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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Ahsan, Shawly Tasnia, Fairooz Tabassum, Nafisa Das, Avishek Hoque, Mohammed Moshiul Siddique, Nazmul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9798350359015

Textual sentiment analysis (TSA) has gained significant attention recently for its wide-ranging applications across various research domains and industries. However, most existing research and sentiment analysis tools are primarily tailored for English texts. The unique linguistic complexities of the Bengali language, coupled with a paucity of comprehensive resources and tools, pose distinctive challenges for TSA in Bengali. This paper introduces an intelligent approach, leveraging transformer-based learning techniques by harnessing the potent capabilities of self-attention mechanisms for dealing with Bengali sentences containing ungrammatical structures or local dialects. To tackle the downstream TSA task in Bengali, this work explores a range of machine learning (ML), deep learning (DL), and transformer-based baselines. Experimental results reveal that the Bangla BERT model outperforms the other baselines, achieving the highest weighted f1-score of 0.69. © 2023 IEEE.

关键词： Natural language processing Sentiment analysis Sentiment corpus Text processing Transformer learning

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Multilabel Aggressive Text Classification from Social Media using Transformer-based Approaches 26

Multilabel Aggressive Text Classification from Social Media ...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Hossain, Jawad Das, Avishek Hoque, Mohammed Moshiul Siddique, Nazmul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9798350359015

The prevalence of multilabel aggressive text content on social media has a detrimental societal impact attracting the attention of government agencies and tech corporations to undertake measures against the spread of it. Hitherto research has focused on high-resource languages like English, leaving low-resource languages like Bengali out of the spotlight. This work presents a transformer-based technique to classify multilabel aggressive texts in Bengali into their targets to aid research in this area. A dataset (EM-BAD) containing 13728 texts is developed into five target classes: Religious Aggression (ReAG), Political Aggression (PoAG), Verbal Aggression (VeAG), Gender Aggression (GeAG), and Racial Aggression (RaAG) to perform the aggressive texts classification. Experimental results demonstrate that the Bangla-BERT with adjusted pooling layer and fine-tuning outdoes all ML, DL, and transformer-base baselines and existing techniques. The Bangla-BERT shows the highest weighted f1-score of 0.89 in the multilabel aggressive text classification task. © 2023 IEEE.

关键词： Aggressive text classification Deep learning Natural language processing Text corpora Text processing

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Bidirectional Long-Short Term Memory with Byte Pair Encoding and Back Translation for Bangla-English Machine Translation 1st

Bidirectional Long-Short Term Memory with Byte Pair Encoding...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Tanvir, Md. Tasnin Oishy, Asfia Moon Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9783031346187

Machine Translation (MT) has emerged as an important research area in the computational intelligence domain to translate huge online resources cost-effectively. Among different MT approaches, Neural based Machine Translation (NMT) is in widespread use nowadays. In NMT, Bidirectional Long-Short Term Memory (BiLSTM) is popular due to its higher accuracy and improved performance. This study investigates different performance improvements with standard BiLSTM for Bangla-English MT (BEMT). Attention (ATT) mechanism, Byte Pair Encoding (BPE) and Back Translation (BT) are integrated independently and combinedly through a systematic approach. Four different models such as BiLSTM+ATT, BiLSTM+ATT+BPE, BiLSTM+ATT+BT and BiLSTM+ATT+BPE+BT have been tested rigorously on a benchmark corpus. The effectiveness of ATT, BPE and BT on a BiLSTM model are identified by comparing the performance of the individual models. Finally, the integrated model of BPE and BT with BiLSTM showed the best generalization ability and outperformed other models. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Long short-term memory

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance 46

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Horvat, M. Jercic, P. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

ISBN: (纸本)9789532331042

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors' intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers' opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey. © 2023 MIPRO Croatian Society.

关键词： Database systems

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An improved variational mode decomposition with adaptive initial center frequency for bearing fault feature extraction

An improved variational mode decomposition with adaptive ini...

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2024 China Automation Congress, CAC 2024

作者： Wang, Ying Lang, Xun Wu, Jiande Li, Yuzhen Li, Peng Zhang, Yufeng Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is significantly influenced by the initial center frequency. An improper setting of can significantly reduce the stability of decomposition, resulting in the loss or masking of feature information by noise. To address the above issue, an improved VMD algorithm is proposed in this work. Our algorithm redefines based on the narrowband characteristics of intrinsic mode function (IMF). Under the new definition, can be adaptively adjusted based on the signal spectrum processed by Wiener filtering. This adjustment significantly enhances the flexibility and anti-mixing performance of the original VMD. To validate our proposed method, comparative experiments were conducted using two sets of fault data from Case Western Reserve University (CWRU). The results demonstrate that our method is more effective and superior in extracting fault features. © 2024 IEEE.

关键词： Variational mode decomposition

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A Literature Review of Evaluation Approaches for Cyber-Physical Production systems 25

A Literature Review of Evaluation Approaches for Cyber-Physi...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Hostos, Hector Avila, Oscar Goepp, Virginie Department of Systems and Computing Engineering School of Engineering Universidad de los Andes Bogota Colombia ICube Laboratory INSA Strasbourg Strasbourg France

ISBN: (纸本)9789897586484

The role of Cyber-physical production systems (CPPSs) as Industry 4.0 enablers has raised the interest to upgrade legacy production systems. However, manufacturers face uncertainty when assessing if the transformation process is worth it. In this context, the aim of this study is to review the works in the existing literature that approach the evaluation of CPPSs in a context of production systems’ transformation. To do so, we adopted a systematic literature review process that comprises the development of a framework of six review questions that help us to analyze and characterize the literature found. From the literature review, this paper presents a conceptual model that aims at establishing the basis for a complete approach for CPPS evaluation. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Industry 4.0

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

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Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies 15

Feature Extraction Methods for Neural Networks in the Classi...

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15th International Joint Conference on Computational Intelligence, IJCCI 2023

作者： Hamilton, Natasha Harkin, Jim McDaid, Liam Liu, Junxiu Furey, Eoghan School of Computing Engineering and Intelligent Systems Ulster University Northland Road Derry United Kingdom Department of Computing Atlantic Technological University Port Road Donegal Ireland

ISBN: (纸本)9789897586743

Failure of large complex structures such as buildings and bridges can have monumental repercussions such as human mortality, environmental destruction and economic consequences. It is therefore paramount that detection of structural damage or anomalies are identified and managed early. This highlights the need to develop automated Structural Health Monitoring (SHM) systems that can continuously allow the safety status of structures to be determined, even in the worst and most isolated conditions, to ultimately help prevent destruction and save lives. Signal processing is a crucial step to detecting structural anomalies and recent work demonstrates the opportunities for neural networks, however the encoding of data for SHM requires the extraction of features due to often, noisy data. This paper focuses on feature extraction methods for artificial neural networks (ANNs) and spiking neural networks (SNNs) and aims to identify bespoke features which enable SNNs to encode data and perform the classification of anomalies. Results show that extraction of particular features in large real-world applications improve the classification accuracy of SNNs. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Feature extraction

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