Mohammed Abdelmajeed^1,*, Jiangbin Zheng¹, Ahmed Murtadha¹, Youcef Nafa¹, Mohammed Abaker², Muhammad Pervez Akhter³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3471-3491, 2025, DOI:10.32604/cmc.2025.059870 - 16 April 2025

Abstract Arabic Dialect Identification (DID) is a task in Natural Language Processing (NLP) that involves determining the dialect of a given piece of text in Arabic. The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect. Despite the effectiveness of these solutions, the performance heavily relies on the amount of labeled examples, which is labor-intensive to attain and may not be readily available in real-world scenarios. To alleviate the burden of labeling data, this paper introduces a novel solution that leverages… More >

Saad Alahmari^1,*, Abdulwhab Alkharashi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 635-659, 2025, DOI:10.32604/cmc.2025.061062 - 26 March 2025

Abstract Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions. This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations, such as transparency, fairness, and explainability, in artificial intelligence driven decision-making. The framework employs an Autoencoder for feature reduction, a Convolutional Neural Network for pattern recognition, and a Long Short-Term Memory network for temporal analysis.… More >

Di Wu^*, Liming Feng, Xiaoyu Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 977-999, 2025, DOI:10.32604/cmc.2025.060319 - 26 March 2025

Abstract Semi-supervised new intent discovery is a significant research focus in natural language understanding. To address the limitations of current semi-supervised training data and the underutilization of implicit information, a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model (SNID-ENSEF) is proposed. Syntactic elimination contrast learning leverages verb-dominant syntactic features, systematically replacing specific words to enhance data diversity. The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency. A neighborhood sample fusion strategy, based on sample distribution patterns, dynamically adjusts neighborhood size and fuses sample More >

Yuqiao Liu¹, Chen Pan¹, YeonJae Oh^2,*, Chang Gyoon Lim^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4593-4629, 2025, DOI:10.32604/cmc.2025.061196 - 06 March 2025

Abstract Virtual Power Plants (VPPs) are integral to modern energy systems, providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data. Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations. We introduce the Memory-Enhanced Autoencoder with Adversarial Training (MemAAE) model to overcome these limitations, designed explicitly for robust anomaly detection in VPP environments. The MemAAE model integrates three principal components: an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors, an adversarial training module that… More >

Wenju Wang¹, Zehua Gu^1,*, Bang Tang¹, Sen Wang², Jianfei Hao²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2389-2414, 2025, DOI:10.32604/cmc.2024.057392 - 17 February 2025

Abstract Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A More >

Xinlai Xing, Junliang Chen^*, Xiaochuan Zhang, Shuran Zhou, Runqing Zhang

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2275-2292, 2025, DOI:10.32604/cmc.2024.056877 - 17 February 2025

Abstract In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >

Yifan Yu, Dazhi Wang^*, Yanhua Chen, Hongfeng Wang, Min Huang

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3761-3780, 2024, DOI:10.32604/cmc.2024.057103 - 19 December 2024

Abstract For data mining tasks on large-scale data, feature selection is a pivotal stage that plays an important role in removing redundant or irrelevant features while improving classifier performance. Traditional wrapper feature selection methodologies typically require extensive model training and evaluation, which cannot deliver desired outcomes within a reasonable computing time. In this paper, an innovative wrapper approach termed Contribution Tracking Feature Selection (CTFS) is proposed for feature selection of large-scale data, which can locate informative features without population-level evolution. In other words, fewer evaluations are needed for CTFS compared to other evolutionary methods. We initially More >

Haizhen Wang^1,2,*, Na Jia^1,2, Yang He¹, Pan Tan^1,2

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3825-3851, 2024, DOI:10.32604/cmc.2024.055497 - 19 December 2024

Abstract As the popularity and dependence on the Internet increase, DDoS (distributed denial of service) attacks seriously threaten network security. By accurately distinguishing between different types of DDoS attacks, targeted defense strategies can be formulated, significantly improving network protection efficiency. DDoS attacks usually manifest as an abnormal increase in network traffic, and their diverse types of attacks, along with a severe data imbalance, make it difficult for traditional classification methods to effectively identify a small number of attack types. To solve this problem, this paper proposes a DDoS recognition method CVWGG (Conditional Variational Autoencoder-Wasserstein Generative Adversarial… More >