Rajni Jindal

Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph

Recommender systems can provide users with relevant items based on each user’s preferences. However, in the domain of mobile applications (apps), existing recommender systems merely recommend apps that users have experienced (rated, commented, or downloaded) since this type of information indicates each user’s preference for the apps. Unfortunately, this prunes the apps which are releavnt but are not featured in the recommendation lists since users have never experienced them. Motivated by this phenomenon, our work proposes a method for recommending serendipitous apps using graph-based techniques. Our approach can recommend apps even if users do not specify their preferences. In addition, our approach can discover apps that are highly diverse. Experimental results show that our approach can recommend highly novel apps and reduce over-personalization in a recommendation list.

Scalable machine-learning algorithms for big data analytics: a comprehensive review

Big data analytics is one of the emerging technologies as it promises to provide better insights from huge and heterogeneous data. Big data analytics involves selecting the suitable big data storage and computational framework augmented by scalable machine‐learning algorithms. Despite the tremendous buzz around big data analytics and its advantages, an extensive literature survey focused on parallel data‐intensive machine‐learning algorithms for big data has not been conducted so far. The present paper provides a comprehensive overview of various machine‐learning algorithms used in big data analytics. The present work is an attempt to identify the gaps in the work already performed by researchers, thus paving the way for further quality research in parallel scalable algorithms for big data. WIREs Data Mining Knowl Discov 2016, 6:194–214. doi: 10.1002/widm.1194

Software defect prediction using neural networks

Defect severity assessment is the most crucial step in large industries and organizations where the complexity of the software is increasing at an exponential rate. Assigning the correct severity level to the defects encountered in large and complex software, would help the software practitioners to allocate their resources and plan for subsequent defect fixing activities. In order to accomplish this, we have developed a model based on text mining techniques that will be used to assign the severity level to each defect report based on the classification of existing reports done using the machine learning method namely, Radial Basis Function of neural network. The proposed model is validated using an open source NASA dataset available in the PITS database. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the model has performed very well in predicting high severity defects than in predicting the defects of the other severity levels. This observation is irrespective of the number of words taken into consideration as independent variables.

Prediction of defect severity by mining software project reports

With ever increasing demands from the software organizations, the rate of the defects being introduced in the software cannot be ignored. This has now become a serious cause of concern and must be dealt with seriously. Defects which creep into the software come with varying severity levels ranging from mild to catastrophic. The severity associated with each defect is the most critical aspect of the defect. In this paper, we intend to predict the models which will be used to assign an appropriate severity level (high, medium, low and very low) to the defects present in the defect reports. We have considered the defect reports from the public domain PITS dataset (PITS A, PITS C, PITS D and PITS E) which are being popularly used by NASA’s engineers. Extraction of the relevant data from the defect reports is accomplished by using text mining techniques and thereafter model prediction is carried out by using one statistical method i.e. Multi-nominal Multivariate Logistic Regression (MMLR) and two machine learning methods viz. Multi-layer Perceptron (MLP) and Decision Tree (DT). The performance of the models has been evaluated using receiver operating characteristics analysis and it was observed that the performance of DT model is the best as compared to the performance of MMLR and MLP models.

Automated classification of security requirements

Requirement engineers are not able to elicit and analyze the security requirements clearly, that are essential for the development of secure and reliable software. Proper identification of security requirements present in the Software Requirement Specification (SRS) document has been a problem being faced by the developers. As a result, they are not able to deliver the software free from threats and vulnerabilities. Thus, in this paper, we intend to mine the descriptions of security requirements present in the SRS document and thereafter develop the classification models. The security-based descriptions are analyzed using text mining techniques and are then classified into four types of security requirements viz. authentication-authorization, access control, cryptography-encryption and data integrity using J48 decision tree method. Corresponding to each type of security requirement, a prediction model has been developed. The effectiveness of the prediction models is evaluated against requirement specifications collected from 15 projects which have been developed by MS students at DePaul University. The result analysis indicated that all the four models have performed very well in predicting their respective type of security requirements.

Application of knowledge based decision technique to predict student enrollment decision

In this paper we are proposing a new Attribute Selection Measure Function (heuristic) on existing C4.5 algorithm. The advantage of heuristic is that the split information never approaches zero, hence produces stable Rule set and Decision Tree. In ideal situation, in admission process in engineering colleges in India, a student takes admission based on AIEEE rank and family pressure. If the student does not get admission in the desired branch of engineering, then they find it difficult to take decision which will be the suitable branch. The proposed knowledge based decision technique will guide the student for admission in proper branch of engineering. Another approach is also made to analyze the accuracy rate for decision tree algorithm (C5.0) and back propagation algorithm (ANN) to find out which one is more accurate for decision making. In this research work we have used the AIEEE2007 Database.

A novel approach for mining frequent patterns from incremental data

Incremental data can be defined as dynamic data that changes as time advances. Mining frequent patterns from such data is costly as most of the approaches need repetitive scanning and generates a large number of candidate keys. It is important to develop an efficient approach to enhance the performance of mining. This paper proposes a novel tree-based data structure for mining frequent pattern of incremental data called Tree for Incremental Mining of Frequent Pattern (TIMFP) which is compact as well as almost balanced. TIMFP is also suitable for interactive mining (build once and mine many). We have compared TIMFP with canonical-order tree (CanTree), Compressed and Arranged Transaction Sequences (CATS) Tree and Incremental Mining Binary Tree (IMBT). The experimental results show that the proposed work has better performance than other data structures compared in the paper in terms of time required for constructing the tree as well as mining frequent patterns from the tree.

U-STRUCT: A Framework for Conversion of Unstructured Text Documents into Structured Form

The term Text Mining or Text Analytics refers to the process of extracting useful patterns or knowledge from text. The data in textual documents can be of two types, either it can be unstructured or semi-structured. Unstructured data is freely naturally occurring text, whereas web documents data (HTML or XML) is semi structured. Since the natural language text is not organized and does not represent context, it needs to be converted into structured form to perform data analysis and mine useful patterns from it. The field of text mining deals with mining useful patterns or knowledge from unstructured text.

Social Networking based E-Learning System on Clouds

With the recent advancements in the modern Information and Communication Technology (ICT), e-Learning has emerged as a new paradigm for learning in the modern world. There are many dimensions such as pedagogical, technological, ethical etc which are to be satisfied by the e-learning service provider to become a better option in compare to the traditional learning techniques. Among all the dimensions, technological and pedagogical dimension are among the critical dimensions, as they address issues concerning content analysis, audience analysis, goal analysis, performance analysis and infrastructural analysis. This paper proposes an E-learning Social networking site which is maintained by Cloud providers. Blending the two technologies, Social networking and Cloud computing, provides a business model for E-learning where construction of e-learning system is entrusted to cloud computing suppliers and social networking helps to improve the teaching quality and content.

Join Query Processing in MapReduce Environment

MapReduce is a framework for processing large data sets, where straightforward computations are performed by hundreds of machines on large input data. Data could be stored and retrieved using structured queries. Join queries are most frequently used and importatnt. So its crucial to find out efficient join processing techniques. This paper provides overview of join query processing techniques & proposes a strategy to find out best suitable join processing algorithm.