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  • Open Access

    ARTICLE

    A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

    Muhammad Naeem Akram1, Muhammad Usman Yaseen1, Muhammad Waqar1, Muhammad Imran1,*, Aftab Hussain2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855

    Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >

  • Open Access

    ARTICLE

    Improved Shark Smell Optimization Algorithm for Human Action Recognition

    Inzamam Mashood Nasir1,*, Mudassar Raza1, Jamal Hussain Shah1, Muhammad Attique Khan2, Yun-Cheol Nam3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2667-2684, 2023, DOI:10.32604/cmc.2023.035214

    Abstract Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted features. This proposed technique is… More >

  • Open Access

    EDITORIAL

    Grad-CAM: Understanding AI Models

    Shuihua Wang1,2, Yudong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1321-1324, 2023, DOI:10.32604/cmc.2023.041419

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

    Asif Rahim1, Yanru Zhong2, Tariq Ahmad3,*, Sadique Ahmad4,*, Mohammed A. ElAffendi4

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1947-1976, 2023, DOI:10.32604/cmc.2023.039340

    Abstract Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP). The model is trained and… More >

  • Open Access

    ARTICLE

    TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation

    Peng Geng1, Ji Lu1, Ying Zhang2,*, Simin Ma1, Zhanzhong Tang2, Jianhua Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127

    Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training. To solve these problems, a dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose depth-wise convolutions can provide position information is… More >

  • Open Access

    ARTICLE

    COVID TCL: A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

    Rui Wen1,*, Jie Zhou2, Zhongliang Shen1, Xiaorui Zhang2,3,4, Sunil Kumar Jha5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889

    Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image… More >

  • 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

    Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

    Jiajia Yu*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 777-797, 2023, DOI:10.32604/iasc.2023.037354

    Abstract This study aims to reduce the interference of ambient noise in mobile communication, improve the accuracy and authenticity of information transmitted by sound, and guarantee the accuracy of voice information delivered by mobile communication. First, the principles and techniques of speech enhancement are analyzed, and a fast lateral recursive least square method (FLRLS method) is adopted to process sound data. Then, the convolutional neural networks (CNNs)-based noise recognition CNN (NR-CNN) algorithm and speech enhancement model are proposed. Finally, related experiments are designed to verify the performance of the proposed algorithm and model. The experimental results show that the noise classification… More >

  • Open Access

    ARTICLE

    Acknowledge of Emotions for Improving Student-Robot Interaction

    Hasan Han1, Oguzcan Karadeniz1, Tugba Dalyan2,*, Elena Battini Sonmez2, Baykal Sarioglu1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1209-1224, 2023, DOI:10.32604/iasc.2023.030674

    Abstract Robot companions will soon be part of our everyday life and students in the engineering faculty must be trained to design, build, and interact with them. The two affordable robots presented in this paper have been designed and constructed by two undergraduate students; one artificial agent is based on the Nvidia Jetson Nano development board and the other one on a remote computer system. Moreover, the robots have been refined with an empathetic system, to make them more user-friendly. Since automatic facial expression recognition skills is a necessary pre-processing step for acknowledging emotions, this paper tested different variations of Convolutional… More >

  • Open Access

    ARTICLE

    Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

    Muhammad Tahir1,*, Farhan Riaz2, Imran Usman1,3, Mohamed Ibrahim Habib1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 4007-4022, 2023, DOI:10.32604/csse.2023.032359

    Abstract The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant features. The invariance characteristics of… More >

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