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 >

T. M. Nithya^1,*, R. Umanesan², T. Kalavathidevi³, C. Selvarathi⁴, A. Kavitha⁵

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2537-2547, 2023, DOI:10.32604/iasc.2022.028804

Abstract Big data analytics is a popular research topic due to its applicability in various real time applications. The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance. Since big data involves numerous features and necessitates high computational time, feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance. This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit (SBOA-OGRU) model for big data classification in Apache Spark. The SBOA-OGRU technique involves the… More >

Salem Alhayani^*, Diane R. Murphy

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2235-2259, 2023, DOI:10.32604/cmc.2023.038003

Abstract The Internet service provider (ISP) is the heart of any country’s Internet infrastructure and plays an important role in connecting to the World Wide Web. Internet exchange point (IXP) allows the interconnection of two or more separate network infrastructures. All Internet traffic entering a country should pass through its IXP. Thus, it is an ideal location for performing malicious traffic analysis. Distributed denial of service (DDoS) attacks are becoming a more serious daily threat. Malicious actors in DDoS attacks control numerous infected machines known as botnets. Botnets are used to send numerous fake requests to overwhelm the resources of victims… More >

Saleh Ateeq Almutairi^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

Qasem Al-Tashi^1,*, Tareq M Shami², Said Jadid Abdulkadir³, Emelia Akashah Patah Akhir³, Ayed Alwadain⁴, Hitham Alhussain³, Alawi Alqushaibi³, Helmi MD Rais³, Amgad Muneer¹, Maliazurina B. Saad¹, Jia Wu¹, Seyedali Mirjalili^5,6,7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1937-1966, 2023, DOI:10.32604/csse.2023.039788

Abstract The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized. While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase (LMuMOGWO) for tackling feature selection problems. The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer (MOGWO): a Lévy flight and a mutation operator. The Lévy flight, a type of random walk with jump size… More >

Amjed Al Fahoum^1,*, Tahani A. Ghobon²

Journal of New Media, Vol.5, No.1, pp. 1-22, 2023, DOI:10.32604/jnm.2023.037583

Abstract A groundbreaking method is introduced to leverage machine learning algorithms to revolutionize the prediction of success rates for science fiction films. In the captivating world of the film industry, extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut. Our study aims to harness the power of available data to estimate a film’s early success rate. With the vast resources offered by the internet, we can access a plethora of movie-related information, including actors, directors, critic reviews, user reviews, ratings, writers, budgets, genres, Facebook likes, YouTube views for movie trailers, and Twitter followers. The… More >

Adnan Hasan Bdair Aighuraibawi^1,2, Selvakumar Manickam^1,*, Rosni Abdullah³, Zaid Abdi Alkareem Alyasseri^4,5, Ayman Khallel⁶, Dilovan Asaad Zebari⁹, Hussam Mohammed Jasim⁷, Mazin Mohammed Abed⁸, Zainb Hussein Arif⁷

Computer Systems Science and Engineering, Vol.47, No.1, pp. 553-574, 2023, DOI:10.32604/csse.2023.037948

Abstract Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system… More >

S. Sureshkumar^1,*, G .K. D. Prasanna Venkatesan², R. Santhosh³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1109-1123, 2023, DOI:10.32604/csse.2023.036267

Abstract Cloud computing technology provides flexible, on-demand, and completely controlled computing resources and services are highly desirable. Despite this, with its distributed and dynamic nature and shortcomings in virtualization deployment, the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties. The Intrusion Detection System (IDS) is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources. DDoS attacks are becoming more frequent and powerful, and their attack pathways are continually changing, which requiring the development of new detection methods. Here the purpose of the study… More >

A. Richard William^1,*, J. Senthilkumar², Y. Suresh², V. Mohanraj²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753

Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection (BFS) in the… More >