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Fake News Classification: Past, Current, and Future

computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Abnormal Data Detection Model Based on Autoencoder and Random Forest Algorithm: Camera Sensor Data in Autonomous Driving Systems

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International Journal of Advanced computer science and Applications 2025年第3期16卷 222-231页

作者： Shengwen, Geng Osman, Mohd Hafeez Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia

This project develops an AI-based anomaly detection system. In the field of autonomous driving, abnormal data will directly affect the safety of autonomous driving systems, especially in terms of abnormal camera sensor data. Sensor failure, environmental changes, or bad weather can lead to the emergence of abnormal data, which can affect the decision-making process and may have disastrous consequences. Based on the above problems, this study addresses this challenge by proposing a hybrid anomaly detection model (called CAE-RF) that combines convolutional autoencoders and random forest algorithms to achieve efficient and accurate identification of abnormal data patterns to improve the safety of autonomous driving systems. The proposed method will use convolutional autoencoders to calculate the reconstruction error and combine the hidden features extracted by the encoder as the input of the random forest to distinguish normal data from abnormal data. The key performance indicators such as accuracy, precision, recall, and F1 score are used to evaluate the model, and the robustness is guaranteed by cross-validation. Experimental results show that the CAE-RF model has an accuracy of 92% in distinguishing normal and abnormal data. Compared with traditional methods, the CAE-RF model achieves higher accuracy and reliability. The implementation of this model can timely identify and process abnormal data, reduce the risks brought by sensor failure or external environment changes, prevent potential accidents, and improve the safety and reliability of the autonomous driving system. © (2025), (science and information Organization). All rights reserved.

关键词： Tachometers

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Understanding technology Acceptance and Use in Social Media Platforms:A Systematic Literature Review and the Development of Research Framework

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Journal of Social Computing 2024年第3期5卷 261-291页

作者： Muhammad Amirul Asyraaf Roslan Nurul Amelina Nasharuddin Masrah Azrifah Azmi Murad the Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSerdang 43400Malaysia

The Systematic Literature Review(SLR)presented in the document focuses on the acceptance of social media(SM)platforms,particularly through the lens of the Unified Theory of Acceptance and Use of technology(UTAUT),technology Acceptance Model(TAM),Theory of Planned Behavior(TPB)and other relevant *** review involves a comprehensive analysis of the most used theories of acceptance models,the methods for selecting studies,and the results and findings from the reviewed *** study also discusses the accomplishment of research goals and creates a research framework to guide future *** geographic scope of the studies is also examined,providing insights into the distribution of studies across different *** review adheres to the PRISMA(Preferred Reporting Items for Systematic Reviews and meta-Analyses)guidelines for reporting systematic reviews and employs a theory-based approach,categorizing selected articles based on their theoretical *** review’s methodology involves the use of empirical evidence that meets predetermined inclusion criteria and addresses specific research *** document provides a detailed account of the steps involved in the systematic review,including the identification,screening,eligibility determination,and selection of *** review also offers implications for theory and practice,addresses limitations,and suggests areas for future research.

关键词： technology acceptance model(TAM) unified theory of acceptance and use of technology(UTAUT) theory of planned behavior(TPB) social media social networking services

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VSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain

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ACM Transactions on Sensor Networks 2024年第2期20卷 1-22页

作者： Tian, Siben Bai, Fenhua Shen, Tao Zhang, Chi Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications China Faculty of Information Technology Beijing University of Technology China

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm's complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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Metaheuristic task offloading approaches for minimization of energy consumption on edge computing: a systematic review

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Discover Internet of Things 2024年第1期4卷 1-30页

作者： Latip, Rohaya Aminu, Jafar Hanafi, Zurina Mohd Kamarudin, Shafinah Gabi, Danlami Faculty of Computer Science and Information Technology Universiti Putra Serdang Malaysia Department of Computer Science Kebbi State University of Science and Technology Aleiro Aleiro Nigeria

A systematic review of metaheuristic Algorithms for energy-efficient task offloading in edge computing is provided. The Grey Wolf Optimizer (GWO) is explored as a promising approach for reducing energy consumption. Future research focuses on hybrid metaheuristic algorithms, machine learning integration, and real-time task offloading © The Author(s) 2024.

关键词： Adversarial machine learning

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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An enhanced hybrid single-image-based structure-guided 0-norm and radiance–reflectance optimization

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Neural Computing and Applications 2025年 1-21页

作者： WangNo, Nitit Thaiklang, Saowaluk Major of Computer Science Faculty of Information Technology Roi Et Rajabhat University Roi Et Thailand Major of Computer Science Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Ratchathani Thailand

The reduced visibility during the winter season in an outdoor setting can be attributed primarily to the presence of haze or fog. Despite adjusting the lens of an optical sensor system for various purposes, such as automated driver assistance, remote sensing, and visual surveillance, the visual quality remains compromised. Owing to the overcast and murky atmosphere, it is difficult to remove these haziness emissions from a single image. To address this problem, we present a novel optimization-based dehazing algorithm that combines radiance and reflectance components with an additional refinement via a structure-guided 0-norm filter. We first estimate the poor reflectance map and optimize our transmission maps by using an estimated diffuse map. In addition, we estimate the removal of dehazing artifacts via a structure-guided 0 transmission map. In terms of qualitative and quantitative measures, compared with simulated pairs of images, the experimental results show that the proposed method is superior to the state-of-the-art algorithms. Moreover, the results of real-world enhancements confirm that the proposed method can provide high-quality images with no undesirable effects. In addition, textures may be removed, and edges can be preserved by the guided 0-norm filter, while the general image enhancement algorithms are being applied. The test results indicate that the proposed algorithm effectively generates enhanced images with greater visibility. The proposed approach has demonstrated superior performance metrics, including a 25.17% improvement in the peak signal-to-noise ratio (PSNR) and a 6.51% enhancement in the structural similarity index (SSI), surpassing the average performance of alternative methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Visibility

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Gender and age-evolution detection based on audio forensic analysis using light deep neural network

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International Journal of Speech technology 2023年第4期26卷 1091-1098页

作者： AL-Shakarchy, Noor D. Rageb, Huda Safoq, Mais Saad Department of Computer Science Faculty of Computer Science and Information Technology Kerbala University Kerbala Iraq

Forensic audio analysis is a foundation stone of many crime investigations. In forensic evidence;the audio file of the human voice is analyzed to extract much information in addition to the content of the speech, such as the speaker’s identity, emotions, gender, origin, etc. The accurate determination of individuals into groups based on their age development stage and their gender are often used as early investigations to differentiate them and determine the legal rights and responsibilities associated with them. This work introduces a light CNN model with a new architecture to detect the human age-evolution being’s stage (kids or adults) at the same time the gender of the adult one (male or female) based on the individual’s voice characteristics, which offers a balance between computational efficiency and model accuracy. The temporal information in the audio file is prepared by scaled and normalized. Then this information is exploited to extract and track the unique and salient audio features that make up the pattern of the feature map for each target class through some convolutional layers followed by maxpooling layers. Finally, The decision is made based on these feature maps by some fully connected layers. Successful and promising results are accomplished in terms of accuracy and loss functions which realize 0.99 and 0.017 respectively over the riched Voxceleb2 dataset. The proposed model underscores the importance of leveraging Light DNNs for gender and age-evolution detection, offering a robust and ethically sound solution for real-world applications in the field of audio forensics such as span speaker identification, victim profiling, deception detection, and more, contributing to the advancement of audio forensic analysis. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Computational efficiency

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