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  • Open Access


    Hybrid Color Texture Features Classification Through ANN for Melanoma

    Saleem Mustafa1, Arfan Jaffar1, Muhammad Waseem Iqbal2,*, Asma Abubakar2, Abdullah S. Alshahrani3, Ahmed Alghamdi4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549

    Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

  • Open Access


    A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines

    Qi Wang1, Zhaoying Liu1, Ting Zhang1,*, Shanshan Tu1, Yujian Li2, Muhammad Waqas3

    Journal on Artificial Intelligence, Vol.4, No.1, pp. 37-48, 2022, DOI:10.32604/jai.2022.027875

    Abstract Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification. However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters. To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM). The framework consists of a feature extraction module… More >

  • Open Access


    Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

    Hiam Alquran1,2, Wan Azani Mustafa3,4,*, Isam Abu Qasmieh2, Yasmeen Mohd Yacob3,4, Mohammed Alsalatie5, Yazan Al-Issa6, Ali Mohammad Alqudah2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5117-5134, 2022, DOI:10.32604/cmc.2022.025692

    Abstract Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The success of this proposed system… More >

  • Open Access


    Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model

    Mahmoud Ragab1,2,3,*, Diaa Hamed4,5

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4185-4200, 2022, DOI:10.32604/cmc.2022.027074

    Abstract Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With this motivation, this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm (FCA-BFS) with optimal support vector machine (OSVM) model, named FCABFS-OSVM for medical data classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique to cluster the medical data… More >

  • Open Access


    Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

    S. Prakash1,*, P. Vishnu Raja2, A. Baseera3, D. Mansoor Hussain4, V. R. Balaji5, K. Venkatachalam6

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1273-1287, 2022, DOI:10.32604/csse.2022.021784

    Abstract Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, the iterative weighted… More >

  • Open Access


    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

    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 (BCIs), the method for identifying… More >

  • Open Access


    Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

    El-Sayed M. El-kenawy1,2, Abdelhameed Ibrahim3,*, Seyedali Mirjalili4,5, Yu-Dong Zhang6, Shaima Elnazer7,8, Rokaia M. Zaki9,10

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4989-5003, 2022, DOI:10.32604/cmc.2022.023884

    Abstract Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector… More >

  • Open Access


    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

    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 this paper, a weighted average… More >

  • Open Access


    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 >

  • Open Access


    Adaptive Multi-Layer Selective Ensemble Least Square Support Vector Machines with Applications

    Gang Yu1,4,5, Jian Tang2,*, Jian Zhang3, Zhonghui Wang6

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981

    Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate sub-sub-models with same KPs and… More >

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