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  • Open Access

    ARTICLE

    A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

    Huanhua Liu, Wei Wang*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

    Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

  • Open Access

    ARTICLE

    Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

    T. Satyanarayana Murthy1, Kollati Vijaya Kumar2, Fayadh Alenezi3, E. Laxmi Lydia4, Gi-Cheon Park5, Hyoung-Kyu Song6, Gyanendra Prasad Joshi7, Hyeonjoon Moon7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1633-1650, 2023, DOI:10.32604/csse.2023.034769

    Abstract Fruit classification utilizing a deep convolutional neural network (CNN) is the most promising application in personal computer vision (CV). Profound learning-related characterization made it possible to recognize fruits from pictures. But, due to the similarity and complexity, fruit recognition becomes an issue for the stacked fruits on a weighing scale. Recently, Machine Learning (ML) methods have been used in fruit farming and agriculture and brought great convenience to human life. An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts. CNN’s (convolutional neural networks) have attained incredible outcomes in image classifiers… More >

  • Open Access

    ARTICLE

    Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

    C. S. S. Anupama1, Rafina Zakieva2, Afanasiy Sergin3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Chomyong Kim8, Yunyoung Nam8,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321

    Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning… More >

  • Open Access

    ARTICLE

    A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

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

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304

    Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

  • 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 presented GBODL-ASC technique, three major… 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 images facilitated by deep transfer… 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 the text classification model and… 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. The current study designs a… 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 a large corpus of protein… 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. Initially, at the preprocessing steps,… More >

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