Ting Chen¹, Kai Pu², Lanzhen Bian³, Min Rao⁴, Jing Hu⁵, Rugang Lu^1,*, Jinyue Xia⁶

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 513-523, 2022, DOI:10.32604/iasc.2022.022510

Abstract The day surgery management mode is mainly decentralized management, with clinical departments as the unit, and with reference to the experience of inter project operation management in benchmark hospitals, the empirical management is implemented. With the development of day surgery, the extensive decentralized management mode has been unable to meet the needs of the current day surgery development situation. At first, the paper carefully analyzes the existing problems in the day surgery process in the day ward of the Children’s Hospital of Nanjing Medical University. And then, the concerns of doctors, nurses, anesthesiologists and other hospital staff in day ward,… More >

Talha Saeed, Chu Kiong Loo^*, Muhammad Shahreeza Safiruz Kassim

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076

Abstract

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

Nithya Rekha Sivakumar^1,*, Ahmed Zohair Ibrahim²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4467-4483, 2022, DOI:10.32604/cmc.2022.019041

Abstract

Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-and-forward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there… More >

Muhammad Adnan¹, Maryam Altalhi², Ala Abdulsalam Alarood³, M.Irfan Uddin^1,*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1857-1872, 2022, DOI:10.32604/iasc.2022.020606

Abstract Corona Virus disease 2019 (COVID-19) has caused a worldwide pandemic of cough, fever, headache, body aches, and respiratory ailments. COVID- 19 has now become a severe disease and one of the leading causes of death globally. Modeling and prediction of COVID-19 have become inevitable as it has affected people worldwide. With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease. In this research study, we used one deep learning algorithm called Artificial Neural Network (ANN) and several ML algorithms such… More >

Fangjun Zuo^*, Meiwei Jia, Guang Wen, Huijie Zhang, Pingping Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 993-1012, 2021, DOI:10.32604/cmes.2021.016870

Abstract In the traditional reliability evaluation based on the Bayesian method, the failure probability of nodes is usually expressed by the average failure rate within a period of time. Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods, this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness. The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function. Based on the solving characteristics of the dynamic fuzzy set and Bayesian network, the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes… More >

Waqar Hafeez¹, Nazrina Aziz^1,2,*, Zakiyah Zain^1,2, Nur Azulia Kamarudin¹

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3891-3902, 2022, DOI:10.32604/cmc.2022.019695

Abstract Acceptance sampling is a statistical quality control technique that consists of procedures for sentencing one or more incoming lots of finished products. Acceptance or rejection is based on the inspection of sampled products drawn randomly from the lot. The theory of previous acceptance sampling was built upon the assumption that the process from which the lots are produced is stable and the process fraction nonconforming is a constant. Process variability is inevitable due to random fluctuations, which may inadvertently lead to quality variation. As an alternative to traditional sampling plans, Bayesian approach can be used by considering prior information of… More >

Waqar Hafeez¹, Nazrina Aziz^1,2,*, Zakiyah Zain^1,2, Nur Azulia Kamarudin¹

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4185-4198, 2022, DOI:10.32604/cmc.2022.018146

Abstract Acceptance sampling is a well-established statistical technique in quality assurance. Acceptance sampling is used to decide, acceptance or rejection of a lot based on the inspection of its random sample. Experts concur that the Bayesian approach is the best approach to make a correct decision, when historical knowledge is available. This paper suggests a Bayesian new group chain sampling plan (BNGChSP) to estimate average probability of acceptance. Binomial distribution function is used to differentiate between defective and non-defective products. Beta distribution is considered as a suitable prior distribution. Derivation is completed for the estimation of the average proportion of defectives.… More >

Rana A. Bakoban, Maha A. Aldahlan^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 129-142, 2022, DOI:10.32604/iasc.2022.018041

Abstract In this article, Bayesian techniques are adopted to estimate the shape parameter of the generalized inverted exponential distribution (GIED) in the case of complete samples. Normal approximation, Lindley’s approximation, and Tierney and Kadane’s approximation are used for deriving Bayesian estimators. Different informative priors are considered, such as Jeffrey’s prior, Quasi prior, modified Jeffrey’s prior, and the extension of Jeffrey’s prior. Non-informative priors are also used, including Gamma prior, Pareto prior, and inverse Levy prior. The Bayesian estimators are derived under the quadratic loss function. Monte Carlo simulations are carried out to make a comparison among estimators based on the mean… More >

Ziyan Yu¹, Cong Zhang^1,*, Naixue Xiong², Fang Chen¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 207-221, 2022, DOI:10.32604/csse.2022.018301

Abstract As soil heavy metal pollution is increasing year by year, the risk assessment of soil heavy metal pollution is gradually gaining attention. Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals. Random Forest (RF) has strong generalization ability and is not easy to overfit. In this paper, we improve the Bagging algorithm and simple voting method of RF. A W-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classification performance of RF on imbalanced datasets. Adaptive Bagging enables trees in… More >

Amer I. Al-Omari¹, Amal S. Hassan², Heba F. Nagy², Ayed R. A. Al-Anzi^3,*, Loai Alzoubi¹

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3795-3811, 2021, DOI:10.32604/cmc.2021.019061

Abstract This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution. Assuming that the observed samples are taken from the upper record ranked set sampling (URRSS) and upper record values (URV) schemes. Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error, linear exponential and precautionary loss functions, in addition, we obtain Bayesian credible intervals. The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution. Then, the behavior of the estimates is examined at various record values. The output of the study… More >