School Of Engineering And Technology

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    Resolving the celestial classification using fine k-NN classifier
    (Institute of Electrical and Electronics Engineers Inc., 2016) Yadav, Sangeeta; Kaur, Amandeep; Bhauryal, Neeraj Singh
    With the rapid growth in space technology, space exploration is on the high demand. With each such type of mission, data is accumulating in heaps. Be it manned or unmanned mission, its credibility is defined by the quality of research which can be conducted on the data collected in such missions through remote or on the capsule experiments. Thus there is huge demand of soft techniques, which can make the space or celestial data as useful as possible. One of the major issues is dearth of automated technique for image classification of celestial bodies. Though many image classification techniques exist, but none of them is totally attributed to celestial bodies. An artificial neural network based classifier is proposed to classify celestial object from its image. Texture features are extracted from 90 images of size of 225-225 of different planets. Different classifiers were applied on this training data. Accuracy of different classifiers is compared to find out the best classifier for space data classification. Different validation schemes are applied and the results are compared to figure out the best validation scheme. ? 2016 IEEE.
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    Comparative performance of fault-prone prediction classes with k-means clustering and MLP
    (Association for Computing Machinery, 2016) Singh, Satwinder; Singla, Rozy
    Software defect in today's era is most important in the field of software engineering. Most of the organizations used various techniques to predict defects in their products before they are delivered. Defect prediction techniques help the organizations to use their resources effectively which results in lower cost and time requirements. There are various techniques that are used for predicting defects in software before it has to be delivered. For example clustering, neural networks, support vector machine (SVM) etc. In this paper two defect prediction techniques:-K-means Clustering and Multilayer Perceptron model (MLP), are compared. Both the techniques are implemented on different platforms. K-means clustering is implemented using WEKA tool and MLP is implemented using SPSS. The results are compared to find which algorithm produces better results. In this paper Object-Oriented metrics are used for predicting defects in the software. ? 2016 ACM.
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    Classification of defective modules using object-oriented metrics
    (Inderscience Enterprises Ltd., 2017) Singh, Satwinder; Singla, Rozy
    Software defect in today's era is crucial in the field of software engineering. Most of the organisations use various techniques to predict defects in their products before they are delivered. Defect prediction techniques help the organisations to use their resources effectively which results in lower cost and time requirements. There are various techniques that are used for predicting defects in software before it has to be delivered, e.g., clustering, neural networks, support vector machine (SVM). In this paper two defect prediction techniques: K-means clustering and multi-layer perceptron model (MLP) are compared. Both the techniques are implemented on different platforms. K-means clustering is implemented using WEKA tool and MLP is implemented using SPSS. The results are compared to find which algorithm produces better results. In this paper object-oriented metrics are used for predicting defects in the software. Copyright ? 2017 Inderscience Enterprises Ltd.