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2022 International Electrical engineering Congress, iEECON 2022

作者： Sonsilphong, Amornthep Sonsilphong, Suphachoke Soemphol, Chaiyong Hormdee, Daranee Khampitak, Kovit Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Data Science Health Informatics Program Faculty of Medicine Khon Kaen Thailand Mahasarakham University Faculty of Engineering Department of Electrical Engineering Mahasarakham Thailand Khon Kaen University Embedded System RD Group Computer Engineering Faculty of Engineering Khon Kaen Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

The research herein proposes a position control system, designed for a single-port surgical robot, based on the lockup table method. The programmable logic controller (PLC) was implemented to move the single- port surgical robot and handle laparoscope and surgical instruments with feedback control. The output responses of the desired angle were investigated under several conditions. The rotating angle allows movement of the laparoscope in the range of -10o to +10o. The error of the desired position of the designed system was less than approximately ± 0.1o. Moreover, the output data from several experiments may be employed to improve the open-loop control systems. This system is also capable of providing computer vision task implementation, enabling semi- automatic or automatic surgery in the future. © 2022 IEEE.

关键词： Laparoscopy

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Effect of High-Flow Nasal Cannula on Capnogram Waveform for studying Non-Invasive Human Ventilation

Effect of High-Flow Nasal Cannula on Capnogram Waveform for ...

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Biomedical engineering Conference (NBEC), IEEE National

作者： Tjokorda Gde Tirta Nindhia Nursyasa Aqila Mohammed Julia Jais Jamaludin Pagupathi Devandran Nabiel Rafa Angel Bhaswara Thalitakum Hutajulu M.B. Malarvili Study Program of Mechanical Engineering Engineering Faculty Udayana University Jimbaran Bali Indonesia School of Biomedical and Health Science Engineering Universiti Teknologi Malaysia Johor Malaysia

This study is conducted to compare three different cannulas; all purpose nasal cannula with 2 prongs, nasal pressure cannula with 2 prongs and single port nasal cannula which are commonly used for capnography device. These cannulas or sometimes called as nasal prong is connected to the device via nose of subjects studied. This procedure is a part of respiratory study to monitor lungs condition and breathing pattern. This study involves 47 subject where the exhaled breath was recorded to make comparison and choose the most suitable and effective for capnography waveform analysis. Utilizing Statistical Package for the Social Sciences (SPSS) to find the normality of all the nasal cannula data based on capnogram waveform. The result from the analysis shows that the frequency distribution for type 1 nasal cannula which is also known as all-purpose nasal cannula had the feature values closer to the standard value. Our study proofs that all purpose nasal cannula sampling is the most suitable to capture exhaled breath for monitoring respiratory conditions.

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Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

Hybrid Encryption Based on Fernet and Rivest Shamir Adleman ...

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

作者： Muhammad Dafi Hisbullah Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Chaerul Umam Folasade Isinkaye Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR.

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Augmented Reality Application for Book Detection with Google Colaboratory and SnapML Lens Studio

Augmented Reality Application for Book Detection with Google...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Galuh Saputri Joko Suwamo Achmad Lutfi Fuadi Agus Heri Yunial Program Study Informatics EngineeringFaculty of Engineering Universitas Pamulang Tangerang Selatan Banten Indonesia

ISBN: (纸本)9781665453905

Artificial intelligence is one of the technological breakthroughs in science, especially in the field of computers. Object detection and recognition presents a new challenge on how to make machines detect objects automatically. To detect an object, the previous system will take the classifier of an object and evaluate it at various locations and various scales in the image. Smartphones currently play an important role in everyday life, but the existence of smartphones can be one of the causes of the user's lack of interest in reading books. With this Augmented Reality-based object detector, it is hoped that users can experiment on smartphones with the Snapchat application installed to detect objects in books that are around them and the number of objects will be counted automatically, it is also expected to attract users' desire to read books. Image recognition identification is taken through the phone's camera which is recognized as a lens. Lens AR in the Lens Studio application is supported by the SnapML machine learning algorithm, this machine learning function is to display book information and the number of books detected. This research was conducted using Cloud Computing such as Google Collaboratory which uses the python programming language, the book object dataset is selected from the COCO class, the model is exported to Open Neural Network Exchange (.ONNX) and then imported into the Lens Studio application. Publish lenses in the Snapchat app. Finally, a model test was carried out to detect objects and the number of objects in books that had been made using a Smartphone. Open the Snapchat application, scan the Snapcode generated by Lens Studio, AR lenses can be used in real time. AR Book Detection has been tested by other users on Snapchat with 18,845 Lens Views, 18,737 Lens Plays and 16 Lens Shares.

关键词： Image recognition Machine learning algorithms Computational modeling Multimedia Web sites Neural networks Object detection Real-time systems

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Biomedical Image Quality Improvement with Spatial Domains 2

Biomedical Image Quality Improvement with Spatial Domains

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2nd International Conference on Innovative and Creative Information Technology, ICITech 2021

作者： Fernando, Erick Siagian, Pandapotan School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia

ISBN: (纸本)9781728197463

Digital image processing aims to improve the quality of an original image so that it can display an image that is relatively better than the original image, so as to obtain the detailed information needed for an analysis need. Quality degradation Biomedical images in computerization usually experience changes in brightness, blur, and contrast stretching. Because of the deterioration in the image quality, doctors and patients cannot obtain the information needed for analysis, therefore the need for biomedical image processing techniques with spatial domain methods to improve the quality and details of information in the image so that it helps doctors in diagnosing. Data analysis is biomedical images like X-ray images, CT-Scan (Computer Tomographic Scan) images, and USG (Ultrasound Graphic) images. A focused study on improving image quality can be done with digital spatial domain image processing techniques. Digital domain image processing is digital image processing based on the manipulation of pixel values in the image directly, in the form of point processing and mask processing. Process testing for each image for Blur level is CT-scan images with a value of 0.98436, ultrasound images with a value of 0.9875, X-ray images with a value of 0.9836. This value is blur level value get near to 1 which means the image becomes clearer. The analysis results prove that the spatial domain method can clarify the object image in this study. The biomedical image quality processing model is proven to be able to improve the quality of the image that is declining due to the digitization process and assist the user in analyzing the biomedical image. © 2021 IEEE.

关键词： Image quality

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary study

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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Biomedical Instrumentation and Technology Conference (IBITeC), International

作者： Arief Purnama Muharram Farhan Hilmi Taufikulhakim Ayu Purwarianti Faculty of Medicine Universitas Indonesia Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Master Program of Informatics Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.

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Real Market Basket Analysis using Apriori and Frequent Pattern Tree Algorithm

Real Market Basket Analysis using Apriori and Frequent Patte...

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2021 International Conference on Computer, Control, informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Christian, Michael Albert Nathanael, Nathanael Mauliani, Annisa Indrawati, Ariani Manik, Lindung Parningotan Akbar, Zaenal Informatics Engineering Program Study Kwik Kian Gie School of Business Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9781450385244

Recently, data mining has been implemented in various fields, including business and telecommunications. Data mining is a technique for extracting and detecting patterns in massive data sets that combines machine learning, statistics, and database systems. One of the most important use-cases in data mining is finding the high-frequency patterns between the set of itemset called association rules. Association rule mining is a well-researched technique for finding some relations between variables in large databases. This paper aims to measure the performance of the Apriori and Frequent Pattern Tree algorithms by comparing them using several points of comparison. Then we compared the outputs, whether they produce the same or different rules, to find out whether the way the two algorithms work is similar or not. After that, we looked for the itemsets that best match the reality in the market by giving them to a user who had transaction data from his spare parts shop. © 2021 ACM.

关键词： Association rules

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Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

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