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  • Open Access

    ARTICLE

    A Robust Approach for Detection and Classification of KOA Based on BILSTM Network

    Abdul Qadir1, Rabbia Mahum1, Suliman Aladhadh2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1365-1384, 2023, DOI:10.32604/csse.2023.037033 - 28 July 2023

    Abstract A considerable portion of the population now experiences osteoarthritis of the knee, spine, and hip due to lifestyle changes. Therefore, early treatment, recognition and prevention are essential to reduce damage; nevertheless, this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians. To overcome the existing challenges in the early detection of Knee Osteoarthritis (KOA), an effective automated technique, prompt recognition, and correct categorization are required. This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and… More >

  • Open Access

    ARTICLE

    Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

    Fawad Ali Shah1, Habib Akbar1, Abid Ali2,3, Parveen Amna4, Maha Aljohani5, Eman A. Aldhahri6, Harun Jamil7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1385-1413, 2023, DOI:10.32604/csse.2023.036144 - 28 July 2023

    Abstract The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave… More >

  • Open Access

    ARTICLE

    A Consistent Mistake in Remote Sensing Images’ Classification Literature

    Huaxiang Song*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1381-1398, 2023, DOI:10.32604/iasc.2023.039315 - 21 June 2023

    Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it appears that training optimization strategies have received less attention in relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison. The overall accuracy (OA) trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline. Results show that… More >

  • Open Access

    ARTICLE

    Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

    Zhiwei Ye1, Ziqian Fang1, Zhina Song1,*, Haigang Sui2, Chunyan Yan1, Wen Zhou1, Mingwei Wang1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2019-2035, 2023, DOI:10.32604/iasc.2023.039949 - 21 June 2023

    Abstract Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of… More >

  • Open Access

    ARTICLE

    Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

    Jahanzaib Latif1, Shanshan Tu1,*, Chuangbai Xiao1, Anas Bilal2, Sadaqat Ur Rehman3, Zohaib Ahmad4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152 - 08 June 2023

    Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization More >

  • Open Access

    ARTICLE

    Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

    Mohamed Yacin Sikkandar1,*, K. Hemalatha2, M. Subashree3, S. Srinivasan4, Seifedine Kadry5,6,7, Jungeun Kim8, Keejun Han9

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 873-889, 2023, DOI:10.32604/csse.2023.035730 - 26 May 2023

    Abstract Recently, COVID-19 has posed a challenging threat to researchers, scientists, healthcare professionals, and administrations over the globe, from its diagnosis to its treatment. The researchers are making persistent efforts to derive probable solutions for managing the pandemic in their areas. One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography (CT) scans. At the same time, the recent advances in machine learning (ML) and deep learning (DL) models show promising results in medical imaging. Particularly, the convolutional neural network (CNN) model can be applied to… More >

  • Open Access

    ARTICLE

    Data Augmentation Using Contour Image for Convolutional Neural Network

    Seung-Yeon Hwang1, Jeong-Joon Kim2,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4669-4680, 2023, DOI:10.32604/cmc.2023.031129 - 29 April 2023

    Abstract With the development of artificial intelligence-related technologies such as deep learning, various organizations, including the government, are making various efforts to generate and manage big data for use in artificial intelligence. However, it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage. There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology. Therefore, this study proposes a mixed contour data augmentation technique, which is a data augmentation technique using contour images, to solve a… 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 - 03 April 2023

    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

    FST-EfficientNetV2: Exceptional Image Classification for Remote Sensing

    Huaxiang Song*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3959-3978, 2023, DOI:10.32604/csse.2023.038429 - 03 April 2023

    Abstract Recently, the semantic classification (SC) algorithm for remote sensing images (RSI) has been greatly improved by deep learning (DL) techniques, e.g., deep convolutional neural networks (CNNs). However, too many methods employ complex procedures (e.g., multi-stages), excessive hardware budgets (e.g., multi-models), and an extreme reliance on domain knowledge (e.g., handcrafted features) for the pure purpose of improving accuracy. It obviously goes against the superiority of DL, i.e., simplicity and automation. Meanwhile, these algorithms come with unnecessarily expensive overhead on parameters and hardware costs. As a solution, the author proposed a fast and simple training algorithm based… More >

  • Open Access

    ARTICLE

    Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

    Mohammed Jasim Mohammed Jasim1, Bzar Khidir Hussan2, Subhi R. M. Zeebaree3,*, Zainab Salih Ageed4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3677-3693, 2023, DOI:10.32604/cmc.2023.037363 - 31 March 2023

    Abstract The major mortality factor relevant to the intestinal tract is the growth of tumorous cells (polyps) in various parts. More specifically, colonic polyps have a high rate and are recognized as a precursor of colon cancer growth. Endoscopy is the conventional technique for detecting colon polyps, and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate. The automated diagnosis of polyps in a computer-aided diagnosis (CAD) method is implemented using statistical analysis. Nowadays, Deep Learning,… More >

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