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  • Open Access

    ARTICLE

    Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

    Hadeel Alsolai1, Mohammed Rizwanullah2,*, Mashael Maashi3, Mahmoud Othman4, Amani A. Alneil2, Amgad Atta Abdelmageed2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936

    Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the… More >

  • Open Access

    ARTICLE

    Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model

    Mohamed Ibrahim Waly*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3159-3174, 2023, DOI:10.32604/csse.2023.035900

    Abstract Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical… More >

  • Open Access

    ARTICLE

    Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

    Jae-Seok Jang1, Hyuk-Yoon Kwon2,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3471-3489, 2023, DOI:10.32604/csse.2023.035570

    Abstract Question-answering (QA) models find answers to a given question. The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets. In this paper, we deal with the QA pair matching approach in QA models, which finds the most relevant question and its recommended answer for a given question. Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies. In contrast, we aim to automatically find the category to which the question belongs by employing… More >

  • Open Access

    ARTICLE

    Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

    Mesfer Al Duhayyim1,*, Badriyya B. Al-onazi2, Mohamed K. Nour3, Ayman Yafoz4, Amal S. Mehanna5, Ishfaq Yaseen6, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2755-2772, 2023, DOI:10.32604/csse.2023.034609

    Abstract Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes.… More >

  • Open Access

    ARTICLE

    An Innovative Bispectral Deep Learning Method for Protein Family Classification

    Isam Abu-Qasmieh, Amjed Al Fahoum*, Hiam Alquran, Ala’a Zyout

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3971-3991, 2023, DOI:10.32604/cmc.2023.037431

    Abstract Proteins are essential for many biological functions. For example, folding amino acid chains reveals their functionalities by maintaining tissue structure, physiology, and homeostasis. Note that quantifiable protein characteristics are vital for improving therapies and precision medicine. The automatic inference of a protein’s properties from its amino acid sequence is called “basic structure”. Nevertheless, it remains a critical unsolved challenge in bioinformatics, although with recent technological advances and the investigation of protein sequence data. Inferring protein function from amino acid sequences is crucial in biology. This study considers using raw sequencing to explain biological facts using… More >

  • Open Access

    ARTICLE

    Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

    Fatma Taher1, Hamoud Alshammari2, Lobna Osman3, Mohamed Elhoseny4, Abdulaziz Shehab5,2,*, Eman Elayat6

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4485-4499, 2023, DOI:10.32604/cmc.2023.036118

    Abstract Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.… More >

  • Open Access

    ARTICLE

    Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

    Hala J. Alshahrani1, Abdulkhaleq Q. A. Hassan2, Khaled Tarmissi3, Amal S. Mehanna4, Abdelwahed Motwakel5,*, Ishfaq Yaseen5, Amgad Atta Abdelmageed5, Mohamed I. Eldesouki6

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4255-4272, 2023, DOI:10.32604/cmc.2023.034821

    Abstract Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive 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… More >

  • Open Access

    ARTICLE

    Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

    T. Gopalakrishnan1, Mohamed Yacin Sikkandar2, Raed Abdullah Alharbi3, P. Selvaraj4, Zahraa H. Kareem5, Ahmed Alkhayyat6,*, Ali Hashim Abbas7

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6195-6212, 2023, DOI:10.32604/cmc.2023.033252

    Abstract The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification… More >

  • Open Access

    ARTICLE

    EsECC_SDN: Attack Detection and Classification Model for MANET

    Veera Ankalu Vuyyuru1, Youseef Alotaibi2, Neenavath Veeraiah3,*, Saleh Alghamdi4, Korimilli Sirisha5

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6665-6688, 2023, DOI:10.32604/cmc.2023.032140

    Abstract Mobile Ad Hoc Networks (MANET) is the framework for social networking with a realistic framework. In the MANET environment, based on the query, information is transmitted between the sender and receiver. In the MANET network, the nodes within the communication range are involved in data transmission. Even the nodes that lie outside of the communication range are involved in the transmission of relay messages. However, due to the openness and frequent mobility of nodes, they are subjected to the vast range of security threats in MANET. Hence, it is necessary to develop an appropriate security… More >

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