YMCA University of Science and Technology

About: YMCA University of Science and Technology is a education organization based out in Faridabad, India. It is known for research contribution in the topics: Web crawler & Web page. The organization has 299 authors who have published 568 publications receiving 4547 citations.

A Multi-Objective Approach for Test Suite Reduction During Testing of Web Applications: A Search-Based Approach

Abstract: During the development and maintenance phases of evolving software, new test cases would be needed for the verification of the accuracy of the modifications as well as for new functionalities leading to an increase in the size of the test suite. Various related objectives are to be kept in mind while reducing the original test suite by removing redundancy and generating a practical representative set of the unique test cases, some of which may need to be maximized and the remaining ones minimized. This paper presents a multi-objective approach for the test suite reduction problem in which one objective is to be minimized and the remaining two maximized. In this study, experiments were performed on diverse versions of four web applications. Various state-of-the-art algorithms and their updated versions were compared with non-dominated sorting genetic algorithm-II (NSGA-II) for performance evaluation. Based on experimental findings, it was concluded that NSGA-II outperforms all other algorithms; moreover, the algorithm attempts to satisfy all the objectives without compromising coverage.

A Noval Approach to Measure the Semantic Similarity for Information Retrieval

Abstract: In this fast paced multitasking world, internet users are increasing day by day so is our database is increasing and manually maintaining similarity between words of database is a troublesome task. Maintaining semantic similitude between words is substantial chore in chromatic areas such as Natural Language processing tasks like Word sense disambiguation, query expansion as well as web chore such as document bunching, community excavating and automatic metadata breeding. With its wide area applications and usage, in a document still it is very tough to calculate the measure for any two words or entities. We propound a formula using Google, computing semantic similarity employing page count (retrieved by Google only) as a metric. The bounced method outperforms or contribute almost same results to chromatic base lines and compared with the previously proposed web-based semantic similarity methods. The results obtained compared with various online tools like UMBC, SEMILAR etc. Moreover, proposed method has less computation complexity as well as significantly improves the exactitude and efficiency of calculating semantic similarity between two words.

A Comparative Study of Clone Detection Approaches

Abstract: Code resemblance or taking a fragment of code from any place and then regenerate it by writing the same either without or with any alteration is termed as code cloning. It is also called as Duplicate code. Most of the studies show that about 5% to 20% of a software system contains cloned code, this is just the outcome of mimeographing the existing fragments of code. One of the most impediment problem of such copied fragments of code is that all the code chunks need to be checked for the same bug reported in the original chunk. In software maintainance, the other prime concern is code refractoring. The state of the art is explored in clone detection. Firstly, we define the terms used in respect to clone detection and its literature and also specify there correlative mappings for the oftenly used type of clone. Secondly, the review of existing detection approaches, clone taxanomies and experimental evaluations of clone detection tools is provided. How clone detection applications when explored, could be beneficial to other areas of software engineering is also presented here in this paper.

Thyroid Carcinoma Prediction Using ACO and Machine Learning Techniques

Abstract: The thyroid gland, a butterfly-shaped nodule in the front of the neck, can develop thyroid carcinoma. Compared to other carcinomas, thyroid carcinoma is one of the most common endocrine carcinomas. Thyroid carcinoma may not create symptoms at first, but it can cause soreness and enlargement in the forward facing of the neck as it spreads. In this study, first thyroid disease is identified and later-on on the basis of symptoms such as Neck Swelling, TSH, Tumor, Neck Pain, Anxiety Thyroid Carcinoma is identified. Over the past few decades, patients with thyroid disease and carcinoma have been on the rise. Thyroid disease is the second most prevalent malignancy; the majority of instances in the general population is benign and malignant in thyroid cancer. As clinical detection of thyroid cells necessitates the utilization of various features in a variety of scales, a traditional method of extraction of features may not provide adequate results. The goal of this paper is to use ant colony optimization algorithm to reduce the number of features scores and to predict accuracy on thyroid carcinoma from the symptoms of thyroid diseases.

Lung Cancer Diagnosis Using X-Ray and CT Scan Images Based on Machine Learning Approaches

Abstract: Lung cancer is one of the biggest threats to mankind. The number of patients who died from lung cancer is too large compared to the total number of cancer diagnoses. Lung cancer is uncontrollable cell proliferation in the lungs and can be recognized as a nodule, which might be benign or malignant. A nodule is a white-colored area on the lungs that can be seen on an X-ray or CT scan image. With the advancement of technology, many interdisciplinary domains are working together. Technologies such as machine learning (ML) have greatly assisted in lung cancer diagnosis using an imaging modality. An X-ray and/or CT scan image is used as the input for ML techniques. It is processed using image processing techniques, and the findings are then classified using ML algorithms that are presented in this study through a pipeline of ML. According to the specifications of the ML pipeline, several intermediate processes such as image preprocessing, lung segmentation and enhancement, nodule detection, feature extraction, and classification were also briefly described with their techniques. The entire study investigates various ML approaches used in lung cancer diagnosis using X-ray and CT scan images.