Search results

The purpose of the paper is to make use of multiple parameters namely; residual energy, closeness to centre and mobility of detection point (DP) for the selection of detection point network (DPN). In the novel method proposed, the path will have less number of DPs participating in the entire DPN.

The proposed novel method will find out the special detection point (SDP) based on three criteria, namely, the amount of mobility for DP, the amount of remaining energy and the amount of distance between two DPs. This proposed method is an attempt to resolve the network lifetime problems during the communication of DPs over a period of time. It is developed for increasing the lifetime ratio, throughput, residual energy, number of alive nodes.

The simulation results of the novel method show the improvement over the existing methods investigated based on the lifetime ratio, throughput, remaining energy and alive nodes.

In the proposed method, the communication is done between different DPs in the network. The commutation is done using SDPs only from one cluster to another cluster. It is proposed for the implementation of energy efficient data sensing in mobile communication networks.

It is a significant mechanism for energy efficient data sensing of one DP to another DP of different clusters in the network. The total energy consumed for a period of time by the network is significantly reduced from the novel method.

This study's aim was to investigate the role of the perceived values of gaming on consumers' perceptions of brands as cool as well as the impacts on the consumer–brand equity relationship. The study proposed a framework highlighting the influences of fashion-branded games on brand coolness and building fashion brands' overall equity. As significant factors affecting gamers, gender and gaming have been studied as moderators affecting the overall proposed framework.

A quantitative method was used to assess the significance of the relationships within the proposed model. Partial least squares structural equation modeling technique was implemented to assess the framework's relationships with a sample size of 248 active online gamers.

The findings indicate that brand equity is positively associated with perceived brand coolness. Furthermore, of the three core online game values, perceived enjoyment was most strongly associated with perceived brand coolness, with other values, such as self-expression and perceived emotional challenge, having a weaker association. The multigroup analysis results further suggest that in the fashion industry, building brand equity through online games is strongly related to perceived brand coolness among female respondents, with the role of perceived brand coolness affecting male respondents to a lesser degree.

The contribution of this study to the existing literature consists in providing a deeper understanding of the impact of branded games on fashion brands' overall equity. The results provide insights for fashion brand managers into the significant effect of fashion gaming collaborations on consumers' behavioral outcomes.

The purpose of this study is to investigate factors influencing the net stable funding ratio (NSFR) in the Islamic banking system. More specifically, the authors analyze the impact of the deposit structure on the liquidity ratio using the two-step generalized method of moments approach during the 2000–2014 period.

Based on IFSB-12 and the GN-6, the authors calculated the NSFR for 35 Islamic banks operating in the Middle East and North Africa (MENA) region.

The findings of this study show the following: first, ratio of profit-sharing investment accounts have a positive impact on the NSFR, while ratio of non profit-sharing investment accounts increase the maturity transformation risk; second, the results highlight that asset risk, bank capital and the business cycle have a positive impact on the liquidity ratio, while the returns on assets, bank size and market concentration have a negative impact; and third, these results support the IFSB’s efforts in developing guidelines for modifying the NSFR to enhance the liquidity risk management of institutions offering Islamic financial services.

The most prominent limitation of this research is the availability of data.

These results will be useful for authorities and policy makers seeking to clarify the implications of adopting the liquidity requirement for banking behavior.

This study contributes to the knowledge in this area by improving our understanding of liquidity risk management during liquidity stress periods. It analyzes the modified NSFR that was adopted by the IFSB. Besides, this study fills a gap in the literature. Previous studies have used the conventional ratios to determinate the main factors of the maturity transformation risk in a full-fledged Islamic bank based on an early version of NSFR. Finally, most studies focus on the NSFR as proposed by the Basel Committee, whereas the authors investigate the case of the dual-banking system in the emerging economies of seven Arab countries in the MENA region.

An effective corporate governance system helps to smoothly run business operations and manage financial matters. To ensure that management behavior is ethical, and their decisions are in the best interest of shareholders, corporate governance plays a vital role. This study aims to examine the impact of corporate governance on the insider trading profitability of listed banks in Pakistan, Bangladesh and India.

The authors take data from the financial statements of 70 listed banks and stock exchanges of the respective countries. The period of the data for our study is from 2010 to 2020. The authors use board independence, the board size, institutional ownership and managerial ownership as measures of corporate governance characteristics. While inside trading profitability is measured with abnormal returns. The authors apply the fixed effect panel regression for hypothesis testing and the two-step dynamic panel system-generalized method of moments (GMM) regression technique for checking the robustness of the findings.

The authors found that corporate governance has a significant impact on insider trading profitability in Pakistan, Bangladesh and India. Board independence and institutional ownership are negatively related while board size and managerial ownership are positively associated with insider trading profitability.

To the best of our knowledge, this study is the first one to explore the role of corporate governance in limiting insider trading on South Asian banks. It recommends that corporations should follow the code of corporate governance for the protection of shareholders' and other investors' profits.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.