Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

3D-TTN: a power efficient cost effective high performance hierarchical interconnection network for next generation green supercomputer.

Green computing is an important factor to ensure the eco-friendly use of computers and their resources. Electric power used in a computer converts into heat and thus, the system takes fewer watts ensuring less cooling. This lower energy consumption allows to be less costly to run as well as reduces the environmental impact of powering the computer. One of the most challenging problems for the modern green supercomputers is the reduction of current power consumptions. Consequently, regular conventional interconnection networks also show poor cost performance. On the other hand, hierarchical interconnection networks (like-3D-TTN) can be a possible solution to those issues. The main focus for this paper is the estimation of power usage at the on-chip level for 3D-TTN with the various other networks along with the analysis of static network performance. In our analysis, 3D-TTN requires about 32.48% less router power usage at the on-chip level and can also achieve near about 21% better diameter performance as well as 12% better average distance performance than the 5D-Torus network. Similarly, it also requires only about 14.43% higher router power usage; however, can achieve 23.21% better diameter performance and 26.3% better average distance than recent hierarchical interconnection network- 3D-TESH. The most attractive feature of this paper is the static hop distance parameter and per watt analysis (power-performance). According to our power-performance results, 3D-TTN can also show better result than the 3D-Mesh, 2D-Mesh, 2D-Torus and 3D-TESH network even at the lowest network level. Moreover, this paper is also featured with the static effectiveness analysis, which ensures cost and time efficiency of 3D-TTN.