Hafza Ayesha Siddiqa¹, Muhammad Irfan¹, Saadullah Farooq Abbasi^2,*, Wei Chen¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970

Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual age 37 ± 05 weeks.… More >

Firas Abedi¹, Hayder M. A. Ghanimi², Abeer D. Algarni³, Naglaa F. Soliman^3,*, Walid El-Shafai^4,5, Ali Hashim Abbas⁶, Zahraa H. Kareem⁷, Hussein Muhi Hariz⁸, Ahmed Alkhayyat⁹

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2791-2814, 2023, DOI:10.32604/csse.2023.038762

Abstract Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp optimization algorithm-based feature selection (COAFS)… More >

Rufus Gikera^1,*, Jonathan Mwaura², Elizaphan Muuro³, Shadrack Mambo³

Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229

Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this issue, may be… More >

Ilyоs Abdullaev¹, Natalia Prodanova², K. Aruna Bhaskar³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Jungeun Kim^8,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1463-1477, 2023, DOI:10.32604/cmc.2023.038417

Abstract Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,… More >

Saleh Ateeq Almutairi^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

Jayashree Shetty¹, Nisha P. Shetty^1,*, Hrushikesh Kothikar¹, Saleh Mowla¹, Aiswarya Anand¹, Veeraj Hegde²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206

Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different… More >

Niharika Gupta¹, Baijnath Kaushik¹, Mohammad Khalid Imam Rahmani^2,*, Saima Anwar Lashari^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 347-366, 2023, DOI:10.32604/cmc.2023.038864

Abstract Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN),… More >

Ramya Nemani¹, G. Jose Moses², Fayadh Alenezi³, K. Vijaya Kumar⁴, Seifedine Kadry^5,6,7,*, Jungeun Kim⁸, Keejun Han⁹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 919-935, 2023, DOI:10.32604/csse.2023.034213

Abstract Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning… More >

K. Chanthirasekaran, Raghu Gundaala^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 471-489, 2023, DOI:10.32604/iasc.2023.036130

Abstract When it comes to decreasing margins and increasing energy efficiency in near-threshold and sub-threshold processors, timing error resilience may be viewed as a potentially lucrative alternative to examine. On the other hand, the currently employed approaches have certain restrictions, including high levels of design complexity, severe time constraints on error consolidation and propagation, and uncontaminated architectural registers (ARs). The design of near-threshold circuits, often known as NT circuits, is becoming the approach of choice for the construction of energy-efficient digital circuits. As a result of the exponentially decreased driving current, there was a reduction in performance, which was one of… More >

Latifah Almuqren¹, Manar Ahmed Hamza^2,*, Abdullah Mohamed³, Amgad Atta Abdelmageed²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4917-4933, 2023, DOI:10.32604/cmc.2023.037738

Abstract Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model precisely recognizes the face and… More >