Hongsheng Su, Shilin Song^*, Xingsheng Wang

Energy Engineering, Vol.121, No.11, pp. 3289-3303, 2024, DOI:10.32604/ee.2024.054643 - 21 October 2024

Abstract This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling More >

Muhammad Ahmad^1,*, Manuel Mazzara², Salvatore Distefano³, Adil Mehmood Khan⁴, Hamad Ahmed Altuwaijri⁵

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 503-532, 2024, DOI:10.32604/cmc.2024.056318 - 15 October 2024

Abstract Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new examples. This paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification (HSIC). By separating training, validation, and test data without overlap, the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was… 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 >

Xiangkui Jiang^*, Zihao Wang, Chao Dong

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4303-4323, 2024, DOI:10.32604/cmc.2024.054640 - 12 September 2024

Abstract

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree (RRT) algorithm, a feedback-biased sampling RRT, called FS-RRT, is proposed based on RRT. Firstly, to improve the sampling efficiency of RRT to shorten the search time, the search area of the random tree is restricted to improve the sampling efficiency. Secondly, to obtain better information about obstacles to shorten the path length, a feedback-biased sampling strategy is used instead of the traditional random sampling, the collision of the expanding node with an obstacle generates feedback information so that the next

… More >

Yi Pan¹, Lei Xie^2,*, Hongye Su²

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 683-696, 2024, DOI:10.32604/iasc.2023.038543 - 06 September 2024

Abstract In the process of fault detection and classification, the operation mode usually drifts over time, which brings great challenges to the algorithms. Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset, the accuracy of these traditional methods usually drops significantly in the case of covariate shift. In this paper, an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process. It effectively alters the drift between the training and testing dataset. Firstly, the mutual information method is… 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 >

Suyi Liu^1,*, Jianning Chi¹, Chengdong Wu¹, Fang Xu^2,3,4, Xiaosheng Yu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450 - 20 June 2024

Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >

Amerah Alabrah^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3897-3912, 2024, DOI:10.32604/cmc.2024.048528 - 20 June 2024

Abstract The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’ private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However,… More >

Bo Zhu^*, Xiaona Jing, Lan Qiu, Runbo Li

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3977-3999, 2024, DOI:10.32604/cmc.2024.048062 - 20 June 2024

Abstract When building a classification model, the scenario where the samples of one class are significantly more than those of the other class is called data imbalance. Data imbalance causes the trained classification model to be in favor of the majority class (usually defined as the negative class), which may do harm to the accuracy of the minority class (usually defined as the positive class), and then lead to poor overall performance of the model. A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article, which is based on a new hybrid… More >

Haoan Gu¹, Kai Zhu¹, Alfred Strauss², Yehui Shi^3,4, Dragoslav Sumarac⁵, Maosen Cao^1,*

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 363-380, 2024, DOI:10.32604/sdhm.2024.042388 - 05 June 2024

Abstract Concrete materials and structures are extensively used in transformation infrastructure and they usually bear cracks during their long-term operation. Detecting cracks using deep-learning algorithms like YOLOv3 (You Only Look Once version 3) is a new trend to pursue intelligent detection of concrete surface cracks. YOLOv3 is a typical deep-learning algorithm used for object detection. Owing to its generality, YOLOv3 lacks specific efficiency and accuracy in identifying concrete surface cracks. An improved algorithm based on YOLOv3, specialized in the rapid and accurate identification of concrete surface cracks is worthy of investigation. This study proposes a tailored… More >