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  • Open Access

    ARTICLE

    An Effective Classifier Model for Imbalanced Network Attack Data

    Gürcan Çetin*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4519-4539, 2022, DOI:10.32604/cmc.2022.031734 - 28 July 2022

    Abstract Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD’99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and high-dimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these… More >

  • Open Access

    ARTICLE

    Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

    Abdullah A Asiri1, Akhtar Badshah2, Fazal Muhammad3,*, Hassan A Alshamrani1, Khalil Ullah4, Khalaf A Alshamrani1, Samar Alqhtani5, Muhammad Irfan6, Hanan Talal Halawani7, Khlood M Mehdar8

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5075-5089, 2022, DOI:10.32604/cmc.2022.031156 - 28 July 2022

    Abstract Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response… More >

  • Open Access

    ARTICLE

    Smartphone Sensors Based Physical Life-Routine for Health Education

    Tamara al Shloul1, Usman Azmat2, Suliman A. Alsuhibany3, Yazeed Yasin Ghadi4, Ahmad Jalal2, Jeongmin Park5,*

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 715-732, 2022, DOI:10.32604/iasc.2022.025421 - 03 May 2022

    Abstract The physical and the mental health of a human being largely depends upon his physical life-routine (PLR) and today’s much advanced technological methods make it possible to recognize and keep track of an individual’s PLR. With the successful and accurate recognition of PLR, a sublime service of health education can be made copious. In this regard, smartphones can play a vital role as they are ubiquitous and have utilitarian sensors embedded in them. In this paper, we propose a framework that extracts the features from the smartphone sensors data and then uses the sequential feature… More >

  • Open Access

    ARTICLE

    MLP-PSO Framework with Dynamic Network Tuning for Traffic Flow Forecasting

    V. Rajalakshmi1,*, S. Ganesh Vaidyanathan2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1335-1348, 2022, DOI:10.32604/iasc.2022.024310 - 24 March 2022

    Abstract Traffic flow forecasting is the need of the hour requirement in Intelligent Transportation Systems (ITS). Various Artificial Intelligence Frameworks and Machine Learning Models are incorporated in today’s ITS to enhance forecasting. Tuning the model parameters play a vital role in designing an efficient model to improve the reliability of forecasting. Hence, the primary objective of this research is to propose a novel hybrid framework to tune the parameters of Multilayer Perceptron (MLP) using the Swarm Intelligence technique called Particle Swarm Optimization (PSO). The proposed MLP-PSO framework is designed to adjust the weights and bias parameters… More >

  • Open Access

    ARTICLE

    Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

    Marwa M. Eid1,*, Fawaz Alassery2, Abdelhameed Ibrahim3, Mohamed Saber4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4627-4641, 2022, DOI:10.32604/cmc.2022.024043 - 14 January 2022

    Abstract Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces 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 - 14 January 2022

    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 More >

  • Open Access

    ARTICLE

    Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation

    Walid Aydi1,3,*, Fuad S. Alduais2,4

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4033-4050, 2022, DOI:10.32604/cmc.2022.023119 - 07 December 2021

    Abstract The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the… More >

  • Open Access

    ARTICLE

    COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers

    Jabeen Sultana1,*, Anjani Kumar Singha2, Shams Tabrez Siddiqui3, Guthikonda Nagalaxmi4, Anil Kumar Sriram5, Nitish Pathak6

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1007-1024, 2022, DOI:10.32604/iasc.2022.021507 - 17 November 2021

    Abstract COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against… More >

  • Open Access

    ARTICLE

    An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

    Abdelhameed Ibrahim1,*, Hattan F. Abutarboush2, Ali Wagdy Mohamed3,4, Mohamad Fouad1, El-Sayed M. El-kenawy5,6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 199-213, 2022, DOI:10.32604/cmc.2022.021886 - 03 November 2021

    Abstract Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In… More >

  • Open Access

    A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

    Shubha Sumesh1, John Yearwood1, Shamsul Huda1 and Shafiq Ahmad2,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474 - 11 October 2021

    Abstract

    Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model

    More >

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