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 >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

Hira Akhtar Butt¹, Khoula Said Al Harthy², Mumtaz Ali Shah³, Mudassar Hussain^2,*, Rashid Amin^4,*, Mujeeb Ur Rehman¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3003-3031, 2024, DOI:10.32604/cmc.2024.057185 - 18 November 2024

Abstract Detecting sophisticated cyberattacks, mainly Distributed Denial of Service (DDoS) attacks, with unexpected patterns remains challenging in modern networks. Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking (SDN) environments. While Machine Learning (ML) models can distinguish between benign and malicious traffic, their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining. In this paper, we propose a novel DDoS detection framework that combines Machine Learning (ML) and Ensemble Learning (EL) techniques to improve DDoS attack detection and mitigation in SDN environments. Our model… More >

Hamad Naeem¹, Amjad Alsirhani^2,*, Faeiz M. Alserhani³, Farhan Ullah⁴, Ondrej Krejcar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2185-2223, 2024, DOI:10.32604/cmes.2024.056308 - 31 October 2024

Abstract When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized… More >

Noor Ullah Bacha¹, Songfeng Lu¹, Attiq Ur Rehman¹, Muhammad Idrees², Yazeed Yasin Ghadi³, Tahani Jaser Alahmadi^4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 707-748, 2024, DOI:10.32604/cmc.2024.054780 - 15 October 2024

Abstract Cross-Site Scripting (XSS) remains a significant threat to web application security, exploiting vulnerabilities to hijack user sessions and steal sensitive data. Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats. This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression (LR), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Networks (DNN). Utilizing the XSS-Attacks-2021 dataset, which comprises 460 instances across various real-world traffic-related scenarios, this framework significantly enhances XSS attack detection. Our approach, which… More >

Seung-Ju Han, Seong-Su Yoon, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1495-1518, 2024, DOI:10.32604/cmes.2024.053457 - 27 September 2024

Abstract The rapid proliferation of Internet of Things (IoT) technology has facilitated automation across various sectors. Nevertheless, this advancement has also resulted in a notable surge in cyberattacks, notably botnets. As a result, research on network analysis has become vital. Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods. In this paper, we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework. The results indicate that using the More >

Oluwaseun Peter Ige^1,2, Keng Hoon Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373 - 27 September 2024

Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

Pouyan Razmjouei¹, Elaheh Moharamkhani², Mohamad Hasanvand³, Maryam Daneshfar⁴, Mohammad Shokouhifar^5,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460 - 12 September 2024

Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

Fiaz Majeed¹, Abdul Razzaq Ahmad Shakir¹, Maqbool Ahmad², Shahzada Khurram³, Muhammad Qaiser Saleem⁴, Muhammad Shafiq^5,*, Jin-Ghoo Choi⁵, Habib Hamam^6,7,8,9,10, Osama E. Sheta¹¹

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 765-781, 2024, DOI:10.32604/iasc.2023.034029 - 06 September 2024

Abstract Machine learning (ML) and data mining are used in various fields such as data analysis, prediction, image processing and especially in healthcare. Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results. Using ML algorithms, researchers have developed applications for decision support, analyzed clinical aspects, extracted informative information from historical data, predicted the outcomes and categorized diseases which help physicians make better decisions. It is observed that there is a huge difference between women… More >