Abstract Comic books are very popular across the world due to the unique experience they provide for all of us in the society without any age limitation. Because of this attraction, which comic books have received, it has proved that comic literature will be able to survive in the twenty first century, even with the existence of multi-dimensional movie theatres as... Comic books are very popular across the world due to the unique experience they provide for all of us in the society without any age limitation. Because of this attraction, which comic books have received, it has proved that comic literature will be able to survive in the twenty first century, even with the existence of multi-dimensional movie theatres as its competitors. While the biggest global filmmakers are busy with making movies from comic books, many researchers have been investigating their time on digitizing the comic stories as it is, expecting to create a new era in the comic world. But most of them have focused only on one or few components of the story. This paper is based on a research which aims to give the full experience of enjoying the comic books for everyone in the world despite of visual and literacy barriers people are having. Proposed solution comes as a web application that translates input image of a comic story into a text format and delivers it as an audio story to the user. The story will be created using extracted components such as characters, objects, speech text and balloons and considering the association among them with the use of image processing and deep learning technologies. Read More Read Less

Abstract Medical image segmentation is a knotty and challenging task. Predominantly, the brain has a complicated structure and its exact segmentation is very essential for identifying the tumors, edema, and necrotic tissues in order to provide proper treatment. In this paper, we have proposed a novel brain tumor classification of MR images using texture features and hybrid kernel based SVM. Our proposed... Medical image segmentation is a knotty and challenging task. Predominantly, the brain has a complicated structure and its exact segmentation is very essential for identifying the tumors, edema, and necrotic tissues in order to provide proper treatment. In this paper, we have proposed a novel brain tumor classification of MR images using texture features and hybrid kernel based SVM. Our proposed approach comprises the following major steps: i) preprocessing ii) Tumor Region Location iii) Feature Extraction and iv) Final Classification. In preprocessing steps, Anisotropic filtering will be applied to diminish the noise and improved quality of the image for further processing. In the next steps to perform the skull stripping and tumor regions are identified using regionprops algorithm. In feature extraction some specific feature will be extracted using texture using GLCM (Gray Level Co-occurrence Matrix). In the classification stage, the hybrid kernel will be designed and apply to training of support vector machine (SVM) to perform automatic detection of tumor in MRI images. For comparative analysis, our proposed approach is compared with the existing works. The accuracy level (93%) for our proposed approach is proved is good at detecting the tumors in the brain MRI images. Read More Read Less

Abstract Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today’s search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as “what food is good for cold weather”, because their models... Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today’s search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as “what food is good for cold weather”, because their models could not infer the underlying meaning of the input text. In this work, we believe that contexts convey valuable evidence that could facilitate the semantic modeling of queries, and take them into consideration for entity recommendation. In order to better model the semantics of queries and entities, we learn the representation of queries and entities jointly with attentive deep neural networks. We evaluate our approach using largescale, realworld search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine 1 and you can fetch it in UC Browser of Alibaba. Results from online A/B test suggest that the impression efficiency of click-through rate increased by 5.1% and page view increased by 5.5%. Read More Read Less