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.

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.

Mild Cognitive Impairment (MCI) is a formative stage of dementia. Its detection may help clinicians to delay the progression. The conversion prediction of patients suffering from MCI to dementia can be done using the structural Magnetic Resonance Imaging data, using the gray matter. This work carries out an extensive empirical analysis exploring various feature extraction methods including Local Binary Pattern, Discrete Wavelet Transform, Histogram of Oriented Gradients, and Gray Level Co-occurrence Matrix along with forward feature selection. The work presents the findings by validating the various pipelines using the ADNI dataset. In total, 32 experiments were carried out. The combination of the Local Binary Pattern with Fisher Discriminant Ratio gives the best result with the linear kernel of SVM. We also explored the effect of the cost parameter on the recital of the proposed pipeline. The work has been compared with the existing works, and the results are encouraging.

Aging leads to reduced cognitive abilities. A person heading toward dementia will also show such signs but with more severity. This stage is referred to as Mild Cognitive Impairment. It has been observed that nearly one-fifth of the people suffering from MCI convert to dementia. The patients suffering from MCI who convert to dementia are called MCI-Converts and those who do not convert are called MCI Non-converts. This work proposes a model that extracts gray matter from the s-MRI brain volume and explores the applicability of combination of Convolutional Neural Network and Recursive Feature Elimination in the diagnosis of MCI. Features of the data obtained using the carefully crafted CNN followed by the selection of the appropriate features using Recursive Feature Elimination are then used to accomplish the task. Empirical analysis has been done to select the parameters of the CNN. The results are better than the state of the art and pave way for the exploitation of the Deep Learning models to classify the Converts and the Non-converts.

Alzheimer's Disease constitutes one of the biggest portions of the diseases related to ageing. Mild Cognitive Impairment may be considered the formative stage of this disease. The automated diagnosis of Mild Cognitive Impairment using Machine Learning will help the clinicians in delaying its progression and will be easy, cheap, and efficient for the patient. This work uses neuropsychological data obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI), containing the results from 12 tests including Mini-Mental State Examination and ADAS-Cog. An extensive empirical analysis is carried out and the most important features are extracted using the proposed pipelines. The Feature Selection is done using both filter and wrapper methods and in total 13 features were selected. It was found that most of the selected features related to tasks associated with memory. The proposed method gives a performance of 0.9817 in terms of F1 Score. Thus, performing better vis-à-vis the state of the art. The proposed pipeline helps to reduce the number of neuropsychological tests to diagnose the disease. This work is one of the components of the projects that use multi-modality data including structural-Magnetic Resonance Imaging, functional-Magnetic Resonance Imaging, Positron Emission Tomography and Neuropsychological data to develop a system for efficient and effective diagnosis of MCI. The project management is done using Agile Methodology. The results are encouraging and pave the way for the development of such a system.

Depression negatively affects the daily life of an individual and may even lead to suicidal tendencies. The problem is compounded by the scarcity of trained psychologists and psychiatrists in developing countries due to which many cases go undetected. The automated diagnosis of depression can, therefore, assist clinicians to screen the patients and help them to handle the symptoms. The advent of wearable devices in the past decade has helped in capturing signals, which can be used to diagnose depression. This work uses a publicly available dataset and develops a model based on the distribution of microstructures from the temporal data to accomplish the given task. The results are encouraging and better than the state-of-the-art. An accuracy of 86.90

Mild Cognitive Impairment (MCI) can be considered a formative stage of dementia and its diagnosis may help the clinicians to impediment its headway. This work classifies MCI- Converts and Non-Converts using the structural Magnetic Resonance Imaging data obtained from the Alzheimer's Disease Neuroimaging Initiative. This work investigates the applicability of six feature extraction methods and their permutations, followed by three feature selection methods including the Diploid Genetic Algorithm (DGA), using the gray matter obtained from whole-brain volume. A total of 33 experiments were conducted, incorporating various pipelines that combine the above methods. The inability of 2-D feature extraction methods to grasp the correlation between the slices can be handled by 3-D feature extraction methods, which are computationally very expensive. To handle this, the reduction of dimensions along the third axis followed by some of the above combinations has also been explored. The Genetic Algorithms and its variant Diploid Genetic Algorithm works well for selecting features extracted from various feature extraction techniques. Further, it also compares the recital of the network with various feature extraction techniques. It was observed that the combination of DWT and LBP-rotation invariant performs better than others.

Dementia constitutes the major chunk of the diseases in elderly population. The preventive diagnostics of dementia may help the clinicians in handling the symptoms and delaying the progression of the disease. Mild cognitive impairment may be considered precursor to dementia. This work uses the structural-Magnetic Resonance Imaging data obtained from Alzheimer’s Disease Neuroimaging Initiative to classify MCI-Converts from Non-Converts. The information from this 3-D data is difficult to handle using 2-D models, in which the correlation between the slices is lost, and the 3-D models, which are computationally expensive. The proposed model is able to find the correlation between slices of brain volume using s-MRI which grasps the correlation between the slices, is computationally efficient, uses less memory and performs better in comparison with the current state of the art. The work applies Independent Component Analysis and Principal Component Analysis to accomplish the above task. In addition to being more accurate than the state-of-the-art, the proposed model yields an accuracy of 87

Depression affects around four per cent of the total world population. The lack of trained professionals in low and middle-income countries has led to more than three-quarters of those affected receiving no treatment. This calls for the development of automated methods for depression detection. This work proposes a model to detect pertinent frames from video data using Kullback-Leibler Divergence and the concept of outliers. The distribution of microstructures of the frames, selected using the proposed method, is fed to the Support Vector Machine to classify those affected with depression from the controls. The proposed method is validated using the data obtained from the Department of Psychiatry, Center of Excellence in Mental Health, Atal Bihari Vajpayee Institute of Medical Sciences and Dr Ram Manohar Lohia Hospital, New Delhi. The results are encouraging and pave the way for the use of this method in developing systems for the automated detection of the disease.

The diagnosis of mild cognitive impairment (MCI), which is deemed a formative phase of dementia, may greatly assist clinicians in delaying its headway toward dementia. This article proposes a deep learning approach based on a triploid genetic algorithm, a proposed variant of genetic algorithms, for classifying MCI converts and non-converts using structural magnetic resonance imaging data. It also explores the effect of the choice of activation functions and that of the selection of hyper-parameters on the performance of the model. The proposed work is a step toward automated convolutional neural networks. The performance of the proposed method is measured in terms of accuracy and empirical studies exhibit the preeminence of our proposed method over the existing ones. The proposed model results in a maximum accuracy of 0.97961. Thus, it may contribute to the effective diagnosis of MCI and may prove important in clinical settings.

The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.

Mild Cognitive Impairment (MCI) is considered as a formative stage of dementia and therefore its diagnosis can significantly assist in providing apposite treatment to the patients to impediment its headway towards dementia. In this paper, a Deep Learning approach is proposed for the classification of MCI-Converts and MCI-Non Converts, using the Structural Magnetic Resonance Imaging data. It investigates the effect of the variation in the number of filters, and the size of the filter on the performance of the model. Furthermore, the features are extracted using the penultimate layer of the proposed architecture. The Fisher Discriminant Ratio is used for the selection of features and the Support Vector Machine for the classification. The results are also compared to those obtained using the Softmax Layer. The proposed pipeline is able to extort germane features, thus improving the classification accuracy. The empirical studies exhibit the supremacy of the proposed method over the existing ones, in terms of accuracy. Consequently, the proposed technique may prove useful in the effectual diagnosis of MCI.

The classification of Mild Cognitive Impairment (MCI) patients, who convert to Alzheimer’s (MCI-C) and do not convert to Alzheimer’s (MCI-NC) is important to understand the progression of the disease. The imaging data, particularly s-MRI helps to understand the disease by capturing the variation in the gray matter. We developed a Deep Learning based method to classify MCI-C and MCI-NC. For each MRI volume Local Binary Pattern (LBP) was applied on each slice and the features so obtained were concatenated to get the feature vector of the volume. The features were obtained using three methods: creating a histogram having 256 bins, by using the Uniform LBP, and using the Uniform-Rotation Invariant LBP. This feature set was fed to a Deep Neural Network having two hidden layers and a Softmax layer. Experiments were conducted to find the optimal architecture of the DNN, which gives the best accuracy. Also, a dropout layer was added to prevent overfitting. The results were also compared with the Support Vector Machine. The data used in the experiment was obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI). We evaluated the model performance using accuracy. It was found that amongst the three variants of LBP, that having 256 bins produced the best results. Also, DNN performed far better as compared to SVM. Amongst the various DNN’s the one having 100 units in the first hidden layer, 50 in the next, and having a dropout of 0.1, gave the best results. The model used Adam optimizer and categorical cross-entropy. It was also noted that the results obtained using raw volumes were inferior as compared to that obtained using LBP. Table 1 shows the results. We propose a Deep Learning model for classifying MCI-C and MCI-NC. The proposed method is capable of finding discriminating features to classify the two sets. It may be the case that LBP is able to infer the difference in the distribution of microstructures in the two classes which helped the DNN to obtain better accuracy as compared to the state of the art.

The use of Convolutional Neural Networks for the classification of volumetric data is contentious because 2-D convolutions miss out on the correlation between the slices of the volume, whilst 3-D networks guzzle extensive computing resources. Moreover, the advent of SIREN activations calls for the investigation regarding the role of activations in such networks. This work proposes a model that uses the Principal Component Analysis to reduce the given data, followed by a circumspectly designed CNN for extracting the pertinent features. The paper also investigates the role of activations in such networks. The method is used to classify the patients converted to Alzheimer's from Mild Cognitive Impairment from those who did not convert. The data is obtained from ADNI. The proposed work gives an accuracy of 94.29, which is better as compared to the state-of-the-art.

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.

The critical path problem, in Software Project Management, finds the longest path in a Directed Acyclic Graph. The problem is immensely important for scheduling the critical activities. The problem reduces to the longest path problem, which is NP as against the shortest path problem. The longest path is an important NP-hard problem, which finds its applications in many other areas like graph drawing, sequence alignment algorithms, etc. The problem has been dealt with using Computational Intelligence. The paper presents the state of the art. The applicability of Genetic Algorithms in longest path problem has also been discussed. This paper proposes a novel Genetic Algorithm-based solution to the problem. This algorithm has been implemented and verified using benchmarks. The results are encouraging.

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.