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Human Hand Gesture Classification Based on Surface Electromyography Signals by Using a Vector–Kernel Convolutional Takagi–Sugeno–Kang Neuro-fuzzy Classifier

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Sensors and Materials 2024年第2-3期36卷 623-637页

作者： Lin, Cheng-Jian Lin, Chun-Jung Lin, Xin-Wei Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Muscle–computer interfaces are devices that can identify the meaning of human bioelectrical signals, such as surface electromyography (sEMG) signals. sEMG signals can be obtained from arm-worn sensors and can be used to classify human gestures. In this paper, we propose a vector–kernel convolutional Takagi–Sugeno–Kang (TSK)-type neuro-fuzzy classifier (VK-CTNFC) to recognize human gestures represented by sEMG signals. First, vector–kernel convolution is used to extract the features of sEMG signals;this modification reduces the model parameters by half and increases the classification accuracy compared with those achieved with a conventional convolutional kernel. Second, the global average pooling method is used instead of the flattening method to improve feature fusion performance. Finally, a TSK-type neuro-fuzzy network is used for gesture classification. The publicly available dataset Ninapro DB1 was used in experiments for verifying the performance of the proposed VK-CTNFC. Data preprocessing was performed by wavelet denoising to smooth the sEMG waveform, and fast Fourier transform was used to convert time-domain sEMG signals into frequency-domain signals. Finally, the processed sEMG signals were input into the VK-CTNFC for training. The experimental results indicate that the proposed VK-CTNFC has an average accuracy of 87.18% and outperforms other reported methods. © MYU K.K.

关键词： Fast Fourier transforms

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Enhanced text-independent speaker recognition using MFCC, Bi-LSTM, and CNN-based noise removal techniques

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International Journal of Speech technology 2024年第4期27卷 1013-1026页

作者： Tiwari, Manish Verma, Deepak Kumar Department of Computer Science and Engineering University Institute of Engineering and Technology Chhatrapati Shahu Ji Maharaj University UP Kanpur India

This research article introduces a novel approach to text-independent speaker recognition by integrating Mel-Frequency Cepstral Coefficients (MFCC) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, with noise removal facilitated by Convolutional Neural Networks (CNNs). The primary objective is to upgrade the robustness and precision of speaker recognition systems in real-world environments where background noise is prevalent. The proposed method begins with the extraction of MFCC features, which effectively capture the timbral characteristics of the speech signal. To enhance these features, we employ a CNN-based noise removal mechanism that reduces background interference, thereby improving the quality of the input signal. The denoised MFCC features are then fed into a Bi-LSTM network, which excels in modeling temporal dependencies and capturing long-range contextual information inherent in speech data. Extensive experiments were conducted on publicly available datasets, demonstrating significant improvements in speaker recognition accuracy under various noise conditions compared to traditional approaches. The integration of CNN for noise removal and Bi-LSTM for temporal feature modeling showcases a synergistic effect, leading to a more robust and reliable speaker recognition system. Our results underscore the effectiveness of combining advanced feature extraction, noise reduction, and deep learning techniques for enhanced speaker recognition in challenging acoustic environments. The accuracy of the proposed method is found to be 98.17% at the Signal to Noise Ratio (SNR) level of 30 dB. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh

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Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a Southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

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A Vision-Based Method for UAV Autonomous Landing Area Detection 6th

A Vision-Based Method for UAV Autonomous Landing Area Detect...

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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Zhang, Qiutong Xia, Qingyuan Wei, Lisheng Deng, Bohai School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory of Rotorcraft Aeromechanics Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783031712524

Automatic identification of the landing area is crucial for UAV (Unmanned Aerial Vehicles) to land correctly and safely. Using passive vision sensors to achieve this objective is a very promising avenue due to their low cost and the potential they provide for performing simultaneous terrain analysis. In this paper, a computer vision method is proposed using an improved U-Net based architecture on UAV imagery to assess the safe landing area. Contrary to past methods, which little attention has been paid to the whole landing process with multiple descending altitude, experiment involves evaluating the landing area by analyzing visual images obtained from different descending heights. In the initial stage of landing, separation was made between water and land, guiding the flight to the land. As descending, classification was applied on images captured near ground into safe/unsafe landing areas and then mapped to the safety score for selection. Experiments on public datasets have shown promising results. © IFIP International Federation for Information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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Optimized deep learning-oriented strategy for predicting the location of delamination and detecting their shapes in composite structures

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Iran Journal of computer science 2025年第2期8卷 403-418页

作者： Demircioğlu, Ufuk Bakır, Halit Mechanical Engineering Department Sivas University of Science and Technology Sivas Türkiye Computer Engineering Department Sivas University of Science and Technology Sivas Türkiye

In this paper, the possibilities of deep learning approaches for sandwich structure delamination detection and prediction have been investigated. The research was divided into three parts: a validation study, a classification analysis of delamination shape, and delamination location forecasting. The best mesh configuration in terms of computational time and accuracy was chosen during the validation study by comparing a COMSOL MULTIPHYSICS model’s results with those obtained in a previously conducted related study. The natural frequencies of sandwich beams with various delamination forms and locations were collected from COMSOL MULTIPHYSICS and used in the classification and regression studies. Deep learning (DL) models were trained to predict the location of delamination and detect the shape of delamination using the obtained dataset. To improve accuracy and generalizability, we proposed tuning the hyperparameters of the model to choose the best DL structures for achieving both regression and shape detection tasks with as high performance as possible. The Bayesian algorithm was used to select the best combination of values for multiple hyperparameters used in the regression and classification models. Furthermore, several optimization phases have been held to explore the hyperparameter space carefully. In the optimization study, various regression and classification metrics were used to determine the optimum model. The outcomes demonstrate that both delamination location prediction and shape detection were accomplished with excellent performance by the optimized deep learning models. Particularly, a mean squared error value of 0.0000043303 was achieved, which allows regression analysis to identify the location of the delamination accurately. Likewise, 0.9944 of accuracy value has been achieved for the shape detection analysis, which allows the model to identify delamination shapes precisely. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2

关键词： Deep learning Finite element method Hyperparameter tuning Sandwich structures delamination

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Improving Emotion Recognition in Audio Signals: Leveraging Novel Features and Deep Learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Role of solute elements in Mg-Mg_(2)Ni hydrogen storage alloys:A first-principles calculation study

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Journal of Magnesium and Alloys 2024年第11期12卷 4574-4593页

作者： Min-Seok Yoon Jae Hur Seo-Hui Park Ui-Jong Lee Guanglong Xu Hyung-Ki Park Byeong-Chan Suh Young Min Kim Won-Seok Ko Department of Materials Science and Engineering Inha UniversityIncheon 22212Republic of Korea College of Materials Science and Engineering Nanjing Tech UniversityNanjing 211816China Functional Materials and Components R&D Group Korea Institute of Industrial TechnologyGangneung 25440Republic of Korea Advanced Metals Division Korea Institute of Materials ScienceChangwon 51508Republic of Korea Advanced Materials Engineering University of Science and TechnologyDaejeon 34113Republic of Korea

The effects of various alloying elements on the performance of Mg-Mg_(2)Ni hydrogen storage alloys were investigated by performing firstprinciples density functional theory *** examined the important characteristics of hydrogen storage alloys by considering both Mg-based solid solution and Mg_(2)Ni-based intermetallic compound phases,where the hydride forms are Mg H2and Mg_(2)NiH4,*** particular,qualitatively valid information for predicting changes in plateau pressures in the pressure-composition-temperature(PCT)curve was provided by calculating changes in the energy of related hydrogenation *** effects of alloying elements on volume changes due to hydrogenation reactions were also obtained to provide additional criteria for the practical use of hydrogen storage *** the Mg_(2)Ni-based intermetallic compound,we examined the site preference of each alloying element,considering the designated stoichiometry of the base *** on the revealed site preferences,the effects of various possible alloying elements on the properties of Mg_(2)Ni-based hydrides were also *** structure analyses were further conducted to elucidate the detailed mechanisms underlying the role of the additional solute elements.

关键词： Mg-Mg_(2)Ni alloys Density functional theory Hydrogen storage alloys Metal hydrides

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Optimizing XGBoost for Enhanced Electrical Theft Detection: A Fusion of Particle Swarm and Jaya Optimization Techniques 4

Optimizing XGBoost for Enhanced Electrical Theft Detection: ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Prusty, Sashikanta Nayak, Debasish Swapnesh Kumar Moharana, Meena Prusty, Sushree Gayatri Priyadarsini Sahoo, Manmohan Rautaray, Jyotirmayee Department of Computer Science & Engineering Bhubaneswar India Centurion University of Technology & Management Department of Computer Science & Engineering Bhubaneswar India Department of Computer Science & Engineering Bhubaneswar India Odisha University of Technology & Research Department of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350383997

The most serious and hazardous for an electrical provider these days are non-technical losses caused due to electricity theft. The economy as a whole is impacted by fraudulent electricity usage, which lowers supply quality, raises generator load, and drives up costs for legal customers. To avoid these issues, this work proposes a smart electrical theft detection system using the XGBoost model combined with two optimization techniques Particle Swarm Optimization (PSO) and Jaya Optimization (JO). Furthermore, to handle the missing instances in the State Grid Corporation of China (SGCC) imbalanced dataset, we implemented a novel LORAS (Local Outlier-Robust Adaptive Synthetic) technique that balances the target class by making fake data points near the edges of different classes. Finally, the results show that the optimized XGBoost-PSO technique significantly classifies into two distinct categories (i.e. theft as '1' or no theft as '0') with having higher F1 Score (PSO Optimized): 0.9552 and AUC (PSO Optimized): 0.9667. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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Dynamic Visual Semantic Sub-Embeddings and Fast Re-Ranking for Image-Text Retrieval

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IEEE Transactions on Multimedia 2025年 27卷 3781-3796页

作者： Wei, Wenzhang Gui, Zhipeng Wu, Changguang Zhao, Anqi Peng, Dehua Wu, Huayi Wuhan University School of Remote Sensing and Information Engineering Wuhan430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

The core of image-text retrieval is to accurately measure the similarity between different modalities in a unified representation space. However, compared to textual descriptions of a certain perspective, the visual modality has more semantic variations. Therefore, images are usually associated with multiple textual captions in databases. Although popular symmetric embedding methods have explored numerous modal interaction approaches, they often learn toward outputting the average representation of multiple semantic variations within image embeddings. Consequently, information entropy in embeddings is increased, resulting in redundancy and decreased accuracy. In this work, we propose a Dynamic Visual Semantic Sub-Embeddings framework (DVSE) to reduce the information entropy. Specifically, we obtain a set of heterogeneous visual sub-embeddings through dynamic orthogonal constraint loss. To encourage the generated candidate image embeddings to capture various semantic variations, we construct a mixed distribution and employ a variance-aware weighting loss to assign different weights to the optimization process. In addition, we develop a Fast Re-ranking strategy (FR) to efficiently evaluate the retrieval results and enhance the performance. We compare the performance with existing set-based method using five image feature encoders and three text feature encoders on three benchmark datasets: MSCOCO, Flickr30 K and CUB Captions. We also show the role of different components by ablation studies and perform a sensitivity analysis of the hyperparameters. The qualitative analysis of visualized bidirectional retrieval and attention maps further demonstrates the ability of our method to encode semantic variations. © 1999-2012 IEEE.

关键词： Sensitivity analysis

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