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As an innovation, Blockchain has transformed numerous industries and sparked the interest of the research community due to its abundance of benefits, opening up diverse research routes in the healthcare sector in the last decade. With Health 4.0 becoming ubiquitous in the healthcare industry, end-user transactions are being carried out on a decentralized network, making Blockchain profitable to meet the demands of the modern healthcare sector. Therefore, a detailed analysis of Blockchain is very crucial. This study emphasizes the evolution of science and the preliminary research of Blockchain in healthcare through bibliometric analysis. All the data are extracted from the Scopus database, and the VOSviewer tool is used for analysis. A total of 1152 Scopus articles published between 2018 and 2022 are examined. Results reveal that in 2022, the field of Blockchain experienced a notable increment in the number of publications and a significant growth rate. IEEE Access became well known in this field and had a large number of citations. It is observed that China and India are the leading countries in terms of publications on Blockchain. This study offers a number of recommendations that amateur and professional researchers can use as a benchmark before commencing a Blockchain investigation in the future.

The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

Purpose - The purpose of this experimental study is to investigate the impact of teaching Oriental Music using Blended Learning (BL) approach for the students of senior secondary level in Sri Lanka specifically focusing on their achievement on required competencies of Oriental Music at Ordinary Level. The study analyzes the academic performance of students with detailed comparison of BL environment and traditional learning environment. Authors propose the application of BL approach to teach Oriental Music and study its impact on improvement of students’ competency. The study conducted with the application of a mixed instructional design model of objectivist and constructivist approaches for the design of the blended learning course in a student centred learning environment. Methodology - The study was directed by using true experimental study design with pretest and posttest control groups. BL was applied to the experimental group and the traditional instruction method was applied to control group. 9 schools from Colombo district were randomly selected for the experimental and control groups covering all the three existing school types of Sri Lanka. The study group consisted of 360 students of Grade 10 and Grade 11 who has been studying Oriental Music as a subject for General Certificate of Education Ordinary Level. To analyze the data Descriptive statistics, Paired samples t test, Independent samples t test were utilized. Findings - The findings of the experiment indicated that students who has studied Oriental Music under BL strategy showed a significant improvement in their music academic performances after the intervention. The mean post-test of the experimental group was 71.75 which is significantly higher than the mean control group which was 52.07. The mean difference was 19.68 1.91. Hence, there is a statistically significant increase in the performance of students who studied Oriental Music under blended learning. Thus, it is clearly evident that the blended instruction was effective. Significance - This study indicated a positive platform to mould and cater the entire teaching learning process by introducing BL strategy to Sri Lankan secondary education system and fulfilled an existing research gap by utilizing BL to teach highly traditional abstract art. Results of the study contributes to the curriculum designing field with novel ideas to adapt blended instructions to teach secondary level students effectively.