According to the World Health Organization (WHO) breast cancer is the major reason of death among women and its impact on women is 2.1 million per year. Only in 2018 approximately 15% (62700) of women are died due to breast cancer. To detect this breast cancer oncologist rely on two methods i.e. early diagnosis and screening. To identify cancers before any symptoms appear screening plays an important role and in screening Mammography is heart of breast cancer detection. Apart from this Clinical Breast Exams, Breast Self-Exam and many other methodologies are used. Screening for breast cancer is too long and time consuming process if approach is manual analysis and it's performed on medical images. It's also unfeasible for huge data sets. That's the reason we required self-automated, efficient and more accurate machine to identify or capture the breast cancer as minimum as possible amount of time. We found the solution of this problem is Deep Learning Method. It provides the results in short period of time as compare to other techniques and giving the better accuracy for detection of Breast cancer. In this paper we focuses on, by using which methodology we got the more accurate results and how much amount of time is required to do this process. In this project we are going to deal with different classifiers like CNN, KNN, Inception V3, SVM and ANN. By using ANN we are going to detect the Breast Cancer. We are also going to compare the results of SVM with ANN Technique.

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In today's era educational organization strongly needs devices which are ready to access and use and also various operating system platforms are required for different learning courses. To achieve this type of environment hardware availability come in front as an important issue along with lots of money required to purchase them. Many educational courses required to run particular software or application on particular operating system platform along with specific hardware configuration. The maintenance of this different versions of operating systems and their installation stuff is hectic process and also required man power. To overcome all this problem there is solution called Cloud OpenStack. Cloud OpenStack allows us to develop an environment on commodity hardware or on existing system present in the educational organization. It's easily handle different version of OS platforms and also monitor and maintain them. Another important thing is many educational organizations still used Virtual machine method to used different OS platforms for various learning courses. This paper come up with implementation of Cloud OpenStack in Educational Organization and analyzing the results. The outcomes of Cloud OpenStack compare with the Virtual Machines method and find out which technique is better and more suitable for academics.

Human race is blessed with the five basic senses such as touch, taste, smell, hearing and the most important of them all 'vision or eyesight'. It is very difficult to survive without any one of them. Unfortunately a mass population across the globe suffers from the ill effects of vision, hampering their daily life. Detecting objects and providing navigational instructions in an indoor environment can considerably improve the day-to-day quality of life of visually impaired people. The motive of this research work is to propose a solution approach for assisting visually impaired population by identifying obstacles in front of them considering indoor environment. This approach focuses on feature extraction and object detection using Convolutional Neural Network (CNN) from a real time video. For this a head mounted image acquisition device may be used to detect the objects from the scene ahead and information of the detected objects is provided to the visually impaired (VI) person through the audio modality. As a first step towards the overall conceptual process, an object detection system is presented in this article, which processes the live video stream captured through the acquisition device. The video is processed frame-by-frame, treating each frame as a separate image and then using the proposed feature extraction and object detection algorithm to identify the objects.

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