Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (37)
  • Open Access

    ARTICLE

    Diabetes Prediction Using ADASYN-Based Data Augmentation and CNN-BiGRU Deep Learning Model

    Tehreem Fatima1, Kewen Xia1,*, Wenbiao Yang2, Qurat Ul Ain1, Poornima Lankani Perera1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 811-826, 2025, DOI:10.32604/cmc.2025.063686 - 09 June 2025

    Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

  • Open Access

    ARTICLE

    Deterministic Convergence Analysis for GRU Networks via Smoothing Regularization

    Qian Zhu1, Qian Kang1, Tao Xu2, Dengxiu Yu3,*, Zhen Wang1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1855-1879, 2025, DOI:10.32604/cmc.2025.061913 - 16 April 2025

    Abstract In this study, we present a deterministic convergence analysis of Gated Recurrent Unit (GRU) networks enhanced by a smoothing regularization technique. While GRU architectures effectively mitigate gradient vanishing/exploding issues in sequential modeling, they remain prone to overfitting, particularly under noisy or limited training data. Traditional regularization, despite enforcing sparsity and accelerating optimization, introduces non-differentiable points in the error function, leading to oscillations during training. To address this, we propose a novel smoothing regularization framework that replaces the non-differentiable absolute function with a quadratic approximation, ensuring gradient continuity and stabilizing the optimization landscape. Theoretically, we rigorously… More >

  • 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

    A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU

    Buchi Reddy Ramakantha Reddy, Ramasamy Lokesh Kumar*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4081-4107, 2024, DOI:10.32604/cmc.2024.057071 - 19 December 2024

    Abstract Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products, leading to suboptimal user experiences. To address this, our study presents a Personalized Adaptive Multi-Product Recommendation System (PAMR) leveraging transfer learning and Bi-GRU (Bidirectional Gated Recurrent Units). Using a large dataset of user reviews from Amazon and Flipkart, we employ transfer learning with pre-trained models (AlexNet, GoogleNet, ResNet-50) to extract high-level attributes from product data, ensuring effective feature representation even with limited data. Bi-GRU captures both spatial and sequential dependencies in user-item interactions. The innovation of this study lies… More >

  • Open Access

    REVIEW

    A Comprehensive Overview and Comparative Analysis on Deep Learning Models

    Farhad Mortezapour Shiri*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

    Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

    Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries

    Tao Yan1, Javed Rashid2,3, Muhammad Shoaib Saleem3,4, Sajjad Ahmad4, Muhammad Faheem5,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

    Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

  • Open Access

    ARTICLE

    An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

    Umar Zaman1, Junaid Khan2, Eunkyu Lee1,3, Sajjad Hussain4, Awatef Salim Balobaid5, Rua Yahya Aburasain5, Kyungsup Kim1,2,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1789-1808, 2024, DOI:10.32604/cmc.2024.056222 - 15 October 2024

    Abstract Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural Network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of insignificant trajectories. Models were trained using key navigational parameters: latitude, longitude, speed, and heading. Spatiotemporal aware processing through trajectory segmentation… More >

  • Open Access

    ARTICLE

    GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification

    Mohammad Shoab*, Loiy Alsbatin*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.053721 - 15 October 2024

    Abstract In recent years, machine learning (ML) and deep learning (DL) have significantly advanced intrusion detection systems, effectively addressing potential malicious attacks across networks. This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things (IoT) environment, leveraging the NSL-KDD dataset. To achieve high accuracy, the authors used the feature extraction technique in combination with an auto-encoder, integrated with a gated recurrent unit (GRU). Therefore, the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization (PSO), and PSO has been employed for training the features. The More >

  • Open Access

    ARTICLE

    Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

    Ahmad Yahiya Ahmad Bani Ahmad1, Jafar Alzubi2, Sophers James3, Vincent Omollo Nyangaresi4,5,*, Chanthirasekaran Kutralakani6, Anguraju Krishnan7

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4791-4812, 2024, DOI:10.32604/cmc.2024.052771 - 12 September 2024

    Abstract In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is… More >

  • Open Access

    ARTICLE

    A Hybrid Manufacturing Process Monitoring Method Using Stacked Gated Recurrent Unit and Random Forest

    Chao-Lung Yang1,*, Atinkut Atinafu Yilma1,2, Bereket Haile Woldegiorgis2, Hendrik Tampubolon3,4, Hendri Sutrisno5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 233-254, 2024, DOI:10.32604/iasc.2024.043091 - 21 May 2024

    Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random… More >

Displaying 1-10 on page 1 of 37. Per Page