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  • Open Access


    Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

    Heba G. Mohamed1, Fadwa Alrowais2, Mohammed Abdullah Al-Hagery3, Mesfer Al Duhayyim4,*, Anwer Mustafa Hilal5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4467-4483, 2023, DOI:10.32604/cmc.2023.036822

    Abstract As the Internet of Things (IoT) endures to develop, a huge count of data has been created. An IoT platform is rather sensitive to security challenges as individual data can be leaked, or sensor data could be used to cause accidents. As typical intrusion detection system (IDS) studies can be frequently designed for working well on databases, it can be unknown if they intend to work well in altering network environments. Machine learning (ML) techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy. This article introduces… More >

  • Open Access


    Research on WNN Greenhouse Temperature Prediction Method Based on GA

    Wenbin Dai1, Lina Wang1,2,*, Binrui Wang1, Xiaohong Cui1, Xue Li1

    Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2283-2296, 2022, DOI:10.32604/phyton.2022.021096

    Abstract Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN)… More >

  • Open Access


    Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

    Zulqurnain Sabir1, Abeer S. Alnahdi2,*, Mdi Begum Jeelani2, Mohamed A. Abdelkawy2,3,*, Muhammad Asif Zahoor Raja4, Dumitru Baleanu5,6, Muhammad Mubashar Hussain7

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 763-785, 2022, DOI:10.32604/cmes.2022.018496

    Abstract The present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are compared with the Runge-Kutta… More >

  • Open Access


    Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

    José Escorcia-Gutierrez1,*, Romany F. Mansour2, Kelvin Beleño3, Javier Jiménez-Cabas4, Meglys Pérez1, Natasha Madera1, Kevin Velasquez1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4221-4235, 2022, DOI:10.32604/cmc.2022.022322

    Abstract Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer… More >

  • Open Access


    Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

    Ajla Kulaglic1,*, Burak Berk Ustundag2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768

    Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples… More >

  • Open Access


    Wind Speed Prediction Modeling Based on the Wavelet Neural Network

    Zhenhua Guo1,2, Lixin Zhang1,*, Xue Hu1, Huanmei Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 625-630, 2020, DOI:10.32604/iasc.2020.013941

    Abstract Wind speed prediction is an important part of the wind farm management and wind power grid connection. Having accurate prediction of short-term wind speed is the basis for predicting wind power. This paper proposes a short-term wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four wavelet neural network models using the Morlet function as the wavelet basis function were developed to forecast short-term wind speed in January, April, July, and October. Predicted wind speed was compared across the four models using the mean square error and regression.… More >

  • Open Access


    A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

    Chen Shen1,*, Youping Chen1, Bing Chen1, Jingming Xie1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 379-397, 2019, DOI:10.32604/cmc.2019.04883

    Abstract Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed. As such, the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system. This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations. The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties. Thus, the controller can reduce the need for manual adjustments. The controller… More >

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