Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

This sales letter may not influence you to be smarter, but the book that we offer will evoke you to be smarter. Yeah, at least you'll know more than others who don't. This is what called as the quality life improvisation. Why should this research methods in social relations? It's because this is your favourite theme to read. If you like this theme about, why don't you read the book to enrich your discussion?

Research methods in social relations

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

Will Tracz, our esteemed editor and Used-Program salesman, has written an entertaining, non-technical book dealing with the practice (and lack of) of software reuse. Its a collection of essays, mostly rehashed (reused?) and updated from various columns and papers published over the years.. Its a short (a bit over 200 pages) easy reading and enjoyable book (I read most of it in one sitting). Some of the essays discuss what was printed in the past and a discussion of the current status of the points.

Safeware: System Safety and Computers

Testing technique selection and evaluation remains a key issue in software testing Industry practitioners need concrete evidence to select proper testing techniques in STLC Despite the large number of empirical studies which attempt to study the testing techniques’ applicability conditions and allied factors, we are still without realistic and generalized results as studies lack a formal foundation and are not complete in all respects Additionally, besides varying significantly in terms of parameters they have taken into consideration, many existing studies show contradictory results Even though the researchers stress on replication of these studies under a common set of guidelines, however, attempts to aggregate results from such replications still has not been fruitful so far As such, to bridge the gap between researchers and industry professionals, we propose to carry out evaluation of testing techniques on a large scale under a unified framework in an open-source fashion so that the realistic and generalized results are obtained in a shorter span of time

Empirical evaluation of software testing techniques in an open source fashion

: File systems have been mostly benchmarked as per the application perspective. This approach hides all the underlying complexities of the system including the actual I/O being done with the secondary storage device like magnetic disk. The IO bound property of a file system is necessarily to be evaluated because the most dominant performance limiting factor of a file system is its I/O operation with secondary storage device. This IO bound property of file system dictates the quantity and frequency of IO that a file system does with secondary storage device. In this paper, we argue system perspective of file system benchmarks and develop a benchmark to evaluate some common disk file systems for IO bound property. The goal of this paper is to better understand the behavior of file systems and unveil the low level complexities faced by file

IO Bound Property: A System Perspective Evaluation & Behavior Trace of File System

One of the major goals of software testing is to increase reliability of the software. As pointed out by many studies, fault detection does not necessarily increase the reliability of the software when all failures are considered to be equivalent to one another. Accordingly, we need to evaluate software testing techniques to check which technique is more suitable and effective in terms of increasing confidence by reducing risk (by detecting and isolating faults which affect reliability most). We here present a novel experiment which compares three defect detection techniques for reliability. Preliminary results suggest that testing techniques differ in terms of their ability to reduce risk in the software.

A Novel Approach for Evaluating Software Testing Techniques for Reliability

Diseases cause huge economic loss to the apple industry every year. Timely identification of these diseases is challenging for the farmers as the symptoms produced by different diseases may be similar and sometimes present simultaneously. This paper is an attempt to provide the timely and accurate identification of apple diseases from plant leaves. In this study, we propose a deep learning approach for identification and classification of apple diseases. First part of this study is dataset creation which includes data collection and labelling. Next, we train a convolutional neural network (CNN) model on the prepared dataset for automatic classification of apple diseases. CNNs are end-to-end learning algorithms which perform automatic feature extraction and learn complex features directly from raw images, making them suitable for a wide variety of computer vision tasks. The model parameters were initialised using transfer learning enabling the proposed model to achieve 97.18% accuracy on the prepared dataset.

Deep learning for apple diseases: classification and identification

Purpose




As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.




Design/methodology/approach




On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator.




Findings




On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.




Originality/value




The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

S. M. K. Quadri

Papers

Empirical evaluation of software testing techniques in an open source fashion

IO Bound Property: A System Perspective Evaluation & Behavior Trace of File System

A Novel Approach for Evaluating Software Testing Techniques for Reliability

Deep learning for apple diseases: classification and identification

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery