The convolutional neural network (CNN) is the type of deep neural networks which has been widely used in visual recognition. Over the years, CNN has gained lots of attention due to its high capability to appropriately classifying the images and feature learning. However, there are many factors such as the number of layers and their depth, number of features map, kernel size, batch size, etc. They must be analyzed to determine how they influence the performance of network. In this paper, the performance evaluation of CNN is conducted by designing a simple architecture for image classification. We evaluated the performance of our proposed network on the most famous image repository name CIFAR-10 used for the detection and classification task. The experiment results show that the proposed network yields the best classification accuracy as compared to existing techniques. Besides, this paper will help the researchers to better understand the CNN models for a variety of image classification task. Moreover, this paper provides a brief introduction to CNN, their applications in image processing, and discuss recent advances in region-based CNN for the past few years.

Product maintenance techniques have significant importance because they are much cost effective and less time consuming to maintain a product or software rather to change it. There are different product maintenance and support techniques. These previous techniques do not solve user/clients bugs, issues and enhancements effectively and efficiently. Scrum is being used now a day as a quick, flexible and holistic methodology to develop software. In Scrum projects there is the much customer involvement is included which help to develop a user oriented product. Users can change their requirements in Scrum. Many techniques have been proposed for product maintenance and support. However, in this paper, there have been a detailed literature review of existing product maintenance techniques and also presented a new proposed model and technique for the product maintenance by using Scrum methodology. This Scrum based model for maintenance is designed and based on the analysis of client request types and severity (priority). In our approach, The Session attendees (Scrum Master, Product Owner and Team) choose that bug, issue or enhancement first which has an urgent type or higher priority request and resolves it then select low priority request or non urgent requests and facilitates the clients in timely manner. In this way this proposed model works effectively and defiantly to meet the customer's demand. A comprehensive study on product maintenance and support has been carried out which adds to the current practices in the scrum. We found that maintenance phase of the scrum has been given less attention in the existing literature. In view of this, we have made an attempt to propose a novel model that focuses on the maintenance phase of scrum.