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Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

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computers, Materials & Continua 2023年第6期75卷 5355-5377页

作者： Shabir Hussain Muhammad Ayoub Yang Yu Junaid Abdul Wahid Akmal Khan Dietmar P.F.Moller Hou Weiyan School of Information Engineering Zhengzhou UniversityZhengzhouChina School of Computer Science and Engineering Central South UniversityChangshaChina Department of Data Science The Islamia University of BahawalpurBahawalpurPakistan Clausthal University of Technology Clausthal-ZellerfeldGermany

As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for *** students,particularly,turned to Twitter to express their struggles and hardships during this difficult *** better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining *** framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 *** the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect ***,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled *** address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier *** resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a *** testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally *** findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to *** analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.

关键词： COVID-19 pandemic situational awareness ensemble learning aspect-based text classification deep learning models international students topic modeling

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Prediction of Breast Cancer using Machine Learning Techniques 2022

Prediction of Breast Cancer using Machine Learning Technique...

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14th International Conference on Contemporary Computing, IC3 2022

作者： Dikshit, Parthh Dey, Bhawna Shukla, Ayush Singh, Akhilesh Chadha, Tarankit Sehgal, Vivek Kumar Computer Science and Engineering Jaypee University of Information Technology India

ISBN: (纸本)9781450396752

Breast cancer is a topic that is frequently discussed these days. It is one of the most widespread diseases and forms of cancer. The National Cancer Institute says that the second most frequent malignancy in women is breast cancer. Every year, around 2000 new instances of breast cancer are diagnosed in males, while approximately 2,30,000 new cases are diagnosed in women. The diagnosis of this disease is crucial so that women can get it treated in its early stages. It is best for an precise and timely diagnosis. This is a critical phase in the therapy and recovery process. Breast cancer detection is done with the help of mammograms, which are basically X-rays of the breasts. It's a tool that is used to detect and help diagnose breast cancer. But detection is not easy due to different kinds of uncertainties in using these mammograms. Breast cancer can be detected using machine learning algorithms. These approaches can be used to create tools for clinicians that can be used to identify and diagnose breast cancer at an early stage. This will significantly improve patient survival rates. © 2022 ACM.

关键词： Machine learning

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Advancements in EEG-Based Emotion and Consciousness Recognition: Techniques, Challenges, and Innovations 1

Advancements in EEG-Based Emotion and Consciousness Recognit...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Jadekar, Ranjana B. Poornima, B. Gowramma, B.H. Puneeth, S.P. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Information Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms of techniques, methodologies, and accuracies, the research highlights the efficacy of both machine learning and deep learning models. The key challenges were identified such as noise removal due to artifacts and variability in patient responses. The findings underscore the potential of advanced computational methods in enhancing the reliability and precision of EEG-based diagnostic tools, paving the way for better clinical interventions and deeper understanding of human emotions. Also, the findings show that using deep learning, the current works have achieved better results for identification of emotion. © 2024 IEEE.

关键词： Electroencephalography

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Rice Disease Recognition using Improved Visual Geometry Group Deep Learning Algorithms 23

Rice Disease Recognition using Improved Visual Geometry Grou...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Lee, Shu-Hung Cheng, Chia-Hsin Tsai, Yu-Shun Huang, Yung-Fa Liu, Shing-Hong Chen, Ching-Mu School of Intelligent Manufacturing and Automotive Engineering Guangdong Business and Technology University Zhaoqing526238 China National Formosa University Department of Electrical Engineering Yunlin Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung Taiwan Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung Taiwan National Penghu University of Science and Technology Department of Electrical Engineering Penghu Taiwan

ISBN: (纸本)9798331528041

Rice diseases are one of the major factors affecting rice production. Traditionally, the identification and assessment of rice diseases have been done manually by experts and farmers, which is time-consuming and cannot provide real-time disease prediction, leading to significant losses. Therefore, there is a need for faster and more efficient methods for rice disease recognition. In this paper, we propose a solution for rice disease detection using artificial intelligence in image recognition. We investigate the performance of the visual geometry group (VGG) deep learning algorithms in recognizing rice diseases. Simulation results show that the VGG16 and VGG19 pretrained models achieve validation accuracies of 67% and 72.5%, respectively. © 2024 IEEE.

关键词： Artificial Intelligence Rice Diseases Visual Geometry Group

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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Risk ranking of rig up operation activities using multi-criteria decision-making methods based on fuzzy environment

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年第1期12卷 289-298页

作者： Ali, Syed Imran Lalji, Shaine Mohammadali Ali, Syed Muhammad Zia, Yassar Kamal Husain, Nusrat Yousaf, Nimra Balobaid, Awatef Salem Yahya, Ashraf Awan, Zahoor Department of Petroleum Engineering NED University Of Engineering & Technology Karachi 75270 Pakistan NED Academy NED University of Engineering &Technology Karachi 75270 Pakistan Department of Electronics & Power Engineering Pakistan Navy Engineering College National University of Sciences and Technology (NUST) Islamabad 44000 Pakistan School of Geography and Environment University of Oxford United Kingdom Department of Computer Science Computer Science and Information Technology College Jazan University Jazan 45142 Saudi Arabia Department of Food Engineering NED University Of Engineering & Technology Karachi 75270 Pakistan

The main aim of this research study is to determine the risk ranking of 29 failure modes of rig-up operations using a well-known and highly effective MCDM method i.e. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). This method takes into account the four criteria which comprise human impact, property damage, environmental effect and company reputation as risk criteria. Three Experts in the field were invited to present the risk ranking of the relative importance of criteria and criteria performance of each risk (as failure mode) by utilizing linguistic variables which are then integrated in triangular fuzzy numbers for further analysis using the Fuzzy TOPSIS method. The evaluation concluded that FM20 (Collision between the offshore platform and boats) is the most dangerous risk with a coefficient of closeness (CCi) equal to 0.992 while the risk level of FM26 (foggy weather) was found least having a coefficient of closeness (CCi) of 0. Subsequently, sensitivity analysis of ranks concluded that FM1 (H2S inaccurate monitoring) is the most sensitive alternative with standard deviation and frequency of rank change was 5.16 and 15 respectively whereas FM20 (Collision between offshore platform and boats) is observed to be the least sensitive alternative. Fuzzy TOPSIS results were found in excellent correlations with the other reliable MCDM methods which include Fuzzy VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) and Fuzzy COPRAS (COmplex PRoportional ASsessment) yielding correlation of around 1. This study proves to be a landmark in the petroleum industry and significantly contributes to verifying safe practices, by taking appropriate actions in minimizing the risk level associated with different activities during rig-up operations. © 2024 The Authors

关键词： Failure mode Fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) Multi-criteria decision-making rig-up operations Risk ranking

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Research on parking guidance system based on computer vision

Research on parking guidance system based on computer vision

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2022 International Conference on Scientific Computation and Applied Statistics, ICSCAS 2022

作者： Liu, Yuan Liu, Xiangju Wang, Shuaimao Tian, Rongqiang School of Computer and Information Engineering Qilu Institute of Technology China

The rapid pace of urbanization has made the urban parking problem more and more important, and various regions have introduced corresponding policies and measures to solve the parking problem, such as building large parking plants, introducing intelligent systems, etc. However, the number of parking spaces in large parking plants is large, the spatial distribution is complex, and it is difficult to find parking spaces. It is very important to improve parking efficiency through intelligent parking guidance and management. The parking space recognition system based on image recognition technology of computer vision can obtain real-time parking space utilization. The vehicle guidance system based on the generalized shortest path can guide incoming vehicles, reduce the time for vehicles to wait and find parking spaces. As the core module of the parking intelligent management system, the intelligent parking space recognition system and vehicle guidance system can significantly improve the parking efficiency. © Published under licence by IOP Publishing Ltd.

关键词： Remote control

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A review of intelligent optimization for group scheduling problems in cellular manufacturing

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Frontiers of engineering Management 2023年第3期10卷 406-426页

作者： Yuting WANG Yuyan HAN Dunwei GONG Huan LI School of Computer Science Liaocheng UniversityLiaocheng 252059China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical *** are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each *** a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization *** this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these *** literature is classified according to the number of objective functions,number of machines,and optimization *** classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also *** but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

关键词： cellular manufacturing group scheduling flowshop literature review

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Synth-to-Segment: MRI Brain Tumor Segmentation with Diffusion Transformers and Attention U-Net 5th

Synth-to-Segment: MRI Brain Tumor Segmentation with Diffusi...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ali, Mudassar Wu, Tong Mansoor, Maryam Hu, Haoji College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China University of Illinois Urbana-Champaign Institute Zhejiang University Zhejiang Hangzhou314400 China Department of Computer Science and Information Technology NED University of Engineering and Technology Sindh Karachi75270 Pakistan

ISBN: (纸本)9789819638628

Accurately segmenting brain tumors from MRI images is very important for effective treatment planning and mostly suffers from a scarcity of annotated datasets and complex tumor morphology. In this paper, the authors propose a new approach by generating high-quality synthetic MRI images using the BraTS 2020 dataset with a Modality-Conditional Diffusion Transformer that has Multi-Scale Attention and a Hierarchical Latent Space model. These synthetic images are combined with real data for training an attention-enhanced U-Net for segmentation. Our approach focuses on critical multi-scale features, therefore improving the accuracy and robustness of segmentation. Experimental results show significant improvements over the state-of-the-art and offer a very accurate and robust solution to brain tumor segmentation, particularly in data-scarce scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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