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  • Open Access

    ARTICLE

    Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

    Sureka Sivavelu, Venkatesh Palanisamy*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407

    Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things

    Bin Li*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3267-3282, 2024, DOI:10.32604/cmc.2024.047193

    Abstract The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing… More >

  • Open Access

    ARTICLE

    Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

    Ying Su1, Morgan C. Wang1, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

    Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

    Bin Wu1,2, Xianyi Chen3, Jinzhou Huang4,*, Caicai Zhang5, Jing Wang6, Jing Yu1,2, Zhiqiang Zhao7, Zhuolin Mei1,2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3177-3194, 2024, DOI:10.32604/cmc.2023.047147

    Abstract In a cloud environment, outsourced graph data is widely used in companies, enterprises, medical institutions, and so on. Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers. Servers on cloud platforms usually have some subjective or objective attacks, which make the outsourced graph data in an insecure state. The issue of privacy data protection has become an important obstacle to data sharing and usage. How to query outsourcing graph data safely and effectively has become the focus of research. Adjacency query is a basic and frequently used operation in… More >

  • Open Access

    ARTICLE

    Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

    Zhaoliang Wu1, Yuewei Wu1,2, Xiaoli Feng1, Jiajun Zou3, Fulian Yin1,2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3391-3412, 2024, DOI:10.32604/cmc.2024.047076

    Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately improving ASQP performance in the… More >

  • Open Access

    ARTICLE

    Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

    Rabia Abid1, Muhammad Rizwan2, Abdulatif Alabdulatif3,*, Abdullah Alnajim4, Meznah Alamro5, Mourade Azrour6

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

    Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the security of e-healthcare systems. The… More >

  • Open Access

    REVIEW

    Trends in Event Understanding and Caption Generation/Reconstruction in Dense Video: A Review

    Ekanayake Mudiyanselage Chulabhaya Lankanatha Ekanayake1,2, Abubakar Sulaiman Gezawa3,*, Yunqi Lei1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2941-2965, 2024, DOI:10.32604/cmc.2024.046155

    Abstract Video description generates natural language sentences that describe the subject, verb, and objects of the targeted Video. The video description has been used to help visually impaired people to understand the content. It is also playing an essential role in devolving human-robot interaction. The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping. Deep learning is changing the shape of computer vision (CV) technologies and natural language processing (NLP). There are hundreds of deep learning models, datasets, and evaluations that can improve the gaps in current research. This article… More >

  • Open Access

    ARTICLE

    Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN

    Jongwook Si1, Sungyoung Kim2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2893-2908, 2024, DOI:10.32604/cmc.2023.046081

    Abstract In the context of high compression rates applied to Joint Photographic Experts Group (JPEG) images through lossy compression techniques, image-blocking artifacts may manifest. This necessitates the restoration of the image to its original quality. The challenge lies in regenerating significantly compressed images into a state in which these become identifiable. Therefore, this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks (GAN). The generator in this network is based on the U-Net architecture. It features a new hourglass structure that preserves the characteristics of the deep layers. In… More >

  • Open Access

    REVIEW

    A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

    Usman Khan1, Muhammad Khalid Khan1, Muhammad Ayub Latif1, Muhammad Naveed1,2,*, Muhammad Mansoor Alam2,3,4, Salman A. Khan1, Mazliham Mohd Su’ud2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101

    Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their… More >

  • Open Access

    ARTICLE

    A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

    Navaneetha Krishnan Muthunambu1, Senthil Prabakaran2, Balasubramanian Prabhu Kavin3, Kishore Senthil Siruvangur4, Kavitha Chinnadurai1, Jehad Ali5,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

    Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >

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