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  • Open Access


    Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

    Uzair Aslam Bhatti1,*, Sibghat Ullah Bazai2, Shumaila Hussain1, Shariqa Fakhar3, Chin Soon Ku4,*, Shah Marjan5, Por Lip Yee6, Liu Jing1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 681-697, 2023, DOI:10.32604/cmc.2023.037958

    Abstract Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages… More >

  • Open Access


    Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

    S. Kiruthika Devi1, Amani Abdulrahman Albraikan2, Fahd N. Al-Wesabi3, Mohamed K. Nour4, Ahmed Ashour5, Anwer Mustafa Hilal6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3191-3207, 2023, DOI:10.32604/csse.2023.036980

    Abstract Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on… More >

  • Open Access


    Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

    K. Kavin Kumar1, P. M. Dinesh2, P. Rayavel3, L. Vijayaraja4, R. Dhanasekar4, Rupa Kesavan5, Kannadasan Raju6, Arfat Ahmad Khan7, Chitapong Wechtaisong8,*, Mohd Anul Haq9, Zamil S. Alzamil9, Ahmed Alhussen10

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1845-1861, 2023, DOI:10.32604/csse.2023.033927

    Abstract A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal. In India, around 15 million cases are diagnosed yearly. To mitigate the seriousness of the tumor it is essential to diagnose at the beginning. Notwithstanding, the manual evaluation process utilizing Magnetic Resonance Imaging (MRI) causes a few worries, remarkably inefficient and inaccurate brain tumor diagnoses. Similarly, the examination process of brain tumors is intricate as they display high unbalance in nature like shape, size, appearance, and location. Therefore, a precise and expeditious prognosis of brain tumors is essential for implementing… More >

  • Open Access


    EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

    R. S. Sandhya Devi1,*, V. R. Vijay Kumar2, P. Sivakumar3

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2249-2263, 2023, DOI:10.32604/csse.2023.032231

    Abstract Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The… More >

  • Open Access


    A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

    Yu Fan1,6, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Chunyan Shao1, Cheng Gong1, Jieren Cheng3,5, Yenwei Chen4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1279-1302, 2023, DOI:10.32604/cmc.2023.031445

    Abstract Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed. This paper proposes… More >

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