Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach

Currently, almost all direct marketing activities take place virtually rather than in person, weakening interpersonal skills at an alarming pace. Furthermore, businesses have been striving to sense and foster the tendency of their clients to accept a marketing offer. The digital transformation and the increased virtual presence forced firms to seek novel marketing research approaches. This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit and finding the most significant characteristics of the clients. Real-world data from a Portuguese bank and national socio-economic metrics are used to model the telemarketing decision-making process. This research makes two key contributions. First, propose a novel genetic algorithm-based classifier to select the best discriminating features and tune classifier parameters simultaneously. Second, build an explainable prediction model. The best-generated classification models were intensively validated using 50 times repeated 10-fold stratified cross-validation and the selected features have been analyzed. The models significantly outperform the related works in terms of class of interest accuracy, they attained an average of 89.07\% and 0.059 in terms of geometric mean and type I error respectively. The model is expected to maximize the potential profit margin at the least possible cost and provide more insights to support marketing decision-making

Multi-author document decomposition based on authorship

University of Technology Sydney. Faculty of Engineering and Information Technology.Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues) and intelligence issues that involves disputed anonymous documents. Among the existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big rooms for improvement. In this thesis, we propose novel approaches for decomposition of a multi-author document written in any language disregarding to topics, based on a Naive-Bayesian model and Hidden Markov Model (HMM). The proposed approaches of the Naive-Bayesian model aim to exploit the difference in its posterior probability to improve the performance of decomposition. Two main procedures are proposed based on Naive-Bayesian model, and they are Segment Elicitation procedure and Probability Indication Procedure. The segment elicitation procedure is proposed to form a strong labeled training dataset. The probability indication procedure is developed to improve the purity of the sentence decomposition. The proposed approaches of the HMM strive to exploit the contextual correlation hidden among sentences when determining their authorships. In this thesis, it is for the first time the sequential patterns hidden among document elements is considered for such a problem. To build and learn the HMM, a new unsupervised learning method is proposed to estimate its initial parameters. The proposed frameworks do not require the availability of any information of authors or document's context other than how many authors have contributed to writing the document. The effectiveness of the proposed algorithms is proved using benchmark datasets which are widely used for authorship analysis of documents. Furthermore, scientific papers are used to demonstrate the performance of the proposed approaches on authentic documents. Comparisons with recent state-the-art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of the proposed approaches

Protecting Digital Images Using Keys Enhanced by 2D Chaotic Logistic Maps

This research paper presents a novel digital color image encryption approach that ensures high-level security while remaining simple and efficient. The proposed method utilizes a composite key r and x of 128-bits to create a small in-dimension private key (a chaotic map), which is then resized to match the color matrix dimension. The proposed method is uncomplicated and can be applied to any image without any modification. Image quality, sensitivity analysis, security analysis, correlation analysis, quality analysis, speed analysis, and attack robustness analysis are conducted to prove the efficiency and security aspects of the proposed method. The speed analysis shows that the proposed method improves the performance of image cryptography by minimizing encryption–decryption time and maximizing the throughput of the process of color cryptography. The results demonstrate that the proposed method provides better throughput than existing methods. Overall, this research paper provides a new approach to digital color image encryption that is highly secure, efficient, and applicable to various images