RESUMO
We demonstrate silicon-based $p \text{-} n \text{-} p$p-n-p floating-base GeSn heterojunction phototransistors with enhanced optical responsivity for efficient short-wave infrared (SWIR) photodetection. The narrow-bandgap GeSn active layer sandwiched between the $p \text{-} {\rm Ge}$p-Ge collector and $n \text{-} {\rm Ge}$n-Ge base effectively extends the photodetection range in the SWIR range, and the internal gain amplifies the optical response by a factor of more than three at a low driving voltage of 0.4 V compared to that of a reference GeSn $p \text{-} i \text{-} n$p-i-n photodetector (PD). We anticipate that our findings will be leveraged to realize complementary metal-oxide-semiconductor-compatible, sensitive, low driving voltage SWIR PDs in a wide range of applications.
RESUMO
BACKGROUND: The ensemble building is a common method to improve the performance of the model in case of regression as well as classification. OBJECTIVE: In this paper we propose a weighted average ensemble model to predict the number of incidence for infectious diseases like typhoid and compare it with applied models for prediction. METHODS: The Monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. The data was processed by three regressions such as support vector regression, neural network and linear regression. RESULTS: To evaluate the prediction error and compare it with different models, different performance measures have been used such as MSE, RMSE and MAE and it was found that proposed ensemble method performed better in terms of forecast measures. CONCLUSION: Our main aim in this paper is to minimize the prediction error; the resulting proposed weighted average ensemble model has shown a significant result in terms of prediction errors.