINs, the model performs the best among all baseline models, especially in the area under the precision-recall curve (AUPRC) by as large as 23%. The results demonstrate that our approach using GNNs can successfully extract high-dimensional and complicated interactions within genomic data.

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