Earticle

This study aims to design a real-time system on STM32F4 to measure the density of liquid flow in construction building materials, which can be manually tested in laboratories or construction sites. The study provides an embedded system capable of sequentially and real-time measuring the velocity profile and pressure drop. Existing devices such as rheometers, which are expensive and require calibration, are used to measure fluid flows. However, this study presents a domestically developed device at a lower cost.

It is critical that information be gathered, handled, and kept in a systematic manner that preserves its integrity and authenticity. Record management procedures are rapidly being recognized as a crucial business practice that assists businesses in establishing and validating their business dealings and related activities. The purpose and essence of any records management system is to provide the right information in the right place, in the right order, and at the right time for the right person at the lowest cost, which is better achieved with a computer-based system. Patient Management Information Systems (PMISs) are designed to manage the information of patients in the hospital and service processes. In Ghana, despite the importance of medical records, most hospitals still use the manual system of patient record management, resulting in poor record management practice, meaning a lack of effective systems for opening, tracking, and indexing files, which have many limitations for proper healthcare. In this study, the SWOT analysis method was used to assess the readiness of Ghana’s healthcare services to implement a patient management information system for healthcare delivery.

Health Tourism is a trendy interest area as it allows individuals to travel another country for receiving health services while experiencing touristic opportunities. There are many benefits of health tourism in terms of costs, services, and experiences. By combining both health and tourism area, it welcomes many research directions in not only healthcare and tourism but also marketing and business management. Among these the area of Health Tourism is intensively connected with digital solutions since it is important to reach out individuals in different countries and enabling them to decide and come to the host country for receiving services. So, the data is very important for establishing innovative Health Tourism applications. However, one question arising is about how to analyze, process and use the data effectively within the purposes of such applications. This paper tries to answer this question from the perspective of Data Mining. As the Data Mining allows creating descriptive and predictive new knowledge from the known data, its use in digital Health Tourism applications has a remarkable value. In this context, this paper firstly ensures the connection between Health Tourism and Data Mining, and then discusses about present state as well as the future. It is believed that the outcomes from this paper will be a triggering factor for further research in Data Mining, which is currently a niche application way in Health Tourism.

Considering the aging of individuals with the increasing population, the demand for technologies that enable people to follow their daily lives unnoticed is increasing day by day. In this article, a high-performance solution to the fall and posture detection problem for CCD camera-based fall detection systems is provided. Fall detection was performed with images obtained with CCD cameras placed in different positions. Within the scope of the proposed method, two different pre-trained CNN structures were trained using two different camera images. Data fusion was applied to the high-level features obtained from these structures. Features that were fusion process applied to different classifiers were given as input and ensemble learning process was applied. Considering the performance metrics of the proposed method, it was predicted that promising results were obtained for fall detection.

In today's digital age where health data is increasingly stored, accessed, and shared electronically, ensuring the privacy and security of patient information has become a major concern. The potential risks associated with unauthorized access, data breaches, and privacy violations require innovative approaches to protect sensitive health data. This is where the power of artificial intelligence (AI) comes into play. AI innovations have emerged as a transformative force in healthcare and offer promising solutions to enhance privacy protection. This paper discusses how healthcare organizations can use artificial intelligence (AI) to protect patient privacy while extracting valuable insights from their data. It also expresses how AI techniques such as differential privacy and fused learning can help organizations collaborate, aggregate their data, and train machine learning models securely without compromising patient privacy. It emphasizes the importance of understanding the challenges and potential limitations of privacy-preserving techniques in this context and encourages stakeholders to collaborate on developing industry standards and best practices for privacy protection.