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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.

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.

Mild Cognitive Impairment (MCI) can be considered as one of the early markers of dementia and can be helpful for clinicians to take corrective measures and delay its progression. This study aims to classify Mild Cognitive Impairment-Converts (MCI-C) and Mild Cognitive Impairment-Non-Converts (MCI-NC) using structural Magnetic Resonance Imaging (s-MRI) by analysing the decay in gray matter using a novel approach. Previous works such as 2D or 3D CNNs had drawbacks: 2D CNNs cannot detect spatial correlation between MRI slices, while 3D CNNs are computationally expensive and less practical to use on edge devices. To overcome these challenges, we propose a novel sequence-based framework inspired by Natural Language Processing (NLP) techniques, designed to capture correlations between MRI slices. The study used s-MRI volumes from 187 subjects (75 MCI converters, 112 MCI non-converters) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI), retaining 106 slices per volume. For each slice, histograms were created using the local binary pattern (LBP) and its variants (Basic LBP, Uniform LBP, and Rotation-Invariant Uniform LBP), reducing the dimensionality and forming feature vectors. These feature vectors were stacked for each MRI volume, creating a train of features. For the classification model, we used a Layer-wise Adaptive Sine Activation (LASA) based Bidirectional Recurrent Neural Network (BiRNN) capable of modelling the temporal and spatial relationships in the data. The trainable frequency parameter in LASA enables the network to adapt both short-term and long-term dependencies, while the bidirectional structure captures forward and backward correlations between slices. Throughout training, the validation accuracy consistently exceeded the accuracy of the training, indicating a strong generalisation performance. Across 30 experiments, our model achieved an average accuracy of 97.4% with a standard deviation of ±0.2, demonstrating its effectiveness and reliability. The high accuracy and edge-device compatibility of this method have the potential to significantly improve clinical practice by allowing early and cost-effective diagnosis of MCI in a wider range of healthcare settings, including remote and resource-constrained environments. This innovative method offers an efficient and practical solution for the early diagnosis of dementia, overcoming the limitations of traditional deep learning based models.

Background Mild Cognitive Impairment (MCI) can be considered as one of the early markers of dementia and can be helpful for clinicians to take corrective measures and delay its progression. This study aims to classify Mild Cognitive Impairment‐Converts (MCI‐C) and Mild Cognitive Impairment‐Non‐Converts (MCI‐NC) using structural Magnetic Resonance Imaging (s‐MRI) by analysing the decay in gray matter using a novel approach. Previous works such as 2D or 3D CNNs had drawbacks: 2D CNNs cannot detect spatial correlation between MRI slices, while 3D CNNs are computationally expensive and less practical to use on edge devices. Method To overcome these challenges, we propose a novel sequence‐based framework inspired by Natural Language Processing (NLP) techniques, designed to capture correlations between MRI slices. The study used s‐MRI volumes from 187 subjects (75 MCI converters, 112 MCI non‐converters) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI), retaining 106 slices per volume. For each slice, histograms were created using the local binary pattern (LBP) and its variants (Basic LBP, Uniform LBP, and Rotation‐Invariant Uniform LBP), reducing the dimensionality and forming feature vectors. These feature vectors were stacked for each MRI volume, creating a train of features. For the classification model, we used a Layer‐wise Adaptive Sine Activation (LASA) based Bidirectional Recurrent Neural Network (BiRNN) capable of modelling the temporal and spatial relationships in the data. The trainable frequency parameter in LASA enables the network to adapt both short‐term and long‐term dependencies, while the bidirectional structure captures forward and backward correlations between slices. Result Throughout training, the validation accuracy consistently exceeded the accuracy of the training, indicating a strong generalisation performance. Across 30 experiments, our model achieved an average accuracy of 97.4% with a standard deviation of ±0.2, demonstrating its effectiveness and reliability. Conclusion The high accuracy and edge‐device compatibility of this method have the potential to significantly improve clinical practice by allowing early and cost‐effective diagnosis of MCI in a wider range of healthcare settings, including remote and resource‐constrained environments. This innovative method offers an efficient and practical solution for the early diagnosis of dementia, overcoming the limitations of traditional deep learning based models.

Background This research aims to improve the prediction of Mild Cognitive Impairment (MCI) conversion to Alzheimer's disease. In order to achieve this, this study focuses on seven specific brain regions identified using the brain atlas. The regions are Hippocampus, Entorhinal cortex, Cerebral cortex, Frontal lobe, Temporal lobe, Parietal lobe, and Occipital lobe. The decay in the gray matter in these regions is associated with the cognitive impairment. This method proposes a novel feature extraction method based on Auto Encoders and then uses these feature to create a graph representing the association between these regions. Method The latent representation of the seven regions is found using a novel auto‐encoder based method. This is followed by the formation of a graph, where each of the above regions are nodes and the distance between these nodes is proportional to the inverse of the similarity between the latent representation of the regions. By examining the relationships between these regions, the study seeks to identify patterns associated with MCI conversion. The method involves flattening the above‐formed graph representation into a 1‐D vector, which serves as a unique feature representation for each brain volume. The classification is done using the Support Vector Machine Linear Kernel and forward feature selection is used for selecting the pertinent features. Result The method has been validated using the data has obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). We collected 75 s‐MRI scans of the patients suffering from MCI who converted to Alzheimer's (MCI‐Converts) and 112 s‐MRI scans of the patients suffering from MCI who did not convert to Alzheimer's (MCI‐Non Converts). The F‐score of the classification is 95.4%, which is better than the state of the art. Conclusion The proposed method provides region‐specific insights, that is it allows for the identification of specific brain regions that are crucial in predicting MCI conversion. It also opens the doors for network analysis of the connections between regions and provides valuable information about the underlying networks involved in MCI conversion. Furthermore, the method also gives promising results and is more generalizable.

This research aims to improve the prediction of Mild Cognitive Impairment (MCI) conversion to Alzheimer's disease. In order to achieve this, this study focuses on seven specific brain regions identified using the brain atlas. The regions are Hippocampus, Entorhinal cortex, Cerebral cortex, Frontal lobe, Temporal lobe, Parietal lobe, and Occipital lobe. The decay in the gray matter in these regions is associated with the cognitive impairment. This method proposes a novel feature extraction method based on Auto Encoders and then uses these feature to create a graph representing the association between these regions. The latent representation of the seven regions is found using a novel auto-encoder based method. This is followed by the formation of a graph, where each of the above regions are nodes and the distance between these nodes is proportional to the inverse of the similarity between the latent representation of the regions. By examining the relationships between these regions, the study seeks to identify patterns associated with MCI conversion. The method involves flattening the above-formed graph representation into a 1-D vector, which serves as a unique feature representation for each brain volume. The classification is done using the Support Vector Machine Linear Kernel and forward feature selection is used for selecting the pertinent features. The method has been validated using the data has obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). We collected 75 s-MRI scans of the patients suffering from MCI who converted to Alzheimer's (MCI-Converts) and 112 s-MRI scans of the patients suffering from MCI who did not convert to Alzheimer's (MCI-Non Converts). The F-score of the classification is 95.4%, which is better than the state of the art. The proposed method provides region-specific insights, that is it allows for the identification of specific brain regions that are crucial in predicting MCI conversion. It also opens the doors for network analysis of the connections between regions and provides valuable information about the underlying networks involved in MCI conversion. Furthermore, the method also gives promising results and is more generalizable.

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.

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.