درخت حوزه‌های تخصصی

This paper surveys recent publications on maritime logistics with a focus on blockchain capabilities. Maritime logistics plays an important role in global trade and an important part of the global economy depends on it. The growth of world trade has led to the creation of various systems that are prone to error and fraud, the current supply chain needs to connect and trust many intermediaries which not only increase abuse and errors but also it will increase cost and time in the chain. Hence, transparency and stability are highly demanded. On the other hand, with the onset of the fourth industrial revolution, a new approach called “blockchain” was introduced which can have very important applications in maritime logistics. As this novel technology is in its early stages of development, its applications in maritime logistics have not been specifically investigated. So, this paper takes a deep dive into blockchain technology capabilities for maritime logistics by reviewing relevant scientific journal papers since 2014. The paper also discusses and challenges the current operational paradigms and by categorizing efforts, provides a taxonomy of research topics in maritime logistics. Finally, the paper in addition to presenting challenges, identifies new avenues for academic research based on current trends and development.

Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.  One of the most growing attacks is on Android systems due to its use in many devices worldwide.  De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance.  There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families.  Hence, understand the distinct ob-jective of malware after identifying their family and category.  This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods.  The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.

The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.

This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

Accelerating digital transformation and the basic needs of businesses to adapt to environmental transformations and complexities have doubled the necessity for extensive stakeholder interactions. Gamification is a powerful tool that facilitates stakeholder interaction and a common understanding of the vision and contributes to the sustainability of businesses. Businesses' sustainability in the digital transformation age depends on the continuous interaction of stakeholders and a comprehensive understanding of all the business pillars, which will be possible through the flow of ideas inside and outside the workplace and by providing innovative processes. In this regard, this study explores the effect of gamification on corporate sustainability by explaining the role of digital transformation, open innovation, and value co-creation. The statistical population of this study comprises top managers and experts in e-businesses who use gamification in their processes. The statistical sample included 117 managers and experts active in this field, and they were selected through convenience sampling. The data collection tool was a questionnaire reliability of which was 0.763 using Cronbach's alpha. Using Smart PLS software, the gathered data were analyzed by structural equation modeling (SEM). Gamification, with a factor of 74%, has a positive effect on open innovation, while a factor of 85% has a positive effect on digital transformation. Gamification, both directly and indirectly, creates value co-creation in businesses, and finally, value co-creation at 78% affects corporate sustainability.