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Analysis and Classification of Fake News Using Sequential Pattern Mining

Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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Robust Device-Free Mmwave Sensing with Specular Reflection Interference Mitigation

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5749-5764页

作者： Liu, Yulin Wang, Jie Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-Free mmWave Sensing (DFWS) could sense target state by analyzing how target activities influence the surrounding mmWave signals. It has emerged as a promising sensing technology. However, when employing DFWS indoors, specular reflection interference arises due to the specular reflectors. This interference often induces ghost targets, impacting the accurate estimation of the number and position of targets, resulting in degradation in sensing performance. To tackle this issue, we delve into the generation mechanism of specular reflection interference and analyze its multi-domain characteristics. Through exploration, we discern its temporal sparsity, spatial symmetry or collinearity, and frequency correlation characteristics, and propose four metrics to measure them, accordingly. Specifically, we propose a temporal characteristic quantitative evaluation metric based on identity matching, spatial symmetry and collinearity quantitative evaluation metrics based on geometric analysis, and a frequency correlation quantitative evaluation metric based on Doppler velocity correction, respectively. Based on these metrics, we design a novel Specular Reflection Interference Mitigation (SRIM) method and develop a robust SRIM-DFWS prototype system based on a 60 GHz mmWave radar to validate our proposed method. Experimental results demonstrate that our proposed method could achieve accurate and effective mitigation of specular reflection interference in device-free target tracking. © 2002-2012 IEEE.

关键词： Radar interference

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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification Methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of Natural Language Processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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Topology-adaptive Bayesian optimization for deep ring echo state networks in speech emotion recognition

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Neural Computing and Applications 2025年第1期37卷 399-416页

作者： Soltani, Rebh Benmohamed, Emna Ltifi, Hela University of Sfax BP 1173 Sfax3038 Tunisia Department of computer science Onaizah college of Engineering and Information Technology Oniazah colleges Saudi Arabia Computer Science and Mathematics Department Faculty of Sciences and Techniques of Sidi Bouzid University of Kairouan Kairouan Tunisia

Speech emotion recognition is a difficult task that is gaining attention in a variety of domains, including psychology, human–computer interaction, and speech processing. To recognize speech emotions, machine learning techniques such as convolutional neural networks and long short-term memory networks have been successfully used. However, these models often fail to capture the long-term temporal dynamics of emotional speech. In this work, data augmentation was performed on all datasets. The generated result was used to extract the discriminant features. Then we apply a deep echo state network with a ring topology. The Bayesian optimization (BO) algorithm was applied to the spectral radius of the reservoir matrix. Our approach reveals term-weighted and unweighted accuracies of (75.45%, 74.79%), (86.89%, 86.14%), (98.99%, 97.62%), and (62%, 61.24%) on EMO-DB, SAVEE, RAVDESS, and TESS datasets. The findings demonstrate that by using BO, we are able to determine an optimal spectral radius. Therefore, this approach balances the minimal complexity of reservoir design and high emotion recognition accuracy. Notably, we demonstrate that a simple, deterministically constructed cycle reservoir within DRESN is comparable to other methodologies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Bayesian networks

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Dempster-shafer deep capsule attention model (DDCAM)

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multimedia Tools and Applications 2025年 1-29页

作者： Mehravaran, Zahra Ghanizadeh, Ahmad Navid Hamidzadeh, Javad Harati, Ahad Mashhad Iran Department of Computer Science Saarland University Saarbrücken Germany Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, this paper proposes a novel framework, Dempster-Shafer Deep Capsule Attention Model (DDCAM), that combines 3D convolution with attention mechanisms to improve feature representation. This model utilizes the Dempster-Shafer Theory to manage uncertainty and optimize feature selection, resulting in more reliable and accurate learning. In this architecture, dense capsule blocks are used to improve gradient flow and mitigate the effects of vanishing gradients. The DDCAM consistently outperforms state-of-the-art capsule networks on a wide range of datasets, especially complex datasets such as MNIST, CIFAR10, SVHN, and Fashion MNIST. DDCAM improves accuracy by 1.29% on CIFAR10 and 0.76% on SVHN compared to existing models, while achieving precision improvements of 1.22% and 1.08% on CIFAR10 and SVHN, respectively. Additionally, experiments conducted on the AffNIST dataset demonstrate the robustness of DDCAM in handling affine transformations, where it achieved significantly higher accuracy compared to existing models. These results illustrate the model’s enhanced ability to capture complex data relationships and improve both accuracy and precision, advancing capsule neural network performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Affine transforms

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Automatic Timbre Transformation using Enhanced Diffusion Model

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IEEE Access 2025年 13卷 91642-91654页

作者： Wu, Cheng-Han Cheewaprakobkit, Pimpa Shih, Timothy K. Lin, Yu-Cheng Liu, Bing-Ze National Central University Department of Computer Science and Information Engineering Taoyuan City32001 Taiwan Asia Pacific International University Faculty of Information Technology Saraburi18180 Thailand Yuan-Ze University Department of Computer Science and Engineering Taoyuan City32003 Taiwan

We present a novel timbre transfer model that uses an enhanced diffusion architecture to convert music from various instruments into Erhu timbre. The Erhu, a traditional Chinese instrument, is difficult to simulate due to its rich vibrato and smooth note transitions. Existing Musical Instrument Digital Interface systems struggle to capture its nuanced dynamics. Our model integrates a Pitch Encoder, a Loudness Encoder, and a Diffusion Decoder. The encoders extract pitch features and dynamic loudness variations, guiding the decoder in generating realistic Erhu timbre. By extracting general musical features, the system generalizes to unseen input types without retraining. Evaluations based on pitch accuracy, cosine similarity, and Fréchet Audio Distance show that our model achieves 96% pitch accuracy and high fidelity in Erhu timbre reproduction. This study demonstrates the potential of diffusion-based timbre transfer models in music generation and provides new directions for future work on both music generation and timbre transfer. © 2013 IEEE.

关键词： Deep learning

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Publication performance and trends in virtual reality research in education fields: a bibliometric analysis

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multimedia Tools and Applications 2025年第10期84卷 6999-7026页

作者： Elaish, Monther M. Yadegaridehkordi, Elaheh Ho, Yuh-Shan Computer Science Department Information Technology Faculty University of Benghazi Benghazi Libya College of Information and Communications Technology School of Engineering and Technology CQUniversity Sydney Australia Trend Research Centre Asia University No. 500 Lioufeng Road Taichung41354 Taiwan

Virtual Reality (VR) technology has the potential to enhance education by providing immersive and engaging learning experiences that can improve teaching and learning outcomes. While there is a growing interest in utilizing VR in education settings, further research is needed to understand its pedagogical effectiveness and address associated considerations and challenges. This bibliometric study comprehensively analyzes 1,157 relevant articles from the Social science Citation Index (SSCI) and the science Citation Index Expanded (SCI-EPANDED) to gain insights into the current state of VR integration in education. The analysis revealed variation in VR adoption and research output across countries and institutions, underscoring the importance of collaboration and knowledge-sharing in the field. Key research areas and trends, such as the use of VR for skill development and training, were identified. Additionally, the study highlighted the need for more research on the pedagogical effectiveness of VR. The findings carry practical implications for guiding future research shaping policy decisions and advocating for a concerted effort to harness VR’s capabilities in education. This study serves as a practical roadmap, promoting the implementation and improvement of VR technology and fostering equitable and inclusive educational practices. © The Author(s) 2024.

关键词： Virtual reality

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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