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Periodica polytechnica Electrical engineering and computer science 2021年第4期65卷 290-301页

作者： Nguyen, Du Duc Pham, Phong Dinh Software Engineering Department Faculty of Information Technology University of Transport and Communications 3 Cau Giay street Hanoi100000 Viet Nam Computer Science Department Faculty of Information Technology University of Transport and Communications 3 Cau Giay street Hanoi100000 Viet Nam

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers. © 2021 Budapest University of Technology and Economics. All rights reserved.

关键词： Semantics

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

arXiv

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

引用

IEEJ Transactions on Electronics, Information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Yuan, Shuai Qin, Hanlin Yan, Xiang Akhtar, Naveed Mian, Ajmal The School of Optoelectronic Engineering Xidian University Xi’an710071 China The School of Computing and Information Systems Faculty of Engineering and IT The University of Melbourne ParkvilleVIC3052 Australia The Department of Computer Science and Software Engineering The University of Western Australia Perth6009 Australia

This is the pre-acceptance version, to read the final version please go to IEEE TRANSACTION ON GEOscience AND REMOTE SENSING on IEEE Xplore. Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full levels. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://***/xdFai/SCTransNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Denoising Medical Images Using Deep Learning in IoT Environment

引用

computers, Materials & Continua 2021年第12期69卷 3127-3143页

作者： Sujeet More Jimmy Singla Oh-Young Song Usman Tariq Sharaf Malebary School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Software Department Sejong UniversitySeoulKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21911Saudi Arabia

Medical Resonance Imaging(MRI)is a noninvasive,nonradioactive,and meticulous diagnostic modality capability in the field of medical ***,the efficiency of MR image reconstruction is affected by its bulky image sets and slow process ***,to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network(SANR_CNN)for eliminating noise and improving the MR image reconstruction *** proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality,and SARN algorithm is used for building a dictionary learning technique for denoising large image *** proposed SANR_CNN model also preserves the details and edges in the image during *** experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and mean squared error(MSE).The proposed SANR_CNN model achieved higher PSNR,SSIM,and MSE efficiency than the other noise removal *** proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.

关键词： Medical resonance imaging convolutional neural network denoising contrast enhancement internet of things rheumatoid arthritis

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

引用

Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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Nusec and Midesec: Datasets for Histopathological Image Analysis

SSRN

引用

SSRN 2024年

作者： Nemati, Nooshin Samet, Refik Sak, Serpil Hancer, Emrah Kirmizi, Bilge Ayca Yildirim, Zeynep Traoré, Mohamed Department of Computer Engineering Ankara University Ankara06830 Turkey Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Software Engineering Mehmet Akif Ersoy University Burdur15039 Turkey Department of Pathology Faculty of Medicine Ankara University Ankara06080 Turkey

Hematoxylin and Eosin (H&E) staining stands as the gold standard protocol for the preparationof histopathology tissue samples in breast cancer research. This staining technique facilitates theidentification and differentiation of various cell types within the tissue, providing crucial informationfor pathologists. While deep learning algorithms have shown promise in automating the analysis ofH&E images, their effectiveness relies on extensive datasets for training. Unfortunately, there is ascarcity of publicly available datasets specifically tailored for nuclei and mitosis detection. To addressthis gap, this paper introduces two publicly available datasets, namely NuSeC (Nuclei Segmentationand Classification) and MiDeSeC (Mitosis Detection, Segmentation, and Classification) to serve asvaluable resources for building deep learning models for histopathological image analysis. The NuSeCdataset comprises 75 images for training and an additional 25 images for testing, encompassing around6000 nuclei structures, and the MiDeSeC dataset involves 35 training and 15 test image sets with morethan 500 mitoses. In both datasets, the QuPath tool as a pixel-level annotator is utilized to generatemask images. Moreover, we design two deep learning-based methodologies to conduct experimentalanalysis on the NuSeC and MiDeSeC datasets. The results indicate the potential to achieve substantialperformance on these datasets, facilitating the construction of new models or analysis of existing ones © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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software Testing Issues Improvement in Quality Assurance 2

Software Testing Issues Improvement in Quality Assurance

引用

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Salahat, Mohammed Said, Raed A. Hamid, Khalid Haseeb, Usama Abdel Maguid Abdel Ghani, Elsaid Abualkishik, Abedallah Iqbal, Muhammad Waseem Inairat, Mohammad University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Superior University Department of Software Engineering Lahore Pakistan Canadian University Dubai Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai United Arab Emirates

ISBN: (纸本)9798350335644

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above. © 2023 IEEE.

关键词： Quality control

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Adaptive Prompt Learning With Distilled Connective Knowledge for Implicit Discourse Relation Recognition

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 586-597页

作者： Bang Wang Zhenglin Wang Wei Xiang Yijun Mo School of Software Engineering and Hubei Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology (HUST) Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Implicit discourse relation recognition (IDRR) aims at recognizing the discourse relation between two text segments without an explicit connective. Recently, prompt learning has been applied to the IDRR task with great performance improvements over various neural network-based approaches. However, the discrete nature of the state-art-of-art prompting approach requires manual design of templates and answers, a big hurdle for its practical applications. In this paper, we propose a continuous version of prompt learning together with connective knowledge distillation, called AdaptPrompt, to reduce manual design efforts via continuous prompting while further improving performance via knowledge transfer. In particular, we design and train a few virtual tokens to form continuous templates and automatically select the most suitable one by gradient search in the embedding space. We also design an answer-relation mapping rule to generate a few virtual answers as the answer space. Furthermore, we notice the importance of annotated connectives in the training dataset and design a teacher-student architecture for knowledge transfer. Experiments on the up-to-date PDTB Corpus V3.0 validate our design objectives in terms of the better relation recognition performance over the state-of-the-art competitors.

关键词： Training Encoding Adaptation models Text recognition Knowledge engineering Feature extraction Bidirectional control Annotations Vocabulary Vectors

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Innovative Poverty Estimation through Machine Learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 IEEE.

关键词： Forecasting

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