1,721,068 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Student Performance Measure by using Different Classification Methods of Data Mining
No full textMaster of Engineering-Software EngineeringThe assessment in outcome based learning is very vital and significant approach toward measuring the student’s performance. There are many traditional methods existing in this context. The data mining is one of the intelligent computing methods which are having widely accepted features that enable the idea of its usage in assessment. Much work has been done to measure the student performance by using different methodologies and modern technologies. In this work, we have gone through the current data sets of students of the university and different classification methods of data mining are used to measure the accuracy of student performance. Based on the analysis of the result, it has been concluded that accuracy and the other measures of SVM is more than the other classification methods
Age-Group prediction of facial images using different classifiers
No full textME-Software Engineering-ThesisA human face provides a lot of information which allows another person to identify their characteristics such as age, gender, etc. So the challenge is to develop an age-group prediction system by using the machine learning method. The task of estimating the human‟s age-group from their frontal facial images is very captivating, but also the challenging one due to the personalized and non-linear pattern of ageing which differs from one person to another. This work examines the problem of predicting the age-group of human on the basis of presenting a facial image with the improved accuracy of estimation. The aim of this study is to build up a framework and subsequently an algorithm that helps in estimating the age-group with the reasonable accuracy of the facial images.
In this work, a method is presented for the age-group prediction in which age-group is predicted by detecting the face or face landmarks using the Viola-Jones algorithm. After detecting the face, features including geometric features, wrinkles features are extracted and then these extracted features are used to train a classifier using Support Vector Machine (SVM) or K-Nearest Neighbors (K-NN). Finally, SVM or K-NN is used to categorize the age into one of the three different groups such as child, adult and old for the test data. The system used self-build database for the age-group classification. Finally, identification rate achieved using k-NN model produces better results than using SVM model as specified in experimental results.Computer Science and Engineering, Thapar University, Patial
Performance Evaluation of Secure OLSR under Link Spoofing Attack
No full textMobile ad hoc network (MANET) is a dynamic in nature which is constantly prone to
many severe attacks. It has a many unique features unlike conventional network. It is
a node resource constraint and self-constructed network. In this thesis, we
summarized basically security vulnerabilities in the optimized link state routing
protocol (OLSR). OLSR is mostly primarily for dense networks. The paramount
feature of OLSR is multipoint relay nodes. In our work, we suggested a prototype to
defend against link spoofing attack. Our main technique is based on interchanging
control packets by 2-hop neighbors. The major ease of our approach is that it offers
protection against link spoofing attack without having knowledge of complete
scenario. In our scenario we do not need particular hardware like Global Positioning
System (GPS). Secure-OLSR is implemented on network simulator. Our simulation
analysis indicates that proposed algorithm is good enough to detect presence of
malicious node under link spoofing attack as compared to current version of OLSR.
Our approach have higher packet delivery ratio, and throughput
Prediction of Parkinson Disorder Based on Kinect Arm Movement by Using Machine Learning Techniques
No full textMaster of Engineering -CSEParkinson Disorder is a gradually increasing neurodegenerative disease. In this, motor and non-motor symptoms can impact on human basic functions to a wide variety of range. This article describes the required features and predicts the Parkinson Disorder in a patient with higher efficacy. In this work, mean vector velocity (MVV) was performed to analyze the essential features and crossed verified it by the Rank of Correlation. Previously many scientists analyzed Parkinson disorder using the fundamental classification models and also the different techniques to identify the postural tremor in the arm. It was found that they concentrated on the slow movement of the patients. But it has been proven that some abnormalities are also present in the medium movement. In this work the main tasks involve are (I) detecting four arm-features (II) calculating the mean instantaneous velocity of that four features and (III) performance evaluation of the four classification models under Artificial Neural Network. Finally we can able to predict the Parkinson disorder for patients with having good accuracy and sensitivity
Software Fault Prediction using Machine Learning Techniques
No full textFault-prediction techniques are aiming towards prediction of the software modules that are faulty so that it could be beneficial in the upcoming phases of software development. Difference performance criteria are being employed in order to boost the performance of the already existing ways. However, the main issue is the perspective of compiling their performances which is ignored constantly. Classification is the most used tech-nique that is being used for the exclusion of faulty from non-faulty modules. Previous work under this topic has been carried out using different techniques. Here, we have used tried to enhance the process by using feature selection method so as to make the prediction more accurate and less time consuming. Moreover, nine fault prediction techniques have been such as Adaboost, multilayer perceptron, decision tree regression, linear regression, Boosting, random forest, support vector machine, bagging, boosting for the prediction of number of faults. The study has been carried out using ten software project datasets from PROMISE repository. Mainly, AAE, ARE and feature selection techniques have been used to evaluate the results of the investigation. The kruskal-wallis test has been applied on the ARE and AAE values to check whether the difference of performance of the various machine learning techniques is statistically significant or not
Selection of Change-prone Software Components using the Expertise of Semantic Web and Intelligent Computing Methods
No full textSoftware component development, like generic software development, deals with “the construction of multi-version software” which will relentlessly be predisposed to changes either to add novel features or to reform source code for accommodating future changes or for removing existing defects as a part of corrective maintenance. Therefore, knowledge regarding the change-prone source code components of any software project or component is vital as they are prospective cradles of modifications and defects and could also depict possible design issues that need to be solved.
This thesis work proposes a novel change-prone software selection mechanism that employs Semantic web technology and Intelligent Computing Methods (ICM). The mechanism essentially selects those code components (Java files) of a software component that will be employed with change in the successive release of the component. Additionally, the work also identifies and establishes a cognitive aspect to the software change process.
We first begin by extensively surveying the existing research in order to understand the topic in concern from three primary standpoints:
(i) Software change prediction with an emphasis on the metrics used for software change prediction along with the prediction techniques employed, statistical tests used and validation strategies applied,
(ii) The cognitive complexity metrics introduced and validated in literature and their use to estimate any software development procedure, and
(iii) Application of Semantic web technologies for software component-based tasks with a key focus on the year wise trend of the research articles and possible justification of the usage of Semantic web technology and tools for a specific phase of component-based software development.
From the literature studied, we observed that it is challenging to modify code fragments from existing software that are difficult to comprehend. However, since systematic software maintenance includes an extensive human activity, cognitive complexity is one of the intrinsic factors that could potentially contribute to or impede an efficient software maintenance practice, the empirical validation of which remains vastly unaddressed. Thusly, we first conduct an experimental analysis in this thesis work in which the software developer’s level of difficulty in comprehending the software: the cognitive complexity, is theoretically computed and empirically evaluated for estimating its relevance to actual software change. For multiple successive releases of two software components (plugin projects written in Java), where the source code of a previous release has been substantively used in a novel release, we calculate the change results and the values of the cognitive complexity for each of the version’s source code Java files. We construct eight datasets and build predictive models using statistical analysis and Machine Learning (ML) techniques. The pragmatic comparative examination of the estimated cognitive complexity against prevailing metrics of software change and software complexity clearly validates the cognitive complexity metric as a noteworthy measure of version to version source code change.
Secondly, several studies exist in literature that focus on finding the association between change-proneness and software metrics. Nevertheless, since the quest for the best classifier for change-prone source code elements is an ongoing process, we then present a pervasive framework for software change prediction by investigating various techniques from the categories of Intelligent Computing Methods (ICMs) and Statistical approaches for creating version to version change-prediction models with respect to Java files. The performance of the models is assessed during two validation scenarios: k-fold intra-release validation and inter-release validation, where the latter is useful for estimating the trend of change-proneness of files in the upcoming versions. Our experiments indicate that the prediction techniques perform differently under the selected validation settings, at the same time confirming the proficiency of the selected prediction techniques in lieu of developing change-proneness prediction models. Such models could aid the software engineers in the initial stages of software development for classifying change-prone Java files for the analyzed plugin project versions, in turn aiding in the trend estimation of change-proneness over future versions.
Lastly, we present the change-prone Java file selection mechanism developed using ICM and Semantic web technology. The framework proceeds with the construction of an ontology in Protégé with respect to the Java files in each of the selected software component versions using static source code metrics as attributes. It then employs Semantic Web Rule Language (SWRL) and Drools inference engine to formulate and induce potential rules acquired via the most appropriate ICM that classifies a Java file as change-prone or stable. The relevant change-prone or stable files are then selected via the Semantic Query-Enhanced Web Rule Language (SQWRL) commonly employed for extracting information from ontologies. Additionally, we demonstrate how information (in the form of patterns or rules) which is constructed by a prediction technique to make relevant predictive decisions can be expressed in the ontology via valid SWRL rules. Since SWRL supports monotonic inference only, this can result in an incomplete inference should the rule contain more than one negation. We provide a methodology to express such rules in SWRL without any loss of relevant inference. The proposed selection mechanism is evaluated on various successive releases of the selected plugin projects and clearly establishes the applicability of Semantic web principles with respect to building a successful change-prone Java file selection mechanism.
More generally, the findings of this thesis identify if cognitive complexity plays a role in software change and if the existing prediction techniques for software change are cogent enough to predict the version to version change-proneness of Java files of software components. It also analyses if the two paradigms: Semantic web technology and an ICM could in fact be employed in unison to select change-prone Java files from a software component version
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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