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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 Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom 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)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

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IEEE Internet of Things Journal 2023年第19期10卷 17254-17264页

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi Macquarie University School of Computing SydneyNSW2109 Australia CSIRO Data61 SydneyNSW1710 Australia Deakin University School of Information Technology Waurn PondsVIC3125 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Qilu University of Technology Shangdong Computer Science Center Jinan250316 China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems. © 2014 IEEE.

关键词： Speech recognition

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Comparison of Cost-effectivnes of Fixed and Two-axis Tracking PV System in Market Conditions 5

Comparison of Cost-effectivnes of Fixed and Two-axis Trackin...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Kljajić, Ružica Kraus, Zorislav Tudan, Marko Marí, Predrag Computer Science and Information Technology Osijek University of Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

PV systems are increasingly encouraged, and different technologies are differentiated between them. This paper provides a comparison of a fixed PV system with the two-axis tracking PV system. Two-axis tracking, although more expensive, provides numerous environmental benefits, primarily in the utilization of the area required for system installation as well as higher production per utilized area. The paper gives a comparison on the example of a small system with a power of 6.4 kW, comparing a fixed and two-axis system in terms of energy produced, income, and payback time. © 2022 IEEE.

关键词： Tracking (position)

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Interpretable SHAP-Driven Machine Learning for Accurate Fault Detection in Software engineering 5th

Interpretable SHAP-Driven Machine Learning for Accurate Fau...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Kassaymeh, Sofian Rjoub, Gaith Dssouli, Rachida Bentahar, Jamal Almobydeen, Shahed Bassam Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031731501

In order to design and develop secure and reliable software, accurate prediction of software errors is crucial. The complex, nonlinear relationship between software features and bugs that occur during development despite precautions and measures taken to prevent them makes it difficult for traditional empirical models to predict these bugs with any degree of accuracy. An integration between the Decision Tree (DT) model and the SHAP (Shapley Additive exPlanations) technique was developed in this work with the aim of predicting software faults and providing informative explanations of the expected results. The synergistic advantages of integrating DT and SHAP allow the creation of an accurate, efficient, and fully interpretable technique. In order to make the process visible and reliable, SHAP provides a global explanation of how features of the developed software affect quality and a local explanation of how features contribute to each prediction. The tendency of software features to influence software quality was revealed by feature dependence analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

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computer Modeling in engineering & Sciences 2023年第9期136卷 2267-2289页

作者： Abdelazim G.Hussien Guoxi Liang Huiling Chen Haiping Lin Department of Computer and Information Science Linköing UniversityLinköing4189Sweden Faculty of Science Fayoum UniversityFayoum63514Egypt Department of Information Technology Wenzhou PolytechnicWenzhou325035China Artificial Intelligence Wenzhou UniversityWenzhou325035China College of Information Engineering Hangzhou Vocational&Technical CollegeHangzhou310018China

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such *** algorithms have received a lot of attention in recent years because of their efficient performance and simple *** Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&***,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal *** this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive *** first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching *** evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are ***,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of *** results show that RDSCA has competitive results with other metaheuristics algorithms.

关键词： Sine cosine algorithm global optimization swarm intelligence meta-heuristic algorithms

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Deep Learning Metaphor Detection with Emotion-Cognition Association

Deep Learning Metaphor Detection with Emotion-Cognition Asso...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Razali, Md Saifullah Halin, Alfian Abdul Chow, Yang-Wai Norowi, Noris Mohd Doraisamy, Shyamala Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia

ISBN: (纸本)9798350397000

The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and performance of each feature sets are given. It is also found that a few sets performed well when they are used independently. However, even the sets that are not useful separately is proven to be very useful after the combination process. © 2022 IEEE.

关键词： Support vector machines

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Dimensioning of RX Coil in Wireless Power Transfer System 5

Dimensioning of RX Coil in Wireless Power Transfer System

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Vinko, Davor Bilandžija, Domagoj Jaman, Branimir Barišić Computer Science and Information Technology Faculty od Electrical Engineering Department of Communications Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

This paper presents the study on how to dimension the receiver's coil in resonant wireless power transfer system. One of the key features of the wireless power transfer system is the efficiency of the power transfer. To achieve high efficiency, two main coil parameters must be optimized: number of coil windings and coil surface area. This paper presents a mathematical model for coil parameter calculation. Comparison with measurement results shows that the accuracy of the calculated values is higher than 90%. © 2022 IEEE.

关键词： Energy transfer

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MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors 23

MotionNeXt: A Deep Learning Neural Network for Recognizing H...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

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

ISBN: (纸本)9798350353952

Recognition of human activities using wearable sensors is essential for applications related to health and well-being. However, the complex data captured by inertial sensors poses challenges in accurately identifying actions. This research introduces MotionNeXt, a residual deep neural network incorporating aggregated transformation. MotionNeXt utilizes residual and multi-kernel blocks to extract spatial and temporal characteristics from raw IMU data. Subsequently, it employs global average pooling, a fully connected layer, and softmax for classification purposes. An attention layer enables the model to focus on specific segments of the input sequence while categorizing each time step. MotionNeXt is evaluated using a publicly available dataset of activities collected from wearable inertial measurement devices. It achieves state-of-the-art accuracy, boasting an F1-score of 99.37%, surpassing previous deep learning methods by 2- 5%. By integrating accelerometer, gyroscope, and magnetometer modalities, MotionNeXt mitigates the limitations of individual inputs, significantly improving accuracy across various models. © 2024 IEEE.

关键词： Deep neural networks

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Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

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computers, Materials & Continua 2022年第3期70卷 6127-6140页

作者： Mohamed Ali Ibrahim A.Abd El-Moghith Mohamed N.El-Derini Saad M.Darwish Higher Institute for Tourism Hotels and ComputerAl-SeyoufAlexandria21533Egypt Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University21526Egypt Department of Computer and Systems Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

Routing is a key function inWireless Sensor Networks(WSNs)since it facilitates data transfer to base *** attacks have the potential to destroy and degrade the functionality ofWSNs.A trustworthy routing system is essential for routing security andWSN *** methods have been implemented to build trust between routing nodes,including the use of cryptographic methods and centralized ***,the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node *** the moment,there is no effective way to avoid malicious node *** a consequence of these concerns,this paper proposes a trusted routing technique that combines blockchain infrastructure,deep neural networks,and Markov Decision Processes(MDPs)to improve the security and efficiency of WSN *** authenticate the transmission process,the suggested methodology makes use of a Proof of Authority(PoA)mechanism inside the blockchain *** validation group required for proofing is chosen using a deep learning approach that prioritizes each node’s *** are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting *** to testing data,our routing system outperforms current routing algorithms in a 50%malicious node routing scenario.

关键词： Wireless sensor networks trusted routing deep neural network blockchain markov decision

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Speech Emotion Recognition by using Philips Fingerprint and Spectral Entropy 22

Speech Emotion Recognition by using Philips Fingerprint and ...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Cao, Xuan Jia, Maoshen Li, Lu Pai, Tun-Wen Faculty of Information Technology Beijing University of Technology Beijing China Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781450396110

Speech Emotion Recognition (SER) has become an indispensable part of human-computer interaction. In order to obtain a higher recognition rate, in this paper, a joint feature based on Philips fingerprint and spectral entropy is used, and it is combined with some underlying features to perform speech emotion recognition on the four emotions of angry, neutral, happy, and sad. Firstly, the Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficient (LPC), logarithmic amplitude-frequency characteristics, Philips fingerprints and spectral entropy are extracted for speech dataset. Secondly, Principal Component Analysis (PCA) is used to reduce the dimension of the extracted feature. Thirdly, SVM is used to train and classify the features after dimension reduction. The experimental results show that by comparing the recognition results with other feature combinations, the method proposed in this paper has good recognition results. © 2022 ACM.

关键词： Principal component analysis

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