Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the "STrutred Analysis of the REtina (STARE) Database" accessed on Oct. 2020 (https://cecas.clemson.edu/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning

The domain of machine learning is confronted with a crucial research area known as class imbalance (CI) learning, which presents considerable hurdles in the precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. To overcome this limitation, we propose a novel graph-embedded intuitionistic fuzzy RVFL for CI learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model offers a plethora of benefits: 1) leveraging graph embedding (GE) to preserve the inherent topological structure of the datasets; 2) employing intuitionistic fuzzy (IF) theory to handle uncertainty and imprecision in the data; and 3) the most important, it tackles CI learning. The amalgamation of a weighting scheme, GE, and IF sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the CI issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.

GRA: Graph Representation Alignment for Semi-Supervised Action Recognition

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCLA (N-UCLA) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness.

A survey on cancer detection via convolutional neural networks: Current challenges and future directions

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection.

Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis

Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly.1.

Redefining Lobe-Wise Ground-Glass Opacity in COVID-19 Through Deep Learning and its Correlation With Biochemical Parameters

During COVID-19 pandemic qRT-PCR, CT scans and biochemical parameters were studied to understand the patients' physiological changes and disease progression. There is a lack of clear understanding of the correlation of lung inflammation with biochemical parameters available. Among the 1136 patients studied, C-reactive-protein (CRP) is the most critical parameter for classifying symptomatic and asymptomatic groups. Elevated CRP is corroborated with increased D-dimer, Gamma-glutamyl-transferase (GGT), and urea levels in COVID-19 patients. To overcome the limitations of manual chest CT scoring system, we segmented the lungs and detected ground-glass-opacity (GGO) in specific lobes from 2D CT images by 2D U-Net-based deep learning (DL) approach. Our method shows > 90% accuracy, compared to the manual method (  ∼ 80%), which is subjected to the radiologist's experience. We determined a positive correlation of GGO in the right upper-middle (0.34) and lower (0.26) lobe with D-dimer. However, a modest correlation was observed with CRP, ferritin and other studied parameters. The final Dice Coefficient (or the F1 score) and Intersection-Over-Union for testing accuracy are 95.44% and 91.95%, respectively. This study can help reduce the burden and manual bias besides increasing the accuracy of GGO scoring. Further study on geographically diverse large populations may help to understand the association of the biochemical parameters and pattern of GGO in lung lobes with different SARS-CoV-2 Variants of Concern's disease pathogenesis in these populations.

DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods.

Multimodal Neuroimaging based Alzheimer's Disease Diagnosis using Evolutionary RVFL Classifier

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

Investigating White Matter Abnormalities Associated with Schizophrenia Using Deep Learning Model and Voxel-Based Morphometry

Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ's regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model.

Reducing rip current drowning: An improved residual based lightweight deep architecture for rip detection

Rip Currents are contributing around 25 fatal drownings each year in Australia. Previous research has indicated that most of beachgoers cannot correctly identify a rip current, leaving them at risk of experiencing a drowning incident. Automated detection of rip currents could help to reduce drownings and assist lifeguards in supervision planning; however, varying beach conditions have made this challenging. This work presents the effectiveness of an improved lightweight framework for detecting rip currents: RipDet+¹, aided with residual mapping to boost the generalization performance. We have used Yolo-V3 architecture to build RipDet⁺ framework and utilize pretrained weight by fully exploiting the detection training set from some base classes which in result quickly adapt the detection prediction to the available rip data. Extensive experiments are reported which show the effectiveness of RipDet+ architecture in achieving a detection accuracy of 98.55%, which is significantly greater compared to other state-of-the-art methods for Rip currents detection.

Graph embedded ensemble deep randomized network for diagnosis of Alzheimer's disease

Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural networks. Ensemble deep random vector functional link (edRVFL) network utilize the strength of two growing fields, i.e., deep learning and ensemble learning. However, edRVFL model doesn't consider the geometrical relationship of the data while calculating the final output parameters corresponding to each layer considered as base model. In the literature, graph embedded frameworks have been successfully used to describe the geometrical relationship within data. In this paper, we propose an extended graph embedded RVFL (EGERVFL) model that, unlike standard RVFL, employs both intrinsic and penalty subspace learning (SL) criteria under the graph embedded framework in its optimization process to calculate the model's output parameters. The proposed shallow EGERVFL model has only single hidden layer and hence, has less representation learning. Therefore, we further develop an ensemble deep EGERVFL (edEGERVFL) model that can be considered a variant of edRVFL model. Unlike edRVFL, the proposed edEGERVFL model solves graph embedded based optimization problem in each layer and hence, has better generalization performance than edRVFL model. We evaluated the proposed approaches for the diagnosis of Alzheimer's disease and furthermore on UCI datasets. The experimental results demonstrate that the proposed models perform better than baseline models. The source code of the proposed models is available at https://github.com/mtanveer1/.

The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions

Beyond fifth generation (5G) communication systems aim towards data rates in the tera bits per second range, with improved and flexible coverage options, introducing many new technological challenges in the fields of network architecture, signal pro- cessing, and radio frequency front-ends. One option is to move towards cell-free, or distributed massive Multiple-Input Multiple-Output (MIMO) network architectures and highly integrated front-end solutions. This paper presents an outlook on be- yond 5G distributed massive MIMO communication systems, the signal processing, characterisation and simulation challenges, and an overview of the state of the art in millimetre wave antennas and electronics.

Recognition of multi-cognitive tasks from EEG signals using EMD methods

Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.

Intuitionistic Fuzzy Weighted Least Squares Twin SVMs

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuitionistic fuzzy TWSVM combined the concept of intuitionistic fuzzy number with TWSVMs to reduce the effect of outliers and distinguish support vectors from noise. Despite these benefits, TWSVMs and intuitionistic fuzzy TWSVMs still suffer from some drawbacks as: 1) the local neighborhood information is ignored among the data points and 2) they solve quadratic programming problems (QPPs), which is computationally inefficient. To overcome these issues, we propose a novel intuitionistic fuzzy weighted least squares TWSVMs for classification problems. The proposed approach uses local neighborhood information among the data points and also uses both membership and nonmembership weights to reduce the effect of noise and outliers. The proposed approach solves a system of linear equations instead of solving the QPPs which makes the model more efficient. We evaluated the proposed intuitionistic fuzzy weighted least squares TWSVMs on several benchmark datasets to show the efficiency of the proposed model. Statistical analysis is done to quantify the results statistically. As an application, we used the proposed model for the diagnosis of Schizophrenia disease.

Ensemble deep random vector functional link network using privileged information for Alzheimer's disease diagnosis

In this paper, deep RVFL and its ensembles are enabled to incorporate privileged information, however, the standard RVFL model and its deep models are unable to use privileged information. Privileged information-based approach commonly seen in human learning. To fill this gap, we incorporate learning using privileged information (LUPI) in deep RVFL model and propose deep RVFL with LUPI framework (dRVFL+). Privileged information is available while training the models. To make the model more robust, we propose ensemble deep RVFL+ with LUPI framework (edRVFL+). Unlike traditional ensemble approach wherein multiple base learners are trained, the proposed edRVFL+ optimises a single network and generates an ensemble via optimization at different levels of random projections of the data. Both dRVFL+ and edRVFL+ efficiently utilise the privileged information which results in better generalization performance. In LUPI framework, half of the available features are used as normal features and rest as the privileged features. However, we propose a novel approach for generating the privileged information. To the best of our knowledge, this is first time that a separate privileged information is generated. The proposed models are employed for the diagnosis of Alzheimer's disease. Experimental results show the promising performance of both the proposed models.

Diagnosis of Schizophrenia: A comprehensive evaluation

Machine learning models have been successfully employed in the diagnosis of Schizophrenia disease. The impact of classification models and the feature selection techniques on the diagnosis of Schizophrenia have not been evaluated. Here, we sought to access the performance of classification models along with different feature selection approaches on the structural magnetic resonance imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy control subjects. We evaluated different classification algorithms based on support vector machine (SVM), random forest, kernel ridge regression and randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the feature selection techniques. Based on the evaluation, SVM based models with Gaussian kernel proved better compared to other classification models and Wilcoxon feature selection emerged as the best feature selection approach. Moreover, in terms of data modality the performance on integration of the grey matter and white matter proved better compared to the performance on the grey and white matter individually. Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease. This indicates that proper selection of the features and the classification models can improve the diagnosis of Schizophrenia.

Brain age prediction with improved least squares twin SVR

Alzheimer's disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of abnormal brain. Several studies have shown that early prediction and treatment initiation can slow the progression of dementia's and hence, the quality of life of those subjects can be improved. We propose a novel regression model trained on a normal brain age pattern to predict the brain age of the new subjects. If the brain age delta (difference between the predicted and chronological age) is positive that implies accelerated atrophy and hence, a risk factor for possible conversion to AD. Machine learning models like support vector regression (SVR) based models have been successfully employed in the regression problems. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR), ε-TSVR and Lagrangian TSVR (LTSVR) models have been used for the regression problems. ε-TVSR and LTSVR models seek a pair of ε-insensitive proximal planes for generation of end regressor. However, SVR and TSVR based models have several drawbacks- i) SVR model is computationally inefficient compared to the TSVR based models. ii) Twin SVM based models involve the computation of matrix inverse which is intractable in real world scenario's. iii) Both TSVR and LTSVR models are based on empirical risk minimization principle risk and hence may be prone to overfitiing. iv) TSVR and LTSVR assume that the matrices appearing in their formulation are positive definite which may not be satisfied in real world scenario's. To overcome these issues, we formulate improved least squares twin support vector regression (ILSTSVR). The proposed ILSTSVR modifies the TSVR by replacing the inequality constraints with the equality constraints and minimizes the slack variables using squares of l₂ norm instead of l₁. Also, we introduce different Lagrangian function to avoid the computation of matrix inverses. The advantages of the proposed ILSTSVR modes are summarised as: i) No matrix inversions are involved in the proposed ILSTSVR model. ii) Structural risk minimization (SRM) principle is embodied in proposed ILSTSVR model which is the marrow of statistical learning and thus avoids the issues of overfitting. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer's disease subjects for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for the brain-age prediction.

Lightweight Face Anti-Spoofing Network for Telehealth Applications

Online healthcare applications have grown more popular over the years. For instance,telehealth is an online healthcare application that allows patients and doctors to schedule consultations,prescribe medication,share medical documents,and monitor health conditions conveniently. Apart from this,telehealth can also be used to store a patients personal and medical information. Given the amount of sensitive data it stores,security measures are necessary. With its rise in usage due to COVID-19,its usefulness may be undermined if security issues are not addressed. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However,face recognition systems are not foolproof. They are prone to malicious attacks like printed photos,paper cutouts,re-played videos,and 3D masks. In order to counter this,multiple face anti-spoofing methods have been proposed. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance,existing methods use a significant amount of parameters,making them resource-heavy and unsuitable for handheld devices. Apart from this,they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. A lightweight model is critical for applications like telehealth that run on handheld devices. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries,classification becomes more accurate. We further demonstrate our models capabilities by comparing the number of parameters,FLOPS,and performance with other state-of-the-art methods.

Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis

Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.

Classification of Alzheimer's disease using ensemble of deep neural networks trained through transfer learning

Deep learning; transfer learning; ensemble learning; Alzheimer's disease.

Predicting brain age using machine learning algorithms: A comprehensive evaluation

Machine learning (ML) algorithms play a vital role in brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals (N = 788) as a training set followed by different regression algorithms (18 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimers disease patients. The prediction accuracy in the independent test set (i.e., CH set) varied in regression algorithms (mean absolute error (MAE) from 4.63 to 7.14 yrs, R2 from 0.76 to 0.88). The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE = 4.63 yrs, R2 = 0.88, 95% CI = [-1.26, 1.42]) and Binary Decision Tree algorithm (MAE = 7.14 yrs, R2 = 0.76, 95% CI = [-1.50, 2.62]), respectively. Our experimental results demonstrate that prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.

Sparse Twin Support Vector Clustering using Pinball Loss

Clustering is a widely used machine learning technique for unlabelled data. One of the recently proposed techniques is the twin support vector clustering (TWSVC) algorithm. The idea of TWSVC is to generate hyperplanes for each cluster. TWSVC, utilising hinge loss, which relies on shortest distance between different clusters, is prone to noise-corrupted datasets and is unstable for re-sampling. In this paper we propose a novel Sparse Pinball loss Twin Support Vector Clustering (SPTSVC). The proposed SPTSVC involves the ϵ-insensitive pinball loss function to formulate a sparse solution. Pinball loss function provides noise-insensitivity and re-sampling stability. The ϵ-insensitive zone provides sparsity to the model and improves testing time. Numerical experiments on synthetic as well as real world benchmark datasets are performed to show the efficacy of the proposed model. An analysis on the sparsity of various clustering algorithms is presented in this work. Statistical tests are performed to show the significance of the various algorithms. Experimental results show that the proposed SPTSVC gives better generalization performance with sparsity comparable to existing algorithms. In order to show the feasibility and applicability of the proposed SPTSVC on biomedical data, experiments have been performed on epilepsy and breast cancer datasets.

Identification of chimera using machine learning

Chimera state refers to the coexistence of coherent and non-coherent phases in identically coupled dynamical units found in various complex dynamical systems. Identification of chimera, on one hand, is essential due to its applicability in various areas including neuroscience and, on the other hand, is challenging due to its widely varied appearance in different systems and the peculiar nature of its profile. Therefore, a simple yet universal method for its identification remains an open problem. Here, we present a very distinctive approach using machine learning techniques to characterize different dynamical phases and identify the chimera state from given spatial profiles generated using various different models. The experimental results show that the performance of the classification algorithms varies for different dynamical models. The machine learning algorithms, namely, random forest, oblique random forest based on Tikhonov, axis-parallel split, and null space regularization achieved more than 96% accuracy for the Kuramoto model. For the logistic maps, random forest and Tikhonov regularization based oblique random forest showed more than 90% accuracy, and for the Hénon map model, random forest, null space, and axis-parallel split regularization based oblique random forest achieved more than 80% accuracy. The oblique random forest with null space regularization achieved consistent performance (more than 83% accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance. This work provides a direction for employing machine learning techniques to identify dynamical patterns arising in coupled non-linear units on large-scale and for characterizing complex spatiotemporal patterns in real-world systems for various applications.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.