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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...

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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Machine Learning-based Modulation Format Identification and Optical Performance Monitoring Techniques Implementation

Machine Learning-based Modulation Format Identification and ...

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2021 Advanced Communication Technologies and Signal Processing, ACTS 2021

作者： Devi, Pukhrambam Puspa Vincent Simatupang, Joni Welman National Institute of Technology Silchar Department of Electronics and Communication Engineering Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung40132 Indonesia President University Study Program of Electrical Engineering Bekasi17530 Indonesia

ISBN: (纸本)9781665423373

In this paper, machine learning-based techniques are used to solve and analyze the modulation format recognition problem. The combination of intelligent software and high-performance hardware provides a large scope for innovation in optical networking. Machine learning algorithms can use a large amount of data available from the network monitors to learn and make the network more robust. This is a problem in optical communication that consists of defining the type of digital modulation process in which an electrical signal should be sent. A dataset to represent realistic transmission behaviors was generated using a simulator based on a Gaussian noise model. A multi-layer perceptron was used and tested with different architectures to show that a high level of accuracy is achievable with machine learning. An analysis of the input features was made by using the select K best features method. Finally, an attempt to visualize the data in 2-dimension was made using the Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) methods to reduce the dimensionality of the input features and see their relationships. © 2021 IEEE.

关键词： Principal component analysis

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Smartphone Application As An Air Quality Monitor Using Raspberry Pi for Reducing Air Pollution 2

Smartphone Application As An Air Quality Monitor Using Raspb...

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

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

ISBN: (纸本)9781728197463

Good air quality is very important in human health. However, air pollution has been polluted everywhere, whether in the house, in cities. This is caused by a growing industry that produces air pollution. Room air pollution and various cities will contribute to air pollution, which will cause problems for human health. This work measures the carbon monoxide level using the MQ-7 sensor and measures the air's carbon dioxide level with the CCS811 sensor and MH-Z19 sensor. All three sensors are connected to the NodeMCU ESPS266, and we call it a NodeMCU module. Tests are carried out in three rooms in the house, and then three NodeMCU modules are used so that the three modules will send data alternately. Raspberry Pi3 (RPi3) functions as a server that collects data and acquires information data from sensors. Data from the CCS811 (TCOV) sensor, MH-Z19 (co2), will be processed first with fuzzy logic rules to make decisions about CO2, which are right, moderate, nasty conditions. The process is carried out on the Raspberry Pi 3 server with MQTT using publication, customer, fuzzy logic, and broker services. The value of the sensor's measurement results can be accessed via a smartphone and provides good air control values. © 2021 IEEE.

关键词： Air quality

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Development of application Information System in Rhee District 1

Development of application Information System in Rhee Distri...

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1st Virtual Conference on Engineering, Science and Technology, ViCEST 2020

作者： Esabella, Shinta Intan, Hikmawati Hidayatullah, Muhammad Andriani, Titi Gunawan, G. Informatics Department Faculty of Engineering Universitas Teknologi Sumbawa Indonesia Electrical Engineering Department Faculty of Engineering Universitas Teknologi Sumbawa Indonesia Physics Education Study Program Faculty of Teacher Training and Education Universitas Mataram Indonesia

Management of patient data at the Technical Implementation Unit of the Community Health Center (UPT. Puskesmas) of Rhee Sumbawa District has not been fully computerized. The need for the development of an Information System to store medical records (history) of patient data. The research aims to develop information systems with descriptive research methods. For software development methods using Waterfall Models and built with Microsoft Visual Studio C# software and databases stored in Microsoft SQL Server software. Software testing uses Black Box Testing, which is testing software in functional terms. The results of research into digital data technology products that function to enter, change, delete data and minimize the time to find the data needed to improve the effectiveness and efficiency of data management. © Published under licence by IOP Publishing Ltd.

关键词： Information management

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Prediction of Bankruptcy among Middle 40 Per Cent (M40) in Malaysia using Support Vector Machine

Prediction of Bankruptcy among Middle 40 Per Cent (M40) in M...

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Student Conference on Research and Development (SCOReD)

作者： Nurbaity Sabri Aqilah Nadiah Md Sahiq Hazwa Hanim Mohamed Hamzah Hartayuni Sain Nur Nabilah Abu Mangshor Anis Amilah Shari College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Melaka Kampus Jasin Melaka Malaysia Faculty of Business and Management Universiti Teknologi MARA Cawangan Melaka Kampus Bandaraya Melaka Malaysia Statistics Study Program Mathematics and Natural Science Faculty Tadulako University Palu Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The Middle 40 Per Cent (M40) demographic comprises a substantial proportion of Malaysia’s consumer base, comprising the median 40% of income earners. This group is a big part of the country’s customer base. The rising cost of living, flat wage growth, and rising family debt make this group especially vulnerable to financial instability. Given these economic pressures, predicting the possibility of bankruptcy in this population is becoming increasingly crucial to allow for early intervention and preventive actions. Support Vector Machines (SVM) are recognized for their efficacy in classification tasks, particularly with small or imbalanced datasets. SVM prediction will be utilized using 14694 samples of the Debt Management program (DMP) dataset from 2016 to 2020. Twenty-three (23) features will be implemented on the dataset and Synthetic Minority Oversampling Technique (SMOTE) to improve the imbalanced datasets. The experiment demonstrates that the SVM classifier obtained 91.1% accuracy when implementing PUK kernels. It suggests that SVM is a plausible alternative model for predicting personal bankruptcy in the M40 group. To anticipate personal bankruptcy for M40, additional prediction models will be evaluated in the future.

关键词： Support vector machines Machine learning algorithms Neural networks Predictive models Prediction algorithms Data models Vectors Bankruptcy Research and development Random forests

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An Intelligent System Based on Evolutionary Algorithm for Scheduling University Course Timetable

SSRN

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SSRN 2022年

作者： Bimantara, I. Made Satria Yuhana, Umi Laili Supriana, I. Wayan Pardede, Eric Department of Informatics Institut Teknologi Sepuluh Nopember 60117 Indonesia Informatics Study Program Faculty of Mathematics and Natural Sciences Udayana University Indonesia Department of Computer Science and Information Technology La Trobe University BundooraVIC3086 Australia

University Course Timetabling (UCT) is a common problem in educational institutions. The preparation of the class schedule must pay attention to available resources without violating set constraints. This research applies Evolutionary Algorithm such as Genetic Algorithm into timetabling system, to replace human expertise in generating optimal lecture schedules. Genetic Algorithm (GA) uses chromosomes to represent the solution of the UCT. One chromosome represents a set of schedule solutions that AG generates. Genes and alleles become components of chromosomes that represent the value of each entity to be optimized. We modeled chromosomes into four gene types, each of which has three allele types, namely day, hour, and room alleles which will be optimized with GA. The N-point and cycle crossovers were implemented into the new chromosome encoding scheme and do not take place at the gene level, but at the allele level by grouping similar allele types before the crossover process is carried out. The success rate is measured by the percentage of the optimal number of schedules generated from the trials. The proposed method provides a success rate of 80 - 85% and can produce an optimal schedule with an average computation time of 8 minutes. GA can reduce human resources and save time in the process of preparing lecture schedules. © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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

作者： Nur Ryan Dwi Cahyo Christy Atika Sari Eko Hari Rachmawanto Cahaya Jatmoko Rabei Raad Ali Al-Jawry Mohamed Ayad Alkhafaji Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department Computer Science Northern Technical University Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Sciece National University of Science and Technology DhiQar Iraq

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3; including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability.

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Development of virtual reality-based Sumenep tourism map application (virtual field trip simulations in science learning)

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AIP Conference Proceedings 2024年第1期2867卷

作者： Badrud Tamam Wanda Ramansyah Nuru Aini Dimas Achmad Indria Sapta Nugraha Roy Wahyu Novianto Science Education Study Program Faculty of Education University of Trunojoyo Madura Bangkalan Indonesia Informatics education study program Faculty of Education University of Trunojoyo Madura Bangkalan Indonesia

This study aims to create a virtual reality-based tourism map application for Sumenep city to support the implementation of science learning field trips at SMPN 1 Kamal. The development follows the ADDIE model, encompassing five stages: (1) Analysis, (2) Design, (3) Development, (4) Implementation, and (5) Evaluation. The product undergoes expert validation, material testing, and student testing. The instruments for validity and testing utilize a Likert scale questionnaire ranging from 0 to 5. Results from the expert validation indicate a score of 93% (very high), demonstrating high validity. Material expert validation yields a score of 90% (very high), and individual trials result in a score of 92% (very high). Small group trials achieve a score of 87% (high), while large group trials yield an 86% score (high). These outcomes lead to the conclusion that the development of the virtual reality-based tourist map application is deemed effective, efficient, and appealing for use in the science learning process.

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Portable Monitoring Systems for Rivers Waste Based on Internet of Things

Portable Monitoring Systems for Rivers Waste Based on Intern...

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International Conference on informatics and Computing (ICIC)

作者： Henderi Mumammad Hudzaifah Nasrullah Laura Belani Nudiyah Po Abas Sunarya Sofa Sofiana Didik Setiyadi Informatics University of Raharja Tangerang Indonesia Management of Retail Study Program University of Raharja Tangerang Indonesia Informatics Engineering Departement Faculty of Engineering Pamulang University Tangerang Indonesia Infotmatics Faculty of Technolgy and Design Universitas Indonesia Mandiri Jakarta Indonesia

ISBN: (纸本)9798350345728

River pollution has become a severe problem in Indonesia. Industrial waste causes water pollution because the waste is not filtered before being discharged into the river. Illegal waste dumping cannot be prosecuted due to insufficient data and unknown disposal times. Therefore, a tool is needed to measure river water waste to measure polluted water waste data. Studies on river water monitoring devices have shown that there are no portable monitoring tools based on the internet of things for river water waste that follows regulatory standards from the Minister of Health. This study aims to produce a portable monitoring tool based on the internet of things for water waste. The system was developed using TDS sensors, turbidity, pH, MQ-135 sensors, and the system is equipped with a GPS module to indicate the location's latitude. The monitoring system is developed through the stages of system design, testing water samples, setting water quality standards and required hardware and components, setting river water quality parameters, and conducting system testing. The test results show that the system can send the data obtained to dashboard things via an internet connection, the TDS sensor error rate is 2.17%, the pH sensor error rate is 1.87%, the turbidity sensor error rate is 1.66%, the MQ135 sensor value is at the quality limit. The internet of things-based portable river waste monitoring system produced through this research helps related parties to identify and handle polluted rivers. Water according to the standards of the Ministry of Health, and the GPS module shows location measurements and information according to the location's latitude on google maps.

关键词： Error analysis Sensor systems Water pollution Sensors Rivers Pollution measurement Internet of Things

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social Sciences (7.8%), Engineering (3.9%), Computer Science (2.6%), Environmental Science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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