Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (253)
  • Open Access

    REVIEW

    Bio-Inspired Algorithms in NLP Techniques: Challenges, Limitations and Its Applications

    Huu-Tuong Ho1, Thi-Thuy-Hoai Nguyen2, Duong Nguyen Minh Huy3, Luong Vuong Nguyen1,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3945-3973, 2025, DOI:10.32604/cmc.2025.063099 - 19 May 2025

    Abstract Natural Language Processing (NLP) has become essential in text classification, sentiment analysis, machine translation, and speech recognition applications. As these tasks become complex, traditional machine learning and deep learning models encounter challenges with optimization, parameter tuning, and handling large-scale, high-dimensional data. Bio-inspired algorithms, which mimic natural processes, offer robust optimization capabilities that can enhance NLP performance by improving feature selection, optimizing model parameters, and integrating adaptive learning mechanisms. This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—across core NLP tasks. We analyze More >

  • Open Access

    ARTICLE

    Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

    Iman S. Al-Mahdi1, Saad M. Darwish1,*, Magda M. Madbouly2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

    Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

  • Open Access

    ARTICLE

    An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing

    Adil Yousif*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025

    Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

  • Open Access

    ARTICLE

    Multi-Objective Optimization of Swirling Impinging Air Jets with Genetic Algorithm and Weighted Sum Method

    Sudipta Debnath1, Zahir Uddin Ahmed2, Muhammad Ikhlaq3,4,*, Md. Tanvir Khan5, Avneet Kaur6, Kuljeet Singh Grewal1

    Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 71-94, 2025, DOI:10.32604/fhmt.2024.059734 - 26 February 2025

    Abstract Impinging jet arrays are extensively used in numerous industrial operations, including the cooling of electronics, turbine blades, and other high-heat flux systems because of their superior heat transfer capabilities. Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution, which can lead to improved system performance and energy savings. This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system. The governing equations are resolved employing the commercial computational fluid dynamics (CFD) software ANSYS Fluent v17. The study focuses on four… More > Graphic Abstract

    Multi-Objective Optimization of Swirling Impinging Air Jets with Genetic Algorithm and Weighted Sum Method

  • Open Access

    ARTICLE

    Recent Advancements in the Optimization Capacity Configuration and Coordination Operation Strategy of Wind-Solar Hybrid Storage System

    Hongliang Hao1, Caifeng Wen2,3, Feifei Xue2,*, Hao Qiu1, Ning Yang2, Yuwen Zhang1, Chaoyu Wang1, Edwin E. Nyakilla1

    Energy Engineering, Vol.122, No.1, pp. 285-306, 2025, DOI:10.32604/ee.2024.057720 - 27 December 2024

    Abstract Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources. This paper proposes a wind-solar hybrid energy storage system (HESS) to ensure a stable supply grid for a longer period. A multi-objective genetic algorithm (MOGA) and state of charge (SOC) region division for the batteries are introduced to solve the objective function and configuration of the system capacity, respectively. MATLAB/Simulink was used for simulation test. The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system, with a combination of a 300 More >

  • Open Access

    ARTICLE

    A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

    Umapathi Krishnamoorthy1,*, Shanmugam Jagan2, Mohammed Zakariah3, Abdulaziz S. Almazyad4,*, K. Gurunathan5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3903-3926, 2024, DOI:10.32604/cmc.2024.055910 - 19 December 2024

    Abstract Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure… More >

  • Open Access

    ARTICLE

    Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force

    Van-Truong Nguyen1,*, Dai-Nhan Duong1, Dinh-Hieu Phan1, Thanh-Lam Bui1, Xiem HoangVan2, Phan Xuan Tan3

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2337-2356, 2024, DOI:10.32604/cmc.2024.055412 - 18 November 2024

    Abstract This paper proposes an adaptive nonlinear proportional-derivative (ANPD) controller for a two-wheeled self-balancing robot (TWSB) modeled by the Lagrange equation with external forces. The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative (NPD) controller and a genetic algorithm, in which the proportional-derivative (PD) parameters are updated online based on the tracking error and the preset error threshold. In addition, the genetic algorithm is employed to adaptively select initial controller parameters, contributing to system stability and improved control accuracy. The proposed controller is basic in design yet simple to implement. The… More >

  • Open Access

    ARTICLE

    A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis

    Hussain AlSalman1, Taha Alfakih2, Mabrook Al-Rakhami2, Mohammad Mehedi Hassan2,*, Amerah Alabrah2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

    Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

  • Open Access

    ARTICLE

    Numerical Simulation and Optimization of the Gas-Solid Coupled Flow Field and Discharging Performance of Straw Crushers

    Yuezheng Lan1, Yu Zhao2,*, Zhiping Zhai1, Meihua Fan2, Fushun Li2

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2565-2583, 2024, DOI:10.32604/fdmp.2024.053362 - 28 October 2024

    Abstract The quality of crushing, power consumption, and discharging performance of a straw crusher are greatly influenced by the characteristics of its internal flow field. To enhance the straw crusher’s flow field properties and improve the efficiency with which crushed material is discharged, first, the main structural parameters influencing the air flow in the crusher are discussed. Then, the coupled gas-solid flow field in the straw crusher is numerically calculated through solution of the Navier-Stokes equations and application of the discrete element method (DEM). Finally, the discharge performance index of the crusher is examined through detailed More >

  • Open Access

    ARTICLE

    A Two-Layer Optimal Scheduling Strategy for Rural Microgrids Accounting for Flexible Loads

    Guo Zhao1,2, Chi Zhang1,2,*, Qiyuan Ren1,2

    Energy Engineering, Vol.121, No.11, pp. 3355-3379, 2024, DOI:10.32604/ee.2024.053130 - 21 October 2024

    Abstract In the context of China’s “double carbon” goals and rural revitalization strategy, the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids. Considering the operational characteristics of rural microgrids and their impact on users, this paper establishes a two-layer scheduling model incorporating flexible loads. The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid, while the lower-layer aims to minimize the total electricity cost for rural users. An Improved Adaptive Genetic Algorithm (IAGA) is proposed to solve the model. Results show that the two-layer scheduling model with More >

Displaying 1-10 on page 1 of 253. Per Page