Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337 - 27 February 2024

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through More >

Muzi Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2465-2493, 2023, DOI:10.32604/cmes.2023.026827 - 03 August 2023

Abstract Modular construction as an innovative method aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness. Despite the obvious advantages, the uptake of modular construction is not booming as expected. However, previous studies have investigated and summarized the barriers to the adoption of modular construction. In this research, a Large-Scale Group Decision Making (LSGDM)- based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders. In addition, the Technology-Organization-Environment (TOE) framework is utilized to identify the barriers based on three More >

K. Ravishankar^*, C. Jothikumar

Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343 - 26 May 2023

Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of… More >

Kamalrulnizam Bin Abu Bakar¹, Fatima Tul Zuhra^2,*, Babangida Isyaku^1,3, Fuad A. Ghaleb¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 785-798, 2023, DOI:10.32604/cmc.2023.034540 - 06 February 2023

Abstract The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization… More >

M. J. Anitha^1,*, R. Hemalatha²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1183-1200, 2023, DOI:10.32604/csse.2023.031888 - 20 January 2023

Abstract Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures. Moreover, identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings. Hence, this paper proposes an efficient strategy to classify the cracks into fine, medium, and thick using a novel bilayer crack detection algorithm. The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification. The proposed algorithm works well in the dark background… More >

W. M. Faizal^1,*, C. Y. Khor¹, Muhammad Nooramin Che Yaakob¹, N. N. N. Ghazali², M. Z. Zainon², Norliza Binti Ibrahim³, Roziana Mohd Razi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 43-61, 2023, DOI:10.32604/cmes.2023.022716 - 05 January 2023

Abstract This paper aims to investigate and present the numerical investigation of airflow characteristics using Turbulent Kinetic Energy (TKE) to characterize the upper airway with obstructive sleep apnea (OSA) under inhale and exhale breathing conditions. The importance of TKE under both breathing conditions is that it show an accurate method in expressing the severity of flow in sleep disorder. Computational fluid dynamics simulate the upper airway’s airflow via steady-state Reynolds-averaged Navier-Stokes (RANS) with k–ω shear stress transport (SST) turbulence model. The three-dimensional (3D) airway model is created based on the CT scan images of an actual More > Graphic Abstract

G. Bhargavi^*, J. Arunnehru

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1257-1272, 2023, DOI:10.32604/iasc.2023.034335 - 05 January 2023

Abstract Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala’s Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and… More >

Firas Abedi¹, Subhi R.M. Zeebaree², Zainab Salih Ageed³, Hayder M.A. Ghanimi⁴, Ahmed Alkhayyat^5,*, Mohammed A.M. Sadeeq⁶, Sarmad Nozad Mahmood⁷, Ali S. Abosinnee⁸, Zahraa H. Kareem⁹, Ali Hashim Abbas¹⁰, Waleed Khaild Al-Azzawi¹¹, Mustafa Musa Jaber^12,13, Mohammed Dauwed¹⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5691-5704, 2023, DOI:10.32604/cmc.2023.034435 - 28 December 2022

Abstract As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical… More >

Touseef Hussain^1,*, Abrar A. Khan¹, Heba I. Mohamed^2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 783-799, 2023, DOI:10.32604/phyton.2022.026045 - 29 November 2022

Abstract Fusarium dry rot is considered to be the most critical soilborne and postharvest disease that damages potato tubers worldwide when they are stored for a long time. This study was performed to demonstrate the effect of crude extract, culture filtrate, and cell suspension obtained from the bacterium Bacillus subtilis HussainT-AMU, on the net house and field. From oil-contaminated soil, through the serial dilution method, biosurfactant bacteria were isolated on nutrient agar medium. To isolate and screen the prospective biosurfactant strain, various biosurfactant screening methods were used. Standard protocols were carried out for morphological, molecular, and chemical… More >

Nebojsa Budimirovic¹, E. Prabhu², Milos Antonijevic¹, Miodrag Zivkovic¹, Nebojsa Bacanin^1,*, Ivana Strumberger¹, K. Venkatachalam³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1685-1698, 2022, DOI:10.32604/cmc.2022.023418 - 24 February 2022

Abstract Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is More >