Nur Syaiful Afrizal¹, Khairul Adib Yusof^1,2,*, Lokman Hakim Muhamad¹, Nurul Shazana Abdul Hamid^2,3, Mardina Abdullah^2,4, Mohd Amiruddin Abd Rahman¹, Syamsiah Mashohor⁵, Masashi Hayakawa^6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-16, 2026, DOI:10.32604/cmc.2025.066421 - 09 December 2025

Abstract Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years. This study introduces a novel reconstruction-based modeling approach enhanced by negative learning, employing a Bidirectional Long Short-Term Memory (BiLSTM) network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals. By penalizing the model for accurately reconstructing seismic anomalies, the negative learning approach effectively magnifies the differences between normal and anomalous data. This strategic differentiation enhances the sensitivity of the BiLSTM network, enabling improved detection of subtle geomagnetic More >

Adel Saad Assiri^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069826 - 10 November 2025

Abstract Customer churn is the rate at which customers discontinue doing business with a company over a given time period. It is an essential measure for businesses to monitor high churn rates, as they often indicate underlying issues with services, products, or customer experience, resulting in considerable income loss. Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth. Traditional machine learning (ML) models often struggle to capture complex temporal dependencies in client behavior data. To address this, an optimized deep learning (DL) approach using a Regularized Bidirectional Long Short-Term… More >

Zheng Zhang^1,2, Jie Hao², Liquan Chen^1,*, Tianhao Hou², Yanan Liu²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068372 - 10 November 2025

Abstract With the increasing severity of network security threats, Network Intrusion Detection (NID) has become a key technology to ensure network security. To address the problem of low detection rate of traditional intrusion detection models, this paper proposes a Dual-Attention model for NID, which combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to design two modules: the FocusConV and the TempoNet module. The FocusConV module, which automatically adjusts and weights CNN extracted local features, focuses on local features that are more important for intrusion detection. The TempoNet module focuses on global information, identifies… More >

Mohamed Kouki¹, Nahid Osman², Mona Gafar³, Ragab A. El-Sehiemy^4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3783-3811, 2025, DOI:10.32604/cmes.2025.071964 - 23 December 2025

Abstract The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO2 emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address… More >

Qingliang Wang¹, Chengkai Liu¹, Zhaohui Zhou¹, Ye Han¹, Luebin Fang², Moxuan Zhao³, Xiao Cao^3,*

Energy Engineering, Vol.122, No.11, pp. 4715-4732, 2025, DOI:10.32604/ee.2025.068105 - 27 October 2025

Abstract Grid-supplied load is the traditional load minus new energy generation, so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid. In addition, with the expansion of the power system and the increase in the frequency of extreme weather events, the difficulty of grid-supplied load forecasting is further exacerbated. Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load, especially under extreme weather conditions. This paper proposes a novel grid-supplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism (CNN-BiLSTM-Attention). The model utilizes transfer… More >

Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025

Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

Mengke Ding¹, Jun Li^1,2,*, Dongyue Gao^1,*, Guotai Zhou², Borui Wang¹, Zhanjun Wu¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 597-612, 2025, DOI:10.32604/cmc.2025.067907 - 29 August 2025

Abstract Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently,… More >

Xiaoping Yang^1,2,3, Jinku Qiu^2,3,4, Xifa Gong⁵, Jin Ye⁵, Fei Yao^5,*, Jiaqiao Chen⁶, Xianzan Luo⁶, Da Qin⁶

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1515-1539, 2025, DOI:10.32604/cmc.2025.067518 - 29 August 2025

Abstract In contemporary society, rapid and accurate optical cable fault detection is of paramount importance for ensuring the stability and reliability of optical networks. The emergence of novel faults in optical networks has introduced new challenges, significantly compromising their normal operation. Machine learning has emerged as a highly promising approach. Consequently, it is imperative to develop an automated and reliable algorithm that utilizes telemetry data acquired from Optical Time-Domain Reflectometers (OTDR) to enable real-time fault detection and diagnosis in optical fibers. In this paper, we introduce a multi-scale Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) deep… More >

Jingxian Ni, Chaomeng Wang, Shiqi Sun, Yuxuan Sun, Gang Ma^*

Energy Engineering, Vol.122, No.9, pp. 3719-3736, 2025, DOI:10.32604/ee.2025.066971 - 26 August 2025

Abstract To improve the fault diagnosis accuracy of a PV grid-connected inverter, a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed. Firstly, unlike the traditional VMD algorithm which relies on manual experience to set parameters (e.g., noise tolerance, penalty parameter, number of decompositions), this paper achieves adaptive optimization of parameters through MPA algorithm to avoid the problem of feature information loss caused by manual parameter tuning, and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters; and then, adopts the PSO algorithm for the Then, the… More >

Lilia-Eliana Popescu-Apreutesei, Mihai-Sorin Iosupescu, Sabina Cristiana Necula, Vasile-Daniel Păvăloaia^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2605-2644, 2025, DOI:10.32604/cmc.2025.064747 - 03 July 2025

Abstract The increasing fluency of advanced language models, such as GPT-3.5, GPT-4, and the recently introduced DeepSeek, challenges the ability to distinguish between human-authored and AI-generated academic writing. This situation is raising significant concerns regarding the integrity and authenticity of academic work. In light of the above, the current research evaluates the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with pre-trained GloVe (Global Vectors for Word Representation) embeddings to detect AI-generated scientific abstracts drawn from the AI-GA (Artificial Intelligence Generated Abstracts) dataset. Two core BiLSTM variants were assessed: a single-layer approach and a dual-layer… More >