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  • Open Access


    Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

    Manar Ahmed Hamza1,*, Siwar Ben Haj Hassine2, Ibrahim Abunadi3, Fahd N. Al-Wesabi2,4, Hadeel Alsolai5, Anwer Mustafa Hilal1, Ishfaq Yaseen1, Abdelwahed Motwakel1

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2581-2596, 2022, DOI:10.32604/cmc.2022.024764

    Abstract Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique More >

  • Open Access


    Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

    Uğur Ayvaz1, Hüseyin Gürüler2, Faheem Khan3, Naveed Ahmed4, Taegkeun Whangbo3,*, Abdusalomov Akmalbek Bobomirzaevich3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5511-5521, 2022, DOI:10.32604/cmc.2022.023278

    Abstract Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals. One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs are successful in processing the voice signal with high accuracies. MFCCs represents a sequence of voice signal-specific features. This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings. Since the human perception of sound is not linear, after the More >

  • Open Access


    Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

    K. O. Mohammed Aarif1,*, P. Sivakumar2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 275-289, 2022, DOI:10.32604/iasc.2022.022805

    Abstract Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the… More >

  • Open Access


    Heart Sound Analysis for Abnormality Detection

    Zainab Arshad1, Sohail Masood Bhatti2,*, Huma Tauseef3, Arfan Jaffar2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1195-1205, 2022, DOI:10.32604/iasc.2022.022160

    Abstract According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop More >

  • Open Access


    Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

    Nasir Mahmood*, Muhammad Usman Ghani Khan

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4411-4428, 2022, DOI:10.32604/cmc.2022.013956

    Abstract This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings. By linking ‘dots’ of police data comprising scattered information of people, groups, logistics, locations, communication, and spatiotemporal characters on different social media groups, the proposed architecture will spawn beneficial information. This useful information will, in turn, help the police both in predicting potential terrorist events and in investigating previous events. Furthermore, this architecture will aid in the identification of criminals and their associates and handlers. Terrorism is psychological warfare, which, in the broadest sense, can be defined as the… More >

  • Open Access


    A New Hybrid Feature Selection Method Using T-test and Fitness Function

    Husam Ali Abdulmohsin1,*, Hala Bahjat Abdul Wahab2, Abdul Mohssen Jaber Abdul Hossen3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3997-4016, 2021, DOI:10.32604/cmc.2021.014840


    Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes

    More >

  • Open Access


    Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures

    Muhammad Riaz1, Harish Garg2, Hafiz Muhammad Athar Farid1, Ronnason Chinram3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 771-800, 2021, DOI:10.32604/cmes.2021.014174

    Abstract This paper aims to introduce the novel concept of the bipolar picture fuzzy set (BPFS) as a hybrid structure of bipolar fuzzy set (BFS) and picture fuzzy set (PFS). BPFS is a new kind of fuzzy sets to deal with bipolarity (both positive and negative aspects) to each membership degree (belonging-ness), neutral membership (not decided), and non-membership degree (refusal). In this article, some basic properties of bipolar picture fuzzy sets (BPFSs) and their fundamental operations are introduced. The score function, accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy More >

  • Open Access


    Statistical Medical Pattern Recognition for Body Composition Data Using Bioelectrical Impedance Analyzer

    Florin Valentin Leuciuc1,2,*, Maria Daniela Craciun1,2, Iulian Stefan Holubiac1, Mazin Abed Mohammed3, Karrar Hameed Abdulkareem4, Gheorghe Pricop1,2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2601-2617, 2021, DOI:10.32604/cmc.2021.014863

    Abstract Identifying patterns, recognition systems, prediction methods, and detection methods is a major challenge in solving different medical issues. Few categories of devices for personal and professional assessment of body composition are available. Bioelectrical impedance analyzer is a simple, safe, affordable, mobile, non-invasive, and less expensive alternative device for body composition assessment. Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape, body mass, energy requirements, physical fitness, health status, and metabolic profile. Thus, this research aims to identify… More >

  • Open Access


    Emotion Recognition Using WT-SVM in Human-Computer Interaction

    Zequn Wang, Rui Jiao, Huiping Jiang*

    Journal of New Media, Vol.2, No.3, pp. 121-130, 2020, DOI:10.32604/jnm.2020.010674

    Abstract With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. Emotion recognition is an important task for the computer to understand social status in BCI. Affective computing (AC) aims to develop the model of emotions and advance the affective intelligence of computers. There are various emotion recognition approaches. The method based on electroencephalogram (EEG) is more reliable because it is higher in accuracy and more objective in evaluation than other external appearance clues such… More >

  • Open Access


    Pattern Recognition of Construction Bidding System Based on Image Processing

    Xianzhe Zhang1,3,∗, Sheng Zhou2,†, Jun Fang1,‡, Yanling Ni3,§

    Computer Systems Science and Engineering, Vol.35, No.4, pp. 247-256, 2020, DOI:10.32604/csse.2020.35.247

    Abstract Bidding for construction projects is a very important and representative field in the industry. The system of bidding for construction projects has made considerable progress over the years due to the accumulation of experience and many contributions to the field. Nowadays, with the rapid development of information technology and the intellectualization of the bidding system for construction projects, the accumulation and processing of data has become an essential element of its development. In order to manage the bidding system of construction engineering reasonably, this paper proposes a system based on image processing and pattern recognition… More >

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