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.

Face detection is one of the most important modern computer vision topics. The importance of face recognition (FR) is increasing in our society for identification purposes. In this paper, we practically demonstrated the authenticity of an automatic security system for automobiles using face recognition technology. Our proposed system is not only a facial recognition system, but also supports authentication via a GSM module for remote monitoring and permission/control by a specific remote security code. The system was implemented and tested using a Raspberry Pi 3 board, Python and OpenCV to program the different modules for face recognition. The implementation results confirm the applicability of the proposed system as it offers the advantages of robustness, high accuracy, reliability and security at the lowest cost. This paper defines a combination of fail-safe algorithms for the GPS/GSM module in addition to face recognition algorithms, which are more suitable in dynamic environments where high uncertainties are expected and require high accuracy and reasonable speed. In future, we aim to develop an intelligent smart home application to meet the upcoming needs.