To check out the health of the patient, digital images are generated every single day and are used by the radiologist for extracting out the details and anomalies. The complicated part is to figure out the disease in those images. By the manual diagnosis of the images through the radiologists, the doctors can get to know exact scenario of the abnormalities in images, but is considerably more difficult with Content Based Image Retrieval (CBIR) to get those finer details from MR images. These CBIR approaches are now frequently employed in the automatic diagnosis of disease from MR images, mammograms, and other sources. Bridging this gap can be done with deep learning feature extraction algorithm and the canny edge detection technique we propose, and accuracy closer to the manual results of a human evaluator can be achieved to a significant extent as part of the goal of sustainable development through innovation.

In today's world Internet is the higher source of all kind of information's. Modern high-traffic websites must serve hundreds of thousands request from user to clients and vice versa. These services return the required information in form of text, images, video etc. In Cloud computing, Load Balancing is required in such situations to avoid overload. A load balancer technique mediates client access requests to servers and intelligently decides which server is best placed to fulfil each request. Restful interfaces are mainly used for implementation of web services and are based on the resource-oriented approach. This paper discusses the some existing load balancing algorithms in cloud computing. In this research paper, Restful services are used for data storage and retrieval from Cloud system. Cloud is a storage mechanism in which one can store, process data on demand. Cloud based on service oriented architecture is known as service oriented cloud computing architecture. This approach has reduced the amount of data used for recovery to almost half and also maintains a secure access control mechanism for authenticated user.

In 2009, National Skill Development (NSD) Policy was reformed to modify the vocational education system in India. The skills development and entrepreneurship policy developed in 2015 tries to solve the challenges of skill development by inspiring early signs, development, and variations. The present study is based on a primary survey conducted in four districts of Sikkim involving 600 respondents from amongst the various stakeholders and examines whether there is any significant difference in the expressed belief held by stake-holders vis-à-vis the challenges identified in the skill eco space in Sikkim. These challenges are namely resistance to relocation by Sikkim's youth for employment, stigma against labor-oriented jobs, preference for government jobs, belief that skilling is for low academic achievers, and lack of industrial opportunities and development in the State of Sikkim. The findings can be utilized for suggesting recommendations and way forward to remove these barriers for better outreach and effective implementation of various schemes by adopting suitable practices.

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