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Naeem Seliya
Researcher at Ohio Northern University
Publications - 83
Citations - 5542
Naeem Seliya is an academic researcher from Ohio Northern University. The author has contributed to research in topics: Software quality & Software metric. The author has an hindex of 31, co-authored 73 publications receiving 4389 citations. Previous affiliations of Naeem Seliya include University of Michigan & Florida Atlantic University.
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Deep learning applications and challenges in big data analytics
Maryam M. Najafabadi,Flavio Villanustre,Taghi M. Khoshgoftaar,Naeem Seliya,Randall Wald,Edin Muharemagic +5 more
TL;DR: This study explores how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.
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A survey on addressing high-class imbalance in big data
TL;DR: This paper provides a large survey of published studies within the last 8 years, focusing on high-class imbalance in big data in order to assess the state-of-the-art in addressing adverse effects due to class imbalance.
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Choosing software metrics for defect prediction: an investigation on feature selection techniques
TL;DR: While some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods, and performances of the defect prediction models either improved or remained unchanged when over 85 metrics were eliminated.
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Analyzing software measurement data with clustering techniques
TL;DR: Cluster analysis with expert input is a viable unsupervised-learning solution for predicting software modules' fault proneness and potential noisy modules.
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Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
TL;DR: It is observed that predictive performances of the models is significantly different across the system releases, implying that in the software engineering domain prediction models are influenced by the characteristics of the data and the system being modeled.