Abdul Majid¹, Muhammad Attique Khan¹, Yunyoung Nam^2,*, Usman Tariq³, Sudipta Roy⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 319-337, 2021, DOI:10.32604/cmc.2021.016816 - 04 June 2021

Abstract The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue… More >

Ye Mu^1,2,3,4, Yuheng Sun¹, Tianli Hu^1,2,3,4, He Gong^1,2,3,4, Shijun Li^1,2,3,4,*, Thobela Louis Tyasi⁵

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 729-737, 2021, DOI:10.32604/iasc.2021.016569 - 20 April 2021

Abstract The rapid development of computer vision technology and digital images has increased the potential for using image recognition for eye disease diagnosis. Many early screening and diagnosis methods for ocular diseases based on retinal images of the fundus have been proposed recently, but their accuracy is low. Therefore, it is important to develop and evaluate an improved VGG model for the recognition and classification of retinal fundus images. In response to these challenges, to solve the problem of accuracy and reliability of clinical algorithms in medical imaging this paper proposes an improved model for early More >

Sameh Abd ElGhany^1,2, Mai Ramadan Ibraheem³, Madallah Alruwaili⁴, Mohammed Elmogy^5,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102 - 22 March 2021

Abstract With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified More >

Zhaoqing Xie^1,*, Qing Liu², Yulian Cao³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 153-166, 2021, DOI:10.32604/iasc.2021.016246 - 17 March 2021

Abstract Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsupervised learning algorithm. More >

Thavavel Vaiyapuri¹, Sachi Nandan Mohanty², M. Sivaram³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar^6,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1881-1897, 2021, DOI:10.32604/cmc.2021.014924 - 05 February 2021

Abstract The latest advancements in highway research domain and increase inthe number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System (ITS). One of the popular research areas i.e., Vehicle License Plate Recognition (VLPR) aims at determining the characters that exist in the license plate of the vehicles. The VLPR process is a difficult one due to the differences in viewpoint, shapes, colors, patterns, and non-uniform illumination at the time of capturing images. The current study develops a robust Deep Learning (DL)-based VLPR model using Squirrel Search Algorithm… More >

Sulaiman Khan¹, Shah Nazir¹, Habib Ullah Khan^2,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 895-915, 2021, DOI:10.32604/cmc.2021.013878 - 12 January 2021

Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control… More >

Shwet Ketu^*, Pramod Kumar Mishra

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1896-1919, 2021, DOI:10.32604/cmc.2020.012423 - 26 November 2020

Abstract Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.… More >

Xing Deng^1,2, Haijian Shao^1,2,*, Liang Shi³, Xia Wang^4,5, Tongling Xie⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 579-596, 2020, DOI:10.32604/cmes.2020.011920 - 12 October 2020

Abstract The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Convolutional Neural Networks) are provided to compare with More >