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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： Puttamat, Wirawan Phaphan, Wikanda Jitpattanakul, Anuchit Pianpailoon, Chainarong Mekruksavanich, Sakorn Chaiyaphum Rajabhat University Faculty of Education Department of Mathematics Chaiyaphum36000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand Research Group in Statistical Learning and Inference 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 Faculty of Education Sakon Nakhon47000 Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE. © 2024 IEEE.

关键词： Multiple linear regression

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Narit Hnoohom Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Nakhon Pathom Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems.

关键词： Deep learning Emotion recognition Accuracy Computational modeling Bidirectional long short term memory Mental health computer architecture Brain modeling Electroencephalography Recording

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Orthopedic Walker Fall Detection and Motion Classification Using Bidirectional Gated Recurrent Unit Neural Network

Orthopedic Walker Fall Detection and Motion Classification U...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. This paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. The dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model’s performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. The proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. The experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. The high precision and recall values in both scenarios demonstrate the model’s reliability for practical healthcare monitoring applications.

关键词： Deep learning Accuracy Measurement units Neural networks Medical services Safety Reliability Fall detection Older adults Monitoring

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A Comparative Study of Validation Methods for Sensor-Based Human Activity Recognition Using Deep Learning Models 10

A Comparative Study of Validation Methods for Sensor-Based H...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok 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)9798331543273

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) - on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches 10

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok 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)9798331543273

关键词： Recurrent neural networks

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Orthopedic Walker Fall Detection and Motion Classification Using Bidirectional Gated Recurrent Unit Neural Network 10

Orthopedic Walker Fall Detection and Motion Classification U...

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作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok 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)9798331543273

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. This paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. The dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model's performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. The proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. The experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. The high precision and recall values in both scenarios demonstrate the model's reliability for practical healthcare monitoring applications. © 2025 IEEE.

关键词： Recurrent neural networks

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A Comparative Study of Validation Methods for Sensor-Based Human Activity Recognition Using Deep Learning Models

A Comparative Study of Validation Methods for Sensor-Based H...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Narit Hnoohom Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Nakhon Pathom Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models – convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) – on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems.

关键词： Deep learning Accuracy Bidirectional long short term memory Ubiquitous computing Data models Human activity recognition Convolutional neural networks Smart devices Wearable sensors Stress

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Review of the Ionospheric Error Correction Techniques in Satellite Navigation

Review of the Ionospheric Error Correction Techniques in Sat...

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第十四届中国卫星导航年会

作者： Xihao Han Jianlei Yang Shuaiyong Zheng Mengzhi Gao Zhigang Huang Xiaoqin Jin Kun Li Peng Yang Zhonghao Zuo Department School of Integrated Circuit Science and Engineering Tianjin University of Technology The 54th Research Institute of China Electronics Technology Group Corporation State Key Laboratory of Satellite Navigation System and Equipment Technology School of Electronic and Information Engineering Beihang University College of Artificial Intelligence Nankai University School of Computer Science and Engineering Tianjin University of Technology

Ionospheric error seriously affects the positioning accuracy of the global navigation satellite ***,BDS,Galileo and GLONASS all adopt different ionospheric error correction *** ionospheric error correction methods in satellite navigation are introduced,and the principle and development of single frequency ionospheric error correction,dual-frequency ionospheric error correction and multi-frequency ionospheric error correction are summarized in this *** the single frequency correction,the grid ionospheric model,Klobuchar model,IRI constraint model and Ne Quick model are summarized,the BDGIM model further improves the ionospheric error correction accuracy on the base of the grid ionospheric model correction by ameliorating spherical harmonics,the Kriging interpolation method improves the interpolation accuracy of the grid ionospheric model correction;In the dualfrequency correction,the ionosphere-free model and the ionospheric error correction methods in the PPP-RTK technology are summarized;In the multifrequency correction,the optimization and improvement of ionospheric error correction technique with the high order correction and geomagnetic field model are ***,the ionospheric error correction techniques and their derivative are analyzed and encapsulated,the development trend and future hotspots of ionospheric error correction technology in satellite navigation are listed and analyzed.

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Energy efficient task offloading from IoHT in delay sensitive edge networks

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Alexandria engineering Journal 2025年 128卷 476-483页

作者： Amit Kumar Jitendra Kumar Samriya Ashok Bhansali Meena Malik Varsha Arya Wadee Alhalabi Eman Alharbi Brij B. Gupta Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Indian Institute of Information Technology Sonepat India Department of Computer Engineering and Applications GLA University Mathura India Department of CSE Chandigarh University Mohali India Hong Kong Metropolitan University (HKMU) Hong Kong Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Immersive Virtual Reality Research Group Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul 02447 South Korea

The edge server connected to the Internet of Things (IoT) is designed to provide optimized resource allocation and task processing, particularly for delay-sensitive applications. This becomes especially challenging when certain tasks demand lower response times compared to others. In this paper, we develop a deep learning model to optimize the task offloading problem in a multi-user Internet of Health Things (IoHT) edge environment. The proposed model, based on a Deep Reinforcement Learning (DRL) architecture, is trained on various input data to manage task offloading across data centres within the edge network. It effectively addresses the challenges of optimizing both offloading probability and power allocation. Simulations are conducted to evaluate the energy efficiency of IoHT devices and their computational offloading capabilities in the cloud. The results demonstrate that the proposed method achieves a higher response rate and greater energy savings compared to existing approaches.

关键词： Delay Edge networks Offloading tasks IOHT

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Bio-Kinetic Façade Inspired by Ogonchu Plant for Cooling Load Reduction Assessed by a Novel Energy Simulation Framework

SSRN

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SSRN 2024年

作者： Fattah, Twana Rasool Mohammed, Daryan Katona, Tamás János Baranyai, Bálint University of Pécs Marcel Breuer Doctoral School Faculty of Engineering and Information Technology Boszorkány Street 2 Pécs7624 Hungary University of Pécs Department of Building Structures and Energy Design Institute of Architecture Faculty of Engineering and Information Technology Boszorkány Street 2 Pécs7624 Hungary University of Pécs Molecular Medicine Research Group Szentágothai Research Centre Pécs Ifjúság Street 20 Pécs7624 Hungary University of Pécs Energy Design Research Group Institute of Architecture Faculty of Engineering and Information Technology Boszorkány Street 2 Pécs7624 Hungary

Due to overheating issues, the building sector consumes a staggering amount of cooling energy in temperate and desert climate zones, which inevitably contributes to global warming. To mitigate this widespread dilemma and reduce the carbon footprint, biomimicry offers ample efficient strategies in various means. Interestingly, cacti astutely incorporated robust and efficient self-cooling mechanisms to cope with the hot climate characteristic of the world’s deserts. Therefore, we designed a multifunctional responsive Bio-kinetic façade (Bio-KF) inspired by the spine net and stoma function of a cactus, specifically, the Ogonchu plant (Pilosocereus chrysostele). The Bio-KF aimed to reduce cooling energy, solar heat gain, glaze, and Carbon dioxide emission in three typologies of detached office buildings with a curtain wall system [Linear (T1), Concentric (T2), and Courtyard (T3)] in Sulaimani and Baghdad, associated with temperate and desert climate zones, respectively. To stimulate total, cooling and lighting energy consumption in buildings with Bio-KF, we developed a novel energy simulation framework using IDA Indoor Climate and Energy (ICE) software, which examined at five different opening angles (0°, 50°, 75°, 100° & 125°). The results revealed improved cooling energy efficiency, CO2 emission, and operational costs ranging from 25% to 67%. In conclusion, the simulation results of Bio-KF promise endeavours to alleviate high energy consumption in these two climate zones of which, these findings necessitate practical translation into real-world applications. © 2024, The Authors. All rights reserved.

关键词： Carbon footprint

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