Multi-objective regression test suite optimization using three variants of adaptive neuro fuzzy inference system.

In the process of software development, regression testing is one of the major activities that is done after making modifications in the current system or whenever a software system evolves. But, the test suite size increases with the addition of new test cases and it becomes in-efficient because of the occurrence of redundant, broken, and obsolete test cases. For that reason, it results in additional time and budget to run all these test cases. Many researchers have proposed computational intelligence and conventional approaches for dealing with this problem and they have achieved an optimized test suite by selecting, minimizing or reducing, and prioritizing test cases. Currently, most of these optimization approaches are single objective and static in nature. But, it is mandatory to use multi-objective dynamic approaches for optimization due to the advancements in information technology and associated market challenges. Therefore, we have proposed three variants of self-tunable Adaptive Neuro-fuzzy Inference System i.e. TLBO-ANFIS, FA-ANFIS, and HS-ANFIS, for multi-objective regression test suites optimization. Two benchmark test suites are used for evaluating the proposed ANFIS variants. The performance of proposed ANFIS variants is measured using Standard Deviation and Root Mean Square Error. A comparison of experimental results is also done with six existing methods i.e. GA-ANFIS, PSO-ANFIS, MOGA, NSGA-II, MOPSO, and TOPSIS and it is concluded that the proposed method effectively reduces the size of regression test suite without a reduction in the fault detection rate.

A model-driven framework for data-driven applications in serverless cloud computing.

In a serverless cloud computing environment, the cloud provider dynamically manages the allocation of resources whereas the developers purely focus on their applications. The data-driven applications in serverless cloud computing mainly address the web as well as other distributed scenarios, and therefore, it is essential to offer a consistent user experience across different connection types. In order to address the issues of data-driven application in a real-time distributed environment, the use of GraphQL (Graph Query Language) is getting more and more popularity in state-of-the-art cloud computing approaches. However, the existing solutions target the low level implementation of GraphQL, for the development of a complex data-driven application, which may lead to several errors and involve a significant amount of development efforts due to various users' requirements in real-time. Therefore, it is critical to simplify the development process of data-driven applications in a serverless cloud computing environment. Consequently, this research introduces UMLPDA (Unified Modeling Language Profile for Data-driven Applications), which adopts the concepts of UML-based Model-driven Architectures to model the frontend as well as the backend requirements for data-driven applications developed at a higher abstraction level. Particularly, a modeling approach is proposed to resolve the development complexities such as data communication and synchronization. Subsequently, a complete open source transformation engine is developed using a Model-to-Text approach to automatically generate the frontend as well as backend low level implementations of Angular2 and GraphQL respectively. The validation of proposed work is performed with three different case studies, deployed on Amazon Web Services platform. The results show that the proposed framework enables to develop the data-driven applications with simplicity.

A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set.

The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS) and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.

Detection and classification of retinal lesions for grading of diabetic retinopathy.

Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.