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ACOVMD: Automatic COVID-19 misinformation detection in Twitter using self-trained semi-supervised hybrid deep learning model

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International Social science Journal 2024年第252期74卷 713-730页

作者： Birunda, S. Selva Devi, R. Kanniga Muthukannan, M. Babu, M. Mahesh Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Kelambakkam India Department of Civil Engineering KCG College of Technology Tamil Nadu Chennai India

During the COVID-19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID-19. Despite the existence of many fake news detection models;annotation inconsistency, memory consumption, accurate and self-trained efficient algorithms for detecting the emerging COVID-19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self-trained semi-supervised model that accurately and automatically detects the reliability of emerging COVID-19 tweets without delay. In this work, COVID-19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self-trained semi-supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self-trained repeatedly and the model gets updated to predict the reliability of upcoming COVID-19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label-seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID-19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance. © 2023 John Wiley & Sons Ltd.

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4290-4302页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Design of Chest Visual Based Image Reclamation Method Using Dual Tree Complex Wavelet Transform and Edge Preservation Smoothing Algorithm

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SN computer science 2024年第4期5卷 1-19页

作者： Kumar, B. P. Pradeep Darshan, S. L. Shiva Naresh, E. Srinidhi, N.N. Shreyas, J. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications. © The Author(s) 2024.

关键词： B quantities DWT Healthcare systems Medical image Visual BIR

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A Natural Light Simulation Mathematical Model for Mixed Reality Simulation Cockpit 13

A Natural Light Simulation Mathematical Model for Mixed Real...

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13th International Conference of Information and Communication technology, ICTech 2024

作者： Liu, Wenhai Li, Yang Feng, XinYuan Liang, GuiChong Li, Ning Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Information Science and Engineering East China University of Science and Technology Shanghai China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (纸本)9798350376258

This paper introduces a mathematical model for simulating natural light, grounded in the simple sky model and advanced sun positioning techniques. By processing inputs like time, geographical coordinates, and weather, it accurately computes natural light scenarios, replicating sun positioning, angles, intensity, and realistic environmental illumination. ©2024 IEEE.

关键词： Azimuth component illumination nature light simulation simulation solar energy sun position Zenith

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Intelligent Systems for Medical Diagnostics with the Detection of Diabetic Retinopathy at Reduced Entropy

Intelligent Systems for Medical Diagnostics with the Detecti...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Sindhu Madhuri, G. Somashekhara Reddy, D. Mahesh, T.R. Rajan, T. Vanitha, K. Shashikala, H.K. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information Technology Karnataka Bengaluru India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India School of Computer Science and Engineering Presidency University Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300826

Diabetes Mellitus (DM) is primarily defined by hyperglycemia, polyuria, and polyphagia and as a result of a complex interaction of hereditary and environmental variables, it has developed into a severechronic metabolic condition. Numerous diabetes problems might arise from uncontrolled high blood sugar. Long-term diabetes causes severe complications, some of which are fatal. Everywhere in the world, the prevalence of diabetes in patients is increasing at epidemic rates. Every year, diabetes and related disorders consume a sizable percentage of the national health budget. Several risk factors influence the etiopathogenesis of the disease and the emergence of the epidemic. The untreatable condition of diabetes may be managed by maintaining self-care in daily life, providing appropriate education about diabetes, and making significant advancements in knowledge, attitudes, skills, and management. Diabetes should be diagnosed as soon as possible because it can lead to several illnesses, including kidney failure, stroke, blindness, heart attacks, and lower limb amputation. This study aims to use relevant variables, create a prediction algorithm using machine learning, and choose the best classifier to produce results that are as close to clinical outcomes as possible with reduced entropy. The proposed approach is on selecting the characteristics that ail in the early detection of Diabetes Miletus utilizing Predictive analysis. The computational techniques K-Nearest Neighbour (KNN) and Decision Tree (DT) have been employed to detect DM at an early stage. The KNN outperforms with the best performance when evaluated against different performance metrics. © 2023 IEEE.

关键词： Decision trees

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Design of Cost Efficient VBIR Technique Using ICA and IVCA

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SN computer science 2024年第5期5卷 1-20页

作者： Pradeep Kumar, B.P. Srinidhi, N.N. Shiva Darshan, S.L. Naresh, E. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced that outperforms the conventional k-means clustering for image retrieval based on vision. The study emphasises how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be improved in conjunction with quantitative analysis. Within the field of DCE-MRI, ACICA stands out as a unique intravascular attention measurer. Utilising the spatial independence of extravascular and intravascular magnetic resonance imaging (MR) data, ACICA offers a strong foundation for DCE-MRI image analysis. It incorporates pharmacokinetic modelling to optimise the time lag, especially useful for arterial curves, and a reference region (RR)-based technique to adjust the intravascular concentration curve. The model's evaluation yields outstanding results, with recall and accuracy ranging from 83 to 99% and 82.8% to 99.6%, respectively. The average recall and precision across datasets are 92.86% and 92.82%).All things considered, this study demonstrates the effectiveness of ACICA in evaluating intravascular responsiveness and presents viable paths for enhancing clinical results and diagnostic precision in the treatment of prostate cancer. © The Author(s) 2024.

关键词： Adaptive complex independent components analysis Computed tomography Dynamic contrast enhancement Magnetic resonance imaging Visual-based image retrieval

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Adversarially Robust Object Detection via Deviation Calibration and Content Preservation

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5900-5911页

作者： Zhang, Xu Peng, Bo Lei, Jianjun Xue, Chao Yao, Yuxuan Huang, Qingming Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tiandy Technologies Tianjin300384 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China

Object detection has achieved a promising development in recent years and played an important role in various applications. However, the performance of object detection networks generally drops significantly when subjected to adversarial attacks. As an effective technique for defending against adversarial attacks, adversarially robust object detection has attracted increasing interest. In this paper, a novel deviation-calibrated and content-preserved network (DCCP-Net) is proposed for adversarially robust object detection by effectively exploring and mitigating the essential negative impact of noise disturbance in the feature space. Specifically, a deviation-calibrated robust feature enhancement module is designed to enhance the feature robustness of adversarial images by removing noise disturbance and supplementing rectified information. Besides, by enabling adversarial image features to imitate corresponding clean image features, a content-preserved consistency information imitation mechanism is proposed to obtain more accurate content information of adversarial images. Extensive experiment results have verified the superiority of the proposed DCCP-Net. © 1991-2012 IEEE.

关键词： Feature extraction Noise Training Object detection Detectors Robustness Perturbation methods Accuracy Calibration Convolution

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Memory-enhanced text style transfer with dynamic style learning and calibration

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science China(Information sciences) 2024年第4期67卷 181-196页

作者： Fuqiang LIN Yiping SONG Zhiliang TIAN Wangqun CHEN Diwen DONG Bo LIU College of Computer Science and Technology National University of Defense Technology Department of Computer Science and Engineering The Hong Kong University of Science and Technology Strategic Assessments and Consultation Institute Academy of Military Sciences

Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate *** results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.

关键词： style transfer memory-enhanced method text generation deep learning text representation

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Deep Learning Architectures Based Sentiment Analysis Systematic Literature Review 5

Deep Learning Architectures Based Sentiment Analysis Systema...

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Anjana Thampy, S. Jane Rubel Angelina, Jeyaraj Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputtur India Lourdes Matha College of Science and Technology Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350334128

Recently, sentiment analysis has attracted a good deal of interest from researchers. Each day, FB, Twitter, Weibo, and other social media, in addition to huge e-trade websites, generate big feedback, which can be used to investigate humans' critiques or emotions. Sentiment analysis will correctly analyze subjective records from this information. Deep learning becomes an effective machine learning approach which learns from a couple of layers of representations or features of the records and outputs a cutting-edge prediction effect. Deep learning overcomes all other machine learning techniques for sentiment analysis as well as offers different architectures for sentiment evaluation. Recently, RNN abbreviated as Recurrent Neural Networks as well as CNN abbreviated as Convolutional Neural Networks has been replaced with Transformer Language models in deep learning *** survey deals with contemporary architectures and challenges in that are in Sentiment Analysis as well as Natural Language Processing. © 2023 IEEE.

关键词： Sentiment analysis

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