Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, Bhawna Goyal², Talal Taha Ali³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287 - 29 November 2023

Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust More >

Mohammed Aly^1,*, Abdullah Shawan Alotaibi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 557-582, 2023, DOI:10.32604/cmc.2023.042493 - 31 October 2023

Abstract Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the… More >

Jiaji Wang¹, Muhammad Attique Khan², Shuihua Wang^1,3, Yudong Zhang^1,3,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2201-2216, 2023, DOI:10.32604/cmc.2023.041191 - 30 August 2023

Abstract Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train… More >

Amel Ali Alhussan¹, Fatma M. Talaat², El-Sayed M. El-kenawy³, Abdelaziz A. Abdelhamid^4,5, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga^1,*, Mona Alnaggar⁷

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368 - 08 June 2023

Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the… More >

Mona Jamjoom¹, Ahmed Elhadad², Hussein Abulkasim^3,*, Safia Abbas⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310 - 08 June 2023

Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, More >

K. Ravishankar^*, C. Jothikumar

Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343 - 26 May 2023

Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of… More >

A. Richard William^1,*, J. Senthilkumar², Y. Suresh², V. Mohanraj²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753 - 26 May 2023

Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with… More >

Saeed Mohsen^1,2,*, Sherif S. M. Ghoneim³, Mohammed S. Alzaidi³, Abdullah Alzahrani³, Ashraf Mohamed Ali Hassan⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758 - 29 April 2023

Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and… More >

Dumitru Baleanu^1,2,3, Ahmad Sami Al-Shamayleh⁴, Rabha W. Ibrahim^5,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3465-3477, 2023, DOI:10.32604/cmc.2023.037162 - 31 March 2023

Abstract Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains… More >

Hussein Ibrahim Hussein¹, Said Amirul Anwar^2,*, Muhammad Imran Ahmad²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025 - 06 February 2023

Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly,… More >