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 >

Nguyen Long Giang¹, Tran Thanh Dai², Le Hoang Son³, Tran Thi Ngan⁴, Nguyen Nhu Son¹, Cu Nguyen Giap^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3097-3124, 2024, DOI:10.32604/cmc.2024.057383 - 18 November 2024

Abstract Attribute reduction through the combined approach of Rough Sets (RS) and algebraic topology is an open research topic with significant potential for applications. Several research works have introduced a strong relationship between RS and topology spaces for the attribute reduction problem. However, the mentioned recent methods followed a strategy to construct a new measure for attribute selection. Meanwhile, the strategy for searching for the reduct is still to select each attribute and gradually add it to the reduct. Consequently, those methods tended to be inefficient for high-dimensional datasets. To overcome these challenges, we use the… More >

Monirah Al-Ajlan^*, Mourad Ykhlef

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024

Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

Lixiang Wang¹, Longfei Wen^2,3, Rong Tian^2,3,*, Chun Feng^1,4,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.011137

Abstract The extended finite element method (XFEM) has been successful in crack analysis but faces challenges in modeling multiple cracks. One challenge is the linear dependence and ill-conditioning of the global stiffness matrix, while another is the geometric description for multiple cracks. To address the first challenge, the Improved XFEM (IXFEM) [1–9] is extended to handle multiple crack problems, effectively eliminating issues of linear dependence and ill-conditioning. Additionally, to overcome the second challenge, a novel level set templated cover cutting method (LSTCCM) [10] is proposed, which combines the advantages of the level set method and cover More >

Wei Lu^1,3, Yujie Chen^2,*, Bo Yu², Dongliang Sun², Wei Zhang², Yanru Yang^1,3, Xiaodong Wang^1,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012265

Abstract Flow boiling offers superior heat transfer performance compared to single-phase flow, therefore holding significant potential for application in thermal management. In mini-channel applications, due to their narrow dimensions, the size characteristics of the channel have a particularly notable impact on bubble dynamics and flow boiling heat transfer performance. This study employs the VOSET method to explore the impact of different aspect ratios (1:3, 1:2, 1:1, 2:1, 3:1) on the heat transfer performance of mini-channels. By maintaining a consistent equivalent diameter across the channels, the study aims to unveil the mechanism by which aspect ratios affect… More >

Gang Wang^*, Yuedong Wu, Xiaoyi Qian, Yi Zhao

Energy Engineering, Vol.121, No.11, pp. 3417-3435, 2024, DOI:10.32604/ee.2024.052268 - 21 October 2024

Abstract A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the More >

Arkan Kh Shakr Sabonchi^*

Journal on Artificial Intelligence, Vol.6, pp. 261-282, 2024, DOI:10.32604/jai.2024.056552 - 18 October 2024

Abstract The phenomenal increase in device connectivity is making the signaling and resource-based operational integrity of networks at the node level increasingly prone to distributed denial of service (DDoS) attacks. The current growth rate in the number of Internet of Things (IoT) attacks executed at the time of exchanging data over the Internet represents massive security hazards to IoT devices. In this regard, the present study proposes a new hybrid optimization technique that combines the firefly optimization algorithm with global searches for use in attack detection on IoT devices. We preprocessed two datasets, CICIDS and UNSW-NB15,… More >

Chunyuan Li^1,2,3, Peng Zhang^1,2,3, Shuangming Wang⁴, Lie Liu⁴, Mingquan Shi^2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1197-1214, 2024, DOI:10.32604/cmc.2024.055893 - 15 October 2024

Abstract With the remarkable advancements in machine vision research and its ever-expanding applications, scholars have increasingly focused on harnessing various vision methodologies within the industrial realm. Specifically, detecting vehicle floor welding points poses unique challenges, including high operational costs and limited portability in practical settings. To address these challenges, this paper innovatively integrates template matching and the Faster RCNN algorithm, presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques. This algorithm meticulously weights and fuses the optimized features of both methodologies, enhancing the overall detection capabilities. Furthermore,… 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 >

Naeem Raza¹, Muhammad Asif Habib¹, Mudassar Ahmad¹, Qaisar Abbas^2,*, Mutlaq B. Aldajani², Muhammad Ahsan Latif³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 911-931, 2024, DOI:10.32604/cmc.2024.055049 - 15 October 2024

Abstract Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle… More >