Facebook Marketing and Its Influence on Consumer Purchase Behaviour in the Context of Bangladesh

Facebook is a widely used social media platform where people usually spend a lot of time. Presently, it is considered a great way to outspread information globally through its artificial intelligence-based targeted control. As a result, Facebook also plays a vital role in the expansion of businesses due to its huge number of audiences around the universe. Nowadays to reach these customers very quickly and easily, businessmen around the world are utilizing Facebook as one of the best tools for marketing purposes. In Bangladesh, almost every person who possesses a smartphone or has internet accessibility uses Facebook as their primary social networking website. This frequent use of Facebook should have an influence on digital marketing. In this paper, we have tried to evaluate the influence of Facebook Marketing on customers’ purchase behavior in the context of Bangladesh through an online survey and some face-to-face interviews. Secondary data are also collected from existing research, journals papers, online reports, and websites. After the analyses, it can be stated that Facebook Marketing, having a list of positive customer feedback, has a great influence on customer purchase behavior in Bangladesh. From the positive reviews of existing customers, it can also be said that the influence rate of new customers is very high as well. Keywords:Facebook Marketing, Customers Buying Behaviour, Influence of Facebook Marketing, Digital Marketing DOI: 10.7176/EJBM/13-21-03 Publication date: November 30th 202

Deep neural network based resource allocation for V2X communications

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm reducing the computational overhead significantly.Comment: Submitted to IEEE VTC 2019-Fall, Honolulu, Hawaii, US

An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data From Mobile Cellular Networks

The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this article, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions

Thinking Out of the Blocks:Holochain for Distributed Security in IoT Healthcare

The Internet-of-Things (IoT) is an emerging and cognitive technology which connects a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. IoT is increasingly being implemented in distributed settings, making footprints in almost every sector of our life. Unfortunately, for healthcare systems, the entities connected to the IoT networks are exposed to an unprecedented level of security threats. Relying on a huge volume of sensitive and personal data, IoT healthcare systems are facing unique challenges in protecting data security and privacy. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from major setbacks of increasing storage and computation requirements with the network size. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes these challenges and is particularly suited for resource constrained IoT scenarios. The performance and thorough security analyses demonstrate that a holochain-based IoT healthcare system is significantly better compared to blockchain and other existing systems.Comment: Submitted to IEE

Learning the wireless V2I channels using deep neural networks

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance