Rajesh Singh¹, Anita Gehlot¹, Ritika Saxena², Khalid Alsubhi³, Divya Anand^1,*, Irene Delgado Noya^4,5, Shaik Vaseem Akram¹, Sushabhan Choudhury²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1217-1233, 2023, DOI:10.32604/cmc.2023.023894 - 22 September 2022

Abstract Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic… More >

Jun-Ping Yao¹, Kai-Yuan Cheng^1,*, Meng-Meng Ge², Xiao-Jun Li¹, Yi-Jing Wang¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5509-5524, 2022, DOI:10.32604/cmc.2022.030150 - 28 July 2022

Abstract Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences, but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts. In these years, combining the knowledge graph with sequential recommendation has gained momentum. The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations, rich association facts can increase the diversity of recommendations, and complex relational paths can hence the interpretability of recommendations. But the information in the knowledge graph, such as entities and relations, often fails to be fully utilized… More >