Harbi Al-Mahafzah¹, Tamer AbuKhalil¹, Bassam A. Y. Alqaralleh^2,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5703-5715, 2022, DOI:10.32604/cmc.2022.030399 - 28 July 2022

Abstract Biometric verification has become essential to authenticate the individuals in public and private places. Among several biometrics, iris has peculiar features and its working mechanism is complex in nature. The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models. With this motivation, the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System (CKHDTL-BIRS). The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification. To achieve this, CKHDTL-BIRS model initially performs Median Filtering More >

Irfan Ahmed Usmani^1,*, Muhammad Tahir Qadri¹, Razia Zia¹, Asif Aziz², Farheen Saeed³

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4369-4392, 2022, DOI:10.32604/cmc.2022.030698 - 16 June 2022

Abstract Knowledge-based transfer learning techniques have shown good performance for brain tumor classification, especially with small datasets. However, to obtain an optimized model for targeted brain tumor classification, it is challenging to select a pre-trained deep learning (DL) model, optimal values of hyperparameters, and optimization algorithm (solver). This paper first presents a brief review of recent literature related to brain tumor classification. Secondly, a robust framework for implementing the transfer learning technique is proposed. In the proposed framework, a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters: batch size… More >

Hussah Nasser AlEisa¹, El-Sayed M. El-kenawy^2,3, Amel Ali Alhussan^1,*, Mohamed Saber⁴, Abdelaziz A. Abdelhamid^5,6, Doaa Sami Khafaga¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447 - 16 June 2022

Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most… More >

Radwa Marzouk¹, Eatedal Alabdulkreem², Sami Dhahbi³, Mohamed K. Nour⁴, Mesfer Al Duhayyim⁵, Mahmoud Othman⁶, Manar Ahmed Hamza^7,*, Abdelwahed Motwakel⁷, Ishfaq Yaseen⁷, Mohammed Rizwanullah⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3905-3920, 2022, DOI:10.32604/cmc.2022.029326 - 16 June 2022

Abstract Oral cancer is the most commonly occurring ‘head and neck cancers’ across the globe. Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public. Since earlier identification of disease is essential for improved outcomes, Artificial Intelligence (AI) and Machine Learning (ML) models are used in this regard. In this background, the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model (AIDTL-OCCM). The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.… More >

Mahmoud Ragab^1,2,3,*, Alaa F. Nahhas⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2849-2864, 2022, DOI:10.32604/cmc.2022.028855 - 16 June 2022

Abstract Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate. One of the commonly utilized imaging modalities for breast cancer is histopathological images. Since manual inspection of histopathological images is a challenging task, automated tools using deep learning (DL) and artificial intelligence (AI) approaches need to be designed. The latest advances of DL models help in accomplishing maximum image classification performance in several application areas. In this view, this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer (DTLRO-HCBC) technique. The proposed… More >

Lixiu Wu^1,*, Song Wang², Qingbing Sang³

Journal of New Media, Vol.4, No.3, pp. 125-135, 2022, DOI:10.32604/jnm.2022.027199 - 13 June 2022

Abstract In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. More >

Zhongyao Du^1,*, Xiaoying Chen¹, Hao Wang², Xuheng Wang¹, Yu Deng¹, Liying Sun¹

Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283 - 23 May 2022

Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss… More >

Zhijie Wang¹, Yue Zhao^1,*, Licheng Wu¹, Xiaojun Bi¹, Zhuoma Dawa², Qiang Ji³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 629-639, 2022, DOI:10.32604/cmc.2022.027092 - 18 May 2022

Abstract As one of Chinese minority languages, Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently. This, along with the relatively small Tibetan corpus, has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model. This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data. We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects: modeling unit selection, transfer learning method, and source language selection. Experimental results show that the More >

Khadija Khan, Samabia Tehsin^*

Journal on Artificial Intelligence, Vol.4, No.1, pp. 1-13, 2022, DOI:10.32604/jai.2022.028092 - 16 May 2022

Abstract Leukemia is the cancer that starts in the blood cells due to the excess production of immature leucocytes that replace the cells with normal blood cells. Physicians rely on their experience to determine the type and subtype of Leukemia from the blood sample. Most people are misdiagnosed when it comes to its subtypes, the error rates can be up to 40% during the classification process. That too depends on the expertise of the physician. This research represents a Convolutional Neural Network based medical image classifier. The proposed technique can classify Leukemia and its five subtypes. More >

Ahmad Alassaf, Mohamed Yacin Sikkandar^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5273-5285, 2022, DOI:10.32604/cmc.2022.025577 - 21 April 2022

Abstract Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images. The early identification of the disease can effectively reduce the severity rate. Deep learning (DL) models can be widely employed to analyze biomedical images, thereby minimizing the misclassification rate. With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images. The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images. In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing… More >