Nanotechnology several time proven effective for the treatment of various diseases. Nano medicine is growing field of research in development of novel drug delivery systems. Targeting autophagy with the help...

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We are living in the era of an information technology-driven world. A lot of data is being generated at every moment with the help of the Internet of Things. To extract information from that data, it becomes essential to classify those data into various categories. Classification of text involves extracting features from text and then classifying them. Categorizing this big chunk of data is cumbersome and timeconsuming, and, in some cases, impossible without the machine's involvement. That has imaged a need to model a machine-based classification algorithm. However, the recent surge in machine learning and deep learning has grown more attraction in this research domain. Literature shows a significant number of research work in topic classification that deals with only English language. But, there are very few topic classification research in the Bangla language due to the scarcity of the Bangla topics database and other linguistic constraints. Among those topic classification work in Bangla, there are few works that involve machine learning or deep learning implementation. In this article, we present the Bangla topic classification methodology using a supervised learning model. We have implemented various word embedding algorithms to embed the text of Bangla newspaper datasets and machine learning

Three-dimensional cerebral registration is mostly performed with atlas based methods. However, in case of compact and pre-constructed (segmented) regions of interest (ROIs) involving only blood vessels which are prone to torturous changes; atlas-based approach does not offer the best output. This article suggests a hierarchical (top to bottom) skeleton based registration approach for similar cases. The method has been applied on five sets of cerebral artery locations with aneurysms in order to evaluate their post invasive structural changes. The algorithm works in a semi-automatic manner where the bifurcation zone has been selected as the reference zone. This landmark matching approach works as the basis of the initial stage, coarse affine transformation. The non-rigid intermediate stage is optional and is dependent on the difference of the comparative angular orientation of the models in three dimensional space. Afterward, a third stage of iterative affine transformation is applied for finer adjustments if there is scope for any. Once registered with limiting boundaries, the branch by branch structural comparisons are interpreted quantitatively with box and whisker plots. In order to verify the proposed method, overlapping for one of the fifteen branch sets has also been evaluated with dice similarity indices. The resulting comparison gives a good support in favour of the proposed method. General TermsMedical image registration, 3D volume registration, skeleton based registration.

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