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  • Open Access

    REVIEW

    Review of Artificial Neural Networks for Wind Turbine Fatigue Prediction

    Husam AlShannaq, Aly Mousaad Aly*

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731 - 20 September 2024

    Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… More >

  • Open Access

    ARTICLE

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

    Djeldjli Halima1,*, Benatiallah Djelloul1, Ghasri Mehdi2, Tanougast Camel3, Benatiallah Ali4, Benabdelkrim Bouchra1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002 - 20 June 2024

    Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

  • Open Access

    ARTICLE

    A Novel Approach to Energy Optimization: Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

    Muhammad Salman Qamar1,*, Ihsan ul Haq1, Amil Daraz2, Atif M. Alamri3, Salman A. AlQahtani4, Muhammad Fahad Munir1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168 - 15 May 2024

    Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs… More >

  • Open Access

    ARTICLE

    A Hybrid Model for Improving Software Cost Estimation in Global Software Development

    Mehmood Ahmed1,3,*, Noraini B. Ibrahim1, Wasif Nisar2, Adeel Ahmed3, Muhammad Junaid3,*, Emmanuel Soriano Flores4, Divya Anand4

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1399-1422, 2024, DOI:10.32604/cmc.2023.046648 - 30 January 2024

    Abstract Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This… More >

  • Open Access

    ARTICLE

    Seakeeping analysis of dead ship condition in fishing ships based on Artificial Neural Networks

    P. Romero-Tello1,2, B. Serván-Camas3, José Enrique Gutiérrez Romero1,2, Javier Ignacio Piazzese4

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.4, pp. 1-6, 2023, DOI:10.23967/j.rimni.2023.10.004 - 25 October 2023

    Abstract In the operation of ships, assessing seakeeping performance is crucial. Historically, this has been done through experimentation in towing tank basins or numerical computations. However, with the rise of Artificial Intelligence (AI) and increased computational resources, there are many opportunities to use AI in predicting seakeeping performance. This research will utilize a pre-trained Artificial Neural Network (ANN) to evaluate the behaviour of fishing vessels in various operational scenarios. One of the key advantages of using these algorithms is the ability to predict a large number of scenarios quickly, compared to traditional methods. By analysing millions More >

  • Open Access

    ARTICLE

    Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

    Mohammed Zakariah1, Salman A. AlQahtani2,*, Abdulaziz M. Alawwad1, Abdullilah A. Alotaibi3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752 - 26 December 2023

    Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are… More >

  • Open Access

    ARTICLE

    Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

    Shoaib Khan, Bilal Khan, Saifullah Jan*, Subhan Ullah, Aiman

    Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579 - 28 December 2023

    Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the More >

  • Open Access

    ARTICLE

    A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

    Bin Li*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649 - 25 June 2023

    Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R2 of 0.847 on the testing. The partial dependence More >

  • Open Access

    ARTICLE

    Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

    Muhammad Hammad Waseem1, Malik Sajjad Ahmed Nadeem1,*, Ishtiaq Rasool Khan2, Seong-O-Shim3, Wajid Aziz1, Usman Habib4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710 - 31 March 2023

    Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the… More >

  • Open Access

    ARTICLE

    Numerical Computation of SEIR Model for the Zika Virus Spreading

    Suthep Suantai1,2, Zulqurnain Sabir3,4, Muhammad Asif Zahoor Raja5, Watcharaporn Cholamjiak6,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2155-2170, 2023, DOI:10.32604/cmc.2023.034699 - 06 February 2023

    Abstract The purpose of this study is to present the numerical performances and interpretations of the SEIR nonlinear system based on the Zika virus spreading by using the stochastic neural networks based intelligent computing solver. The epidemic form of the nonlinear system represents the four dynamics of the patients, susceptible patients S(y), exposed patients hospitalized in hospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIR model. The computing numerical outcomes and performances of the system are examined by using the artificial neural networks (ANNs) and the scaled conjugate gradient (SCG) for the training of the networks, More >

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