Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Dazhi E¹, Jiale Liu², Ming Zhang^1,*, Huiyuan Jiang², Keming Mao²

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3853-3880, 2024, DOI:10.32604/cmc.2024.057538 - 19 December 2024

Abstract Imbalance is a distinctive feature of many datasets, and how to make the dataset balanced become a hot topic in the machine learning field. The Synthetic Minority Oversampling Technique (SMOTE) is the classical method to solve this problem. Although much research has been conducted on SMOTE, there is still the problem of synthetic sample singularity. To solve the issues of class imbalance and diversity of generated samples, this paper proposes a hybrid resampling method for binary imbalanced data sets, RE-SMOTE, which is designed based on the improvements of two oversampling methods parameter-free SMOTE (PF-SMOTE) and… More >

Ching-Chi Hsu^1,*, Hsin-Hao Lin¹, Kao-Shang Shih²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.011792

Abstract Decompression surgery is one of the useful methods to relieve the pressure on the spinal cord and nerves [1]. In computational simulation, various bone material modelling strategies have been used to model cortical bone and cancellous bone of spinal vertebrae [2,3]. However, the effects of the bone material modelling strategies on the biomechanics of the thoracolumbar spine are unclear. Thus, this study aimed to investigate the biomechanics of the thoracolumbar spine with various bone modelling strategies using a patient-specific finite element modelling technique.
Three-dimensional finite element models of the human thoracolumbar spine were developed from the… More >

Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, Ahmed Ali⁷, Pitshou Bokoro⁷

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

Chenghai Yu, Zhilong Lu^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413 - 18 November 2024

Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >

Fahim Nasir¹, Abdulghani Ali Ahmed^1,*, Mehmet Sabir Kiraz¹, Iryna Yevseyeva¹, Mubarak Saif²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

P. K. A. Chitra¹, S. Appavu alias Balamurugan², S. Geetha³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*, Keejun Han⁸

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373 - 13 September 2024

Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l₂₁ norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >

Congcong Ma^1,2, Jiaqi Mi¹, Wanlin Gao^1,2, Sha Tao^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506 - 12 September 2024

Abstract Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes. This task is prevalent in practical scenarios such as industrial fault diagnosis, network intrusion detection, cancer detection, etc. In imbalanced classification tasks, the focus is typically on achieving high recognition accuracy for the minority class. However, due to the challenges presented by imbalanced multi-class datasets, such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries, existing methods often do not perform well in multi-class imbalanced data… More >

Min-Gyu Kim¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221 - 20 August 2024

Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

Jiqiang Chen, Ranran Han, Dongqing Zhang, Litao Ma^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5067-5082, 2024, DOI:10.32604/cmc.2024.051494 - 20 June 2024

Abstract Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent… More >