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 >

Abdelwahed Berguiga^1,2,*, Ahlem Harchay^1,2, Ayman Massaoudi^1,2, Mossaad Ben Ayed³, Hafedh Belmabrouk⁴

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667

Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… More >

Seyoung Lee¹, Wonsuk Choi¹, Insup Kim², Ganggyu Lee², Dong Hoon Lee^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583

Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These metrics take into consideration various… More >

XIANBAO FANG^1,#, MINGYAN TANG^1,#, ZIYANG YU^1,#, JIAQI DING¹, CHONG CUI², HONG ZHANG^1,*

BIOCELL, Vol.47, No.9, pp. 2059-2068, 2023, DOI:10.32604/biocell.2023.030565

Abstract Objective: Key genes were screened to analyze molecular mechanisms and their drug targets of endometriosis by applying a bioinformatics approach. Methods: Gene expression profiles of endometriosis and healthy controls were obtained from the Gene Expression Omnibus database. Significant differentially expressed genes were screened using the limma package. Correlation pathways were screened by Spearman correlation analysis on the echinoderm microtubule-associated protein-like 4 (EML4) and enrichment in endometriosis pathways and estimated by the GSVA package. Immune characteristics were assessed by the “ESTIMATE” R package. Potential regulatory pathways were determined by enrichment analysis. The SWISS-MODE website was used in homology modeling with EML4… More > Graphic Abstract

Zi Han^1,*, Zhentian Huang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 245-272, 2024, DOI:10.32604/cmes.2023.029708

Abstract In the context of global mean square error concerning the number of random variables in the representation, the Karhunen–Loève (KL) expansion is the optimal series expansion method for random field discretization. The computational efficiency and accuracy of the KL expansion are contingent upon the accurate resolution of the Fredholm integral eigenvalue problem (IEVP). The paper proposes an interpolation method based on different interpolation basis functions such as moving least squares (MLS), least squares (LS), and finite element method (FEM) to solve the IEVP. Compared with the Galerkin method based on finite element or Legendre polynomials, the main advantage of the… More > Graphic Abstract

Gagan Deep^*, Jyoti Verma

Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329

Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI… More >

Hengyang Liu, Chuncheng Huang^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1745-1761, 2023, DOI:10.32604/cmc.2023.040710

Abstract In the field of medical images, pixel-level labels are time-consuming and expensive to acquire, while image-level labels are relatively easier to obtain. Therefore, it makes sense to learn more information (knowledge) from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs. In this paper, using Pixel-Level Labeled Images for Multi-Task Learning (PLDMLT), we focus on grading the severity of fundus images for Diabetic Retinopathy (DR). This is because, for the segmentation task, there is a finely labeled mask, while the severity grading task is without classification… More >

Yongsoo Lee, Yeeun Lee, Eungyu Lee, Taejin Lee^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1701-1719, 2023, DOI:10.32604/cmc.2023.040235

Abstract Cybersecurity increasingly relies on machine learning (ML) models to respond to and detect attacks. However, the rapidly changing data environment makes model life-cycle management after deployment essential. Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models. However, detecting drift in unsupervised environments can be challenging. This study introduces a novel approach leveraging Shapley additive explanations (SHAP), a widely recognized explainability technique in ML, to address drift detection in unsupervised settings. The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers… More >

Rayan A. Alsemmeari^1,*, Mohamed Yehia Dahab², Badraddin Alturki¹, Abdulaziz A. Alsulami³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1853-1870, 2023, DOI:10.32604/cmc.2023.038589

Abstract Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative… More >