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Fingerprint detection is one of the primary methods for identifying individuals. Gray Level Co-occurrence Matrix (GLCM) is the oldest and prominent statistical textual feature extraction method applied in many fields for texture analysis. GLCM holds the distribution of co-occurring intensity patterns at a given offset over a given image. However, images occupy excessive space in storage by its original sizes. Thus, Discrete Wavelet Transform (DWT) based compression has become popular especially for reducing the size of the fingerprint images. It is important to investigate whether GLCM-based classification can be utilized efficiently on DWT-compressed fingerprint images. In this paper, we analyze the performance of GLCM-based classification on DWT-compressed fingerprint images. We performed satisfying simulations for different levels of DWTcompressed images. Simulation results identify that classification performance sharply decreases by the increase of DWT-compression level. Besides, instead of utilizing all Haralick features, it is recognized that eight of them are the most prominent ones that affect the accuracy performance of the classification.

Mobile phones are considered extremely crucial for their daily usage due to their unique features such as mobility, availability, and compatibility. Moreover, the need for mobile apps periodically increases in terms of the variety of end-users as well as mobile platforms. Iraq is an emerging country concerning the development and requirements of mobile apps. There are numerous challenges faced by this trend on mobile phone app requirements, development, and usage. Consequently, the major consideration is how to build a suitable mobile app that can be adopted, adapted, and customized according to Iraqi market requirements. An essential fact worth mentioning is the increase in unemployed computer departments alumni in Iraq. Those who can develop mobile apps and software need to recruit suitable candidates. The purpose of this article is to develop a customized flight booking mobile application to partially reduce and solve the problem of unemployed computer alumni. The target of this application is to reduce time and effort for passengers and offer unique features. The developed application is the adoption of previous applications and the improvement of their inadequacies. By following a six-phase methodology to set a procedural technique for optimizing the use of the mobile application as a source of revenue for unemployed alumni as well as being beneficial for the customer. The validation and verification of the proposed application of this current research are done by evaluating and executing the mobile application. The results of this paper meet the main objective, where, the special features have been implemented and tested. In addition, the application has been installed on mobile phones and tested. Furthermore, the income from this application is obtained through purchasing it or subscribing to it for a specific duration. We encourage researchers as well as alumni who are targeted in this field to be attentive to cutting-edge technology and advancement and the concept of this paper.

The visual elements play an essential role in E-learning utilisations that impact the effectiveness of the learning users. That requires highlighting the effect of new elements of E-learning on the learning outcomes of students as well as users. The study was carried out in two phases. In the first phase, a literature review was conducted to identify the most relevant studies on the subject. This paper investigates empirical identification and examines both E-learning text and non-text related to the category of visual materials. The user experience design perspective covers literature surveys, interviews, and questionnaires. Research has been done on the types and functions of the visual elements of E-learning. Therefore, based on the existing E-learning Levin visual model elements, including "organisational", "descriptive", "interpretative", "deformable", "decorative", and "social", distinguish the correlation degree of each element with learning content and persistence. The result shows better user satisfaction enhancement and promotion of E-learning's learning effectiveness and persistence. In addition, from the perspective of user experience, it is found that social elements are the potential needs of users, and sociality is one of the characteristics of digital learning.