In this paper, visible image watermarking algorithm based on biorthogonal wavelet
transform is proposed. The watermark (logo) of type binary image can be embedded in the
host gray image by using coefficients bands of the transformed host image by biorthogonal
transform domain. The logo image can be embedded in the top-left corner or spread over the
whole host image. A scaling value (α) in the frequency domain is introduced to control the
perception of the watermarked image. Experimental results show that this watermark
algorithm gives visible logo with and no losses in the recovery process of the original image,
the calculated PSNR values support that. Good robustness against attempt to remove the
watermark was s

Preserving and saving energy have never been more important, thus the requirement for more effective and efficient heat exchangers has never been more important. However, in order to pave the way for the proposal of a truly efficient technique, there is a need to understand the shortcomings and strengths of various aspects of heat transfer techniques. This review aims to systematically identify these characteristics two of the most popular passive heat transfer techniques: nanofluids and helically coiled tubes. The review indicated that nanoparticles improve thermal conductivity of base fluid and that the nanoparticle size, as well as the concentrations of the nanoparticles plays a major role in the effectiveness of the nanofluids.

Association rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time.  In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D

Fraud Includes acts involving the exercise of deception by multiple parties inside and outside companies in order to obtain economic benefits against the harm to those companies, as they are to commit fraud upon the availability of three factors which represented by the existence of opportunities, motivation, and rationalization. Fraud detecting require necessity of indications the possibility of its existence. Here, Benford’s law can play an important role in direct the light towards the possibility of the existence of financial fraud in the accounting records of the company, which provides the required effort and time for detect fraud and prevent it.

Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota