Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.

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.

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.

Due to the availability of several social media platforms and their use in sending text messages, it is necessary to provide an easy and safe way to protect messages from being hacked especially in the presence of intruders and data thieves, and taking into consideration that most of messages are confidential and personal, it is necessary to provide an easy and safe way to protect messages from being hacked. In this research paper, a simple and easy method of message cryptography will be proposed. The method divides a message into blocks with fixed sizes. The block size ranges from 2 to 60. The method uses a secret color image to generate an array with a size equal to the number of resulted blocks. The array will then be used as a private key. Each element of the private key will be used to calculate the number of rotation digits for the associated block in order to apply block rotation left operation. The proposed method will be examined using the parameters: Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), and throughput. The proposed method will be compared with other standard methods of message cryptography, such as Data Encryption Standard (DES), Triple-DES (3DES), Advanced Encryption Standard (AES), and Blow Fish (BF). Experimental results show that the proposed method is enough secured based on using secret image, block size, and calculated Rotation Left Digits (RLD) for each block.

Some digital data circulated through various social media, regardless of its nature, requires high-level protection and security for various reasons.In this research, a multi-purpose method will be presented to be used in encrypting SMS messages, text files regardless of their size, digital images of all kinds and sizes, and audio files of all kinds and sizes.The proposed method will be examined to prove its efficiency, and the practical results will be compared with the implementation results of other internationally approved methods to show the extent to which the method improves efficiency indicators.It will be shown how to use digital color image to generate a highly secure private key which will make the process of hacking impossible.The Quality of the encrypted and decrypted images will be examined to justify the use of the proposed method.

Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32% and 92.67% in terms of geometric mean and accuracy respectively, utilizing less than 10% of the total feature space. The empirical results show that the modified genetic algorithm outperforms Chi(2) and PCA feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works.

The human fingerprint is very important for human identification. Therefore, many works have been done to enhance the possibility of detecting a human fingerprint. This research article will explain how to build a high-performance fingerprint classification system. In the study presented in this research paper, the Kmeans clustering method will be used for several reasons, the most important of which is the flexibility of this method by controlling the number of values in the features vector of the fingerprint and more than one alternative can be used to form the features, as it is not sensitive to the process of rotating the fingerprint image. The key idea is to normalize the created fingerprint database and exploit the concept of deep learning to minimize the error between the calculated output and the desired target output. The artificial neural network architecture of different types of ANN will be expanded vertically and horizontally. These architectures will be trained and tested; the obtained training and testing results will be used to make some useful recommendations for using an artificial neural network as a recognition tool in the fingerprint recognition system.

Document clustering groups documents of certain similar characteristics in one cluster. Document clustering has shown advantages on organization, retrieval, navigation and summarization of a huge amount of text documents on Internet. This paper presents a novel, unsupervised approach for clustering single-author documents into groups based on authorship. The key novelty is that we propose to extract contextual correlations to depict the writing style hidden among sentences of each document for clustering the documents. For this purpose, we build an Hidden Markov Model (HMM) for representing the relations of sequential sentences, and a two-level, unsupervised framework is constructed. Our proposed approach is evaluated on four benchmark datasets, widely used for document authorship analysis. A scientific paper is also used to demonstrate the performance of the approach on clustering short segments of a text into authorial components. Experimental results show that the proposed approach outperforms the state-of-the-art approaches.

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 involve disputed anonymous documents. Among 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 room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.

This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components. The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships. For this purpose, we adopt Hidden Markov Model (HMM) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships. An unsupervised learning method is developed to initialize the HMM parameters. Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests. As an example of its applications, the proposed approach is applied for attributing authorship of a document and has also shown promising results.

This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the precision of the clustering assignment and the accuracy of the classification process of our method. Experimental results show that the proposed method outperforms two state-of-the-art methods.