Performance Evaluation of Imputation Methods For Incomplete Datasets

In this study, we compare the performance of four different imputation strategies ranging from the commonly used Listwise Deletion to model based approaches such as the Maximum Likehood on enhancing completeness in incomplete software project data sets. We evaluate the impact of each of these methods by implementing them on six different real-time software project data sets which are classified into different categories based on their inherent properties. The reliability of the constructed data sets using these techniques are further tested by building prediction models using stepwise regression. The experiment results are noted and the findings are finally discussed

Implications of Integrating Test-Driven Development into CS1/CS2 Curricula

Many academic and industry professionals have called for more testing in computer science curricula. Test-driven development (TDD) has been proposed as a solution to improve testing in academia. This paper demonstrates how TDD can be integrated into existing course materials without reducing topic coverage. Two controlled experiments were conducted in a CS1/CS2 course in Winter 2008. Following a test-driven learning approach, unit testing was introduced at the beginning of the course and reinforced through example. Results indicate that while student work loads may increase with the incorporation of TDD, students are able to successfully develop unit tests while learning to program

Application of Deep Learning to Sentiment Analysis for recommender system on cloud

© 2017 IEEE. Sentiment analysis of short texts like single sentences and reviews available on different social networking sites is challenging because of the limited contextual information. Based on the sentiments and opinions available, developing a recommendation system is an interesting concept, which includes strategies that combine the small text content with prior knowledge. In this paper, we explore a new application of Recursive Neural Networks (RNN) with deep learning system for sentiment analysis of reviews. The proposed RNN-based Deep-learning Sentiment Analysis (RDSA) recommends the places that are near to the user\u27s current location by analyzing the different reviews and consequently computing the score grounded on it. Deep Learning is used to optimize the recommendations depending on the sentiment analysis performed on the different reviews, which are taken from different social networking sites. The Experiments performed indicate that the RNN based Deep-learning Sentiment Analysis (RDSA) improvises the behavior by increasing the accuracy of the sentiment analysis, which in turn yields better recommendations to the user and thus helps to identify a particular position as per the requirement of the user need

A survey on Zero touch network and Service Management (ZSM) for 5G and beyond networks

Abstract Faced with the rapid increase in smart Internet-of-Things (IoT) devices and the high demand for new business-oriented services in the fifth-generation (5G) and beyond network, the management of mobile networks is getting complex. Thus, traditional Network Management and Orchestration (MANO) approaches cannot keep up with rapidly evolving application requirements. This challenge has motivated the adoption of the Zero-touch network and Service Management (ZSM) concept to adapt the automation into network services management. By automating network and service management, ZSM offers efficiency to control network resources and enhance network performance visibility. The ultimate target of the ZSM concept is to enable an autonomous network system capable of self-configuration, self-monitoring, self-healing, and self-optimization based on service-level policies and rules without human intervention. Thus, the paper focuses on conducting a comprehensive survey of E2E ZSM architecture and solutions for 5G and beyond networks. The article begins by presenting the fundamental ZSM architecture and its essential components and interfaces. Then, a comprehensive review of the state-of-the-art for key technical areas, i.e., ZSM automation, cross-domain E2E service lifecycle management, and security aspects, are presented. Furthermore, the paper contains a summary of recent standardization efforts and research projects towards the ZSM realization in 5G and beyond networks. Finally, several lessons learned from the literature and open research problems related to ZSM realization are also discussed in this paper