In the public health sector and the field of medicine, the popularity of data mining and its usage in knowledge discovery and databases (KDD) are rising. The growing popularity of data mining has discovered innovative healthcare links to support decision making. For this reason, there is a great possibility to better diagnose patient’s diseases and maintain the quality of healthcare services in hospitals. So, there is an urgent need to make disease diagnosis possible by discovering the hidden patterns from the patients’ history information in developing countries. This work is a step towards how to use the extracted knowledge to enhance the quality of healthcare facilities. In this paper, we have proposed a web-centered hospital information management system (HIMS) that identifies frequent patterns from the data with eye disorder patients using the association rule-based Apriori data mining technique. The proposed framework has the capability to overcome all the key issues and problems in the current hospital information management system regarding data analysis and reporting services. For this purpose, data were collected from more than 1000 university students (China citizens) both online and manually (printed questionnaire). After applying the Apriori algorithm on the collected data, we revealed that almost 140 individuals out of 1035 had myopia (near-sighted disorder), at current age of 22 years, and that there were no male patients found with myopia. We concluded that their clinical relevance and utility can generate favorable results from prospective clinical studies by mapping out the habits or lifestyles that potentially lead to fatal diseases. In the future, we plan to extend this work to fully automate HIMS to help practitioners to diagnose the reasons of various diseases by extracting patient lifestyle patterns.

The lack of attention to the correlation between the attributes of usability requirements leads to several problems with software development. This paper presents a novel framework that focuses on the mapping of usability requirements attributes to the linguistic assessment from the users using fuzzy logic. Our proposed framework prioritizes conflicting usability requirements attributes. For implementation, we have used MATLAB Fuzzy Logic Toolbox. This proposed framework is aimed at helping the requirement analyst in taking better decisions by automating the whole process of identifying and resolving usability requirements conflicts. The major task in the proposed system involves determining the numerical value for each attribute considering their respective importance in different quantitative and qualitative evaluation standards. On the basis of numerical value, conflicts and their respective severities are identified.

Improving the quality of satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve enhanced performance. Different algorithms have been proposed to enhance the quality of satellite images. However, satellite images enhancement is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to enhance the resolution and contrast of satellite images. To improve the quality of satellite images, in this study, first, the resolution of an image is improved. For resolution enhancement, first, the input image is decomposed into four frequency components (LL,LH,HL,and HH) using the stationary wavelet transform (SWT). Second, Singular value matrices (SVMs) U_A and V_A which contains high-frequency elements of an input image are obtained using singular value decomposition (SVD). Third, the high-frequency components (LH,HL) of an input image are obtained using discrete wavelet transform (DWT) and corrected by SVMs and SWT. Next, the interpolation factor is added and the high-resolution image is obtained using inverse discrete wavelet transform (IDWT). Second, the contrast of the image is optimized. For the contrast enhancement, the image is decomposed using DWT into sub-bands such as (LL,LH,HL,and HH). Next, the singular value matrix (SVM) of the LL sub-band is obtained which contains the illumination information. Then, SVM is modified to enhance the contrast. Finally, the image reconstructed using the IDWT. In this paper, the results from the method above are compared with existing approaches. The proposed method achieves high performance and yields more insightful results over conventional technique.

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.

Recent advances in information and communication technology have greatly increased the popularity of online education. The COVID-19 pandemic has further accelerated the adoption of online education due to the need for educational institutions to adopt online learning models. On the other hand, the online education system faces several challenges, such as lack of usability to overcome access barriers and scalability issues. These problems stand in the way of widespread adoption of the online education system. The usability of online education websites is one of the fundamental challenges because it directly affects students’ learning experience. This study focuses on the challenges related to usability of online education websites. If an online education website has usability issues, students may have difficulty using the learning materials provided. This makes it difficult to achieve the required level of service, which can lead to a poor reputation for the website. Therefore, in order to develop a well-functioning online education website, it is necessary to ensure that all usability requirements (URs) are met efficiently. Considering the different backgrounds, knowledge, and skills of students, they may have different preferences regarding usability requirements. Therefore, prioritization of URs is necessary to focus on the most preferred requirements. In this study, we apply the fuzzy analytic hierarchy process (FAHP) to analyze the usability requirements in the requirements analysis phase. We consider the online education website of Virtual University of Pakistan, a leading online university in the country with more than 0.1 million active students. The result of this work is a flexible approach to identify precise usability requirements for developing a functionally sound website with an optimized and easy-to-use user interface that will ultimately improve the quality of education.

The proposed system for automobile security is a face detection and recognition application that control the automobile to be operated or restricted. This system is established for all types of door locks and particularly for automobiles. By using this methodology, resulted a better quality product with respect to documentation standards, code optimization, user acceptance due to adequately efficient, maintainable, consistence and cheaper software. With the up-to-date and influential technology, the system is not only expected to be workable, but also sufficiently efficient in terms of execution speed and response time. In our work we studied the limitations of the face recognition techniques and try to give the best solution with benefits of both fisherface and eigenface recognition methods at minimum cost. As the main aim of this work is to develop a low cost security system for a common man to keep his automobile save but it does not only protects the automobile but also assist in catching the thief by saving his image. This paper defines an efficient face detection and recognition algorithms using a cross platform of EmguCV as .Net wrapper to the Intel OpenCV library, Visual Studio and C# .Net, with hardware components essentially a Computer Stick and an Arduino uno.