Firas Abedi¹, Hayder M. A. Ghanimi², Abeer D. Algarni³, Naglaa F. Soliman^3,*, Walid El-Shafai^4,5, Ali Hashim Abbas⁶, Zahraa H. Kareem⁷, Hussein Muhi Hariz⁸, Ahmed Alkhayyat⁹

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2791-2814, 2023, DOI:10.32604/csse.2023.038762

Abstract Data mining plays a crucial role in extracting meaningful knowledge from large-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp optimization algorithm-based feature selection (COAFS)… More >

Ahmad Alassaf^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2773-2789, 2023, DOI:10.32604/csse.2023.035899

Abstract Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare. Deep Learning (DL) models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models. On the other hand, skin lesion-based segregation and disintegration procedures play an essential role in earlier skin cancer detection. However, artefacts, an unclear boundary, poor contrast, and different lesion sizes make detection difficult. To address the issues in skin lesion diagnosis, this study creates the UDLS-DDOA model, an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder (UDLS) optimized by Dynamic Differential Annealed Optimization (DDOA). Pre-processing, segregation,… More >

Shorouq Alshawabkeh, Li Wu^*, Daojun Dong, Yao Cheng, Liping Li, Mohammad Alanaqreh

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 63-77, 2023, DOI:10.32604/cmc.2023.042183

Abstract Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC). Our findings reveal that Model… More >

C. Rajathi, P. Rukmani^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 733-756, 2023, DOI:10.32604/cmc.2023.041507

Abstract Considering the recent developments in the digital environment, ensuring a higher level of security for networking systems is imperative. Many security approaches are being constantly developed to protect against evolving threats. An ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior studies. This research work aimed to create a more diverse and effective ensemble model. To this end, selected six classification models, Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) from existing study to run as independent models. Once the… More >

Muhammad Fezan Afzal¹, Imran Khan¹, Javed Rashid^1,2,3, Mubbashar Saddique^4,*, Heba G. Mohamed⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3653-3670, 2023, DOI:10.32604/cmc.2023.041627

Abstract Software Product Line (SPL) is a group of software-intensive systems that share common and variable resources for developing a particular system. The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints (CTC). CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things (IoT) devices because different Internet devices and protocols are communicated. Therefore, managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex, time-consuming, and hard. However, the CTC… More >

Hui Xu, Yalin Hu^*, Weidong Cao, Longjie Han

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.039227

Abstract The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimization-seeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the harris hawk optimization (HHO) and… More >

Mehwish Zafar¹, Javeria Amin², Muhammad Sharif¹, Muhammad Almas Anjum³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860

Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed,… More >

Shuangxing Deng, Man Li^*, Huachun Zhou

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2919-2939, 2023, DOI:10.32604/iasc.2023.039985

Abstract Security service function chaining (SFC) based on software-defined networking (SDN) and network function virtualization (NFV) technology allows traffic to be forwarded sequentially among different security service functions to achieve a combination of security functions. Security SFC can be deployed according to requirements, but the current SFC is not flexible enough and lacks an effective feedback mechanism. The SFC is not traffic aware and the changes of traffic may cause the previously deployed security SFC to be invalid. How to establish a closed-loop mechanism to enhance the adaptive capability of the security SFC to malicious traffic has become an important issue.… More >

Motasem S. Alsawadi^1,*, El-Sayed M. El-kenawy², Miguel Rio¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440

Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across all input layers and samples.… More >

Hachemi Bennaceur, Meznah Almutairy, Norah Alhussain^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2687-2706, 2023, DOI:10.32604/iasc.2023.038723

Abstract The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new… More >