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  • Open Access

    ARTICLE

    Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

    Kwok Tai Chui1,*, Brij B. Gupta2,3,4,5,6,*, Varsha Arya7,8,9, Miguel Torres-Ruiz10

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762

    Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >

  • Open Access

    ARTICLE

    Opinion Mining on Movie Reviews Based on Deep Learning Models

    Mian Muhammad Danyal1, Muhammad Haseeb1, Sarwar Shah Khan2,*, Bilal Khan1, Subhan Ullah1

    Journal on Artificial Intelligence, Vol.6, pp. 23-42, 2024, DOI:10.32604/jai.2023.045617

    Abstract Movies reviews provide valuable insights that can help people decide which movies are worth watching and avoid wasting their time on movies they will not enjoy. Movie reviews may contain spoilers or reveal significant plot details, which can reduce the enjoyment of the movie for those who have not watched it yet. Additionally, the abundance of reviews may make it difficult for people to read them all at once, classifying all of the movie reviews will help in making this decision without wasting time reading them all. Opinion mining, also called sentiment analysis, is the process of identifying and extracting… More >

  • Open Access

    ARTICLE

    Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

    Puja S. Prasad1, Adepu Sree Lakshmi1, Sandeep Kautish2, Simar Preet Singh3, Rajesh Kumar Shrivastava3, Abdulaziz S. Almazyad4, Hossam M. Zawbaa5, Ali Wagdy Mohamed6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 725-739, 2024, DOI:10.32604/cmes.2023.030640

    Abstract Pupil dynamics are the important characteristics of face spoofing detection. The face recognition system is one of the most used biometrics for authenticating individual identity. The main threats to the facial recognition system are different types of presentation attacks like print attacks, 3D mask attacks, replay attacks, etc. The proposed model uses pupil characteristics for liveness detection during the authentication process. The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities. The proposed framework consists of two-phase methodologies. In the first phase, the pupil’s diameter is calculated by applying stimulus (light) in one eye… More >

  • Open Access

    ARTICLE

    Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

    Mohammed Zakariah1, Salman A. AlQahtani2,*, Abdulaziz M. Alawwad1, Abdullilah A. Alotaibi3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752

    Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are slowing computation. In addition, networked… More >

  • Open Access

    ARTICLE

    Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

    Shoaib Khan, Bilal Khan, Saifullah Jan*, Subhan Ullah, Aiman

    Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579

    Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the potential of ML techniques in… More >

  • Open Access

    ARTICLE

    Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

    Mohammed Abdalsalam1,*, Chunlin Li1, Abdelghani Dahou2, Natalia Kryvinska3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1427-1467, 2024, DOI:10.32604/cmes.2023.029911

    Abstract One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database… More >

  • Open Access

    ARTICLE

    Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset

    Rosepreet Kaur Bhogal1,*, V. Devendran2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2759-2772, 2023, DOI:10.32604/csse.2023.034862

    Abstract Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1 (CNN_RNN_1), used to find the… More >

  • Open Access

    REVIEW

    Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

    Yong Li1,4, Qiming Liang2,*, Bo Gan3, Xiaolong Cui4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1-23, 2023, DOI:10.32604/cmc.2023.042494

    Abstract Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream models, the temporal models, the… More >

  • Open Access

    ARTICLE

    Using Speaker-Specific Emotion Representations in Wav2vec 2.0-Based Modules for Speech Emotion Recognition

    Somin Park1, Mpabulungi Mark1, Bogyung Park2, Hyunki Hong1,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1009-1030, 2023, DOI:10.32604/cmc.2023.041332

    Abstract Speech emotion recognition is essential for frictionless human-machine interaction, where machines respond to human instructions with context-aware actions. The properties of individuals’ voices vary with culture, language, gender, and personality. These variations in speaker-specific properties may hamper the performance of standard representations in downstream tasks such as speech emotion recognition (SER). This study demonstrates the significance of speaker-specific speech characteristics and how considering them can be leveraged to improve the performance of SER models. In the proposed approach, two wav2vec-based modules (a speaker-identification network and an emotion classification network) are trained with the Arcface loss. The speaker-identification network has a… More >

  • Open Access

    ARTICLE

    Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

    Yih Bing Chu*, Zhi Min Lim, Bryan Keane, Ping Hao Kong, Ahmed Rafat Elkilany, Osama Hisham Abusetta

    Journal of Cyber Security, Vol.5, pp. 33-46, 2023, DOI:10.32604/jcs.2023.045422

    Abstract The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud, particularly in credit card transactions. Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising from potentially fraudulent activities. However, a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations. While sampling techniques can significantly reduce computational time, the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed. Such… More >

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