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COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction

Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 339-349页

作者： Misra, Neeraj Kumar Das, Sanchali Satpathy, Sambit Addula, Santosh Reddy Trivedi, Munesh Chandra School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Information Technology University of the Cumberlands Williamsburg United States Computer Science and Engineering PSSCIVE Bhopal Bhopal India

Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. International occurrences of the COVID-19 pandemic have resulted from the rapid global spread of the SARS-CoV-2 virus. In this study, we examine the ML and data analysis techniques for diagnosis, treatment, and impact of COVID-19, which are based on artificial intelligence (AI). Moreover, we provide an overview of the current state of the art in AI-based machine learning and deep learning techniques as well as the datasets, tools, and performance of the tools that can be used to combat COVID-19. In this survey, we have attempted to provide a comprehensive overview of the most recent state-of-the-art approaches available to researchers in Mathematical and Statistical analysis and the larger medical community as a whole. It also includes information on how data and ML can be used to improve COVID-19 status and encourage further research to prevent COVID-19 outbreaks. Future directions and problems are also described in detail. © The Institution of Engineers (India) 2024.

关键词： COVID-19

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Video forgery localization using inter-frame denoising and intra-frame segmentation

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Multimedia Tools and Applications 2025年 1-17页

作者： Banerjee, Debanik Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India Department of Information Science and Engineering Siddaganaga Institute of Technology Karnataka Tumkur572103 India

Video forgery detection has been necessary with recent spurt in fake videos like Deepfakes and doctored videos from multiple video capturing devices. In this paper, we provide a novel technique of detecting fake videos by creating an ensemble network, based on statistical and deep learning methods to detect interframe forgery and intraframe forgery in forged videos separately. In this paper, Noise signature extraction of a particular image capturing sensor and an Autoencoder-based Convolutional Neural Network model (CNN) are used to localize the forged regions. We have trained the model to localize Deepfake video forgeries as well as copy-paste forgeries with effective results in the test data. The proposed fake video detector can be applied at the back-end of on-line video aggregating services and check their authenticity to verify the genuineness of videos. The results achieved have shown better performances in detecting fake videos compared to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 439-450页

作者： Li, Xiaocan Wen, Jigang Xie, Kun Xie, Gaogang Liang, Wei Hunan University College of Computer Science and Electronics Engineering Changsha China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Chinese Academy of Sciences Computer Network Information Center China

Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GEANT, demonstrate the effectiveness of the proposed TRDN. © 2016 IEEE.

关键词： Data accuracy

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Robust Stability of Discrete-time Genetic Regulatory Networks with Reaction Diffusion

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IAENG International Journal of Applied Mathematics 2024年第9期54卷 1756-1764页

作者： Zou, Chengye Yang, Yongwei Li, Haifeng Shang, Yubao Liu, Yunong Information Science and Engineering Yanshan University Qinhuangdao066004 China School of Mathematics and Computer Science Tongling University Anhui Tongling244000 China Computer and Information Science Qinghai Institute of Technology Xining810016 China Information Science and Engineering Yanshan University Qinhuangdao066004 China

In the realm of discrete-time modeling for gene regulatory networks, significant focus has been placed on addressing the time lags inherent in the process of DNA transcription to RNA and the subsequent translation of mRNA to proteins. These temporal delays have been consistently incorporated into discrete gene regulatory network models. However, true gene regulatory networks are also subject to spatial variables, due to the uneven distribution of protein and mRNA concentrations. The integration of reaction-diffusion terms is thus essential to fully represent the impact of spatial dynamics on gene regulatory networks. In such networks, reaction-diffusion dynamics highlight the complex interactions between neighboring spatial regions, where closeness leads to mutual influences on their functional activities. Based on this conceptual groundwork, this study introduces a discrete-time gene regulatory network model that includes the mutual interconnections between spatial areas. To guarantee the model's robust stability, we have established delay-dependent stability criteria using carefully designed Lyapunov-Krasovskii functions, framed within the context of linear matrix inequalities. The robustness and effectiveness of our approach are demonstrated through a numerical example presented in this work. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Stability criteria

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FMeAR: FACS Driven Ensemble Model for Micro-Expression Action Unit Recognition

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SN computer science 2024年第5期5卷 598页

作者： Chauhan, Anjaly Jain, Shikha Department of Computer Science & amp Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial expression, it is difficult to determine when spontaneous and low-intensity micro-expression occurs. The paper presents FMeAR which is a Facial action coding scheme (FACS) driven ensemble model for the micro-expression action unit recognition. Considering the simultaneous triggering of multiple facial AUs (action units), the problem is designed as a multiple-binary-class framework. First, each frame of the video is extracted and pre-processed thoroughly to eliminate any noise in the input. Then, features are extracted using an LBP descriptor from the pre-processed frames. To reduce the processing time, the size of the feature vector is reduced using PCA. Finally, reduced features are processed using a multiple ensemble model (comprising of Support Vector Machine, Linear Discriminant Analysis, and Decision Tree classifiers) to identify the micro-expressions action units. The presented model, FMeAR, outperforms several baseline frameworks and shows an accuracy of 95.3% for the CASME dataset, 95.9% for CASME-II, and 92.6% for MEVIEW. Intensive experiments demonstrated the effectiveness and generalization of FMeAR emphasizing its potential for various applications such as deception detection, depression detection, medical diagnosis, and many more. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Action units Ensemble-model FACS FMeAR Micro-expression

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Exploring Personalized Internet of Things(PIoT),social connectivity,and Artificial Social Intelligence(ASI):A survey

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High-Confidence Computing 2024年第3期4卷 136-162页

作者： Bisma Gulzar Shabir Ahmad Sofi Sahil Sholla Department of Information Technology National Institute of TechnologySrinagar 190001India Department of Computer Science and Engineering Islamic University of Science and TechnologyGazipur 1700India

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day *** emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and *** proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social *** research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information ***,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT ***,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful *** paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ***,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID *** study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.

关键词： Personal Internet of Things(PIoT) Artificial Social Intelligence(ASI) Hyperpersonalization

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Constructing multi-modal emotion recognition model based on convolutional neural network

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Multimedia Tools and Applications 2024年 1-26页

作者： Kuo, Jong-Yih Hsieh, Ti-Feng Lin, Ta-Yu Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

As society advances, an increasing number of individuals spend significant time interacting with computers daily. To enhance the human-computer interaction experience, it has become crucial to augment the computer’s ability for emotion recognition. This capability holds excellent importance as machines become capable of responding to us in a more natural and contextually relevant manner, aligned with our current emotional states. Examples of such applications include caregiving and social robots. Accurate recognition of human emotions, followed by the ability to determine the most appropriate responses, can significantly enhance user experiences. The most commonly employed methods in emotion recognition include observing facial expressions, audio, and conversational content. The multi-modal emotion recognition lacks the explicit mapping relation between emotion state and audio and image features. This study proposes a fusion method for audio-visual emotion recognition. The audio and video data are preprocessed separately. The audio emotion features and visual expression features were then extracted using two distinct feature extractors. The audio emotion feature extractor, denoted as audio-net, employs a 2D CNN architecture capable of processing image-based Mel-spectrograms as input data. The facial expression feature extractor, visual-net, uses a 3D CNN architecture to process sequences of facial expression images. Fusing the visual and auditory features and enhancing feature correlation using the deep canonical correlation analysis (DCCA) method. This research uses the eNTERFACE05 dataset and reaches 89.13% accuracy in classifying emotions. The result shows that considering audio and facial features at the same time can the model better recognize the emotion people are having. © The Author(s) 2024.

关键词： Convolutional neural networks

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Multispectral point cloud superpoint segmentation

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science China(Technological sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Multiclass Classification for Cyber Threats Detection on Twitter

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computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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Security Threats to xApps Access Control and E2 Interface in O-RAN

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 1197-1203页

作者： Hung, Cheng-Feng Chen, You-Run Tseng, Chi-Heng Cheng, Shin-Ming National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

Open Radio Access Networks (O-RANs) represent a novel wireless access network architecture that decomposes traditional RAN functions and makes them openly accessible. O-RANs enable real-time coordination, RAN performance optimization, and management through RAN Intelligent Controllers (RICs) and their related xApps. Due to the openness of O-RAN, developers have the flexibility to download various pre-developed xApps from the Internet for deployment. They can even develop their xApps to enhance the flexibility and innovation of the RAN. The current O-RAN official WG11 has defined numerous specifications for secure implementations. However, not only is accurately detecting malicious xApps a significant challenge, but the existing H-Release also does not fully adhere to the specifications in its implementation. This could potentially introduce security risks and threats. In this paper, we implement an experimental O-RAN H-Release environment and discover significant threats from the absence of specified access control permissions for xApps. Malicious attackers can illegally access APIs to utilize other services or launch attacks on legitimate xApps and E2 nodes through the E2 interface, potentially causing a complete RAN disruption. We used the identified vulnerabilities to design three attacks, providing detailed explanations and a comprehensive analysis of their impact on the system. Finally, we also submit the discovered threats to CVEs (CVE-2023-42358 and CVE-2023-41628), providing a reference for O-RAN officials to improve security in the future. © 2020 IEEE.

关键词： Real time systems

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