Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- AUC (1)
- Accuracy (1)
- Application store (1)
- Classification (1)
- Computational photography (1)
-
- Correlation detection (1)
- Cost Ratio (1)
- Cost Sensitive Classification (1)
- Curve matching (1)
- Digital micro-mirror device (1)
- Distributed computing (1)
- Fragmented image reassembly (1)
- Frequency division multiplexing (1)
- Functional Requirements (1)
- Graph data processing (1)
- Longest common sub-sequence (1)
- Non-functional Requirements (1)
- ROC (1)
- Requirements elicitation (1)
- Smoothing (1)
- Summarization (1)
- Total Misclassification Cost (1)
Articles 1 - 4 of 4
Full-Text Articles in Engineering
Effective Methods And Tools For Mining App Store Reviews, Nishant Jha
Effective Methods And Tools For Mining App Store Reviews, Nishant Jha
LSU Doctoral Dissertations
Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major …
Distributed Iterative Graph Processing Using Nosql With Data Locality, Ayam Pokhrel
Distributed Iterative Graph Processing Using Nosql With Data Locality, Ayam Pokhrel
LSU Master's Theses
A tremendous amount of data is generated every day from a wide range of sources such as social networks, sensors, and application logs. Among them, graph data is one type that represents valuable relationships between various entities. Analytics of large graphs has become an essential part of business processes and scientific studies because it leads to deep and meaningful insights into the related domain based on the connections between various entities. However, the optimal processing of large-scale iterative graph computations is very challenging due to the issues like fault tolerance, high memory requirement, parallelization, and scalability. Most of the contemporary …
Image Processing Applications In Real Life: 2d Fragmented Image And Document Reassembly And Frequency Division Multiplexed Imaging, Houman Kamran Habibkhani
Image Processing Applications In Real Life: 2d Fragmented Image And Document Reassembly And Frequency Division Multiplexed Imaging, Houman Kamran Habibkhani
LSU Doctoral Dissertations
In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied.
In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the …
Evaluating Classifiers' Optimal Performances Over A Range Of Misclassification Costs By Using Cost-Sensitive Classification, Ramy Al-Saffar
Evaluating Classifiers' Optimal Performances Over A Range Of Misclassification Costs By Using Cost-Sensitive Classification, Ramy Al-Saffar
LSU Master's Theses
We believe that using the classification accuracy is not enough to evaluate the performances of classification algorithms. It can be misleading due to overlooking an important element which is the cost if classification is inaccurate. Furthermore, the Receiver Operational Characteristic (ROC) is one of the most popular graphs used to evaluate classifiers performances. However, one of the biggest ROC’s shortcomings is the assumption of equal costs for all misclassified data. Therefore, our goal is to reduce the total cost of decision making by selecting the classifier that has the least total misclassification cost. Nevertheless, the exact misclassification cost is usually …