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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

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

引用

7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

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. © 2024 IEEE.

关键词： Lung cancer

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Development of Detection Kidney Function Stand Alone System and design based on Oral Ammonia Levels 7

Development of Detection Kidney Function Stand Alone System ...

引用

7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： William Hansen, Natasha Angeline Mariana, Yosica Sitepu, Elioenai Astuti, Winda Siswanto, Melinda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Biotechnology Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical. © 2024 IEEE.

关键词： Support vector machines

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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

引用

19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Automatic Anti-Swing Gantry Crane Based on Gradient Descent Optimization Proportional Integrated Derivative (PID): A Comparison Simulation and Real Experiment

Automatic Anti-Swing Gantry Crane Based on Gradient Descent ...

引用

2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Qadri, Munzir Harjanto, Danang Karuna Miharja, Jonathan Astuti, Winda Mariana, Yosica Tan, Sofyan Solihin, Mahmud Iwan University of Muhammadiyah Jakarta Engineering Faculty Mechanical Engineering Department Jakarta10510 Indonesia Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Binus Aso School of Engineering Bina Nusantara University Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Ucsi University Departement of Mechanical & Mechatronics Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350381290

Transporting payload with precise swing angles and transfer times poses a challenging task in building construction, which is why gantry crane systems are extensively utilized. Various control strategies, including open-loop and closed-loop systems, have been proposed to tackle this issue. However, most of these controllers rely on a model and parameters specific to the crane system, making the process of modeling and parameter identification complex and time-consuming. To address this challenge, this work presents a practical and simulation control method for automatic gantry cranes, which is evaluated simulation and experimentally. The lab-scale Gantry crane is developed in this work, which consists of mechanical and electrical parts. Proportional Integral and Derivative (PID) and Proportional Derivative (PD) controllers which applied optimization method are implemented in the electrical part of the system. The PID and PD controller applied in controlled the position and swing angle, respectively. The Gradient Decent optimization method is the best optimization method in determining the parameter of the controller, since it has the best simulation performance in the output of the system. The experimental result is developed and compared between simulation and lab-scale Gantry crane hardware implementation part. The performance of experimental results is shown appropriately approaching the performance of the simulation result, with the approximately settling time. © 2023 IEEE.

关键词： Proportional control systems

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A Smart Campus Framework in Developing Countries: Systematic Literature Approach 10

A Smart Campus Framework in Developing Countries: Systematic...

引用

10th International Conference on ICT for Smart Society, ICISS 2023

作者： Prasetyaningtyas, Sekar Wulan Meliala, Janita S. Pratiwi, Chairani Putri Peranginangin, Ezra Master-Program Bina Nusantara University Binus Business School Jakarta Indonesia Business Creation-Program Bina Nusantara University Binus Business School Jakarta Indonesia Binus Business School Binus University Binus Entrepreneurship Center and Management Department Jakarta Indonesia Binus ASO School of Engineering Bina Nusantara University Product Design Program Industrial Engineering Department Jakarta Indonesia

ISBN: (纸本)9798350339543

This study aims to conduct a systematic literature analysis of the recent research papers on the framework of smart campuses in developing countries. We were using Publish or Perish v8 software to conduct the systematic literature analysis. We were using specific keywords such as 'SMART CAMPUS' and 'DEVELOPING COUNTRIES from the Scopus database. We got 26 papers, ranging from 2015-2023. After several analyses, we were using 16 papers as the basis of this systematic literature review. Through descriptive analysis, we try to respond to these two research questions: 1) The framework of smart campuses in developing countries, and 2) The challenges in utilizing smart campuses in developing countries. The framework of smart campuses that we used consists of 5 indicators: smart society, smart economy, smart environment, smart governance, and smart technology. Based on the analysis, most smart campuses in development focus their activities on smart society and smart governance. While the challenges come from the lack of expertise, lack of facilities, and uncertain economic condition. Several strategies are needed to support the smart campus in developing countries' higher education. © 2023 IEEE.

关键词： Developing countries

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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

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

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Development of Detection Kidney Function Stand Alone System and design based on Oral Ammonia Levels

Development of Detection Kidney Function Stand Alone System ...

引用

International Conference of Computer and Informatics engineering (IC2IE)

作者： William Natasha Angeline Hansen Yosica Mariana Elioenai Sitepu Winda Astuti Melinda Siswanto 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 Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Biotechnology Engineering Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

关键词： Training Accuracy Machine learning algorithms Embedded systems Nearest neighbor methods Vectors Decision trees Kidney Random forests Testing

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Objective Εvaluations Based on Urban Soundscape in Waterfront Recreation Spaces 13th

Objective Εvaluations Based on Urban Soundscape in Waterfro...

引用

13th International Conference on Cross-Cultural design, CCD 2021, Held as Part of the 23rd HCI International Conference, HCII 2021

作者： Lin, Wei Wu, Yi-Ming Zhang, Hui-Zhong Lin, Hsuan School of Architecture Feng Chia University Taichung Taiwan Program for Civil Engineering Water Resources Engineering and Infrastructure Planning Feng Chia University Taichung Taiwan Department of Product Design Tainan University of Technology Tainan Taiwan

ISBN: (纸本)9783030770792

Space and form play an important role in the current geometry of recreational space and cognition of sound field. Therefore, there is different objective physical quantity information of sound energy. These concerned sounds are related to urban development and historical and cultural characteristics and will be highly recognizable sounds. It can be seen from the above that in urban recreational streets, through the objective physical characteristics of the street sound field sorted out, the positive evaluation can be predictably given, and the correspondence between the subjective feelings of the people will be explored. If the physical quantity of sound energy discusses the homogeneity of normalization of the soundscape and comprehensive physical quantity information, it is afraid to ignore the characteristics of spatial qualitative and internal variability. The currently research results propose the urban soundscape acoustic environment factors of waterfront recreation spaces, confirm and draw distinctive sound energy distribution maps and sound field perception. Understand the public’s sensitivity and preference for sound, and explore the correlation and consistency of measurement and computer simulation correspondence. © 2021, Springer Nature Switzerland AG.

关键词： Acoustic fields

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Automatic Anti-Swing Gantry Crane Based on Gradient Descent Optimization Proportional Integrated Derivative (PID): A Comparison Simulation and Real Experiment

Automatic Anti-Swing Gantry Crane Based on Gradient Descent ...

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International Conference on Electrical engineering and Informatics, ICEEI

作者： Munzir Qadri Danang Karuna Harjanto Jonathan Miharja Winda Astuti Yosica Mariana Sofyan Tan Mahmud Iwan Solihin Mechanical Engineering Department Engineering Faculty University of Muhammadiyah Jakarta Jakarta Indonesia Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Product Design program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Departement of Mechanical & Mechatronics Engineering UCSI University Kuala Lumpur Malaysia

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Segmenting Alzheimer's Disease MRI Slices for Hippocampal Volume Calculation

Segmenting Alzheimer's Disease MRI Slices for Hippocampal Vo...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hsiao-Chuan Tan Gilang Titah Ramadhan Wei-Cheng Shen Wen-Thong Chang Chih-Yang Lin Ben-Yi Liau Yih-Kuen Jan Yori Pusparani Chi-Wen Lung Department of Artificial Intelligence PhD Program Asia University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Creative Product Design Asia University Taichung Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana America Department of Digital Media Design Asia University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Alzheimer's is a neurodegenerative disease that quietly steals human memory. This study analyzed hippocampal volume in Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognition (NC) using MRI image slices from the ADNI database and YOLOv8 instance segmentation. We used 300 images to segment the left and right hippocampal. This study focused on volume calculation from five MRI image slices. The results showed 0.98 (AD), 0.92 (MCI), and 0.90 (NC) and revealed significant volumetric differences showed in the NC class (P = 0.045). This suggests early neuroanatomical changes can occur without cognitive symptoms, highlighting the potential of deep learning in the early detection of neurodegenerative diseases.

关键词： Instance segmentation Deep learning Accuracy Databases Magnetic resonance imaging Cognition Alzheimer's disease

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