In this dissertation we investigate the applicability of different adaptive techniques to improve the effectiveness and efficiency of the regression testing. Initially, we introduce the concept of regression testing. We then perform a literature review of current practices and state-of-the-art regression testing techniques. Finally, we advance the regression testing techniques by performing four empirical studies in which we use different types of information (e.g. user session, source code, code commit, etc.) to investigate the effectiveness of each software metric on fault detection capability for different software environments. In our first empirical study, we show the effectiveness of applying user session information for test case prioritization. In our next study, we apply learning from the previous study, and implement a collaborative filtering recommender system for test case prioritization, which uses user sessions and change history information as input parameter, and return the risk score associated with each component. Results of this study show that our recommender system improves the effectiveness of test prioritization; the performance of our approach was particularly noteworthy when we were under time constraints. We then investigate the merits of multi-objective testing over single objective techniques with a graph-based testing framework. Results of this study indicate that the …

Vehicular ad-hoc networks (VANETs) are used for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The IEEE 802.11p/WAVE (Wireless Access in Vehicular Environment) and with WAVE Short Messaging Protocol (WSMP) has been proposed as the standard protocol for designing applications for VANETs. This communication protocol must be thoroughly tested before reliable and efficient applications can be built using its protocols. In this paper, we perform on-road experiments in a variety of scenarios to evaluate the performance of the standard. We use commercial VANET devices with 802.11p/WAVE compliant chipsets for both BSM (basic safety messages) as well as video streaming applications using WSMP as a communication protocol. We show that while the standard performs well for BSM application in lightly loaded conditions, the performance becomes inferior when traffic and other performance metric increases. Furthermore, we also show that the standard is not suitable for video streaming due to the bursty nature of traffic and the bandwidth throttling, which is a major shortcoming for V2X applications.

Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. To achieve the therapeutic goals of UC, which are to first induce and then maintain disease remission, doctors need to evaluate the severity of UC of a patient. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, in our previous works, we developed two different approaches in which one is using the image textures, and the other is using CNN (convolutional neural network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. But, we found that the image texture based approach could not handle larger number of variations in their patterns, and the CNN based approach could not achieve very high accuracy. In this paper, we improve our CNN based approach in two ways to provide better accuracy for the classification. We add more thorough and essential preprocessing, and generate more classes to accommodate large variations in their patterns. The experimental results show that the proposed preprocessing can improve the overall accuracy …

With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked. Literature based discovery (LBD) is in practice for many years to identify plausible associations between previously unrelated concepts. In this paper, we present a new, completely automatic and interactive system that creates a graph-based knowledge base to capture multifaceted complex associations among biomedical concepts. For a given pair of input concepts, our system auto-generates a list of ranked subgraphs uncovering possible previously unnoticed associations based on context information. To rank these subgraphs, we implement a novel ranking method using the context information obtained by performing random walks on the graph. In addition, we enhance the system by training a Neural Network Classifier to output the likelihood of the two concepts being likely related, which provides better insights to the end user.

Researchers have been involved for decades in search of an efficient skin detection method. Yet current methods have not overcome the major limitations. To overcome these limitations, in this dissertation, a clustering and region growing based skin detection method is proposed. These methods together with a significant insight result in a more effective algorithm. The insight concerns a capability to define dynamically the number of clusters in a collection of pixels organized as an image. In clustering for most problem domains, the number of clusters is fixed a priori and does not perform effectively over a wide variety of data contents. Therefore, in this dissertation, a skin detection method has been proposed using the above findings and validated. This method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholding or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.

Current unsupervised approaches for keyphrase extraction compute a single importance score for each candidate word by considering the number and quality of its associated words in the graph and they are not flexible enough to incorporate multiple types of information. For instance, nodes in a network may exhibit diverse connectivity patterns which are not captured by the graph-based ranking methods. To address this, we present a new approach to keyphrase extraction that represents the document as a word graph and exploits its structure in order to reveal underlying explanatory factors hidden in the data that may distinguish keyphrases from non-keyphrases. Experimental results show that our model, which uses phrase graph representations in a supervised probabilistic framework, obtains remarkable improvements in performance over previous supervised and unsupervised keyphrase extraction systems.