Studying the role of Islamic religious beliefs on depression during COVID-19 in Malaysia

Depression is one of the most common psychological disorders and many people in the world suffer from this disorder. Every year, thousands of suicides occur because of depression. Whilst anxiety is considered a common phenomenon of our era, it has existed throughout human history. Nevertheless, there have always been signs of religion and religious beliefs in the study of human communities and the history of civilisations. Despite rapid advancements made in solving the physical problems of human beings, the science of medicine has not taken an effective step toward solving humans’ psychological issues, although they play a considerable role in the emergence of physical diseases. Religion can affect the mental health of individuals and society through various mechanisms. In general, the role of religion and religious beliefs on the health of the individual and society is very important. A great deal of peace of mind can be achieved based on faith and moral beliefs and practices. People with religious beliefs have an optimistic viewpoint towards life, and their religious beliefs turn a dark life into a bright one even when all hopes are lost in the battle of life. Given the importance of this issue, the present study aimed to evaluate the role of Islamic religious beliefs of Muslim students on depression during the coronavirus disease 2019 (COVID-19) pandemic in Malaysia. A field study was performed on 3500 Muslim students of Kuala Lumpur in 2021 by simple random sampling method. Data were collected using standardised questionnaires, and data analysis was performed in SPSS (Statistical Package for the Social Sciences). According to the results, people with a higher level of religious beliefs suffered less from depression, which confirmed the negative and significant relationship between Islamic religious beliefs and depression. According to the results of multiple regression analysis related to the components of the independent variable in SPSS, all components of Islamic beliefs had a significant role in reducing COVID-19-induced depression. Meanwhile, action required (t-value: 2.30; beta: 0.55) and religious activities (t-value: 2.24; beta: 0.54) had the most effect on reducing depression induced by COVID-19 disease. Contribution: The findings of this study could be used to treat people’s depression during the COVID-19 pandemic by taking their Islamic religious beliefs into account

A Diamond Shaped Multilevel Inverter With Dual Mode of Operation

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

The s-Commerce usage and acceptance modelling in Malaysia

The evolution of technology acceptance theories and models have started since the beginning of the 20th century and it is still evolving. This evolution is happened in different theoretical perspectives, such as: cognitive, affective, motivational, and behavioral intentions and reactions for individuals. Nowadays, understanding the reason of accepting or rejecting any new technology by users has become one of the most important areas in the IT field. The social media applications are benefited and enhanced the E-Commerce, Electronic Marketing (E-Marketing), and Electronic Shopping (E-Shopping) usage behaviors to get any information of any offered commodity in the easiest, fastest, and most familiar way, that will increase the retail profit as well. Social Commerce (S-Commerce) has become one of the most important fields and one of the fastest growing areas of the high technology sector development, especially in the trading and commercial environments. In this scope, it is presenting here the theories and models which were developed to study the acceptance by users and their adoption for new technology. this study adheres to the methodology of quantitative research, which offers a numerical measurement and analysis of the factors that determine adoption for samples 30 as a pilot study in Malaysia as a limit of this research specifically among 2 Malaysian Universities, that will lead to distribute the updated survey around 484 samples later. That results a high ratio of questionnaire validity and the effectiveness of the research hypothesis also found that the new model identifies the factors affecting S-Commerce usage behavior and continued usage intention, find the relationship between education and S-Commerce usage behavior and found such relationship between age and S- Commerce usage behavio

Electronic strategies and their importance in teaching and learning arabic language for non-native speakers and the sciences of Qur’an

Reaching successful language education requires correct scientific steps based on clear methodological foundations that a person adopts to achieve his aspirations in this field. For this reason, there are differing views among researchers and scientists as they search for the most scientific necessities to reach an effective curriculum in second language education. The theories of modern researchers in this field varied, since the theories of e-learning emerged into the scientific arena, and the divergence between them increased even more since the emergence of applied linguistics, especially educational linguistics, as it is a branch of applied linguistics, and it intersects with the education sciences in the interest in educational problems that have a linguistic basis. It is a science that studies the teaching of languages and its techniques, and the forms of organizing the learning situations to which the learner is subject and taking into account their reflection on the individual and society in terms of developing mental abilities, enhancing conscience and directing social ties. With the theories of learning that differ among themselves in explaining the best way for how human learning takes place and the best ways in that. That is why this research focused on showing language levels, objectives of linguistic communication, stages of language acquisition and linking it to the communication process, as well as clarifying some e-learning strategies

Quality assurance standards of e-learning and the basis for their application in preparation of language study plans and the science of holy Qur’an

Ensuring the quality of E-Learning in the field of higher education requires in the first place an institutional vision accompanied by commitment, leadership, and proper planning, while ensuring that this is available to the partners. The E-Learning policy should conform to the overall vision of the institution and the tasks and services they provide. Leaders and managers must be able to explain the reasons for choosing E-Learning as an appropriate learning strategy for the students who serve them. Online education components should include the quality components that were identified in the first part of this post. Additionally, organizations need to comply with the regulations governing online learning and ensure that they are reflected in policies and practices.This research also seeks to establish the rules and foundations of specialized education in the field of teaching Arabic to non-native speakers, and link them to Quran. There is no doubt that all scientific institutions aim to ensure the quality of the application of their academic standards that set them to achieve what they want in the field of raising the level of targeted scientific seekers. Therefore, this paper raises a fundamental research problem, namely, ways to ensure scientific quality. This paper referred to a number of questions, including: What are the standards? What does quality mean in the preparation of language teaching plans and Quranic sciences? And how to find academic content that combines between Arabic and Quranic sciences? What possibilities are there to ensure that learners are improved through quality standards? This paper discusses the concept of educational quality and its role in achieving the objectives of the educational process, addressing the role of scientific standards in the field of the preparation of study plans, indicating the possibilities that must be available to help in thesuccess of the implementation of study plans prepared to teach Arabic and Quranic sciences, taking the descriptive analytical approach. Thus, this paper added a new dimension to the studies that preceded it in this area, where it is to indicate the criteria required to achieve the objectives set by linking the teaching of Arabic language with the science of the Quran. The paper recommends further studies on ensuring that quality standards are applied in the educational process

Enhancing AI interpretation and decision-making: Integrating cognitive computational models with deep learning for advanced uncertain reasoning systems

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach

Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

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

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques