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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science North Bangkok Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

With ongoing advancements in remote sensor technologies, the concept of wearable sensors has gained widespread acceptance and grown ubiquitous due to its broad applicability in situations including ambient assisted living, innovative healthcare, and home appliances. In this context, the demand for Heterogeneous Human Activity Recognition (HHAR) has expanded considerably, as individuals desire their daily activities to be collected and analyzed to provide valuable information. This wearable sensing data is taken in the form of time-series data utilizing different wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to offer other movement characteristics for each individual tracked from their smartphones and smartwatches. This data may be analyzed using different deep learning architectures to provide an additional platform for capturing and classifying the numerous sensor-based actions an individual could be undertaking. This research compares HHAR models based on deep learning techniques. We study the influence of smartphone and wristwatch sensor data on model performance and classification of human activities. We train and evaluate deep learning models to achieve our study objective using a publically available standard dataset (HHAR) with a 5-fold cross-validation technique. Using smartphone sensors, research findings demonstrate that the RNN-based model is superior to CNN-based models, with the most fantastic accuracy of 98.42%. © 2022 IEEE.

关键词： Smartphones

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Single-and Multiuser Virtual Reality Training for Forklift Driving 2

Single-and Multiuser Virtual Reality Training for Forklift D...

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2nd IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2023

作者： Froland, Tord Hettervik Ravyse, Werner Luimula, Mika Heldal, Ilona Western Norway University of Applied Sciences Chemistry and Biomedical Laboratory Sciences Department of Safety Bergen Norway School of ICT: Futuristic Interactive Technologies Research Group Turku University of Applied Sciences Turku Finland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350338638

Hands-on skills training is a constant requirement in a multitude of practical work settings and virtual reality (VR) is often found to be a valuable training opportunity to safely prepare students for their future work environments. One of the main benefits of utilizing VR-based training is providing high grades of presence and involvement. Although the user experiences of students are a key factor that impacts the use of a VR simulation, it becomes more challenging to decide whether single-or multi-user experiences should be planned and developed. This paper presents the results from a between-subjects case study comparing the user experiences of single-and multi-user VR training for forklift driving. Findings highlight the importance of considering various aspects of VR technology to design VR training environments for high user experiences, including the impact of age, group dynamics, and freedom of movement in the environment. The benefits and challenges of single-and multi-user VR training for high experiences and implications for utilizing serious games in VR-based training are discussed. © 2023 IEEE.

关键词： Virtual reality

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Deep Learning Networks for Eating and Drinking Recognition based on Smartwatch Sensors

Deep Learning Networks for Eating and Drinking Recognition b...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Smartwatches are becoming more popular for recognizing and monitoring human actions in everyday life. These wearable devices are equipped with various IMU sensors for ubiquitous data processing and recording of human physical activity data. Sensor-based human activity recognition (HAR) has risen to the top of the list of the most active research topic due to its widely real-life applications in various practical domains, such as healthcare monitoring, sports and exercise tracking, and misbehavior prevention. Many machine learning and deep learning approaches have been recently proposed to solve the problem of human activity recognition, focusing on activities of daily living. However, an exciting and challenging HAR topic deals with more complex human activities such as eating-related activities. This paper proposes a sensor-based HAR framework using data from eating-related activities recorded by a smartwatch sensor. In this framework, five d eep learning networks (CNN, LSTM, BiLSTM, Stacked LSTM, CNN-LSTM, and LSTM-CNN) are evaluated for their recognition of eating-related activities. To ensure the model's dependability, data from eating-related activities on the standard publicly available dataset WISDM-HARB are utilized to evaluate the proposed framework using state-of-the-art metrics: accuracy and confusion matrices. Experiment findings demonstrate that the S tacked LSTM model outperforms other deep learning models, achieving an accuracy of 97.37%. © 2022 IEEE.

关键词： Learning systems

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A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals 19

A Deep Residual-based Model on Multi-Branch Aggregation for ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Mekruksavanich, Sakorn Hnoohom, Narit 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665485845

Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography (EMG), and electrodermal activity (EDA) has been frequently utilized for the SER for learning-based approaches. This study introduces a convolutional neural network motivated by ResNeXt to facilitate multimodal awareness. The proposed model, named StressNeXt, can extract high-level insights from raw bio-signal signals and classify emotional expressions effectively. We undertake a series of investigations using a publicly released standard dataset (WESAD) to determine the optimal implementation of the proposed solution for recognizing stress and emotion. After incorporating preliminary fusion events, we examined deep learning models using 5-fold cross-validation. Our study demonstrates that the suggested technique can comprehend robust multimodal representations with an accuracy of 87.73% utilizing EDA. Additionally, the identification was designed to provide better to 99.92% by fusing with accelerometer sensor data. © 2022 IEEE.

关键词： Emotion Recognition

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Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data

Hyperparameter Tuning in Convolutional Neural Network for Fa...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

People have been encouraged to wear masks and avoid touching their faces in public as part of the new measures to prevent the spread of coronavirus 2019 (COVID-19). During the COVID-19 epidemic, few research have examined the effect of everyday living on the frequency of facial touch activity. To develop a face touching avoidance system, deep learning algorithms have been proposed and have demonstrated their amazing performance. However, an important drawback of deep learning is its extensive dependence on hyperparameters. The results of deep learning algorithms may vary depending on hyperparameters, such as the size of the filters, the number of filters, the batch size, the number of epochs, and the training optimization technique used. In this paper, we present an effective approach for hyperparameter tuning of convolutional neural networks (CNNs) for efficiently recognized face touching activities based on accelerometer data. Two hyperparameter tuning methods (Grid search and Bayesian optimization) were evaluated in order to construct the CNN with high performance. The experiment results show that Bayesian optimization can provide suitable hyperparameters for CNNs for face touching recognition with the highest accuracy of 96.61%. © 2022 IEEE.

关键词： Convolutional neural networks

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Modeling and Numerical Simulation of Non-Linear FSI Systems for Energy Harvesting Based on Ocean Waves and Beams with Piezoelectric Patches

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International Journal of applied and Computational mathematics 2022年第4期8卷 1-27页

作者： Belkourchia, Yassin Bakhti, Hamzah Azrar, Lahcen Research Center STIS M2CS Department of Applied Mathematics and Informatics ENSAM Mohammed V University Rabat Morocco Department of Mechanical Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

In this work, a numerical approach is adopted to solve the fluid-structure interaction (FSI) model for energy harvesting from ocean wave motion. A mathematical model is presented based on the nonlinear Navier-Stokes equations for the ocean water flow and the large deformation model of beam vibration based on the Euler-Bernoulli theory with piezoelectric patches. The FSI model is solved by coupling the mixed finite element method with the differential quadrature method (DQM) for the spatial discretization of the fluid and structure equations. An implicit time scheme and the Newton-Raphson method are used to solve the flow equations, while an adaptive nonlinear Newmark scheme is implemented to obtain the non-linear dynamic response of the beam deflections. In order to evaluate the root mean square (RMS) of the generated electric power from the piezoelectric patches, the obtained numerical results are coupled with the mathematical expressions of the generated charge and the voltage. The RMS values are investigated with respect to the ratio of cantilever length H to the ocean depth L_y, the ratio of distance d of the cantilever from the entrance to the distance of the flow channel L_x and the ratio of the width b to the thickness h of the cantilever. Also, the effect of the Reynolds number and the number of the attached piezoelectric patches are discussed. The obtained results show that the RMS increases when H/ L_y and b/h increase, while it decreases when d/ L_x increases. As a result, an increase in the RMS values by more than 4 Watt when increasing the Reylonds number from 100 to 300. Also, the RMS reaches a fixed value when we use more than 3 piezoelectric patches in the cantilver. The presence work is of a great importance in term of design and optimization of new method for energy harvesting from ocean wave via piezoelectric structures. © 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Differential quadrature Method Energy harvesting Finite Element Method Fluid-Structure Interaction NonLinear Vibration of beam Piezoelectric patch

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Recognizing Stationary and Locomotion Activities using LSTM-XGB with Smartphone Sensors 13

Recognizing Stationary and Locomotion Activities using LSTM-...

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13th IEEE International Conference on Software engineering and Service Science, ICSESS 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

Nowadays, stationary and locomotion activity recognition, also known as SLAR, is becoming increasingly important in a variety of domains, such as indoor localization, fitness activity tracking, and elderly care. Currently used methods typically involve handcrafted feature extraction, a process that is both difficult and requires specialized knowledge, and results can still be subpar. We proposed a deep learning technique for SLAR called LSTM-XGB that uses data from inertial sensors in smartphones to reduce the effort required for feature development and selection. The proposed LSTM-XGB consists of multiple stacked LSTM layers to automatically learn the temporal features of the input, followed by XGBoost for label prediction in the final layer. The results showed that the proposed LSTM-XGB technique, which automatically extracts features, outperforms conventional machine learning that requires manual feature extraction. We also showed that sensor data from three sensors (accelerometer, linear acceleration, and gyroscope) can be combined. This achieved higher accuracy than other combinations or single sensors. © 2022 IEEE.

关键词： Feature extraction

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Deep Learning Approaches for Unobtrusive Human Activity Recognition using Insole-based and Smartwatch Sensors 3

Deep Learning Approaches for Unobtrusive Human Activity Reco...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology 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: (数字)9781665469388

ISBN: (纸本)9781665469388

In this digital age, human activity recognition (HAR) has been playing an increasingly important role in almost all aspects of life. Healthcare systems for monitoring the activities of daily living (ADL), security environments for automatically detecting abnormal activity and notifying the pertinent authorities, and increasing human contact with computers are all examples of HAR applications. The data collecting tools used in HAR research can also be categorized (sensors or cameras). In sensor-based HAR, various wearable sensors have been investigated for their usefulness in effectively detecting both simple and complex human activities. In this work, we studied HAR using signal data acquired from insole-based and smartwatch sensors. To achieve our research goal, we proposed a ResNeXt inspired deep learning (DL) network called HARNeXt. The proposed model was evaluated on a public data set named 19NonSense data that collected activity signal data recorded from insole-based and smartwatch sensors. In addition, we compared the proposed model to baseline DL models. The results showed that the HARNeXt outperformed other DL models and achieved the highest F1-score of 97.03%. © 2022 IEEE.

关键词： Wearable sensors

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Multi-stage biomedical feature selection extraction algorithm for cancer detection

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SN applied Sciences 2023年第5期5卷 131页

作者： Keshta, Ismail Deshpande, Pallavi Sagar Shabaz, Mohammad Soni, Mukesh Bhadla, Mohit kumar Muhammed, Yasser Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Engineering Pune India Arba Minch University Arba Minch Ethiopia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India Department of Information Technology Ahmedabad Institute of Technology Ahmedabad India College of Technical Engineering Al-Farahidi University Baghdad Iraq

Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained;in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced. © 2023, The Author(s).

关键词： Feature Selection

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Online Legal Literacy Platform

Online Legal Literacy Platform

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Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), International Conference on

作者： I Kala Hariharan K Kavisri S Kaviyarasu D Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore

The Motor Vehicle Act is a complex legal framework that regulates various aspects of motor vehicle usage in India. However, many individuals lack knowledge and understanding of the Act, which can lead to unsafe driving practices, non-compliance with regulations, and legal violations. Furthermore, the Act is subject to frequent amendments, making it challenging for individuals to stay up to date on the latest regulations. To address these issues, this project proposes an Online Legal Information Platform for the Motor Vehicle Act. The platform utilizes advanced technologies such as BERT fine-tuning and natural language processing techniques to match user input scenarios with relevant sections of the Act. Users can input their scenarios and the platform suggests the ideal law that can be utilized in the given situation. The system displays matching laws with their title and description and generates a summary for the selected law to facilitate easy comprehension. Additionally, voice-to-text feature is incorporated in order to assist people with visual impairments. The platform aims to bridge the gap between legal language and everyday usage, promoting better compliance with road safety laws. The system will be user-friendly and accessible to individuals with limited legal knowledge, making it easier for them to navigate the legal system. Future work includes expanding the platform to include other regional languages as well to increase its availability to people all around the nation.

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