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The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

The purpose of this paper is to explore the nature of fatwa and the dissemination of fatwa rulings among Muslim consumers using two studies. Results from these studies show that contemporary fatwa often extend beyond Muslims' religious beliefs and practices. By advising Muslims on the brands and product categories that are permissible and prohibited for consumption, fatwa rulings can cause boycotts and bans of products or brands.

Content analyses are performed in Study 1 to explore the types of fatwa rulings that are declared by the Malaysian fatwa authorities. In Study 2, a survey instrument is used to collect responses from Muslims regarding their sources of fatwa ruling for two products, their religious motivation and gender. Cluster analysis and an independent χ²‐test are used to test the study's predictions.

Fatwa rulings in the area of social issues and economics have a known tendency to affect Muslim consumers' marketplace behavior. Muslim consumers tend to acquire information on more controversial fatwa rulings through less formal sources, compared to less controversial fatwa rulings. Several clusters of Muslim consumers were found to have their sources of fatwa associated with the product category, gender of respondent, and religious orientation.

The findings are limited to Malaysian Muslim University students and Malaysia's fatwa system. This use of young Malaysian Muslims probably restricts the findings to this cohort, rather than the general population of Malaysian Muslims.

The paper offers insights into how fatwa rulings affect marketplace behaviors and how information sources are accessed and fatwa information is disseminated among Muslim consumers. The findings lead to suggestions on how marketers can manage fatwa rulings concerning their products.

The paper provides an understanding of the nature and types of fatwa rulings circulating in the Muslim world, and identifies factors related to Muslims' knowledge of fatwa rulings.

This paper aims to examine the assumption used in previous studies that all Muslims adopt and believe the same law on the prohibition of bank interest and to investigate the indirect effect of religiosity on customers’ decision for using Islamic banking services.

This study uses an exploratory approach and the natural experimental design with seemingly causal models. A total of 363 questionnaires were distributed to three groups of bank customers, i.e. Islamic banks customers, conventional banks customers and customers of both banks (121 respondents in each group).

The results show that the role of religiosity in the customers’ decision for using the Islamic banking services depends on religious norms variable. Religiosity affects the decision of customers in the traditional group, but it does not have any effect for the contemporary group. Other findings suggest that religiosity indirectly affects the decision for using the Islamic banks through intervening variables of trust and information source.

This is the first paper to investigate the relationship between religiosity and customers’ decision for using the Islamic banking services by considering the religious norm variable. This paper also examines indirect affects of religiosity to the Islamic banks’ choice through intervening variables of trust and information source.

This study aims to develop a comprehensive insolvency model tailored to Islamic banks, ensuring alignment with Shariah principles throughout pre-insolvency, bankruptcy and post-bankruptcy stages.

The research adopts a qualitative research method, using a desktop research approach. Primary sources and secondary sources are examined to gather information and draw conclusions.

This study presents a comprehensive insolvency model designed for Islamic banks, rooted in Shariah principles. The model covers pre-insolvency, bankruptcy (taflis) and post-bankruptcy stages, incorporating key Shariah parameters to ensure adherence to Islamic finance principles. It addresses challenges such as adapting to dynamic financial landscapes and varying interpretations of Shariah principles. Notably, the model recognizes the separate legal personality of Islamic banks and emphasizes transparency, fairness and compliance with religious obligations. In the post-bankruptcy stage, directors are urged to voluntarily settle remaining debts, aligning with ethical and Shariah-compliant standards.

The study contributes to the stability and growth of Shariah-compliant financial systems by extending insolvency principles to Islamic banks, providing a foundation for future research and policymaking specific to this context.