Evaluating M-Commerce Systems Success: Measurement and Validation of the DeLone and McLean Model of IS Success in Arabic Society (GCC Case Study)

This study focused on testing and verifying the 2003 DeLone and McLean Model (otherwise known as the Information System Success [ISS] Model), which represents the achievement of success in electronic systems, including smartphone commercial applications. Previous studies indicated that the DeLone and McLean Model has not been validated experimentally in the context of m-commerce, as there exist some differences between m-commerce and e-commerce. Moreover, the ISS model, for the m-commerce field, has been highly debated in terms of constructs such as perceived usefulness and IS use. These constructs create discrepancies in the acceptance of the ISS Model for the m-commerce field, especially in communities that have different technological requirements than other global communities. Previous studies focusing on the relationship between culture and electronic systems indicated that there are differences in the communities’ requirements that will directly affect the success of those electronic systems in Arabic communities. According to previous studies, there are verification shortages in the ISS model used to evaluate the success of m-commerce systems. The ISS model consists of six dimensions, which are system quality, information quality, service quality, user satisfaction, intention to use, and net benefit. The structural equation modelling technique was applied to the data for this model, which was collected by questionnaire. Responses were gathered from 803 actual users of online purchasing systems from three Arabic Gulf countries (171 from Qatar, 246 from the United Arab Emirates [UAE], and 386 from Saudi Arabia [KSA]). According to empirical evidence on the intention to use construct, which in turn is affected significantly by system quality and information quality constructs, reusing m-commerce applications is associated with quality of systems and information requirements in commercial applications. The results of this study on Arabic society will be beneficial for many future studies, such as ones determining the target characteristics of Arabic technology users and, especially, what features can be added to increase the level of satisfaction with m-commerce applications. This paper contributes several important implications to the field and discusses the additions and limitations that should be addressed in future studies

Analysis of enterprise management technology and innovation based on multilinear regression model

With the continuous innovation of scientific research, technological level and social and economic development, the technological innovation of enterprises in the continuous management is also facing a new breakthrough and upgrade. Especially for enterprises in the era of big data, in order to better respond to the new demands of the development of the new era, enterprise managers should make effective technological innovation from the global economic growth trend on the basis of clarifying their own development advantages. Therefore, on the basis of understanding the multiple linear regression model and its construction conditions, this paper analyses the influencing variables in the actual development according to the current management technology and innovation of enterprises, and obtains the clear results of the model research

Factors Affecting Information Security and the Implementation of Bring Your Own Device (BYOD) Programmes in the Kingdom of Saudi Arabia (KSA)

In recent years, desktop computer use has decreased while smartphone use has increased. This trend is also prevalent in the Middle East, particularly in the Kingdom of Saudi Arabia (KSA). Therefore, the Saudi government has prioritised overcoming the challenges that smartphone users face as smartphones are considered critical infrastructure. The high number of information security (InfoSec) breaches and concerns has prompted most government stakeholders to develop comprehensive policies and regulations that introduce inclusive InfoSec systems. This has, mostly, been motivated by a keenness to adopt digital transformations and increase productivity while spending efficiently. This present study used quantitative measures to assess user acceptance of bring your own device (BYOD) programmes and identifies the main factors affecting their adoption using the unified theory of acceptance and use of technology (UTAUT) model. Constructs, such as the perceived business (PT-Bs) and private threats (PT-Ps) as well as employer attractiveness (EA), were also added to the UTAUT model to provide the public, private, and non-profit sectors with an acceptable method of adopting BYOD programmes. The factors affecting the adoption of BYOD programmes by the studied sectors of the KSA were derived from the responses of 857 participants

Image steganography technique based on bald eagle search optimal pixel selection with chaotic encryption

In the digital era, information security becomes a challenging process that can be mitigated by the utilization of cryptography and steganography techniques. Earlier studies on steganography have the risk of exposing confidential data by an anonymous user. For resolving, the limitations related to the existing algorithms, one of the efficient solutions in encryption-based steganography. Encryption techniques act as an important part in protect actual data from illegal access. This study focuses on the design of Bald Eagle Search Optimal Pixel Selection with Chaotic Encryption (BESOPS-CE) based image steganography technique. The presented BESOPS-CE technique effectively hides the secret image in its encrypted version to the cover image. For accomplishing this, the BESOPS-CE technique employs a BES for optimal pixel selection (OPS) procedure. Besides, chaotic encryption was executed for encrypting the secret image, which is then embedded to choose pixel points of the cover image. Finally, embedding and extraction processes are carried out. The inclusion of the encryption process aids in accomplishing an added layer of security. A comprehensive simulation study was used to report on the BESOPS-CE approach's increased performance, and the results are examined from many angles. A thorough comparative analysis revealed that the BESOPS-CE model outperformed more contemporary methods

Factors Affecting Information Security and the Implementation of Bring Your Own Device (BYOD) Programmes in the Kingdom of Saudi Arabia (KSA)

In recent years, desktop computer use has decreased while smartphone use has increased. This trend is also prevalent in the Middle East, particularly in the Kingdom of Saudi Arabia (KSA). Therefore, the Saudi government has prioritised overcoming the challenges that smartphone users face as smartphones are considered critical infrastructure. The high number of information security (InfoSec) breaches and concerns has prompted most government stakeholders to develop comprehensive policies and regulations that introduce inclusive InfoSec systems. This has, mostly, been motivated by a keenness to adopt digital transformations and increase productivity while spending efficiently. This present study used quantitative measures to assess user acceptance of bring your own device (BYOD) programmes and identifies the main factors affecting their adoption using the unified theory of acceptance and use of technology (UTAUT) model. Constructs, such as the perceived business (PT-Bs) and private threats (PT-Ps) as well as employer attractiveness (EA), were also added to the UTAUT model to provide the public, private, and non-profit sectors with an acceptable method of adopting BYOD programmes. The factors affecting the adoption of BYOD programmes by the studied sectors of the KSA were derived from the responses of 857 participants

Network monitoring and processing accuracy of big data acquisition based on mathematical model of fractional differential equation

Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through situational factors, a fuzzy neural network security situation which is optimised based on an improved gravitational search algorithm combined with fractional differential equation analysis, as an Evaluation model, is proposed. In order to quickly and accurately predict the situation value of the network security situation at that moment, a method for situation prediction of long-term and short-term memory networks based on an improved Nadam algorithm to optimise the online update mechanism is proposed. Note that the situation time series obtained from online assessment cannot be used in a better and efficient manner. The model can minimise the cost function and update the model more effectively by updating the model parameters online Prediction accuracy. In order to improve the problem of slow convergence speed during model network training, the Look-ahead method is used to improve Nesterov's adaptive gradient momentum estimation algorithm to accelerate the model's convergence. Finally, the simulation results analyse and compare the prediction model, which not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model

Beam control method for multi-array antennas based on improved genetic algorithm

In the rapid development of modern science and technology, the human communication system has developed to 5G, and antenna in the related equipment research and technology application has also shown a positive role. With the increasing demand for antennas and their application scope, people have put forward more requirements on the beam shape of antennas. Therefore, it is very important to control the beam of multi-array antenna based on improved genetic algorithms in the new era. Therefore, on the basis of understanding the genetic algorithm and its application content, this paper studies the future development of related technologies based on the design content and test results of the broadband low-side lobe and high-gain microstrip display antenna developed in the new era

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches