Deterministic compartmental models are predominantly used in the modeling of infectious diseases, though stochastic considered more realistic, yet complicated to estimate due missing data. In this paper we present a novel algorithm for estimating Susceptible-Infected-Recovered and Susceptible-Exposed-Infected-Recovered (SIR/SEIR) epidemic model within Bayesian framework, which can be readily ex...

Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis . However, its robustness may be questioned because of the small size training datasets. Though issue could solved by data augmentation, polarization modalities are subject to physical feasibility constraints unaddressed classical augmentation techniques. To address this i...

Shadow detection is an important branch of computer vision. Recently, convolutional neural network (CNN)-based methods for shadow have achieved better performance than based on manually designed features. However, CNNs are extremely hungry data and the training CNN-based detector requires time-consuming expensive pixel-level annotations. To alleviate this problem in detection, a method augmenta...

Sorting out the legal documents by their subject matter is an essential and time-consuming task due to large amount of data. Many machine learning-based text categorization methods exist, which can resolve this problem. However, these algorithms not perform well if they do have enough training data for every category. Text augmentation Data a widely used technique in learning applications, espe...

Mosquitoes are responsible for the most number of deaths every year throughout world. Bangladesh is also a big sufferer this problem. Dengue, malaria, chikungunya, zika, yellow fever etc. caused by dangerous mosquito bites. The main three types mosquitoes which found in aedes, anopheles and culex. Their identification crucial to take necessary steps kill them an area. Hence, convolutional neura...

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been ...