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Study on Optimal Machine Learning Approaches for Kidney Failure Detection System Based on Ammonia Level in the Mount 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Phandinata, Nicholas Puji, Muhammad Nurul Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No.28 Central Jawa Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Patients with kidney failure can emit bad breath with a certain level of ammonia content. The interpretation of ammonia levels in patients with renal failure was identified in this work. This research used the Chronic Kidney Disease dataset from UCI Machine Learning Repository. The dataset was processed first to get the value of eGFR (estimated Glomerular Filtration Rate) and ppb (parts per billion) of ammonia. Based on the eGFR value, the severity of kidney failure was divided into 5 categories, namely normal (Stage 1), mild (stage 2), moderate (stage 3), severe (stage 4) and failure (stage 5). The values of eGFR features are used as input for the machine learning technique in order to predict the level of kidney failure. Four different types of machine learning techniques, namely Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-nearest neighbors (KNN), are applied and compared. The last process was to identify kidney failure using the K-Nearest Neighbors (KNN) method based on eGFR and ppb of ammonia dataset. AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 89.9% and 95.65% for training and testing accuracy, respectfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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Development of Wireless Integrated Early Warning System (EWS) for Hospital Patient 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Steady Oey, Endra Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No. 28 Central Java Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

The implementation of the Early Warning System (EWS) in hospitalized patients aims to identify worsening of the patient’s condition based on the assessment of vital signs. Currently, the EWS is still applied manually by nurses by filling out observation sheets to assess the risk of patient deterioration, so that it can be used. There was an error filling in by the human error factor. In addition, the lack of nurses in Indonesia makes the implementation of EWS not optimal. Therefore, a system for measuring vital signs and risk assessment of patient deterioration was created automatically which nurses can monitor through the website. The results of this study are a system design prototype consisting of five data retrieval devices and a Raspberry Pi as a web server. Each device sends data to the server wireless using the MQ Telemetry Transport (MQTT) protocol. The test results show that the system can retrieve vital signs data automatically every hour and displays a risk that is one level lower than manual EWS implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deterioration

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Design and Development of an Internet of Things (IoT)-Based Application and System to Detect Stunting in Infants and Toddlers

Design and Development of an Internet of Things (IoT)-Based ...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Theresa Tiffany James Jeremia Zener Sukra Lie Automotive and Robotics Engineering Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350380439

ISBN: (纸本)9798350380446

Currently, the process of monitoring children’s growth in Indonesia relies on manual methods for collecting anthropometric data. These methods pose a risk of data recording errors. Additionally, the ratio of healthcare workers to children in Indonesia is 1:90, a significant disparity that hinders efforts to reduce the prevalence of stunting in the country. To support the third Sustainable Development Goal (SDG), which emphasizes “Good Health and Well-being,” it is crucial to address issues in the stunting management process, from data collection to monitoring. The design and development of this iOS-based mobile application aim to assist in monitoring child growth and provide guidance and steps for prevention and treatment. Besides application development, it also involves the design and development of an Internet of Things (IoT) system to aid in the collection of children’s anthropometric data. The research methodology includes studying stunting cases in Indonesia, anthropometric data calculation methods, stunting prevention and management steps, application design and development, automatic anthropometric data collection, and online data storage and transmission using IoT technology. The developed application and system have been tested and successfully integrated. The IoT device has an accuracy rate of 99.83% for weight measurement calibration and 98.68% for height or length measurement calibration. From the testing results of the child condition calculation method in the application, an accuracy rate of 100% was achieved.

关键词： Weight measurement Pediatrics Accuracy Prevention and mitigation Data collection Mobile applications Calibration Internet of Things Monitoring Testing

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Emotion Recognition based on Heart Rate Detection using Logistic Regression for Automation Food Healing Application 8

Emotion Recognition based on Heart Rate Detection using Logi...

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8th International Conference on Education and Technology, ICET 2022

作者： Antonio, Nicholas Jason Nobel, Yosse Ricardo Ho, Hwi-Chie Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Bina Nusantara University Industrial Engineering Department Jakarta Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

Daily pressure activity especially in work place or any society condition may causing stress or pressure for the people deal with that condition. This stress may causing of decreasing of work performance and achievement in the daily activity. Furthermore in worse condition, people may went to psychiatrist consulting regarding the condition in order to heal the stress or pressure feeling. In this work, food recommendation healing in order to improve the mood based on mood recognition using heartbeat rate detection system is developed. Mood detection results will be used to recommend foods that can improve the mood. the mood detection system work based on automatic intelligent heartbeat rate identification. This system installed on an Android cellphone, so its make the system more flexible and portable. the system has also connected to the web, in order used by doctors to monitor the patient's condition if the patient is being monitoring due to health mental diseases treatment. This system has worked properly with mood detection based on heartbeat rate with 96% and 97%, training and testing accuracy, respectively. Furthermore, this system is connected to the website for monitoring purposes for the psychiatrist to monitor their patient intern of mood changing fluctuation © 2022 IEEE.

关键词： Data handling

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN)

Development of Detection Lung Disease Stand Alone System Bas...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Edgar Caesar Liewarnata Yosica Mariana Sofyan Tan Suryadiputra Liawatimena Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification.

关键词： Training Accuracy Tuberculosis Pulmonary diseases Lungs Stethoscope Recording Convolutional neural networks Mel frequency cepstral coefficient Testing

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Design Of A ‘ Drive Wave ‘ System based on Communications System Remote Real-time Running Text and Speech to Text Converter using Light Emitting Diode (LED) Display

Design Of A ‘ Drive Wave ‘ System based on Communications ...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Juan Felix Christopher Sandy Setiawan Elioenai Sitepu Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Drive Wave is a device that can help drivers communicate with other road users by displaying familiar words and icons as well as a speech to text feature that can make it easier for drivers to communicate with other road users. in this work, a system design prototype consisting of two devices, namely Remote and LED message board. The most spoken words used and icons displayed on the LED message board are controlled wireless using a Remote using the ESP-NOW protocol and Bluetooth Low Energy (Bluetooth LE). The two ways of he LED message board controlled by the remote are manual and automatic speech to text converter system controlled the LED message board displayed. the manual way, the user pressed the button to choice the word to be displayed. in the automatic voiced control, the LED displayed the word uttered by the user. the automatic voiced controlled applied Mel-frequency cepstral coefficients (MFCC) as feature extraction techniques and Long Short-Term Memory (LSTM) method for word identification methods. The results showed that the automatic voice controlled 99.78% and 100% for training and validating accuracy, respectively. the real testing result showed that 24 times testing which have 100% accuracy.

关键词： Meters Pediatrics Roads Drives Light emitting diodes Mel frequency cepstral coefficient Long short term memory Speech to text Vehicles Testing

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How Fast Can a Robotic Drummer Beat Using Dielectric Elastomer Actuators?

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IEEE robotics and Automation Letters 2024年第3期9卷 2638-2645页

作者： Wakle, Sudhir Lin, Tse-Han Huang, Shu Basu, Sumit Lau, Gih-Keong National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Indian Institute of Technology Kanpur Mechanical Engineering Department Kanpur208016 India National Yang Ming Chiao Tung University Department of Graduate Degree Program of Robotics Hsinchu30010 Taiwan Industrial Technology Research Institute Mechanical and Mechatronics System Research Labs Zhudong 31057 Taiwan National Yang Ming Chiao Tung University Department of Mechanical Engineering International College of Semiconductor Technology Hsinchu30010 Taiwan

Fast drumming presents a speed challenge to many robotic arms. To simultaneously meet the needs for speed, stroke, and force, we proposed and tested a new double-saddle dielectric elastomer actuator (DEA) as the artificial biceps for driving a lightweight robotic drummer. This work finds that fast force induction is instrumental to the fast drumming by a DEA-driven drumstick. While a pure-shear series of DEA under a large pre-stretch and lateral reinforcement can generate a large isotonic stroke, it is not fast in isometric force induction. Instead, this work found a double-saddle DEA, which is a degenerated two-segment pure-shear DEA with a middle laterally buckled beam, managed to induce a faster isometric force at a 0.5 s down time constant when its ultimate actuation tapered due to substantial strain stiffening effect. As such, this double-saddle DEA-driven drummer suffered less from the dynamic stroke decrement than the 8-segment DEA did. A 4.4 g DEA managed to swing freely a 7.8 g drumstick up to nearly 60° at 6 kV and a maximum tip speed of up to 0.45 m/s. The drumming frequency upon 5.5 kV activation was up to 2-2.5 Hz. In comparison, an 8-segment pure-shear DEA-driven drummer failed to reach the drum when pulsed at 2 Hz. Interestingly, a slower stroke excited multiple drumbeats due to secondary bounces. To achieve a faster drumming to match a human drummer, it is foreseen that a harder dielectric elastomer material will help drive a faster soft robot. © 2016 IEEE.

关键词： Dielectric materials

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Automatic wall slant angle map generation using 3D point clouds

Automatic wall slant angle map generation using 3D point clo...

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作者： Kim, Jeongyun Yun, Seungsang Jung, Minwoo Kim, Ayoung Cho, Younggun Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of Department of Robotics Engineering Yeungnam University Gyeongsan Korea Republic of

Recently, quantitative and repetitive inspections of the old urban area were conducted because many structures exceed their designed lifetime. The health of a building can be validated from the condition of the outer wall, while the slant angle of the wall widely serves as an indicator of urban regeneration projects. Mostly, the inspector directly measures the inclination of the wall or partially uses 3D point measurements using a static light detection and ranging (LiDAR). These approaches are costly, time-consuming, and only limited space can be measured. Therefore, we propose a mobile mapping system and automatic slant map generation algorithm, configured to capture urban environments online. Additionally, we use the LiDAR-inertial mapping algorithm to construct raw point clouds with gravity information. The proposed method extracts walls from raw point clouds and measures the slant angle of walls accurately. The generated slant angle map is evaluated in indoor and outdoor environments, and the accuracy is compared with real tiltmeter measurements. 1225-6463/$ © 2021 ETRI

关键词： Conformal mapping

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Development of a Portable Face Recognition System with GPS Integration Using Raspberry Pi

Development of a Portable Face Recognition System with GPS I...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Muhammad Nurul Puji Winda Astuti Rahmanda Wulandari William Jonathan Edwards Telaumbanua Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Business Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Face recognition systems are a rapidly evolving computer vision technology capable of accurately identifying faces. Additionally, the development of SBCs (Single Board Computers) has advanced significantly, enabling fast computational processing. The integration of face recognition systems on SBCs enables the creation of embedded face recognition systems. With the addition of GPS devices, it is possible to develop a mobile embedded face recognition system, which can contribute to enhancing infrastructure security and sustainability. This research demonstrates that A portable face recognition system based on Raspberry Pi 5 and a GPS module has been successfully developed. The system successfully detects target faces and able to send location coordinates through the Telegram application. Testing results show that the system's effective range is 1 to 4 meters, with an effective angle of 80° to 100° (from the left horizontal). The tests also indicate that systems without video output generally have higher FPS compared to systems displaying video.

关键词： Meters Computer vision Technological innovation Face recognition Streaming media Real-time systems Sustainable development Optimization Global Positioning System Testing

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