The determinants of smart government systems adoption by public sector organizations in Saudi Arabia

This study investigates the determinants of smart government systems that are used in public service organizations in Saudi Arabia. The world's developed nations have conducted studies on smart government systems, but little research has been done on the Middle East, particularly in Saudi Arabia. This study fills the lacuna in the literature. Based on a number of theories including the Technology, Organization, and Environment framework (TOE), Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Models (TAM), the study established an integrated conceptual research model. Online survey questionnaires were sent to 2060 employees in four ministries and after the second reminder a total of 427 completed answers were received, of which 419 (22% response rate) were deemed useable for the analysis. Multivariate statistical analysis was used to analyze the data and results indicated that 51% of the variance (R2 = 0.51) of employees' perceptions of smart government systems is explained by independent determinants. Findings show that security concerns (t (419) = 2.051, p < 0.041), ICT strategy (t (419) = 4.215, p < 0.000), managerial support (t (419) = 5.027, p < 0.000), incentives (t (419) = 5.263, p < 0.000), and trust (t (419) = -1.957, p < 0.050) are significant predictors of smart government systems acceptance. Meanwhile cultural values (t (419) = 0.669, p < 0.504) and religious values (t (419) = 1.082, p < 0.280) have no significant effect on the attitude to smart system adoption. Perception was found to have a strong significant effect on adoption of smart government systems (t (419) = 8.411, p < 0.000). These results have significant implications for the Saudi government's drive to implement smart government systems in all its agencies

Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

In this paper, a joint spatio&ndash;radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users&rsquo; grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27.6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62.5 % compared to the full CSI feedback

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

Hyperspectral imaging (HSI) plays a major role in agricultural remote sensing applications. Its data unit is the hyperspectral cube that contains spatial data in 2D but spectral band data of all the pixels in 3D. The classification accuracy of HSI was significantly enhanced by deploying either spatial or spectral features. HSIs are developed as a significant approach to achieve growth data monitoring and distinguish crop classes for precision agriculture, based on the reasonable spectral response to the crop features. The latest developments in deep learning (DL) and computer vision (CV) approaches permit the effectual detection and classification of distinct crop varieties on HSIs. At the same time, the hyperparameter tuning process plays a vital role in accomplishing effectual classification performance. The study introduces a dandelion optimizer with deep transfer learning-based crop type detection and classification (DODTL-CTDC) technique on HSI. The DODTL-CTDC technique makes use of the Xception model for the extraction of features from the HSI. In addition, the hyperparameter selection of the Xception model takes place using the DO algorithm. Moreover, the convolutional autoencoder (CAE) model is applied for the classification of crops into distinct classes. Furthermore, an arithmetic optimization algorithm (AOA) can be employed for optimal hyperparameter selection of the CAE model. The performance analysis of the DODTL-CTDC technique is assessed on the benchmark data set. The experimental outcomes demonstrate the betterment of the DODTL-CTDC method in the crop classification process

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection