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  • Open Access

    ARTICLE

    Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects

    Nadeem Jabbar Chaudhry1,*, M. Bilal Khan2, M. Javaid Iqbal1, Siddiqui Muhammad Yasir3

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 245-259, 2022, DOI:10.32604/jai.2022.038875

    Abstract Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB camera. Defects must be detected early to take timely corrective action due to production concerns. For image classification up till now, a model-based method has been utilized, which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free surfaces. The problem of detecting steel surface defects has grown in importance as a result of the vast range of steel applications in end-product… More >

  • Open Access

    ARTICLE

    ResCD-FCN: Semantic Scene Change Detection Using Deep Neural Networks

    S. Eliza Femi Sherley1,*, J. M. Karthikeyan1, N. Bharath Raj1, R. Prabakaran2, A. Abinaya1, S. V. V. Lakshmi3

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931

    Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a… More >

  • Open Access

    ARTICLE

    Le vandalisme dans l’information géographique volontaire

    Du concept à la détection non supervisée d’anomalie

    Quy Thy Truong1 , Guillaume Touya2, Cyril de Runz3

    Revue Internationale de Géomatique, Vol.29, No.1, pp. 31-56, 2019, DOI:10.3166/rig.2019.00073

    Abstract Since vandalism is a serious matter for the quality of Volunteered Geographic Information, this paper aims at exploring machine learning techniques that enable its detection. First, a focus on the various definitions of vandalism highlights the complexity of this concept. This focus comprises a case study on proven vandals in OpenStreetMap (OSM). Second, we present an experimental vandalism detection on OSM data using a clustering-based outlier detection. The analysis of initial results leads to a discussion about the construction of an OSM vandalism corpus that would be useful in a supervised learning context.

    RÉSUMÉ
    Dans un contexte où le vandalisme de… More >

  • Open Access

    ARTICLE

    Shadow detection and correction using a combined 3D GIS and image processing approach

    Safa Ridene1 , Reda Yaagoubi1, Imane Sebari1, Audrey Alajouanine2

    Revue Internationale de Géomatique, Vol.29, No.3, pp. 241-253, 2019, DOI:10.3166/rig.2019.00091

    Abstract While shadow can give useful information about size and shape of objects, it can pose problems in feature detection and object detection, thereby, it represents one of the major perturbator phenomenons frequently occurring on images and unfortunately, it is inevitable. “Shadows may lead to the failure of image analysis processes and also cause a poor quality of information which in turn leads to problems in implementation of algorithms.” (Mahajan and Bajpayee, 2015). It also affects multiple image analysis applications, whereby shadow cast by buildings deteriorate the spectral values of the surfaces. Therefore, its presence causes a deterioration in the visual… More >

  • Open Access

    ARTICLE

    Line Fault Detection of DC Distribution Networks Using the Artificial Neural Network

    Xunyou Zhang1,2,*, Chuanyang Liu1,3, Zuo Sun1

    Energy Engineering, Vol.120, No.7, pp. 1667-1683, 2023, DOI:10.32604/ee.2023.025186

    Abstract A DC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits, such as high efficiency and easy control. However, a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability. This study proposes an artificial neural network (ANN)-based fault detection and protection method for DC distribution networks. The ANN is applied to a classifier for different faults on the DC line. The backpropagation neural network is used to predict the line current, and the fault detection threshold is obtained on the basis of the difference between the… More >

  • Open Access

    ARTICLE

    PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform

    Wenbo Li, Qi Wang*, Shang Gao

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 921-938, 2023, DOI:10.32604/iasc.2023.038257

    Abstract Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise… More >

  • Open Access

    ARTICLE

    Mirai Botnet Attack Detection in Low-Scale Network Traffic

    Ebu Yusuf GÜVEN, Zeynep GÜRKAŞ-AYDIN*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 419-437, 2023, DOI:10.32604/iasc.2023.038043

    Abstract The Internet of Things (IoT) has aided in the development of new products and services. Due to the heterogeneity of IoT items and networks, traditional techniques cannot identify network risks. Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity. While the use of artificial intelligence eliminates the need to define rules, the training and retraining processes require additional processing power. This study proposes a methodology for analyzing constrained devices in IoT environments. We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices.… More >

  • Open Access

    ARTICLE

    Leaky Cable Fixture Detection in Railway Tunnel Based on RW DCGAN and Compressed GS-YOLOv5

    Suhang Li1, Yunzuo Zhang1,*, Ruixue Liu2, Jiayu Zhang1, Zhouchen Song1, Yutai Wang1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1163-1180, 2023, DOI:10.32604/iasc.2023.037902

    Abstract The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures. To ensure safety, checking the regular leaky cable fixture is necessary to eliminate the potential danger. At present, the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time. The faulty fixture is also insufficient and difficult to obtain, seriously affecting the model detection effect. To solve these problems, an innovative detection method is proposed in this paper. Firstly, we presented the Res-Net and Wasserstein-Deep Convolution GAN (RW-DCGAN)… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

    H. Shalma, P. Selvaraj*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1145-1161, 2023, DOI:10.32604/iasc.2023.036853

    Abstract The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images. A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map. 3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps. This work uses the consistency check method to find an accurate depth map for identifying occluded pixels. The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation. The improved depth map quality within a… More >

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