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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Marwa M. Eid^1,*, Fawaz Alassery², Abdelhameed Ibrahim³, Bandar Abdullah Aloyaydi⁴, Hesham Arafat Ali^1,3, Shady Y. El-Mashad⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2467-2482, 2022, DOI:10.32604/cmc.2022.025220 - 29 March 2022

Abstract The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can… More >

Seung-Yeon Hwang¹, Jeong-Joon Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2649-2663, 2022, DOI:10.32604/cmc.2022.022593 - 29 March 2022

Abstract Artificial intelligence, which has recently emerged with the rapid development of information technology, is drawing attention as a tool for solving various problems demanded by society and industry. In particular, convolutional neural networks (CNNs), a type of deep learning technology, are highlighted in computer vision fields, such as image classification and recognition and object tracking. Training these CNN models requires a large amount of data, and a lack of data can lead to performance degradation problems due to overfitting. As CNN architecture development and optimization studies become active, ensemble techniques have emerged to perform image… More >

Muhammad Irfan^1,*, Nasir Ayub², Faisal Althobiani³, Zain Ali⁴, Muhammad Idrees⁵, Saeed Ullah², Saifur Rahman¹, Abdullah Saeed Alwadie¹, Saleh Mohammed Ghonaim³, Hesham Abdushkour³, Fahad Salem Alkahtani¹, Samar Alqhtani⁶, Piotr Gas⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2141-2158, 2022, DOI:10.32604/cmc.2022.025466 - 24 February 2022

Abstract One of the major concerns for the utilities in the Smart Grid (SG) is electricity theft. With the implementation of smart meters, the frequency of energy usage and data collection from smart homes has increased, which makes it possible for advanced data analysis that was not previously possible. For this purpose, we have taken historical data of energy thieves and normal users. To avoid imbalance observation, biased estimates, we applied the interpolation method. Furthermore, the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.… More >

Pagnchakneat C. Ouk¹, Wooguil Pak^2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 381-398, 2022, DOI:10.32604/cmc.2022.024540 - 24 February 2022

Abstract Although Android becomes a leading operating system in market, Android users suffer from security threats due to malwares. To protect users from the threats, the solutions to detect and identify the malware variant are essential. However, modern malware evades existing solutions by applying code obfuscation and native code. To resolve this problem, we introduce an ensemble-based malware classification algorithm using malware family grouping. The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number. It also adopts… More >

Amal Abdullah Mohammed Alteaimi^1,*, Mohamed Tahar Ben Othman^1,2

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 577-595, 2022, DOI:10.32604/cmc.2022.023591 - 24 February 2022

Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with… More >

Fatmah Alanazi^*, Kamal Jambi, Fathy Eassa, Maher Khemakhem, Abdullah Basuhail, Khalid Alsubhi

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 923-938, 2022, DOI:10.32604/iasc.2022.024668 - 08 February 2022

Abstract Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and… More >

R. Muthaiyan^1,*, M. Malleswaran²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 891-907, 2022, DOI:10.32604/iasc.2022.024635 - 08 February 2022

Abstract Brain tumours are composed of cells where the growth is unrestrained. Though the incidence rate is lower, it is a serious threatening disease to human lives. For effective treatment, an accurate and quick method to classify Magnetic Resonance Imaging (MRI) is required. To identify the meaningful patterns and to interpret images, pattern recognition algorithms are developed. In this work, an extension of Shearlet transform named Bendlets is employed to interpret MRI images and decision making is done by ensemble learning using k-Nearest Neighbor (kNN), Naive Bayesian and Support Vector Machine (SVM) classifiers. The Bendlet and Ensemble More >

K. Poongodi^1,*, A. Sabari²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038 - 08 February 2022

Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which More >

S. Prakash^1,*, P. Vishnu Raja², A. Baseera³, D. Mansoor Hussain⁴, V. R. Balaji⁵, K. Venkatachalam⁶

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1273-1287, 2022, DOI:10.32604/csse.2022.021784 - 08 February 2022

Abstract Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the… More >