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"Bhasin, Harsh"
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Python Programming Using Problem Solving
Python is a robust, procedural, object-oriented, and functional language. The features of the language make it valuable for web development, game development, business, and scientific programming. This book deals with problem-solving and programming in Python. It concentrates on the development of efficient algorithms, the syntax of the language, and the ability to design programs in order to solve problems. In addition to standard Python topics, the book has extensive coverage of NumPy, data visualization, and Matplotlib. Numerous types of exercises, including theoretical, programming, and multiple-choice, reinforce the concepts covered in each chapter.FEATURES:Concentrates on the development of efficient algorithms, the syntax of the language, and theability to design programs in order to solve problemsFeatures both standard Python topics and also extensive coverage of NumPy, data visualization, and Matplotlib problem-solving techniques
eBook
A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment
Background
The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data.
Methods
This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine.
Results
The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data.
Conclusion
The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.
Journal Article
On the applicability of diploid genetic algorithms
The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. It is also important to analyse the applicability of the premise of the dominance techniques applied so far in order to implement the technique. The work presents a systematic review of diploid genetic algorithms, examines the premise of the dominance relation used in different works and discusses the future scope. The work also discusses the biological basis of evaluating dominance. The work is important as the future of machine learning relies on techniques that are robust as well as efficient.
Journal Article
Applicability of Cellular Automata in Cryptanalysis
Cryptanalysis refers to finding the plaintext from the given cipher text. The problem reduces to finding the correct key from a set of possible keys, which is basically a search problem. Many researchers have put in a lot of effort to accomplish this task. Most of the efforts used conventional techniques. However, soft computing techniques like Genetic Algorithms are generally good in optimized search, though the applicability of such techniques to cryptanalysis is still a contentious point. This work carries out an extensive literature review of the cryptanalysis techniques, finds the gaps there in, in order to put the proposed technique in the perspective. The work also finds the applicability of Cellular Automata in cryptanalysis. A new technique has been proposed and verified for texts of around 1000 words. Each text is encrypted 10 times and then decrypted using the proposed technique. The work has also been compared with that employing Genetic Algorithm. The experiments carried out prove the veracity of the technique and paves way of Cellular automata in cryptanalysis. The paper also discusses the future scope of the work.
Journal Article
On the applicability of diploid genetic algorithms in dynamic environments
Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other.
Journal Article