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Automatic Speech Emotion Recognition: a Systematic Literature Review

International Journal of Speech Technology 2024年第1期27卷 267-285页

作者： Mustafa, Haidy H. Darwish, Nagy R. Hefny, Hesham A. Computer Science Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Information Systems and Technology Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt

Automatic Speech Emotion Recognition (ASER) has recently garnered attention across various fields including artificial intelligence, pattern recognition, and human–computer interaction. However, ASER encounters numerous challenges such as a shortage of diverse datasets, appropriate feature selection, and suitable intelligent recognition techniques. To address these challenges, a systematic literature review (SLR) was conducted following established guidelines. A total of 60 primary research papers spanning from 2011 to 2023 were reviewed to investigate, interpret, and analyze the related literature by addressing five key research questions. Despite being an emerging area with applications in real-life scenarios, ASER still grapples with limitations in existing techniques. This SLR provides a comprehensive overview of existing techniques, datasets, and feature extraction tools in the ASER domain, shedding light on the weaknesses of current research studies. Additionally, it outlines a list of limitations for consideration in future work. © The Author(s), under exclusive licence to Springer science+business Media, LLC, part of Springer Nature 2024.

关键词： Speech recognition

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Comparative Study and Analysis in Text Summarization Literature 6

Comparative Study and Analysis in Text Summarization Literat...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Boghdady, Eman W. Shaaban, Essam Haggag, Mohamed H. Department of Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of computers and artificial intelligence Beni-Suef University Cairo Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

ISBN: (纸本)9798350373875

This study aims to give a concise summary of the research conducted in text summarizing across different languages using diverse methodologies. The report offered a set of facts with sufficient correctness, precision, and efficacy. We identified many characteristics and categorizers for text summarizing. This work aids academics in doing a comprehensive investigation on different methods of text summarizing. © 2024 IEEE.

关键词： Text processing

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Segmentation of Water Bodies from Satellite Images Using Spatial-Channel Attention UNet

Segmentation of Water Bodies from Satellite Images Using Spa...

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2024 International Conference on Smart systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Anitha, M. Chitra, P. Meenaragavi, P. Thiagarajar College of Engineering Department of Computer Applications Madurai625 015 India Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350378177

Precise division of aquatic areas is essential for efficient environmental supervision and administration. It enables the precise delineation and tracking of aquatic ecosystems, facilitating the assessment of water quality and the identification of environmental hazards such as pollution and habitat degradation. By offering high-resolution data and enabling thorough coverage of large areas, advanced technologies like satellite images play a crucial role in improving water body segmentation and expanding the capacity to monitor and manage water resources effectively and sustainably. In this study, the focus is on leveraging Sentinel-2 satellite imagery for accurate water body segmentation. The dataset is meticulously curated and comprises Sentinel-2 satellite images. Masks are then created to isolate water bodies from the original images using advanced deep-learning architectures, including UNet, Deeplabs, and SegNet. Furthermore, the performance of UNet is enhanced by incorporating attention mechanisms such as channel attention, spatial attention, and combined spatial and channel attention. These algorithms are used to generate output masks, which are then compared with the existing masks from the dataset. Subsequently, the evaluation was performed using metrics such as accuracy, binary cross- entropy loss, and mean intersection over union, providing comprehensive insights into their efficacy. Notably, the findings highlight the remarkable accuracy achieved by the UNet model enhanced with spatial and channel attention mechanisms, reaching an impressive 96.7%. This notable progress emphasizes the possibility of achieving better accuracy in segmenting water, which in turn aids real-time monitoring and informed decision-making in environmental management. © 2024 IEEE.

关键词： Decision making

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Rf-based fingerprinting for indoor localization: deep transfer learning approach

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3393-3403页

作者： Safwat, Rokaya Shaaban, Eman Al-Tabbakh, Shahinaz. M. Emara, Karim Computer Systems Faculty of Information and Computer Science Ain Shams University El-Khalyfa El-Mamoun Street Abbasya Cairo11517 Egypt Department of Physics Faculty of Women for Art sciences and Education Ain Shams University Asmaa Fahmi Street Al Golf Nasr City Cairo11517 Egypt

Transfer Learning (TL) has emerged as a powerful approach for improving the performance of Deep Learning systems in various domains by leveraging pre-trained models. It was proven that features learned by deep learning can smoothly be reused across similar domains. Deep transfer learning schemes compensate for limited training data via transfer learning of a rich data environment. This paper investigates the effectiveness of applying TL schemes in indoor localization. It proposes four deep TL models where the knowledge is transferred from the rich-measurement data source domain to multiple target domains with limited data measurements. The architecture of the source domain is based on Convolutional Neural Network (CNN), where the four deep TL models for the target domain are: standalone feature extractor, integrated feature extractor, selective fine-tuning, and weight initialization. We employed a dataset of RF fingerprinting measurement signals representing common interior conditions, including extremely crowded, medium cluttered, low cluttered, and open environments, to test the effectiveness of the proposed TL models. We measured the accuracy and computation time of target-domain models trained, with varied percentages of restricted data sizes: 40%, 30%, 20%, 15%, 10%, 5%, and 2.5%. The experimental results show that all TL models are effective in achieving significant improvement in accuracy when compared to non-transferred models, even with minimal training data size. However, the proper determination of the TL model and the amount of training data profoundly influence the performance results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Fabrication of SiC particle dispersed AZ91D magnesium alloy composite by injection molding method and their properties

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第3期74卷 141-146页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Graduate School of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Okayama Soja-shi719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Co. Suwa-shi Nagano Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Soja-shi Japan

AZ91D magnesium alloy chips were coated with SiC powder using a binder, and injection molding was attempted using SiC powder coated magnesium chips as raw material. The metallurgical structure of the injection molded products showed uniform dispersion of SiC particles in the matrix, and there were no significant defects such as voids at the SiC particle-matrix interface. The hardness and 0.2% proof stress of the injection molded products tended to improve with increasing SiC content, while the elongation decreased with increasing SiC content. The Young's modulus of the injection molded products improved with increasing SiC content. It was confirmed that the specific bending rigidity of the injection molded products also improves due to the significant increase in the Young's modulus, although the addition of SiC leads to an increase in density. © 2024 The Japan Institute of Light Metals.

关键词： Silicon carbide

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and Engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and Engineering.

关键词： Wavelet transforms

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Sailfish Optimizer Algorithm for Effective Toxic Gas Detection Sensor Placement in IIoT

Sailfish Optimizer Algorithm for Effective Toxic Gas Detecti...

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2024 International Conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Senthilraja, P. Ponkaviarasu, S. Vijayabharathi, K.V. Sabarish, H. Kumar, N. Jagadish K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637215 India Sri Sai Ram Engineering College Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

Toxic gas detection in industrial contexts is a key safety problem, necessitating the strategic positioning of sensors for efficient monitoring. However, the ideal placement of these sensors is a difficult optimisation problem due to factors such as gas dispersion patterns, facility layout, and economic considerations. Traditional methodologies frequently fail to provide efficient solutions, resulting in suboptimal sensor combinations and possible safety risks. This study presents an innovative solution to this problem using the Sailfish Optimizer algorithm within the context of the Industrial Internet of Things (IIoT). The Sailfish Optimizer Algorithm (SOA), inspired by sailfish cooperative hunting behaviour, provides a novel solution to optimisation issues that involve exploring and exploiting search space. The programme quickly navigates across solution spaces by simulating the coordinated movement of sailfish, seeking optimal configurations that minimise the risk caused by poisonous gases while maximising coverage and lowering expenses. By comparing the performance of the Sailfish Optimizer algorithm to standard optimisation techniques, we demonstrate its superiority in achieving optimal sensor combinations. Furthermore, we assess the algorithm's capacity to solve real issues including scalability, adaptability, and integration with current IIoT infrastructure. © 2024 IEEE.

关键词： Plant layout

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3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

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computers, Materials & Continua 2023年第9期76卷 2861-2877页

作者： Kainat Nazir Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Usman Tariq Jae-Hyuk Cha Medical Imaging and Diagnostics Lab NCAIDepartment of Computer ScienceCOMSATS University IslamabadIslamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWahPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved *** a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert ***,this process is time-consuming and ***,a computerized technique is required for brain tumor detection in MRI *** the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing *** standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model *** 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,***,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

关键词： Brain tumor segmentation connect component analysis deep learning kronecker convolution magnetic resonance imaging

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A deep learning model based glaucoma detection using retinal images

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Journal of Intelligent and Fuzzy systems 2024年第1期1卷 1-12页

作者： Ruby Elizabeth, J. Kesavaraja, D. Ebenezer Juliet, S. Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology T.M. Palayam Tamil Nadu Coimbatore India Department of Computer Science and Engineering Dr. Sivanthi Aditanar College of Engineering Tuticorin District Tamil Nadu Tiruchendur India Scope Vit Tamil Nadu Vellore India

The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achie

关键词： Ophthalmology

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Neutrosophic Set Hybrid MCDM Methodology for Choosing Best Surfactant-Free Microemulsion Oils within Performance and Emission Criteria Over a Wide Range of Engine Loads

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Neutrosophic Sets and systems 2023年 56卷 190-199页

作者： Abdel-Monem, Ahmed Hassan, Mohammed K. Abdelhafeez, Ahmed Mohamed, Shimaa S. Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt Egypt Faculty of Information Systems and Computer Science October 6th University Cairo Egypt

Microemulsion fuels, also known as surfactant-free fuels, are fuels made from a combination of two immiscible liquids, hydrocarbon fuel, and water, with a trace quantity of a co-solvent. Surfactants are often used in conventional microemulsion fuels, however surfactant-free microemulsion fuels instead depend on the thermodynamic features of the combination to generate a stable emulsion. In this paper, the multi-criteria decision-making (MCDM) model for choosing microemulsion fuel with surfactant-free. Various helpful and harmful criteria were evaluated for ten fuels at varying motor loads, according to performance and emission characteristics. This paper integrated the neutrosophic set with the TOPSIS method. The neutrosophic set is used to deal with uncertain data. The TOPSIS method is used to rank the different fuels. © (2023). All Rights Reserved.

关键词： Microemulsions

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