Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Irfan Khan¹, Xianchao Zhang^1,*, Ramesh Kumar Ayyasamy^2,*, Saadat M. Alhashmi³, Azizur Rahim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1611-1636, 2025, DOI:10.32604/cmes.2025.058566 - 27 January 2025

Abstract The rapid growth of machine learning (ML) across fields has intensified the challenge of selecting the right algorithm for specific tasks, known as the Algorithm Selection Problem (ASP). Traditional trial-and-error methods have become impractical due to their resource demands. Automated Machine Learning (AutoML) systems automate this process, but often neglect the group structures and sparsity in meta-features, leading to inefficiencies in algorithm recommendations for classification tasks. This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso (MSGL) to address these limitations. Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional More >

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 - 29 November 2023

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… More >

Fuad A. M. Al-Yarimi^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 633-645, 2023, DOI:10.32604/cmc.2023.038858 - 31 October 2023

Abstract Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and… More >

Romany F. Mansour^1,*, Eatedal Alabdulkreem²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1161-1169, 2023, DOI:10.32604/csse.2023.023307 - 15 June 2022

Abstract The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image More >

Nisha Mary¹, Bilal Khan¹, Abdullah A. Asiri², Fazal Muhammad^3,*, Salman Khan³, Samar Alqhtani⁴, Khlood M. Mehdar⁵, Hanan Talal Halwani⁴, Muhammad Irfan⁶, Khalaf A. Alshamrani²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6595-6616, 2022, DOI:10.32604/cmc.2022.032384 - 28 July 2022

Abstract Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well… More >

Sadaf Hafeez¹, Yazeed Yasin Ghadi², Mohammed Alarfaj³, Tamara al Shloul⁴, Ahmad Jalal¹, Shaharyar Kamal¹, Dong-Seong Kim^5,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4935-4952, 2022, DOI:10.32604/cmc.2022.023841 - 28 July 2022

Abstract Independent human living systems require smart, intelligent, and sustainable online monitoring so that an individual can be assisted timely. Apart from ambient assisted living, the task of monitoring human activities plays an important role in different fields including virtual reality, surveillance security, and human interaction with robots. Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions. In this paper, we propose multiple methods such as random occupancy pattern, spatio temporal cloud, way-point trajectory, Hilbert transform, Walsh Hadamard transform and bone More >

Nisha Mary¹, Bilal Khan^1,*, Abdullah A Asiri², Fazal Muhammad³, Samar Alqhtani⁴, Khlood M Mehdar⁵, Hanan Talal Halwani⁴, Turki Aleyani⁴, Khalaf A Alshamrani²

Journal of Intelligent Medicine and Healthcare, Vol.1, No.1, pp. 11-31, 2022, DOI:10.32604/jimh.2022.030161 - 14 June 2022

Abstract Prognosis of HD is a complex task that requires experience and expertise to predict in the early stage. Nowadays, heart failure is rising due to the inherent lifestyle. The healthcare industry generates dense records of patients, which cannot be managed manually. Such an amount of data is very significant in the field of data mining and machine learning when gathering valuable knowledge. During the last few decades, researchers have used different approaches for the prediction of HD, but still, the major problem is the uncertainty factor in the output data and also there is a… More >

Tianyi Jiang¹, Shuangshuang Ying², Zhou Fang¹, Xue Song¹, Yinggang Sun², Dongyang Zhan^3,4, Chao Ma^2,*

Journal of New Media, Vol.4, No.2, pp. 85-95, 2022, DOI:10.32604/jnm.2022.027175 - 13 June 2022

Abstract In the medical field, the classification and analysis of blood samples has always been arduous work. In the previous work of this task, manual classification maneuvers have been used, which are time consuming and laborious. The conventional blood image classification research is mainly focused on the microscopic cell image classification, while the macroscopic reagent processing blood coagulation image classification research is still blank. These blood samples processed with reagents often show some inherent shape characteristics, such as coagulation, attachment, discretization and so on. The shape characteristics of these blood samples also make it possible for… More >

Suzhen Wang¹, Zhanfeng Zhang^1,*, Shanshan Geng¹, Chaoyi Pang²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3721-3731, 2022, DOI:10.32604/cmc.2022.015378 - 07 December 2021

Abstract As society has developed, increasing amounts of data have been generated by various industries. The random forest algorithm, as a classification algorithm, is widely used because of its superior performance. However, the random forest algorithm uses a simple random sampling feature selection method when generating feature subspaces which cannot distinguish redundant features, thereby affecting its classification accuracy, and resulting in a low data calculation efficiency in the stand-alone mode. In response to the aforementioned problems, related optimization research was conducted with Spark in the present paper. This improved random forest algorithm performs feature extraction according More >