S. Priya^1,*, K. Pradeep Mohan Kumar²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3339-3353, 2023, DOI:10.32604/csse.2023.030630

Abstract An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets.… More >

A. M. Hafiz¹, M. Hassaballah^2,3,*, Abdullah Alqahtani³, Shtwai Alsubai³, Mohamed Abdel Hameed⁴

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2651-2666, 2023, DOI:10.32604/csse.2023.031720

Abstract With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging and real-world tasks. Given the scope of this area, various techniques are found in the literature. One such notable technique, Multiple Deep Q-Network (DQN) based RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed. As more complex DQNs come to the fore, the overall complexity of these multi-entity systems has increased many… More >

I. Mettildha Mary^1,*, K. Karuppasamy²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2667-2685, 2023, DOI:10.32604/csse.2023.031114

Abstract CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can… More >

Thien-Phuc Doan, Ngoc-Tu Chau, Jungsoo Park, Souhwan Jung^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3059-3073, 2023, DOI:10.32604/csse.2023.038664

Abstract Nowadays, with the significant growth of the mobile market, security issues on the Android Operation System have also become an urgent matter. Trusted execution environment (TEE) technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security. However, for Android, TEE technologies still contain restrictions and limitations. The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE. Another issue of hardware-based TEE is the cross-platform problem. Since every mobile device supports different TEE vendors, it becomes an obstacle for developers to migrate their trusted applications to other… More >

Huaxiang Song^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3959-3978, 2023, DOI:10.32604/csse.2023.038429

Abstract Recently, the semantic classification (SC) algorithm for remote sensing images (RSI) has been greatly improved by deep learning (DL) techniques, e.g., deep convolutional neural networks (CNNs). However, too many methods employ complex procedures (e.g., multi-stages), excessive hardware budgets (e.g., multi-models), and an extreme reliance on domain knowledge (e.g., handcrafted features) for the pure purpose of improving accuracy. It obviously goes against the superiority of DL, i.e., simplicity and automation. Meanwhile, these algorithms come with unnecessarily expensive overhead on parameters and hardware costs. As a solution, the author proposed a fast and simple training algorithm based on the smallest architecture of… More >

Samar Elbedwehy^1,3,*, T. Medhat², Taher Hamza³, Mohammed F. Alrahmawy³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3637-3652, 2023, DOI:10.32604/csse.2023.038376

Abstract One of the issues in Computer Vision is the automatic development of descriptions for images, sometimes known as image captioning. Deep Learning techniques have made significant progress in this area. The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. This paper aims to find optimized models for these two subsystems. For the image feature extraction subsystem, the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image. For the caption generation lingual subsystem, this… More >

Jianguo Zheng, Shuilin Chen^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3619-3635, 2023, DOI:10.32604/csse.2023.038025

Abstract To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy… More >

Qiong Luo^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3141-3158, 2023, DOI:10.32604/csse.2023.038017

Abstract The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources, and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries. The existing RippleNet model doesn’t consider the influence of key nodes on recommendation results, and the recommendation accuracy is not high. Therefore, based on the RippleNet model, this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model. The performance of the model is verified by experiments, which… More >

Abdulaziz Aldribi^1,2,*, Aman Singh^2,3, Jose Breñosa^3,4

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3865-3881, 2023, DOI:10.32604/csse.2023.037748

Abstract Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve the intrusion detection mechanism’s performance.… More >

Sana Zahir¹, Rafi Ullah Khan¹, Mohib Ullah¹, Muhammad Ishaq¹, Naqqash Dilshad², Amin Ullah^3,*, Mi Young Lee^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2741-2754, 2023, DOI:10.32604/csse.2023.037706

Abstract The analysis of overcrowded areas is essential for flow monitoring, assembly control, and security. Crowd counting’s primary goal is to calculate the population in a given region, which requires real-time analysis of congested scenes for prompt reactionary actions. The crowd is always unexpected, and the benchmarked available datasets have a lot of variation, which limits the trained models’ performance on unseen test data. In this paper, we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene. The proposed model consists of encoder and decoder networks comprising batch-free normalization layers… More >