Usman Ahmad¹, Muhammad Junaid Ali², Faizan Ahmed Khan³, Arfat Ahmad Khan⁴, Arif Ur Rehman¹, Malik Muhammad Ali Shahid⁵, Mohd Anul Haq^6,*, Ilyas Khan⁷, Zamil S. Alzamil⁶, Ahmed Alhussen⁸

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2125-2140, 2023, DOI:10.32604/csse.2023.031008

Abstract Building an automatic fish recognition and detection system for large-scale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with… More >

S. Gnanapriya^1,*, K. Rahimunnisa²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1105-1119, 2023, DOI:10.32604/iasc.2023.032832

Abstract The performance of Hand Gesture Recognition (HGR) depends on the hand shape. Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks. The crucial segmentation task is extremely complicated because of the background complexity, variation in illumination etc. The proposed modified UNET and ensemble model of Convolutional Neural Networks (CNN) undergoes a two stage process and results in proper hand gesture recognition. The first stage is segmenting the regions of the hand and the second stage is gesture identification. The modified UNET segmentation model is trained using… More >

Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280

Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the… More >

Romany F. Mansour^1,*, Sara A. Althubiti², Fayadh Alenezi³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265

Abstract Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph… More >

A. Geetha, N. B. Prakash^*

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1041-1055, 2022, DOI:10.32604/csse.2022.023680

Abstract Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma. Glaucoma is an incurable and unavoidable eye disease that damages the vision of optic nerves and quality of life. Classification of Glaucoma has been an active field of research for the past ten years. Several approaches for Glaucoma classification are established, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). In contrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training… More >

Muhamad Erza Aminanto^1,2,*, Ibnu Rifqi Purbomukti³, Harry Chandra², Kwangjo Kim⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5301-5314, 2022, DOI:10.32604/cmc.2022.026749

Abstract Internet of Things (IoT) networks leverage wireless communication protocols, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into an image. This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network (CNN) models, EfficientNet. We… More >

Haleem Farman¹, Jamil Ahmad^1,*, Bilal Jan², Yasir Shahzad³, Muhammad Abdullah¹, Atta Ullah⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2073-2089, 2022, DOI:10.32604/cmc.2022.018961

Abstract Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN), where its powerful features extraction… More >

Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 619-635, 2022, DOI:10.32604/cmc.2022.018562

Abstract As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore,… More >