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A Deep Learning Model to Detect Fake News about COVID-19

Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing 4

Enabling Robust Security in MQTT-Based IoT Networks with Dyn...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj Department of Computer Science Engineering Global Academy of Technology Bengaluru India Department of Information Science Engineering RV College of Engineering Bengaluru India

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm's adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism's efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided. © 2025 The Authors. Published by Elsevier B.V.

关键词： Shift registers

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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

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Journal of engineering and Applied science 2024年第1期71卷 8页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine Information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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XLNet Transfer Learning Model for Sentimental Analysis

XLNet Transfer Learning Model for Sentimental Analysis

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Dhivyaa, C.R. Nithya, K. Sendooran, G. Sudhakar, R. Kumar, K.Sathis Kumar, S.Santhosh Computer Science and Engineering Kongu Engineering College Erode India Computer Technology-PG Kongu Engineering College Erode India Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350333602

In natural language processing, an important objective is to perform sentiment analysis, which involves categorizing textual content based on whether it expresses a positive, negative, or neutral sentiment. Sentiment analysis systems face challenges such as ambiguity, subjectivity, contextual understanding, and domain adaptation. These challenges make accurately determining sentiment in text a complex task. To address these challenges, the proposed objective for sentiment analysis on movie review datasets is to develop a transfer learning-based XLNet model. The utilization of transformer-based models has resulted in notable advancements across several NLP tasks in recent years. In this work, the feasibility of employing the XLNet model for sentiment analysis is examined, which involves fine-tuning the XLNet model on a labeled sentiment analysis dataset. First, the dataset is preprocessed, and the XLNet model is loaded. In addition, the classification layer is added to the model and transfer learning is applied for fine-tuning on the sentiment analysis dataset. The effectiveness of the proposed work is evaluated on a test set, and various metrics such as accuracy, precision, recall, and F1 score are reported. Experimental results indicate that the XLNet model attained goood results than other transformer based models on movie review dataset for sentiment analysis and it shows the effectiveness of transfer learning with XLNet in the sentiment analysis domain. © 2023 IEEE.

关键词： Sentiment analysis

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Novel Face Mask Detection Technique using Machine Learning to control COVID’19 pandemic

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Materials Today: Proceedings 2023年 80卷 3714-3718页

作者： Gupta, Sandeep Sreenivasu, S.V.N. Chouhan, Kuldeep Shrivastava, Anurag Sahu, Bharti Manohar Potdar, Ravindra Department of Electrical Engineering JECRC University Jaipur India Department of Computer Science and Engineering Narasaraopeta Engineering College Andhra Pradesh Narasaraopet India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Lakshmi Narain College of Technology and Science Indore India Department of Computer Science and Engineering Dr. D.Y. Patil Institute of Technology Pimpri Pune India Department of Electronics and Telecommunication Engineering Bhilai Institute of Technology Chhattisgarh Durg India

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of antiviral drugs is one of the reasons for the spreading of COVID-19 virus. This disease is spreading continuously and easily due to some common mistakes by people, like breathing, coughing and sneezing by infected persons. The main symptom is the normal flu. Therefore, in the present condition, the best precaution for this disease is the face mask, which covers both areas of mouth & nose. According to the government and the World Health Organization, everyone should wear a face mask in busy places like hospitals and marketplaces. In today's environment, it's difficult to tell if someone is wearing a mask or not, and physical inspection is impractical since it adds to labour costs. In this research, we present a mask detector that uses a machine learning facial categorization system to determine whether a person is wearing a mask or not, so that it may be connected to a CCTV system to verify that only persons wearing masks are allowed in. © 2021

关键词： COVID-19

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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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Research on Pupil Segmentation Network Based on Improved TransUNet 8

Research on Pupil Segmentation Network Based on Improved Tra...

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8th International Conference on computer Graphics and Virtuality, ICCGV 2025

作者： Jianting, Liu Huazhong, Xiang Zexi, Zheng Dawei, Zhang Songlin, Zhuang College of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781510689213

To address the critical challenge of pupil segmentation accuracy in medical image analysis, this paper proposes an innovative pupil segmentation method based on an enhanced TransUNet deep neural network. The proposed architecture introduces three key improvements: a novel multi-kernel self-selective attention mechanism that optimizes local feature extraction through weighted fusion of different convolution modules;a modified cross-attention Transformer that enables efficient parallel processing of spatial information in both horizontal and vertical dimensions;and an advanced feature fusion module that effectively integrates global and local information through multi-scale feature weighting. Extensive experiments on the OpenEDS dataset demonstrate the method's superior performance, achieving state-of-the-art results with average precision of 97.7%, average recall of 97.41%, and average Dice coefficient of 98.15%. These significant improvements in segmentation accuracy not only advance the field of medical image analysis but also provide a robust foundation for applications in ophthalmological diagnosis, human-computer interaction, and cognitive science research. The proposed methodology offers valuable insights into designing efficient attention mechanisms for medical image segmentation tasks, contributing to both theoretical understanding and practical applications in healthcare imaging systems. © 2025 SPIE.

关键词： Deep neural networks

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Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

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computer Systems science & engineering 2023年第3期44卷 2129-2145页

作者： V.Thanikachalam M.G.Kavitha V.Sivamurugan Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Computer Science and Engineering University College of Engineering PtukottaiRajamadam614701India

In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision *** the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing computer Aided Diagnosis(CAD)models for DR recognition and *** this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of *** presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus *** addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this ***,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for *** model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal *** utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model *** presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation *** resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

关键词： Diabetic retinopathy machine learning internet of things deep belief network image segmentation

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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