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  • Open Access


    Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

    Hong Zhang1,*, Qi Wang1, Lixing Chen1, Jiaming Zhou1, Haijian Shao2

    Energy Engineering, Vol.120, No.8, pp. 1867-1883, 2023, DOI:10.32604/ee.2023.028453

    Abstract Induction motors (IMs) typically fail due to the rate of stator short-circuits. Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories, non-destructive fault detection is universally perceived as a difficult issue. This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs. Based on the publicly available thermal images (IRT) dataset related to condition… More > Graphic Abstract

    Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

  • Open Access


    HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy

    S. Deva Kumar, S. Venkatramaphanikumar*, K. Venkata Krishna Kishore

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165

    Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge the model’s width by substituting… More >

  • Open Access


    Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

    Tusongjiang Kari1, Zhiyang He1, Aisikaer Rouzi2, Ziwei Zhang3, Xiaojing Ma1,*, Lin Du1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617

    Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

  • Open Access


    Breast Cancer Diagnosis Using Artificial Intelligence Approaches: A Systematic Literature Review

    Alia Alshehri, Duaa AlSaeed*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

    Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

  • Open Access


    An Optimal Framework for Alzheimer’s Disease Diagnosis

    Amer Alsaraira1,*, Samer Alabed1, Eyad Hamad1, Omar Saraereh2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 165-177, 2023, DOI:10.32604/iasc.2023.036950

    Abstract Alzheimer’s disease (AD) is a kind of progressive dementia that is frequently accompanied by brain shrinkage. With the use of the morphological characteristics of MRI brain scans, this paper proposed a method for diagnosing moderate cognitive impairment (MCI) and AD. The anatomical features of 818 subjects were calculated using the FreeSurfer software, and the data were taken from the ADNI dataset. These features were first removed from the dataset after being preprocessed with an age correction algorithm using linear regression to estimate the effects of normal aging. With these preprocessed characteristics, the extreme learning machine served as a classifier for… More >

  • Open Access


    Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

    S. Sivasubramaniam*, S. P. Balamurugan

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 111-126, 2023, DOI:10.32604/iasc.2023.035199

    Abstract Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine, for example, traditional Chinese medicine (TCM), Japanese traditional herbal medicine, and traditional Korean medicine (TKM). The diagnosis procedure is mainly based on the expert's knowledge depending upon the visual inspection comprising color, substance, coating, form, and motion of the tongue. But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective. Therefore, computer-aided tongue analyses have a greater potential to present objective and more consistent health assessments. This manuscript introduces a novel Simulated Annealing with Transfer Learning… More >

  • Open Access



    L. Chenga , Y. L. Zhanga,† , J. F. Lib

    Frontiers in Heat and Mass Transfer, Vol.19, No.1, pp. 1-7, 2022, DOI:10.5098/hmt.19.32

    Abstract The internal characteristics during rotational speed fluctuation have an important influence on centrifugal pump to avoid or utilize its transient performance. In this paper, a circulation piping system that includes a low-specific-speed centrifugal pump is established to study the energy distribution characteristics in a centrifugal pump during speed fluctuation. The unsteady flow in the entire system is numerically calculated with a user-defined function, the sliding grid method, and the RNG k-ε turbulence model. Then, the energy distribution of the transient flow field in the centrifugal pump model during speed fluctuation is diagnosed with vortex dynamics by using flow section and… More >

  • Open Access


    Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer


    Oncology Research, Vol.31, No.2, pp. 141-156, 2023, DOI:10.32604/or.2023.028548

    Abstract Though significant improvements have been made in the treatment methods for ovarian cancer (OC), the prognosis for OC patients is still poor. Exploring hub genes associated with the development of OC and utilizing them as appropriate potential biomarkers or therapeutic targets is highly valuable. In this study, the differentially expressed genes (DEGs) were identified from an independent GSE69428 Gene Expression Omnibus (GEO) dataset between OC and control samples. The DEGs were processed to construct the protein-protein interaction (PPI) network using STRING. Later, hub genes were identified through Cytohubba analysis of the Cytoscape. Expression and survival profiling of the hub genes… More >

  • Open Access


    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


    Assessment on Fault Diagnosis and State Evaluation of New Power Grid: A Review

    Bo Yang1, Yulin Li1, Yaxing Ren2, Yixuan Chen3, Xiaoshun Zhang4, Jingbo Wang5,*

    Energy Engineering, Vol.120, No.6, pp. 1287-1293, 2023, DOI:10.32604/ee.2023.027801

    Abstract This article has no abstract. More >

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