Javaria Amin¹, Muhammad Sharif², Muhammad Almas Anjum³, Yunyoung Nam^4,*, Seifedine Kadry⁵, David Taniar⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2451-2467, 2021, DOI:10.32604/cmc.2021.014199 - 13 April 2021

Abstract Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3… More >

Amjed Basil Abdulkareem¹, Nor Samsiah Sani^1,*, Shahnorbanun Sahran¹, Zaid Abdi Alkareem Alyessari¹, Afzan Adam¹, Abdul Hadi Abd Rahman¹, Abdulkarem Basil Abdulkarem²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 305-320, 2021, DOI:10.32604/iasc.2021.015413 - 01 April 2021

Abstract The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet)… More >

Ming Li¹, Zhong Fang², Wanwan Cao¹, Yong Ma^1,*, Shang Wu¹, Yang Guo¹, Yu Xue³, Romany F. Mansour⁴

Computer Systems Science and Engineering, Vol.38, No.1, pp. 39-46, 2021, DOI:10.32604/csse.2021.016189 - 01 April 2021

Abstract With the rapid development and popularization of new-generation technologies such as cloud computing, big data, and artificial intelligence, the construction of smart grids has become more diversified. Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents, which is essential to ensure the normal operation of the power system, energy management and planning. Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method. It uses the unique out-of-bag error of random More >

Rahmat Hidayat^1,2,*, Iwan Tri Riyadi Yanto^1,3, Azizul Azhar Ramli¹, Mohd Farhan Md. Fudzee¹, Ansari Saleh Ahmar⁴

Computer Systems Science and Engineering, Vol.38, No.1, pp. 119-130, 2021, DOI:10.32604/csse.2021.015628 - 01 April 2021

Abstract

Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately. This study discusses data classification using a fuzzy soft set method to predict target classes accurately. This study aims to form a data classification algorithm using the fuzzy soft set method. In this study, the fuzzy soft set was calculated based on the normalized Hamming distance. Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function. In the classification step, a generalized normalized Euclidean

… More >

Mohammed Ibrahim Ahmed Al-mashhadani¹, Theyazn H. H. Aldhyani^2,*, Mosleh Hmoud Al-Adhaileh³, Alwi M. Bamhdi⁴, Mohammed Y. Alzahrani⁵, Fawaz Waselallah Alsaade⁶, Hasan Alkahtani^1,6

Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992 - 01 April 2021

Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is… More >

Pengfei Gong^1,2, Qun Wang^2,*, Junjun Zhu³

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1389-1408, 2021, DOI:10.32604/cmc.2021.016483 - 22 March 2021

Abstract Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management. Therefore, it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly. The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas. This study used a multilevel hierarchical structural model to determine emergency-response classification. In the model, accident attributes, urban road network vulnerability, and institutional resilience were… More >

Muhammad Attique Khan¹, Tallha Akram², Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307 - 22 March 2021

Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a… More >

G. Madhu^1,*, A. Govardhan², B. Sunil Srinivas³, Kshira Sagar Sahoo⁴, N. Z. Jhanjhi⁵, K. S. Vardhan¹, B. Rohit⁶

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 903-919, 2021, DOI:10.32604/cmc.2021.016114 - 22 March 2021

Abstract Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully… More >

Aqib Ali^1,2, Wali Khan Mashwani³, Samreen Naeem², Muhammad Irfan Uddin⁴, Wiyada Kumam⁵, Poom Kumam^6,7,*, Hussam Alrabaiah^8,9, Farrukh Jamal¹⁰, Christophe Chesneau¹¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 391-407, 2021, DOI:10.32604/cmc.2021.016037 - 22 March 2021

Abstract The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach… More >

Nany Katamesh, Osama Abu-Elnasr^*, Samir Elmougy

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 589-606, 2021, DOI:10.32604/cmc.2021.015761 - 22 March 2021

Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by… More >