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  • Open Access

    ARTICLE

    Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

    Rohit Raja1, Chetan Swarup2, Abhishek Kumar3,*, Kamred Udham Singh4, Teekam Singh5, Dinesh Gupta6, Neeraj Varshney7, Swati Jain8

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2015-2031, 2022, DOI:10.32604/cmc.2022.022904

    Abstract As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while… More >

  • Open Access

    ARTICLE

    ANN Based Reduced Switch Multilevel Inverter in UPQC for Power Quality Improvement

    Y. Alexander Jeevanantham1,*, S. Srinath2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 909-921, 2022, DOI:10.32604/iasc.2022.022907

    Abstract A unified power quality conditioner (UPQC) plays a crucial role in the Power quality improvement of a power system. In this paper, a reduced switch multilevel inverter is with artificial neural network, soft computing technique control is proposed for UPQC. This proposed topology is employed for the mitigation of various power quality issues such as voltage sag, voltage swell, power factor, harmonics, and restoration time of voltage compensation. To show the enriched performance of the proposed topology comparative analysis is made with other two topologies of UPQC such as Conventional UPQC and UPQC using cascaded H bridge (CHB) five-level Inverter.… More >

  • Open Access

    ARTICLE

    E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

    Sanaa A. A. Ghaleb1,3,4, Mumtazimah Mohamad1, Syed Abdullah Fadzli1, Waheed A.H.M. Ghanem2,3,4,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4749-4766, 2022, DOI:10.32604/cmc.2022.020472

    Abstract Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and… More >

  • Open Access

    ARTICLE

    Predicting Mobile Cross-Platform Adaptation Using a Hybrid Sem–ANN Approach

    Ali Alkhalifah*

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 639-658, 2022, DOI:10.32604/csse.2022.022519

    Abstract Owing to constant changes in user needs, new technologies have been introduced to keep pace by building sustainable applications. Researchers and practitioners are keen to understand the factors that create an attractive user interface. Although the use of cross-platform applications and services is increasing, limited research has examined and evaluated cross-platforms for developing mobile applications for different operating systems. This study evaluates cross-platform features, identifying the main factors that help to create an attractive user adaptation when building sustainable applications for both Android and iOS. Flutter and React Native were selected so end-users could test their features using the cross-platform… More >

  • Open Access

    ARTICLE

    Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model

    Yiping Sun1,2, Qiang Bai1, Xuefeng Zhao1, Meng Tao1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1149-1170, 2022, DOI:10.32604/cmes.2022.017760

    Abstract A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures. The acoustic performance field has become a key research direction worldwide. Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings, this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network (ANN). First, the mapping relationship between the input characteristics and reflection… More >

  • Open Access

    ARTICLE

    Application of ANFIS Model for Thailand’s Electric Vehicle Consumption

    Narongkorn Uthathip1,*, Pornrapeepat Bhasaputra1, Woraratana Pattaraprakorn2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 69-86, 2022, DOI:10.32604/csse.2022.020120

    Abstract Generally, road transport is a major energy-consuming sector. Fuel consumption of each vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square… More >

  • Open Access

    ARTICLE

    DDoS Detection in SDN using Machine Learning Techniques

    Muhammad Waqas Nadeem, Hock Guan Goh*, Vasaki Ponnusamy, Yichiet Aun

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 771-789, 2022, DOI:10.32604/cmc.2022.021669

    Abstract Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a… More >

  • Open Access

    ARTICLE

    IoT Devices Authentication Using Artificial Neural Network

    Syed Shabih Ul Hasan1, Anwar Ghani1, Ikram Ud Din2, Ahmad Almogren3,*, Ayman Altameem4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3701-3716, 2022, DOI:10.32604/cmc.2022.020624

    Abstract User authentication is one of the critical concerns of information security. Users tend to use strong textual passwords, but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile phones. Textual passwords are slightly unprotected and are easily attackable. The attacks include dictionary, shoulder surfing, and brute force. Graphical passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of use. This paper proposes a Process-based Pattern Authentication (PPA) system for Internet of Things (IoT) devices that does not require a server to maintain… More >

  • Open Access

    ARTICLE

    Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

    Nebojsa Bacanin1, Khaled Alhazmi2,*, Miodrag Zivkovic1, K. Venkatachalam3, Timea Bezdan1, Jamel Nebhen4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4199-4215, 2022, DOI:10.32604/cmc.2022.020449

    Abstract In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing… More >

  • Open Access

    ARTICLE

    A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

    José Escorcia-Gutierrez1,4,*, Jordina Torrents-Barrena4, Margarita Gamarra2, Natasha Madera1, Pedro Romero-Aroca3, Aida Valls4, Domenec Puig4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2971-2989, 2022, DOI:10.32604/cmc.2022.020074

    Abstract Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the… More >

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