As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning‐based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real‐time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password‐sized text and paragraph‐sized text—that achieves target fair results compared to the existing algorithms in real‐time deep learning networks for IoT applications.

This study presents a novel multilevel inverter structure that can operate in both switched capacitor and asymmetric DC source modes. In the first mode, it can produce seven-level output voltage employing two switched capacitors and one single DC supply. The five-level output voltage is produced while operating the second mode. The voltage ratio between the input and output voltage for the capacitor mode is 1:3 (triple voltage gain). During the first mode, the capacitor of the inverter is self -balanced whereas the inverter can produce higher voltage output in the DC source mode. The proposed inverter reduces the total standing voltage in both modes of operations as it can generate the output voltage without requiring any additional H-bridge circuit. The feasibility and predominate features of the proposed inverter have been established by comparing with existing topologies in terms of power components count. Results obtained from this study are validated using simulation employing sinusoidal pulse width modulation (SPWM). A hardware prototype has also been developed for further validation.

Artificial neural networks (ANN) consider classification as one of the most dynamic research and application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by back propagation algorithm [1]. A neural network represent a mathematical models of information processing, benefited from the human biological systems (i.e. a brain or nerve cell), where the neutral network can be trained and learned the same as a human brain does. The learning will be done by changing the weight during the training process and by using certain formula. One of the most known neural networks is the Back Propagation Network. This net has been used in variety of application areas. One of the, the classification of certain objects by known only a portion of information of the object to be classified. In this paper we shall use the back propagation network to classify the human blood groups, also we shall use the same program to be same analysis to find the best number of neurons in hidden layer that gives lower number of iteration.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

An Electronic Government employs the Information and Communication Technology (ICT) to improve the functions of public organizations and their activities. And some references define the e-Government as an application running over the internet, or just an intermediary that interacts between government and external environment. The essential objective of this study is to inspect the innovation diffusion theory with the external factor, the social influences. This research is a quantitative study using survey approach. In the current research, the target population exists in Jordan. The citizen opinions are very important for the investigation on using egovernment behavior. Findings in this paper propose some factors that are important for the behavior intention in egovernment in Jordan. The most significant factor is the behavior intention to use appears to be adoptions and facilitated by the usefulness of e-government services, and other benefits of governments lead to more acceptances among Jordan's citizens.

In recent years, researchers have paid special attention to religiosity and the practice of religious beliefs. If people put religiosity at the forefront of their affairs and maintain the roots of religion in various aspects of work and family life, they will see God present and watchful in doing all things, and the result of such a vision will be the successful performance of deeds and walking the path of perfection. Having a heartfelt belief in the value of work and adhering to it will result in a greater desire to work. Religiosity is a variable that can be related to job variables. In addition, it can play an important role in the behaviours of employees. In Islam, work is a virtue and is necessary to balance the personal and social life of the individual. Given the importance of this issue, the present study aimed to evaluate the relationship between Islamic religiosity and job satisfaction of Muslim teachers in Malaysia. In total, 2200 Muslim teachers of Kuala Lumpur and Penang were selected for the study by simple random sampling. Data were collected by using standard questionnaires and analysed by Pearson’s correlation coefficient in Statistical Package for the Social Sciences (SPSS). According to the results, there was a significant and positive relationship between teachers’ Islamic religiosity and job satisfaction as P = 0.784 and T = 0.000 which was less than the Sig level of 0.05.Contribution: According to Islamic teachings, working for a Muslim is like worship. Based on the findings of this research, teachers with higher Islamic religiosity enjoy higher job satisfaction.