Feature generation for optimization of marketing campaign

Abstract. Utilizing the gaming data for optimizing the entire gaming paradigm has revolutionized the thought process of developers and gamers alike. The significance of the gaming data can be judged from the fact that it is being used productively by the marketing agencies to develop algorithms that could predict the behavior of a certain gamer and the reaction to updates. The core idea behind the solution proposed and implemented in this thesis is focused on making the marketing campaigns more impactful. According to the facts from credible online resources, i.e., Statista.com, the business-to-business (B2B) organizations spent over $12.3 billion on marketing campaigns. Since one of the major aims of a marketing campaign is customer acquisition, which is also referred to as demand generation, measuring the success rate of the marketing campaign is also of great importance. Besides, the conventional Customer Relation Managers (CRMs) don’t have such features using which, the businesses can monitor the effectiveness of the marketing campaigns. The system this thesis proposes aims to analyze the gaming data, which can be used to extract features for refined marketing campaigns. To analyze and precisely classify the gaming data, this thesis proposes an algorithm running behind a full-fledged marketing campaign that can yield optimal results and which can be further refined to predict the future purchase behavior of the users in such marketing campaigns. To accomplish this task, the Random Forest Classifier is the one, which this thesis proposes and has been implemented to optimize feature selection in order to enhance the profit revenue of the business. The promising results of empirical research and studies have proven the capability of the random forest classifier, and after employing it in the research, it has been established that the mentioned classifier is absolutely capable of extracting significant features on the basis of the gaming data sets that were provided. More importantly, this study has indicated that the Random Forest classifier gives better results in predicting the purchase likelihood, which is an essential milestone for our project. It should be noted that the solution we have proposed does not only serve to predict the purchase likelihood, but it can also be preferably utilized for other aims and objectives which are related to optimizing the marketing campaigns

Enhancing Physical Layer Security in AF Relay Assisted Multi-Carrier Wireless Transmission

In this paper, we study the physical layer security (PLS) problem in the dual hop orthogonal frequency division multiplexing (OFDM) based wireless communication system. First, we consider a single user single relay system and study a joint power optimization problem at the source and relay subject to individual power constraint at the two nodes. The aim is to maximize the end to end secrecy rate with optimal power allocation over different sub-carriers. Later, we consider a more general multi-user multi-relay scenario. Under high SNR approximation for end to end secrecy rate, an optimization problem is formulated to jointly optimize power allocation at the BS, the relay selection, sub-carrier assignment to users and the power loading at each of the relaying node. The target is to maximize the overall security of the system subject to independent power budget limits at each transmitting node and the OFDMA based exclusive sub-carrier allocation constraints. A joint optimization solution is obtained through duality theory. Dual decomposition allows to exploit convex optimization techniques to find the power loading at the source and relay nodes. Further, an optimization for power loading at relaying nodes along with relay selection and sub carrier assignment for the fixed power allocation at the BS is also studied. Lastly, a sub-optimal scheme that explores joint power allocation at all transmitting nodes for the fixed subcarrier allocation and relay assignment is investigated. Finally, simulation results are presented to validate the performance of the proposed schemes.Comment: 10 pages, 7 figures, accepted in Transactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT

Channel Impulse Response-based Distributed Physical Layer Authentication

In this preliminary work, we study the problem of {\it distributed} authentication in wireless networks. Specifically, we consider a system where multiple Bob (sensor) nodes listen to a channel and report their {\it correlated} measurements to a Fusion Center (FC) which makes the ultimate authentication decision. For the feature-based authentication at the FC, channel impulse response has been utilized as the device fingerprint. Additionally, the {\it correlated} measurements by the Bob nodes allow us to invoke Compressed sensing to significantly reduce the reporting overhead to the FC. Numerical results show that: i) the detection performance of the FC is superior to that of a single Bob-node, ii) compressed sensing leads to at least $20\%$ overhead reduction on the reporting channel at the expense of a small ($<1$ dB) SNR margin to achieve the same detection performance.Comment: 6 pages, 5 figures, accepted for presentation at IEEE VTC 2017 Sprin

Feeding the world sustainably - efficient nitrogen use

Globally, overuse of nitrogen (N) fertilizers in croplands is causing severe environmental pollution. In this context, Gu et al. suggest environmentally friendly and cost-effective N management practices and Hamani et al. highlight the use of microbial inoculants to improve crop yields, while reducing N-associated environmental pollution and N-fertilizer use