The explosive growth of the Internet of Things (IoT) has highlighted the urgent need for strong network security measures. The distinctive difficulties presented by Internet of Things (IoT) environments, such as the wide variety of devices, the intricacy of network traffic, and the requirement for real-time detection capabilities, are difficult for conventional intrusion detection systems (IDS) to adjust to. To address these issues, we propose DCGR_IoT, an innovative intrusion detection system (IDS) based on deep neural learning that is intended to protect bidirectional communication networks in the IoT environment. DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. Furthermore, complex gated recurrent networks (CGRNs) are used for the temporal feature extraction module, which is utilized by DCGR_IoT. Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. The effectiveness of the DCGR_IoT was proven through extensive evaluations of the UNSW-NB15, KDDCup99, and IoT-23 datasets, which resulted in a high detection accuracy of 99.2%. These results demonstrate the DCG potential of DCGR-IoT as an effective solution for defending IoT networks against sophisticated cyber-attacks.

Particularly compared to other diseases, heart disease (HD) claims the lives of the greatest number of people worldwide. Many priceless lives can be saved with the help of early and effective disease identification. Medical tests, an electrocardiogram (ECG) signal, heart sounds, computed tomography (CT) images, etc. can all be used to identify HD. Of all sorts, HD signal recognition from ECG signals is crucial. The ECG samples from the participants were taken into consideration as the necessary inputs for the HD detection model in this study. Many researchers analyzed the risk factors of heart disease and used machine learning or deep learning techniques for the early detection of heart patients. In this paper, we propose a modified version of the LeNet-5 model to be used as a transfer model for cardiovascular disease patients. The modified version is compared to the standard version using four evaluation metrics: accuracy, precision, recall, and F1-score. The achieved results indicated that when the LeNet-5 model was modified by increasing the number of used filters, this increased the model's ability to handle the ECGs dataset and extract the most important features from it. The results also showed that the modified version of the LeNet-5 model based on the ECGs image dataset improved accuracy by 9.14 percentage points compared to the standard LeNet-5 model.

Cloud computing has its characteristics along with so me important issues that should be handled to improve the performance and increase the efficiency of the cloud platform. These issues are related to resources management, fault tolerance, and security. The purpose of this research is to handle the resource management problem, wh ich is to allocate and schedule virtual mach ines of cloud computing in a way that help providers to reduce makespan time of tasks. In this paper, a hybrid algorith m for dynamic tasks scheduling over cloud's virtual machines is introduced. This hybrid algorith m me rges the behaviors of three effective techniques from the swarm intelligence techniques that are used to find a near optimal solution to d ifficu lt co mb inatorial problems. It exp loits the advantages of ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. Experimental results reinforce the strength of the proposed hybrid algorith m. They also prove that the proposed hybrid algorith m is the best and outperformed ant colony optimization, particle swarm optimization , artificial bee colony and other known algorithms.

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. Evaluated on the 300W-LP and AFLW2000-3D datasets, our method outperforms existing approaches, reducing Chamfer Distance by 62.7% and Earth Mover’s Distance by 57.1% on 300W-LP. Moreover, our framework demonstrates superior robustness to variations in head positioning, occlusion, noise, and lighting conditions while achieving significantly faster processing times.

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the “fuzzy iceberg lattice” and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a ‘fuzzy min-max basis algorithm’ to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice’s ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules.

Formal fuzzy concept analysis is an effective data analysis and mining technique in the real world. However, deriving formal fuzzy concepts is an NP problem that demands substantial time and storage resources. With the continuous exponential growth of real-world data, there is a need to regularly update the extracted list of fuzzy concepts. This research paper presents a novel and efficient algorithm to update the extracted fuzzy concepts when inserting new data objects. The proposed algorithm eliminates the need for regenerating fuzzy formal concepts by reprocessing the entire dataset. Instead, it processes only the changed part and merges it with the old list of fuzzy concepts. We have evaluated the proposed approach over various datasets of different types: quantitative, categorical, and synthesized fuzzy data. The experimental results demonstrate that the proposed algorithm outperforms the traditional approach of fuzzy concept extraction by updating only the extracted fuzzy concepts rather than recreating them from scratch, especially in the case of massive data sets.