Saba Awan¹, Zahid Mehmood^2,*, Hassan Nazeer Chaudhry³, Usman Tariq⁴, Amjad Rehman⁵, Tanzila Saba⁵, Muhammad Rashid⁶

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4609-4626, 2022, DOI:10.32604/cmc.2022.019404

Abstract To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different… More >

Adnan Muhammad Shah, KangYoon Lee^*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1719-1735, 2022, DOI:10.32604/iasc.2022.019666

Abstract Online physician reviews (OPRs) critically influence the patients’ consultation decisions on physician rating websites. The increasing number of OPRs contributes to the challenge of information overload. The worth of development needs to be explored further. Based on the OPRs collected from RateMDs and Healthgrades, and Plutchik’s wheel on human emotions framework, the purpose of this study was to examine the impact of emotional intensity (positive and negative) incorporated in OPRs on review helpfulness (RH). The proposed model was empirically tested using data from two physician rating websites and applying a mixed-methods approach (text mining and econometrics). The results suggested that… More >

Prabhakaran Mathialagan^*, Malathy Chidambaranathan

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 929-946, 2022, DOI:10.32604/iasc.2022.020163

Abstract Nasal cavity and paranasal sinus tumours that occur in the respiratory tract are the most life-threatening disease in the world. The human respiratory tract has many sites which has different mucosal lining like frontal, parred, sphenoid and ethmoid sinuses. Nasal cavity tumours can occur at any different mucosal linings and chances of prognosis possibility from one nasal cavity site to another site is very high. The paranasal sinus tumours can metastases to oral cavity and digestive tracts may lead to excessive survival complications. Grading the respiratory tract tumours with dysplasia cases are more challenging using manual pathological procedures. Manual microscopic… More >

Fulu Wei^1,2, Zhenggan Cai¹, Yongqing Guo^1,*, Pan Liu², Zhenyu Wang³, Zhibin Li²

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 753-767, 2021, DOI:10.32604/iasc.2021.014661

Abstract The differences in traffic accident severity between urban and rural areas have been widely studied, but conclusions are still limited. To explore the factors influencing the occurrence of roadside accidents in urban and rural areas, 3735 roadside traffic accidents from 2017 to 2019 were analyzed. Fourteen variables from the aspects of driver, vehicle, driving environment, and other influencing factors were selected to establish a Bayesian binary logit model of roadside crashes. The deviance information criterion and receiver operating characteristic curve were used to test the goodness of fit for the traffic crash model. The results show that: (1) the Bayesian… More >

Abdullah Baz^1,*, Hosam Alhakami²

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1155-1174, 2021, DOI:10.32604/cmc.2020.013215

Abstract The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom… More >

Engy El-shafeiy¹, Aboul Ella Hassanien², Karam M. Sallam^3,*, A. A. Abohany⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1745-1755, 2021, DOI:10.32604/cmc.2020.013066

Abstract Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of… More >

Nazeer Muhammad¹, Rubab², Nargis Bibi³, Oh-Young Song⁴, Muhammad Attique Khan^5,*, Sajid Ali Khan⁶

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257

Abstract Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue, this article proposes a fully… More >

Mohd Nizam Mat Bah¹, Mohd Hanafi Sapian¹, Mohammad Tamim Jamil¹, Nisah Abdullah¹, Emieliyuza Yusnita Alias¹, Norazah Zahari²

Congenital Heart Disease, Vol.13, No.6, pp. 1012-1027, 2018, DOI:10.1111/chd.12672

Abstract Objectives: There is limited data on congenital heart disease (CHD) from the lower‐ and middle‐income country. We aim to study the epidemiology of CHD with the specific objective to estimate the birth prevalence, severity, and its trend over time.
Design: A population‐based study with data retrieved from the Pediatric Cardiology Clinical Information System, a clinical registry of acquired and congenital heart dis‐ ease for children.
Setting: State of Johor, Malaysia.
Patients: All children (0‐12 years of age) born in the state of Johor between January 2006 and December 2015.
Intervention: None.
Outcome measure: The birth prevalence, severity, and temporal trend… More >

Jessica L. Schachter¹, Manisha Patel², Samuel R. Horton³, A. Mike Devane⁴, Alex Ewing⁵, Gary A. Abrams⁶

Congenital Heart Disease, Vol.13, No.5, pp. 764-770, 2018, DOI:10.1111/chd.12650

Abstract Objective: As the population of patients with Fontan circulation surviving into adult‐ hood increases, hepatic cirrhosis has grown to be a significant cause of morbidity and mortality. Early detection of advanced hepatic fibrosis is imperative for proper inter‐ vention and consideration for heart or combined heart/liver transplantation. Noninvasive biomarkers and elastography have been evaluated for their diagnostic utility with variable results in the Fontan population.
Design: The cohort included 14 patients age 26.4 SD 7.5 who underwent Fontan surgery. All patients were evaluated with FibroSURE, shear wave elastography (SWE), hepatic duplex sonography, and liver biopsy. Liver fibrosis on biopsy was… More >

Xiangao Jiang¹, Megan Coffee^{2, 3, *}, Anasse Bari^{4, *}, Junzhang Wang⁴, Xinyue Jiang⁵, Jianping Huang¹, Jichan Shi¹, Jianyi Dai¹, Jing Cai¹, Tianxiao Zhang⁶, Zhengxing Wu¹, Guiqing He¹, Yitong Huang⁷

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 537-551, 2020, DOI:10.32604/cmc.2020.010691

Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations… More >