V. Ravindra Krishna Chandar^1,*, M. Thangamani²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 165-181, 2022, DOI:10.32604/iasc.2022.022339 - 15 April 2022

Abstract

During patient monitoring, false alert in the Intensive Care Unit (ICU) becomes a major problem. In the category of alarms, pseudo alarms are regarded as having no clinical or therapeutic significance, and thus they result in fatigue alarms. Artifacts are misrepresentations of tissue structures produced by imaging techniques. These Artifacts can invalidate the Arterial Blood Pressure (ABP) signal. Therefore, it is very important to develop algorithms that can detect artifacts. However, ABP has algorithmic shortcomings and limitations of design. This study is aimed at developing a real-time enhancement of independent component analysis (EICA) and time-domain

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Xianhe Yue^*, Shunshe Luo

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.4, pp. 1195-1203, 2022, DOI:10.32604/fdmp.2022.020649 - 06 April 2022

Abstract Because carbonate rocks have a wide range of reservoir forms, a low matrix permeability, and a complicated seam hole formation, using traditional capacity prediction methods to estimate carbonate reservoirs can lead to significant errors. We propose a machine learning-based capacity prediction method for carbonate rocks by analyzing the degree of correlation between various factors and three machine learning models: support vector machine, BP neural network, and elastic network. The error rate for these three models are 10%, 16%, and 33%, respectively (according to the analysis of 40 training wells and 10 test wells). More >

Hongyu Mu^1,2,*, Yinyan Wang¹, Hong Teng², Xingtian Zhao², Xiaolong Zhang², Yan Jin², Shiyang Hao², Jingfeng Zhao²

Energy Engineering, Vol.119, No.3, pp. 1103-1116, 2022, DOI:10.32604/ee.2022.017273 - 31 March 2022

Abstract The aerodynamics and heat transfer performance in the rear-mounted automobile cabin have an important influence on the engine's safety and the operational stability of the automobile. The article uses STAR-CCM and GT-COOL software to establish the 3D wind tunnel model and engine cooling system model of the internal combustion engine. At the same time, we established a 3D artificial coupling model through parameter transfer. The research results show that the heat transfer coefficient decreases with the increase of the comprehensive drag coefficient of the nacelle. This shows that the cabin flow field has an important More >

Yi Zheng¹, Youyou Wang², Chen Zou¹, Bicheng Hu², Min Zhao², Xinxing Wu^2,*

Oncologie, Vol.24, No.1, pp. 147-161, 2022, DOI:10.32604/oncologie.2022.019236 - 31 March 2022

Abstract Tumor-associated macrophages (TAMs) are important components in tumor microenvironment. This study intended to explore the influence of TAMs on cervical cancer cells proliferation and migration. The expression levels of TAMs markers, CD68 and CD163, in tissues were examined by immunohistochemistry and increased with the progression of cervical lesions (p < 0.05). TAMs with M2-like phenotype (PMA(Polymethacrylate) induced THP-1 cells) were noticed to promote the proliferation of cervical cancer cells and improve the migration ability of tumor cells. These enhancements were attributed to secreting soluble components and the physical contact between macrophages and tumor cells. The tumor More >

Mahmoud Ragab^1,2,3,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4143-4155, 2022, DOI:10.32604/cmc.2022.027075 - 29 March 2022

Abstract Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes More >

Mahmoud Ragab^1,2,3,*, Diaa Hamed^4,5

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4185-4200, 2022, DOI:10.32604/cmc.2022.027074 - 29 March 2022

Abstract Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With this motivation, this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm (FCA-BFS) with optimal support vector machine (OSVM) model, named FCABFS-OSVM for medical data classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique More >

Muhammad Zubair¹, Muhammad Kashif Hanif^1,*, Eatedal Alabdulkreem², Yazeed Ghadi³, Muhammad Irfan Khan¹, Muhammad Umer Sarwar¹, Ayesha Hanif¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3705-3718, 2022, DOI:10.32604/cmc.2022.026408 - 29 March 2022

Abstract The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures. For this reason, it is important to design methods for accurate protein secondary structure prediction. Most of the existing computational techniques for protein structural and functional prediction are based on machine learning with shallow frameworks. Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem. In this study, deep learning based models, i.e., convolutional neural network and long short-term memory for protein secondary structure prediction were proposed. The input to proposed More >

Abdullah S. AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*, Maha Farouk S. Sabir¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2895-2907, 2022, DOI:10.32604/cmc.2022.026405 - 29 March 2022

Abstract As higher education institutions (HEIs) go online, several benefits are attained, and also it is vulnerable to several kinds of attacks. To accomplish security, this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish security. The incorporation of the strategies into business is a tendency among several distinct industries, comprising education, have recognized as game changer. Consequently, the HEIs are highly related to the requirement and knowledge of the learner, making the education procedure highly effective. Thus, artificial intelligence (AI) and machine learning (ML) models have shown significant interest in HEIs. This study… More >

Fariza Sabrina^1,*, Shaleeza Sohail², Farnaz Farid³, Sayka Jahan⁴, Farhad Ahamed⁵, Steven Gordon⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3777-3797, 2022, DOI:10.32604/cmc.2022.026363 - 29 March 2022

Abstract With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability… More >

Md. Mottahir Alam^1,*, Ahteshamul Haque², Mohammed Ali Khan³, Nebras M. Sobahi¹, Ibrahim Mustafa Mehedi^1,4, Asif Irshad Khan⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3999-4017, 2022, DOI:10.32604/cmc.2022.026353 - 29 March 2022

Abstract The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure… More >