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  • Open Access

    ARTICLE

    Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

    Emad Abd Al Rahman1, Nur Intan Raihana Ruhaiyem1,*, Majed Bouchahma2, Kamarul Imran Musa3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3007-3028, 2023, DOI:10.32604/iasc.2023.032580

    Abstract This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system. The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening. Mammography has been the most utilized breast imaging technique to date. Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors. Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment plan once a patient has… More >

  • Open Access

    ARTICLE

    AWSD: An Aircraft Wing Dataset Created by an Automatic Workflow for Data Mining in Geometric Processing

    Xiang Su1, Nan Li1,*, Yuedi Hu1, Haisheng Li2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2935-2956, 2023, DOI:10.32604/cmes.2023.026083

    Abstract This paper introduces an aircraft wing simulation data set (AWSD) created by an automatic workflow based on creating models, meshing, simulating the wing flight flow field solution, and parameterizing solution results. AWSD is a flexible, independent wing collection of simulations with specific engineering requirements. The data set is applicable to handle computer geometry processing tasks. In contrast to the existing 3D model data set, there are some advantages the scale of this data set is not limited by the collection source, the data files have high quality, no defects, redundancy, and other problems, and the models and simulation are all… More > Graphic Abstract

    AWSD: An Aircraft Wing Dataset Created by an Automatic Workflow for Data Mining in Geometric Processing

  • Open Access

    ARTICLE

    An Improved Deep Structure for Accurately Brain Tumor Recognition

    Mohamed Maher Ata1, Reem N. Yousef2, Faten Khalid Karim3,*, Doaa Sami Khafaga3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1597-1616, 2023, DOI:10.32604/csse.2023.034375

    Abstract Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the… More >

  • Open Access

    ARTICLE

    MCMOD: The Multi-Category Large-Scale Dataset for Maritime Object Detection

    Zihao Sun1,*, Xiao Hu2, Yining Qi2, Yongfeng Huang2, Songbin Li3

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1657-1669, 2023, DOI:10.32604/cmc.2023.036558

    Abstract The marine environment is becoming increasingly complex due to the various marine vehicles, and the diversity of maritime objects poses a challenge to marine environmental governance. Maritime object detection technology plays an important role in this segment. In the field of computer vision, there is no sufficiently comprehensive public dataset for maritime objects in the contrast to the automotive application domain. The existing maritime datasets either have no bounding boxes (which are made for object classification) or cover limited varieties of maritime objects. To fulfil the vacancy, this paper proposed the Multi-Category Large-Scale Dataset for Maritime Object Detection (MCMOD) which… More >

  • Open Access

    ARTICLE

    Blood Vessel Segmentation with Classification Model for Diabetic Retinopathy Screening

    Abdullah O. Alamoudi1,*, Sarah Mohammed Allabun2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2265-2281, 2023, DOI:10.32604/cmc.2023.032429

    Abstract Biomedical image processing is finding useful in healthcare sector for the investigation, enhancement, and display of images gathered by distinct imaging technologies. Diabetic retinopathy (DR) is an illness caused by diabetes complications and leads to irreversible injury to the retina blood vessels. Retinal vessel segmentation techniques are a basic element of automated retinal disease screening system. In this view, this study presents a novel blood vessel segmentation with deep learning based classification (BVS-DLC) model for DR diagnosis using retinal fundus images. The proposed BVS-DLC model involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. Primarily, the… More >

  • Open Access

    ARTICLE

    Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

    Ghada Atteia1, El-Sayed M. El-kenawy2,3, Nagwan Abdel Samee1,*, Mona M. Jamjoom4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, Ahmad Taher Azar8,9, Nima Khodadadi10,11, Reham A. Ghanem12, Mahmoud Y. Shams13

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1883-1900, 2023, DOI:10.32604/cmc.2023.031723

    Abstract The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose a new algorithm for feature… More >

  • Open Access

    ARTICLE

    The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

    S. Navaneethan1,*, P. Siva Satya Sreedhar2, S. Padmakala3, C. Senthilkumar4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 125-135, 2023, DOI:10.32604/csse.2023.034546

    Abstract The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm… More >

  • Open Access

    ARTICLE

    An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

    Samah Alshathri1, Ayman El-Sayed2, Walid El-Shafai3,4,*, Ezz El-Din Hemdan2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 819-834, 2023, DOI:10.32604/csse.2023.034095

    Abstract Recently, the Internet of Things (IoT) has been used in various applications such as manufacturing, transportation, agriculture, and healthcare that can enhance efficiency and productivity via an intelligent management console remotely. With the increased use of Industrial IoT (IIoT) applications, the risk of brutal cyber-attacks also increased. This leads researchers worldwide to work on developing effective Intrusion Detection Systems (IDS) for IoT infrastructure against any malicious activities. Therefore, this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure. A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is… More >

  • Open Access

    ARTICLE

    ECG Heartbeat Classification Under Dataset Shift

    Zhiqiang He*

    Journal of Intelligent Medicine and Healthcare, Vol.1, No.2, pp. 79-89, 2022, DOI:10.32604/jimh.2022.036624

    Abstract Electrocardiogram (ECG) is widely used to detect arrhythmia. Atrial fibrillation, atrioventricular block, premature beats, etc. can all be diagnosed by ECG. When the distribution of training data and test data is inconsistent, the accuracy of the model will be affected. This phenomenon is called dataset shift. In the real-world heartbeat classification system, the heartbeat of the training set and test set often comes from patients of different ages and genders, so there are differences in the distribution of data sets. The main challenge in applying machine learning algorithms to clinical AI systems is dataset shift. Test-time adaptation (TTA) aims to… More >

  • Open Access

    ARTICLE

    A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

    ZEKUN XIN1,#, YUDAN MA2,#, WEIQIANG SONG3, HAO GAO3, LIJUN DONG3, BAO ZHANG1,*, ZHILONG REN3,*

    BIOCELL, Vol.47, No.3, pp. 555-567, 2023, DOI:10.32604/biocell.2023.026254

    Abstract Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO,… More >

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