Deep learning approach has been used extensively in image analysis tasks. However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. In this work, the performance of deep learning methods on different 3D data representations has been reviewed. Based on the categorization of the different 3D data representations proposed in this paper, the importance of choosing a suitable 3D data representation which depends on simplicity, usability, and efficiency has been highlighted. Furthermore, the origin and contents of the major 3D datasets were discussed in detail. Due to growing interest in 3D object retrieval and classification tasks, the performance of different 3D object retrieval and classification on ModelNet40 dataset were compared. According to the findings in this work, multi views methods surpass voxel-based methods and with increased layers and enough data augmentation the performance can still be increased. Therefore, it can be concluded that deep learning together with a suitable 3D data representation gives an effective approach for improving the performance of 3D shape analysis. Finally, some possible directions for future researches were suggested.

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data warehousing did not find its way easily and readily into healthcare and medicine, not like others financial institutions, Healthcare presents unique challenges for the architect of a data warehouse and the information needs for Health data warehouse were fundamentally different and complicated. When it come to health care, data warehousing is completely stagnant and still there is a need for data warehouse in healthcare. This paper presents a proposed architecture for Health care data warehouse for Diabetes diseases which could be use to monitor Diabetes disease, measure cost of infections and to detect prescription errors in addition it can also be used by healthcare executive managers, doctors, physicians and other health professionals to support the capture, healthcare process and analysis of data, and offer the potential of radically altering the practice and delivery of healthcare and medical research.

An online integrated information system for Bayero University Kano, is a web-based application that provides inputs and outputs information support to admin/users in order to access and update university student record. Bayero University Kano is one of the most recognized conventional universities in Nigeria with a large number of students both undergraduate and post graduate. Papers and pens is the usual method use in student registration and record which is time consuming and waste of resources. In view of the availability of new technologies, this paper mainly concentrates on improving the manual methods by adopting a Browser Server structure which was used to design an online integrated information system for Bayero University Kano. The entire application was developed in java myeclipse environment using servlets and java server pages (JSP) technologies and SQL 2005 serve as the database backend. The system designed was generally accepted and it has proven it is importance in academic usage.

In the shape analysis community, decomposing a 3D shape into meaningful parts has become a topic of interest. 3D model segmentation is largely used in tasks such as shape deformation, shape partial matching, skeleton extraction, shape correspondence, shape annotation and texture mapping. Numerous approaches have attempted to provide better segmentation solutions; however, the majority of the previous techniques used handcrafted features, which are usually focused on a particular attribute of 3D objects and so are difficult to generalize. In this paper, we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful sub-meshes. The first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different views. Contrasting viewpoints, on the other hand, might not have been associated, and a 3D region could correlate into totally distinct outcomes depending on the viewpoint. To address this, we ran each view through (shared weights) CNN and Bolster block in order to create a probability boundary map. The Bolster block simulates the area relationships between different views, which helps to improve and refine the data. In stage two, the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each view. Finally, a layer that is fully connected is used to return coherent edges, which are then back project to 3D objects to produce the final segmentation. Experiments on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.