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  • Open Access

    ARTICLE

    A Hybrid Transfer Learning Framework for Enhanced Oil Production Time Series Forecasting

    Dalal AL-Alimi1, Mohammed A. A. Al-qaness2,3,*, Robertas Damaševičius4,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3539-3561, 2025, DOI:10.32604/cmc.2025.059869 - 17 February 2025

    Abstract Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions… More >

  • Open Access

    ARTICLE

    Enhancing User Experience in AI-Powered Human-Computer Communication with Vocal Emotions Identification Using a Novel Deep Learning Method

    Ahmed Alhussen1, Arshiya Sajid Ansari2,*, Mohammad Sajid Mohammadi3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2909-2929, 2025, DOI:10.32604/cmc.2024.059382 - 17 February 2025

    Abstract Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the… More >

  • Open Access

    ARTICLE

    Detecting Ethereum Ponzi Scheme Based on Hybrid Sampling for Smart Contract

    Yuanjun Qu, Xiameng Si*, Haiyan Kang, Hanlin Zhou

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3111-3130, 2025, DOI:10.32604/cmc.2024.057368 - 17 February 2025

    Abstract With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD… More >

  • Open Access

    ARTICLE

    A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation

    Yuheng Yin, Jiahao Song*, Minghui Yang

    Energy Engineering, Vol.122, No.2, pp. 709-731, 2025, DOI:10.32604/ee.2024.059021 - 31 January 2025

    Abstract The lithium battery is an essential component of electric cars; prompt and accurate problem detection is vital in guaranteeing electric cars’ safe and dependable functioning and addressing the limitations of Back Propagation (BP) neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series, and the limitations of Long-Short Term Memory (LSTM) neural network models in terms of risk of overfitting. A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis. First, a lithium battery model is constructed… More > Graphic Abstract

    A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation

  • Open Access

    ARTICLE

    Ketogenic diet with oxyresveratrol and zinc inhibits glioblastoma and restores memory function and motor coordination

    TANVI VIJAY GUJARAN1,#, VIGNESH BALAJI EASWARAN1,#, RUNALI SANKHE1, PUGAZHANDHI BAKTHAVATCHALAM2,3, HERMAN SUNIL DSOUZA4, K. SREEDHARA RANGANATH PAI1,*

    Oncology Research, Vol.33, No.2, pp. 381-395, 2025, DOI:10.32604/or.2024.049538 - 16 January 2025

    Abstract Background: To date, there is no effective cure for the highly malignant brain tumor glioblastoma (GBM). GBM is the most common, aggressive central nervous system tumor (CNS). It commonly originates in glial cells such as microglia, oligodendroglia, astrocytes, or subpopulations of cancer stem cells (CSCs). Glucose plays an important role in the, which energy metabolism of normal and cancer cells, but cancer cells exhibit an increased demand for glucose is required for their differentiation and proliferation. The main aim of this study is to explore the anti-cancer efficacy of the ketogenic diet against GBM. Also,… More >

  • Open Access

    ARTICLE

    Experimental Study and a Modified Model for Temperature-Recovery Stress of Shape Memory Alloy Wire under Different Temperatures

    Zhi-Xiang Wei1, Wen-Wei Wang2,*, Yan-Jie Xue3, Wu-Tong Zhang2, Qiu-Di Huang2

    Structural Durability & Health Monitoring, Vol.19, No.2, pp. 347-364, 2025, DOI:10.32604/sdhm.2024.054559 - 15 January 2025

    Abstract To investigate the performance of utilizing the shape memory effect of SMA (Shape Memory Alloy) wire to generate recovery stress, this paper performed single heating recovery stress tests and reciprocating heating-cooling recovery stress tests on SMA wire under varying initial strain conditions. The effects of various strains and different energized heating methods on the recovery stress of SMA wires were explored in the single heating tests. The SMA wire was strained from 2% to 8% initially, and two distinct heating approaches were employed: one using a large current interval for rapid heating and one using… More >

  • Open Access

    ARTICLE

    Data-Driven Method for Predicting Remaining Useful Life of Bearings Based on Multi-Layer Perception Neural Network and Bidirectional Long Short-Term Memory Network

    Yongfeng Tai1, Xingyu Yan2, Xiangyi Geng3, Lin Mu4, Mingshun Jiang2, Faye Zhang2,*

    Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

    Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

  • Open Access

    ARTICLE

    An Iterated Greedy Algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem

    Binhui Wang, Hongfeng Wang*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 371-388, 2025, DOI:10.32604/cmc.2024.058885 - 03 January 2025

    Abstract The distributed permutation flow shop scheduling problem (DPFSP) has received increasing attention in recent years. The iterated greedy algorithm (IGA) serves as a powerful optimizer for addressing such a problem because of its straightforward, single-solution evolution framework. However, a potential draw-back of IGA is the lack of utilization of historical information, which could lead to an imbalance between exploration and exploitation, especially in large-scale DPFSPs. As a consequence, this paper develops an IGA with memory and learning mechanisms (MLIGA) to efficiently solve the DPFSP targeted at the mini-mal makespan. In MLIGA, we incorporate a memory… More >

  • Open Access

    ARTICLE

    Steel Surface Defect Detection Using Learnable Memory Vision Transformer

    Syed Tasnimul Karim Ayon1,#, Farhan Md. Siraj1,#, Jia Uddin2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

    Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

  • Open Access

    ARTICLE

    Robust Network Security: A Deep Learning Approach to Intrusion Detection in IoT

    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 >

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