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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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Mapping Research Trends and Visualization of E-learning in Entrepreneurship in the Last Ten Years 1

Mapping Research Trends and Visualization of E-learning in E...

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1st International Conference on Electrical and Information technology, IEIT 2021

作者： Maulana, Fairuz Iqbal Candra Pratama, Febby Permana, Frihandhika Khaeruddin Herasmara, Ridho School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

ISBN: (纸本)9781665417860

E-learning is a system or concept of education or learning process that utilizes information technology in the teaching and learning process anywhere and anytime. E-learning is widely applied in various fields of science. In this study, we draw scientific efforts on e-learning and entrepreneurship from publications in the Scopus database. We reviewed 218 academic documents published over the last 10 years, from 2011 to 2020. Our method is a global bibliometric analysis by visualizing connected networks using Vosviewer. The method used consists of five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. We researched several countries, affiliations, authors, subject areas, to types of publications that have relations in this field. Among the most published and indexed articles by Scopus, papers published by researchers in the China have the highest number of publications is 36 academic documents, followed by United States 18 academic publications, and Italy with 16 academic publications. The results showed that it had a limited impact, and through visualization, the network presented research gaps that future researchers should fill. The processed data shows the pattern and trend of increasing the number of international publications on E-learning in the field of entrepreneurship indexed by Scopus. Finally, we address the limitations of this study. © 2021 IEEE.

关键词： Visualization

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study Case on Implementation Elevator 2 Floor System on PLC OMRON CP1E N30

Study Case on Implementation Elevator 2 Floor System on PLC ...

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International Conference on Education and technology (ICET)

作者： Muhammad Afnan Habibi Arif Wibawa Irham Fadlika Mochamad Farhan Ali Irfani Mohamad Farel Firmansyah Angger Baskoro Study Program of Power Plant Engineering Technology Malang Indonesia Dept. of Electrical Engineering Universitas Negeri Malang Malang Indonesia Dept. of Electrical Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

The swift progression of industrial technology underscores the need for adept professionals capable of skillfully implementing and managing intricate systems. Vocational education assumes a pivotal role in cultivating competent intermediaries proficient in translating theoretical knowledge into practical applications. At the core of industrial automation lies the programmable Logic Controller (PLC), a specialized digital computer that interfaces with sensors, switches, and actuators to execute real-time control programs, thus facilitating meticulous industrial processes. This study concentrates on the practical deployment of a two-floor elevator system utilizing the OMRON CP1E N30 PLC. Elevators, integral to contemporary structures, mandate precision for secure vertical conveyance. Leveraging input signals from elevator buttons, sensors, and switches, the PLC orchestrates efficient motor control and dependable door operations. This hands-on application underscores the PLC’s significance within real-time control contexts. Nonetheless, Vocational Higher Education graduates frequently grapple with the challenge of transitioning to industry settings due to limited practical exposure. This partnership harmonizes with the center’s objective of enriching vocational education by integrating sophisticated technologies like PLCs, equipping the workforce with the acumen to bridge the gap between theoretical foundations and the pragmatic demands of industry.

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2023年第3期50卷 858页

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm 1

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Analysis Implementation of Chatbots to Increase Knowledge of Stunting Prevention in Indonesian Society with Bibliometric

Analysis Implementation of Chatbots to Increase Knowledge of...

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International Conference on Information Management and technology (ICIMTech)

作者： Indrajani Sutedja Lutfi Handayani Maryuni Hendry Mohammad Reza Faisal Information Systems Department School of Information Sytems Bina Nusantara University Jakarta Indonesia Faculty of Nursing and Midwifery Midwifery Study Program Binawan University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Computer Science FMIPA Lambung Mangkurat University Banjarmasin Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The healthcare industry uses AI, because it is more efficient. In Indonesia, the accuracy of measurements and transmission of children's growth and development is often wrong. Many small hospitals usually don't have length measuring equipment, so the results are inaccurate. This research aims to determine the effectiveness of the stunting prevention program in Indonesia using an information system. The methodology used is a qualitative method, to conduct subjective research and analysis. The results of the research are to improve the website/mobile application information system which can display a child's growth and development history and simple educational information related to nutrition. Digital recording methods can speed up the process of updating data with a central database in real time. The value of this research is to reach the public to utilize technology and obtain personalized and interactive information directly to individuals. The theoretical implications of this research focus on information dissemination and understanding how chatbots influence knowledge acquisition and behavior change in the context of stunting prevention can contribute to advancing these theories in the field of public health. Managerial implications can provide cost effectiveness as Chatbots offer potentially cost-effective solutions and data-driven insights where managers can leverage chatbot analytics.

关键词： Prevention and mitigation Scalability Bibliometrics Refining Chatbots Real-time systems Stakeholders Artificial intelligence Sustainable development Research and development

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High Level Learning Approach for High Level Collision Avoidance Using Sensors 1

High Level Learning Approach for High Level Collision Avoida...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Naibaho, Lamhot Razauddin Vertivendan, L. Karunakaran, V. Oberoi, Ashish Vishwath, N.C. Ajay Christian University of Indonesia English Language Education Study Program Faculty of Letters and Languages Indonesia Gulf Colleges Saudia Arabia Department of Computer and Information Sciences Saudi Arabia Galgotias University Department of CSE Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of CSE Bangalore India RIMT University Department of CSE Godindgarh Mandi India Parul Institute of Engineering and Technology Parul University Aeronautical Department India

ISBN: (纸本)9781665456876

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways. © 2022 IEEE.

关键词： Recurrent neural networks

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers

Observing Generator Performance by Finite Element Analysis C...

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technology and Policy in Energy and Electric Power (ICT-PEP), International Conference on

作者： Muhammad Afnan Habibi Mochamad Farhan Ali Irfani Langlang Gumilar Arya Kusumawardana Aripriharta Angger Baskoro Study Program of Power Plant Engineering Technology Universitas Negeri Malang Malang Indonesia Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia Dept. Electrical Engineering and Computer Sciences National Yang Ming Chiao Tung University Hsinchu Taiwan

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies.

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