Cloud computing fitness for e-government implementation: importance-performance analysis

The means through which governments deliver services and the way they operate may be considerably enhanced through cloud computing. It can help to address e-government implementation challenges and revolutionize e-government systems in terms of cost savings and the professional use of resources. The aim of this paper is to analyze the importance and performance of the factors that influence the fitness of cloud computing for e-government implementation. This paper integrates the task technology fit model with the diffusion of innovation theory to address this issue. Yemeni public institutions were identified as sources for data collection and 292 information technology employees participated as sample respondents for a structured questionnaire. Security, compatibility, relative advantage, and tasks were the variables found to affect the fitness of cloud computing for e-government activities. However, no impact was seen from the standpoints of trialability and complexity of the technology. In terms of assessing the fitness of cloud computing for e-government services, a greater understanding among policy formulators was sought through the importance-performance matrix analysis (IPMA). The results of IPMA can help identifying areas for strategic focus to assess cloud computing as an alternative technology to implement e-government services

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets

A fast and effective multiple kernel clustering method on incomplete data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the initialized incomplete data. Then, it constructs a new multiple-kernel-based data space, referred to as K-space, from multiple sources to learn kernel combination coefficients. Finally, it seamlessly integrates an incomplete-kernel-imputation objective, a multiple-kernel-learning objective, and a kernel-clustering objective in order to achieve absent multiple kernel clustering. The three stages in this process are carried out simultaneously until the convergence condition is met. Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is significantly better than state-of-the-art competitors. Meanwhile, the proposed method gains fast convergence speed

Reinforcement learning based resource management for 6G-enabled mIoT with hypergraph interference model

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput

Use of E-Learning by University Students in Malaysian Higher Educational Institutions: A Case in Universiti Teknologi Malaysia

This paper examines university students' intention to utilize e-learning. In this paper, we apply and use the theory of a technology acceptance model. We employ the structural equation modeling approach with a SmartPLS software to investigate students' adoption process. Findings indicate that the content of e-learning and self-efficacy has a positive impact and substantially associated with perceived usefulness and student satisfaction, which impact university students' intention to utilize e-learning. Although e-learning has gained acceptance in universities around the world, the study of the intention to use e-learning is still largely unexplored in Malaysia. The developed model is employed to explain the university student's intention to utilize e-learning. The study concludes that university students in Malaysia have positive perceptions toward e-learning and intend to practice it for educational purposes

Extending the theory of planned behavior (TPB) to explain online game playing among Malaysian undergraduate students

As the world moves into the web 2.0 era, everyone can connect virtually, and online game playing has become a trend. Online games are played over computer networks, usually over the Internet. Online games entail a number of advantages, such as the ability to connect to multiplayer games, although single-player online games are also rather popular. This exploratory study focused on modeling the determinants of actual use of online game playing. Many researchers have shown perceived enjoyment and flow experience as important drivers of actual use of online game playing. The theory of planned behavior has been used in this study. Data were collected from 1584 Universiti Sains Malaysia students with different backgrounds using a structured questionnaire. The findings show that perceived enjoyment has the strongest influence on actual use. Other variables found to influence actual usage include the level of perceived behavioral control, subjective norms, attitude, perceived enjoyment, and flow experience. Implications of this research for future researchers will also be discussed. We hope this research will increase researchers’ interest in further development in this sector and that the model will assist the games industry to identify factors that increase actual use by players

E-Government service delivery by a local government agency: the case of E-Licensing

The advancement in technologies has changed the way services are delivered (Dabholkar, 2000). The licensing department of a local authority in Penang, Malaysia is the major department involved in the processing and issuance of various types of licenses. The traditional method of processing of licenses manually has been a subject of criticism by license applicants and the public due to the delay in processing and the inefficient feedback mechanism. Over a period of two years, the licensing department has been working closely with the system designers by providing input on the construction of the E-Licensing system. All the time spent and the investment would go to waste if the employees do not intend to use the system. This study is focused on factors affecting intention to use of technology enabled service delivery (E-Licensing) by employees. The research model by Moore and Benbasat (1991) was adapted and used in this study. Six hypotheses were formulated to test the relationship proposed. The data collected from a sample of 92 respondents was used in the analysis of the hypothesis. The findings from this study show that (i) perceived usefulness/relative advantage is positively related to intention to use E-Licensing. (ii) Perceived ease of use and result demonstrability is positively related to intention to use E-Licensing. (iii) Visibility/observability is positively related to intention to use E-Licensing. This study will enable the licensing department to formulate, design and introduce measures to encourage usage of E-Licensing

A high-capacity MAC protocol for UAV-enhanced RIS-assisted V2X architecture in 3D IoT traffic

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case

Deep learning based secure transmissions for the UAV-RIS assisted networks: Trajectory and phase shift optimization

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches

Improving the Production of Secondary Metabolites via the Application of Biogenic Zinc Oxide Nanoparticles in the Calli of <i>Delonix elata</i>: A Potential Medicinal Plant

The implementation of nanotechnology in the field of plant tissue culture has demonstrated an interesting impact on in vitro plant growth and development. Furthermore, the plant tissue culture accompanying nanoparticles has been showed to be a reliable alternative for the biosynthesis of secondary metabolites. Herein, the effectiveness of zinc oxide nanoparticles (ZnONPs) on the growth of Delonix elata calli, as well as their phytochemical profiles, were investigated. Delonix elata seeds were collected and germinated, and then the plant species was determined based on the PCR product sequence of ITS1 and ITS4 primers. Afterward, the calli derived from Delonix elata seedlings were subjected to 0, 10, 20, 30, 40, and 50 mg/L of ZnONPs. The ZnONPs were biologically synthesized using the Ricinus communis aqueous leaf extract, which acts as a capping and reducing agent, and zinc nitrate solution. The nanostructures of the biogenic ZnONPs were confirmed using different techniques like UV–visible spectroscopy (UV), zeta potential measurement, Fourier transform infrared spectra (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Adding 30 mg/L of ZnONPs to the MS media (containing 2.5 µM 2,4-D and 1 µM BAP) resulted in the highest callus fresh weight (5.65 g) compared to the control and other ZnONP treatments. Similarly, more phenolic accumulation (358.85 µg/g DW) and flavonoid (112.88 µg/g DW) contents were achieved at 30 mg/L. Furthermore, the high-performance liquid chromatography (HPLC) analysis showed significant increments in gallic acid, quercetin, hesperidin, and rutin in all treated ZnONP calli compared to the control. On the other hand, the gas chromatography and mass spectroscopy (GC-MS) analysis of the calli extracts revealed that nine phytochemical compounds were common among all extracts. Moreover, the most predominant compound found in calli treated with 20, 30, 40, and 50 mg/L of ZnONPs was bis(2-ethylhexyl) phthalate, with percentage areas of 27.33, 38.68, 22.66, and 17.98%, respectively. The predominant compounds in the control and in calli treated with 10 mg/L of ZnONPs were octadecanoic acid, 2-propenyl ester and heptanoic acid. In conclusion, in this study, green ZnONPs exerted beneficial effects on Delonix elata calli and improved their production of bioactive compounds, especially at a dose of 30 mg/L