Sami Gazzah^1,*, Rida Bayi², Soulaimane Kaloun², Omar Bencharef²

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 677-692, 2022, DOI:10.32604/iasc.2022.019360 - 22 September 2021

Abstract A new virus called SARS-CoV-2 appeared at the end of space 2019 in Wuhan, China. This virus immediately spread throughout the world due to its highly contagious nature. Moreover, SARS-CoV-2 has changed the way of our life and has caused a huge economic and public health disaster. Therefore, it is urgent to identify positive cases as soon as possible and treat them as isolated. Automatic detection of viruses using computer vision and machine learning will be a valuable contribution to detecting and limiting the spread of this epidemic. The delay introduction of X-ray technology as… More >

Rehan Ullah Khan^*, Waleed Albattah, Suliman Aladhadh, Shabana Habib

Computer Systems Science and Engineering, Vol.40, No.2, pp. 765-777, 2022, DOI:10.32604/csse.2022.019757 - 09 September 2021

Abstract Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article… More >

Schahrazad Soltane^*, Sameer Alsharif , Salwa M.Serag Eldin

Computer Systems Science and Engineering, Vol.40, No.2, pp. 629-644, 2022, DOI:10.32604/csse.2022.019333 - 09 September 2021

Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and… More >

Yahia Said^1,2,*, Mohammad Barr¹, Taoufik Saidani^2,3, Mohamed Atri^2,4

Computer Systems Science and Engineering, Vol.40, No.2, pp. 607-618, 2022, DOI:10.32604/csse.2022.018479 - 09 September 2021

Abstract Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are More >

G. Jayandhi^1,*, J.S. Leena Jasmine², S. Mary Joans²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 491-503, 2022, DOI:10.32604/csse.2022.016376 - 09 September 2021

Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces… More >

T. Vetriselvi¹, E. Laxmi Lydia², Sachi Nandan Mohanty^3,4, Eatedal Alabdulkreem⁵, Shaha Al-Otaibi⁶, Amal Al-Rasheed⁶, Romany F. Mansour^7,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2049-2064, 2022, DOI:10.32604/cmc.2022.020110 - 07 September 2021

Abstract Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles… More >

A. Sheryl Oliver¹, P. Suresh², A. Mohanarathinam³, Seifedine Kadry⁴, Orawit Thinnukool^5,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2031-2047, 2022, DOI:10.32604/cmc.2022.019876 - 07 September 2021

Abstract Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated… More >

Ahmed Reda^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809 - 07 September 2021

Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a More >

HongGeun Ji^1,2, Soyoung Oh¹, Jina Kim³, Seong Choi^1,2, Eunil Park^1,2,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 669-678, 2022, DOI:10.32604/cmc.2022.019521 - 07 September 2021

Abstract In the field of natural language processing (NLP), the advancement of neural machine translation has paved the way for cross-lingual research. Yet, most studies in NLP have evaluated the proposed language models on well-refined datasets. We investigate whether a machine translation approach is suitable for multilingual analysis of unrefined datasets, particularly, chat messages in Twitch. In order to address it, we collected the dataset, which included 7,066,854 and 3,365,569 chat messages from English and Korean streams, respectively. We employed several machine learning classifiers and neural networks with two different types of embedding: word-sequence embedding and the… More >

Shubham Mahajan^1,*, Akshay Raina², Mohamed Abouhawwash^3,4, Xiao-Zhi Gao⁵, Amit Kant Pandit¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1541-1556, 2022, DOI:10.32604/cmc.2022.019496 - 07 September 2021

Abstract Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction More >