K. Kavin Kumar¹, P. M. Dinesh², P. Rayavel³, L. Vijayaraja⁴, R. Dhanasekar⁴, Rupa Kesavan⁵, Kannadasan Raju⁶, Arfat Ahmad Khan⁷, Chitapong Wechtaisong^8,*, 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 - 09 February 2023

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… More >

Qixin Lan, Binqiang Chen^*, Bin Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2017-2037, 2023, DOI:10.32604/cmes.2023.025307 - 06 February 2023

Abstract Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance… More >

Samra Rehman¹, Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Abdullah Alqahtani⁴, Khean Vesal⁵, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324 - 06 February 2023

Abstract Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation. The authors present computer vision techniques for detecting and classifying fruit leaf diseases. Examples of computer vision techniques are preprocessing original images for visualization of infected regions, feature extraction from raw or segmented images, feature fusion, feature selection, and classification. The following are the major challenges identified by researchers in the literature: (i) low-contrast infected regions extract irrelevant and redundant information, which misleads classification accuracy; (ii) irrelevant and redundant information may increase computational time and reduce the designed… More >

S. J. K. Jagadeesh Kumar¹, P. Parthasarathi², Mofreh A. Hogo³, Mehedi Masud⁴, Jehad F. Al-Amri⁵, Mohamed Abouhawwash^6,7,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2363-2378, 2023, DOI:10.32604/iasc.2023.033800 - 05 January 2023

Abstract Women from middle age to old age are mostly screened positive for Breast cancer which leads to death. Times over the past decades, the overall survival rate in breast cancer has improved due to advancements in early-stage diagnosis and tailored therapy. Today all hospital brings high awareness and early detection technologies for breast cancer. This increases the survival rate of women. Though traditional breast cancer treatment takes so long, early cancer techniques require an automation system. This research provides a new methodology for classifying breast cancer using ultrasound pictures that use deep learning and the… More >

Fatma Harby¹, Adel Thaljaoui¹, Durre Nayab², Suliman Aladhadh^3,*, Salim EL Khediri^3,4, Rehan Ullah Khan³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420 - 28 December 2022

Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of More >

Muhammed Mutlu Yapici¹, Rukiye Karakis^2,*, Kali Gurkahraman³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5049-5067, 2023, DOI:10.32604/cmc.2023.035584 - 28 December 2022

Abstract Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay… More >

R. S. Sandhya Devi^1,*, 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 - 03 November 2022

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… More >

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 >

Zahid Ullah^1,*, Mona Jamjoom²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1457-1473, 2023, DOI:10.32604/cmc.2023.032752 - 22 September 2022

Abstract Alzheimer’s disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD’s progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD.… More >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Roobaea Alroobaea³, Jasem Almotiri³, Muhammad Mohsin Malik¹, Sabah M. Alzahrani³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1069-1091, 2023, DOI:10.32604/cmc.2023.032719 - 22 September 2022

Abstract Environmental sound classification (ESC) involves the process of distinguishing an audio stream associated with numerous environmental sounds. Some common aspects such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording make the ESC task much more complicated and complex. This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources. In this research, the performance of transformer and convolutional neural networks (CNN) are investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz,… More >