Stevica Cvetkovic^1,*, Nemanja Savic¹, Ivan Ciric²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 967-979, 2023, DOI:10.32604/iasc.2023.032526 - 29 September 2022

Abstract Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter. To achieve highly accurate results as well as provide real-time execution, we proposed a deep transfer learning approach over the state-of-the-art deep learning object detector. Our method, denoted as YOLOHANDS, is built on top of the You Only Look Once (YOLO) deep learning architecture, which is modified to adapt to the single class hand detection task. The model transfer is performed by modifying the higher convolutional layers including the last fully connected More >

C. Narmadha¹, T. Kavitha², R. Poonguzhali², V. Hamsadhwani³, Ranjan walia⁴, Monia⁵, B. Jegajothi^6,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3613-3626, 2023, DOI:10.32604/iasc.2023.029323 - 17 August 2022

Abstract At present days, object detection and tracking concepts have gained more importance among researchers and business people. Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process. This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model. The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model. The AIA-RFRCNN model makes use… More >

C. D. Prem Kumar^1,*, S. Saravana Sundaram²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 881-899, 2023, DOI:10.32604/iasc.2023.025766 - 06 June 2022

Abstract The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL… More >

Areej A. Malibari¹, Jaber S. Alzahrani², Marwa Obayya³, Noha Negm^4,5, Mohammed Abdullah Al-Hagery⁶, Ahmed S. Salama⁷, Anwer Mustafa Hilal^8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6443-6459, 2022, DOI:10.32604/cmc.2022.031324 - 28 July 2022

Abstract Osteosarcoma is a type of malignant bone tumor that is reported across the globe. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the detection and classification of malignancies in biomedical images. In this regard, the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning (BOIC-EHODTL) model. The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma. At the initial stage, Gabor Filter (GF) is applied as a pre-processing technique to get rid of the noise from images. In addition,… More >

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 >

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 >

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 >

R. P. Narmadha^1,*, N. Sengottaiyan², R. J. Kavitha³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679 - 22 September 2021

Abstract In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims… More >