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Author

Aggarwal

Bio: Aggarwal is an academic researcher. The author has contributed to research in topics: Semantic analytics & Software analytics. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

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Proceedings ArticleDOI
01 Nov 2017
TL;DR: A semi-supervised approach is proposed that uses the concept of random-walk for prediction of binary labels for edges in undirected and unweighted networks.
Abstract: Mining of signed networks where the links/edges between nodes have a positive or negative sign/label, is getting the attention of researchers and practitioners due to its wide realworld applicability in various domains. Label prediction for nodes in a network is a well-known and explored problem. However, the prediction of labels for edges in a network is relatively less explored, and very challenging and interesting problem. In this paper, we consider the problem of binary label prediction for edges in undirected and unweighted networks. The prediction of binary labels has a number of applications in realworld like friend/foe prediction, recommendation, trust/distrust prediction in social networks, and categorization. In this work, a semi-supervised approach is proposed that uses the concept of random-walk for prediction of binary labels for edges. In this paper, we demonstrate the viability and the effectiveness of the proposed approach using a real-world network.

3 citations

15 Dec 2017
TL;DR: This thesis presents an automation approach using machine learning in which it is shown how to improve text classification performance, and how this approach can reach practically acceptable performance levels even in certain abstract classification problems.
Abstract: Automating repetitive processes and replacing manual tasks with automated systems is an area of research that will greatly impact and transform our lives during the 21st century. Automation comes in many forms and we are now at the start of an era, after which repetitive non-creative tasks will be handled mainly by machines. In this thesis, two analytics approaches are presented that can be used to automate text processing tasks. The first is an automation approach using machine learning in which we show how we can improve text classification performance, and how we, through these improvements, can reach practically acceptable performance levels even in certain abstract classification problems. We test the developed methods on problematic web content categories, such as violence, racism, and hate. The second is an automation approach that uses network analytics to automatically process texts. We use this approach to automate processing of financial news and to automatically extract new information. We show that through automating the process, we can extract company specific sentimentrisks that a person would not identify simply by reading the news articles. Lastly, we show that the risks we have extracted can be used to identify companies that are at higher risk of stock price decrease.

2 citations

Journal Article
TL;DR: The main results on social influence diffusion research from the field of computer science in the last decade, which covers the three main areas -influence diffusion modeling, influence diffusion learning, and influence diffusion optimization, were summarized.

2 citations

Proceedings ArticleDOI
09 Jun 2017-Rice
TL;DR: The clustering approach to classify the random walk trajectories from the synthetic bus station video by using the machine learning approach and the agglomerative clustering algorithm which is used to group the abnormal trajectories with the similar spatial patterns and normal trajectory with similar spatial pattern.
Abstract: In this paper we have proposed the clustering approach to classify the random walk trajectories from the synthetic bus station video. Bus station one of the most crowded locations that consist of more than thousands of passengers or travelers waiting for the buses to travel to the destination point. These crowded locations can be highly prone to accidents or terrorist activities. Work is classified into two steps i.e Firstly we find out the trajectories from the image by using the machine learning approach after that we apply the agglomerative clustering algorithm which is used to group the abnormal trajectories with the similar spatial patterns and normal trajectories with similar spatial patterns. Keywords—Path detection, Anomaly Detection, Trajectories,

1 citations