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检索条件"机构=Computer Science Applied Computer Technology"

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Comparison Model for Forecasting the Total Population in Bangkok 9

Comparison Model for Forecasting the Total Population in Ban...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Tipwong, Nattanicha Jitpattanakul, Anuchit Phaphan, Wikanda Nontapa, Chalermrat Mekruksavanich, Sakorn King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Chiang Mai University Faculty of Science Department of Statistics Chiang Mai50200 Thailand Research Group in Statistical Learning and Inference Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

The primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok's overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, Department of Provincial Administration. This dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. The second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. The evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis. © 2024 IEEE.

关键词： Mean square error

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A novel electrical muscle stimulation device for neurorehabilitation applications with adaptable parameter optimization using AI algorithms 12

A novel electrical muscle stimulation device for neurorehabi...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Arsenidis, Arsenios Moraitopoulos, Alexandros Athanasiou, Alkinoos Bamidis, Panagiotis Stefaneas, Petros Astaras, Alexander NTUA School of Applied Mathematical and Physical Sciences Department of Physics Athens Greece AUTH Medical Physics and Digital Innovation Laboratory School of Medicine Thessaloniki54636 Greece American College of Thessaloniki Computer Science Division of Science & Technology Thessaloniki Greece

ISBN: (纸本)9798350321074

The application of short trains of electric pulses, of appropriate frequency and power, to muscle or nerve tissue can induce action potentials which in turn lead to muscle contractions. Timing these action potentials appropriately can help enhance functional movements, a methodology called Functional Electrical Stimulation (FES). The motor and sensory recruitment of muscle and nerves by means of FES has been demonstrated to elicit short- and long-term neurophysiological changes in the central nervous system and thus constitutes a powerful therapeutic rehabilitation tool. The most significant signal parameters affecting the quality of muscle contraction and duration of cortical reorganization caused by Functional Electrical Stimulation Therapy (FEST) are pulse intensity (amplitude), frequency, pulse width and the time delay between pulses. While these parameters are particular to the stimulating signal waveform, there are other key factors at play which determine both the duration and quality of changes. Such factors are electrode placement and synchronization of voluntary command with the induced contraction, which partially determine the successful completion of a movement task. The work presented in this paper aims to study, calibrate, automate and optimize the adaptation process of the aforementioned FEST parameters with respect to induced muscle contraction intensity and perceived discomfort reported by the subject, using both conventional AI and machine learning methodologies. © 2023 IEEE.

关键词： Muscle

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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A Lightweight Deep Residual Network for Recognizing Activities in Daily Living Using Channel State Information 14

A Lightweight Deep Residual Network for Recognizing Activiti...

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14th IEEE International Conference on Software Engineering and Service science, ICSESS 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350336269

The field of human activity recognition (HAR) focuses on predicting human motion and actions by analyzing data from various sensors. HAR tasks can benefit from vision-based and sensor-based approaches, which offer high data quality. However, it's essential to recognize that these methods have drawbacks, such as intrusiveness and privacy concerns. With the increasing prevalence of Wi-Fi devices, Wi-Fi technology has gained popularity for tracking everyday activities, particularly among senior individuals, making it an appealing choice in healthcare applications. Channel State Information (CSI), a crucial attribute of Wi-Fi signals, can be leveraged to recognize different human actions. This study introduces CSI-ResNeXt, a lightweight deep residual network designed to classify human actions using CSI data from daily living scenarios. A series of tests were conducted to evaluate the network's performance, using a standard CSI-HA dataset. The results of the experiments demonstrated that the proposed lightweight deep residual network achieved an impressive accuracy of 99.17% and an F1-score of 99.10%, surpassing the performance of previous deep learning models. © 2023 IEEE.

关键词： Classification (of information)

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Statistical Features and PageRank Scoring Fusion for Arabic Text Summarization

Statistical Features and PageRank Scoring Fusion for Arabic ...

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2022 International Conference on Intelligent technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Bahakam, Omer Salim Binwahlan, Mohammed Salem Farag Mogaibel, Hassen A. Al-Ahgaff University Faculty of Computer Science and Engineering Department of Information Technology Hadhramout Yemen Seiyun University College of Applied Sciences Department of Information Technology Hadhramout Yemen

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

online text content is increasing daily, making it a big challenge for readers to find the required information. So, providing a gist of the available text content can help those readers save their time and effort. This operation is completed by Automatic Text Summarization (ATS), which creates a summary for the entire text. Automatic Text Summarization can be achieved by many techniques, such as graph theory, statistics, machine learning, and clustering methods. Each of the above techniques takes care of a specific issue of text which other techniques do not. Text summarization for the Arabic language suffers from a lack of resources and language complexity. So, it is worthwhile to take the advantages of two or more text summarization techniques and fuse them together to improve their performance and enhance their summary quality. This study proposes a method that combines two of the ATS methods, which are the statistical based method and the graph-based modified PageRank algorithm. Graph-based techniques take care of relationships among document sentences, whereas statistical-based techniques take care of signalizing important sentences. Experiment results show that proposed method achieved a good performance. The Essex Arabic Summaries Corpus (EASC) was used as evaluation data. © 2022 IEEE.

关键词： Graph theory

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Efficient Recognition of Complex Human Activities Based on Smartwatch Sensors Using Deep Pyramidal Residual Network 15

Efficient Recognition of Complex Human Activities Based on S...

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15th International Conference on Information technology and Electrical Engineering, ICITEE 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350304466

Human activity recognition (HAR) has become a hot topic in artificial intelligence research due to the rapid development of smart wearable technologies. The goal of HAR is to accurately identify human actions using various data sources, such as video, images, and sensor data from wearable devices. Recent research in HAR has achieved promising results using learning-based methods, especially deep learning techniques. However, achieving state-of-the-art results remains a challenge for researchers. This study proposes a new approach to HAR that uses deep learning to classify human activities from smartwatch sensor data. We propose the use of a one-dimensional deep pyramidal residual network (1D-PyramidNet) for accurate human action identification. We evaluate the performance of our model against baseline models using the DHA dataset, a benchmark dataset for HAR that includes wristwatch sensor data for 11 complex human activities. The experimental results show that our 1D-PyramidNet model outperforms the baseline models, including CNN, LSTM, BiLSTM, GRU, and BiGRU. This confirms that the use of 1D-PyramidNet can improve the identification capabilities of HAR systems, achieving a maximum accuracy of 96.64%. © 2023 IEEE.

关键词： Wearable sensors

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Deep Learning Approaches for HAR of Daily Living Activities Using IMU Sensors in Smart Glasses 8

Deep Learning Approaches for HAR of Daily Living Activities ...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350396546

In the latest days, study into the development of intelligent technologies has proven valuable, contributing to attempts to improve the quality of human existence. Smart glass is one of the intelligent wearable devices that can be used for various purposes, including healthcare monitoring, fall detection, sleep tracking, and human activity recognition (HAR). Smart-phones and smartwatches are the primary wearables utilized in sensor-based HAR to collect human motions for training recognition models based on physical movement. These wearable tools, nevertheless, are more intrusive than smart glasses. Using IMU sensor data acquired via smart glasses, we investigate deep learning algorithms for detecting people's activities of daily living (ADL). This work proposes a hybrid deep neural network that automatically extracts spatial-temporal information from raw data to enhance identification p erformance. We performed tests to evaluate deep learning models using a publically available benchmark dataset, UCA-EHAR, which included IMU sensor data from multiple ADL from smart eyewear. The recommended CNN-LSTM model achieved the best effectiveness with the highest F1-score of 93.20%, as determined by experimental findings. © 2023 IEEE.

关键词： Deep neural networks

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Deep Learning Approaches for Classification of Road Types Using Wearable Sensors Embedded in Smart Glasses 8

Deep Learning Approaches for Classification of Road Types Us...

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作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Witayarat, Apiwat Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

The Internet of Vehicles (IoV) is an architecture of the intelligent transportation system that combines automotive, transportation, and information exchange to increase road safety. The categorization of roadways not only improves the passenger's comfort and safety but also provides autonomous cars with safe navigation paths. In this paper, we present an identification method based on movement data from smart glasses (electroocu-lography, acceleration, and angular velocity) to categorize four kinds of roads often experienced: highway, city road, highway, undeveloped region, and housing estate. We developed a deep pyramidal residual network that automatically recovers spatial-temporal data and efficiently identifies road kinds. We performed experiments to evaluate deep learning models utilizing a publicly available benchmark dataset, including sensor data acquired from intelligent eyewear. Experimentally, we discovered that the suggested 1D-PyramidNet model obtained the most incredible interpretation with the most increased accuracy (92.23%) and outperformed all other deep learning models. © 2023 IEEE.

关键词： Wearable sensors

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The Iterative Compromise Ranking Analysis (ICRA) - The New Approach to Make Reliable Decisions

The Iterative Compromise Ranking Analysis (ICRA) - The New A...

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Special Sessions in the Advances in Information Systems and Technologies Track, AIST 2022 and 17th Conference on Information Systems Management, ISM 2022 held as part of the Federated Conference on computer science and Information Systems, FedCSIS 2022

作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Shekhovtsov, Andrii Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

In the field of multi-criteria decision-making, compromise is often sought because it is highly desirable for decision-making. However, over the years, many methods have been developed for decision-making, between which discrepancies in the final rankings are often present. For this reason, it is worth noting the possibility of a compromise between different multi-criteria decision-making methods. One such solution is the Iterative Compromise Ranking Analysis (ICRA), which, by means of an iterative evaluation of the preferences of alternatives, leads to a compromise between the methods under consideration. This work presents an example of a solution to a theoretical decision problem, for which five methods were used: TOPSIS, VIKOR, MARCOS, MABAC and EDAS. In addition, an empirical analysis of the compromise solution was carried out to check the effect of parameters on the number of iterations needed to reach a compromise and the differences between the rankings proposed by the methods and the compromise ranking. The work showed that this is an interesting tool that can find its use in the field of multi-criteria decision-making as well as can be used to analyze the behaviour of multi-criteria decision-making methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Spread Analysis and Prediction of Covid-19 in India using Machine Learning

Spread Analysis and Prediction of Covid-19 in India using Ma...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Anuradha Yenkikar C. Narendra Babu Department of Computer Science and Engineering indRamaiah University of Applied Sciences Bangalore Karnataka India Pune Institute of Computer Technology Pune India

Since its breakout, the coronavirus (covid-19) has wreaked devastation all over the world. There hasn’t been a nation that hasn’t been affected by it, and India is no exception. Finding a treatment for this sickness and stopping its spread is one of the hardest problems humanities has ever faced. World Health organization (WHO) figures show that the mortality rate and the number of people with covid-19 are both increased exponentially in some countries during the peak waves. In this study, we use machine learning approaches to forecast the number of covid-19 confirmations, recoveries, and mortality cases over a period of time and assess the coronavirus outbreak in India. The polynomial regression (PR), support vector regression (SVR), and an autoregressive integrated moving average (ARIMA) model are three techniques that are used. The findings demonstrate that, in terms of prediction outcomes, the ARIMA model provides the least Root Mean Squared Error (RMSE), closely followed by polynomial regression. SVR doesn’t perform well since predictions are either too low or too high. Overall, the proposed system can significantly aid in comprehending the pattern of spread in other nations and assist governmental bodies in taking action to lessen its effects in future.

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