One-Shot Federated Learning (OSFL) enables collaborative training in a single round, eliminating the need for iterative communication, making it particularly suitable for use in resource-constrained and privacy-sensitive applications. This survey offers a thorough examination of One-Shot FL, highlighting its distinct operational framework compared to traditional federated approaches. One-Shot FL supports resource-limited devices by enabling single-round model aggregation while maintaining data locality. The survey systematically categorizes existing methodologies, emphasizing advancements in client model initialization, aggregation techniques, and strategies for managing heterogeneous data distributions. Furthermore, we analyze the limitations of current approaches, particularly in terms of scalability and generalization in non-IID settings. By analyzing cutting-edge techniques and outlining open challenges, this survey aims to provide a comprehensive reference for researchers and practitioners seeking to design and implement One-Shot FL systems, advancing the development and adoption of One-Shot FL solutions in real-world, resource-constrained settings.

• Presents an in-depth review of generative AI use cases across domains such as healthcare, education, finance, law, and architecture. • Identifies key misuse scenarios, including misinformation, hallucination, and privacy risks associated with generative models. • Comprehensively surveys mitigation strategies and ethical alignment techniques. • Offers a unified framework comparing prior surveys and emphasizes open research challenges in trustworthy GenAI deployment. Generative Artificial Intelligence (GenAI) refers to the aspect of Artificial Intelligence (AI) that deals with generating content such as texts, images, music, videos, etc. GenAI has seen a surge in interest recently, particularly with the emergence of tools like ChatGPT, which demonstrate the potential of AI to generate contextually relevant and human-like outputs across various domains. GenAI has also enhanced existing language, image, and speech models, improving their performance on domain-specific tasks. This improvement has led to better integration in existing systems, including content creation, image generation, and enhanced decision-making processes. GenAI has facilitated personalized content delivery, provided data-driven insights, and automated complex tasks, enhancing efficiency and precision across various domains. While the advancements in GenAI have been greatly beneficial in multiple domains, there are many instances of misuse and abuse. This paper aims to provide a comprehensive review of the history of GenAI, its use cases, recent developments, downsides, and solutions to address the problem of its misuse in various sectors.

Alzheimer’s disease (AD) is a neurological condition primarily impacting the elderly and is known for its progressive decline in normal brain functioning. The magnetic resonance imaging (MRI) modality enables disease diagnoses by identifying atrophy patterns and structural changes. Imaging captures anatomical details effectively using axial, sagittal, and coronal planes. The axial plane of MRI provides a cross-sectional view, whereas the coronal plane allows visualization in the anterior-posterior direction. The sagittal plane offers a lateral view and aids in examining asymmetry and bilateral structures of the brain’s anatomy. In this paper, the features of the sagittal plane are extracted using Resnet-50, a deep-learning network. These extracted features are fed to the classifier for AD diagnosis. This paper presents a l1 regularization-based random vector functional link network (RVFL) classifier for AD diagnosis. The optimization problem of the proposed l1 regularization-based RVFL is solved using the Split Bregman iterative method. l1 regularization-based RVFL classifier is more generalizable and produces sparse output. The sparse output indicates a lower number of non-zero elements in the output compared to the standard RVFL network which uses l2 regularization. The experiments are performed on the publicly accessible Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, compared with other feed-forward networks. Results highlight the better performance of the proposed model for AD diagnosis than state-of-the-art approaches.

The random vector functional link (RVFL) neural network has shown significant potential in overcoming the constraints of traditional artificial neural networks, such as excessive computation time and suboptimal solutions. However, RVFL faces challenges when dealing with noise and outliers, as it assumes all data samples contribute equally. To address this issue, we propose a novel robust framework, R2VFL, RVFL with Huber weighting function and class probability, which enhances the model’s robustness and adaptability by effectively mitigating the impact of noise and outliers in the training data. The Huber weighting function reduces the influence of outliers, while the class probability mechanism assigns less weight to noisy data points, resulting in a more resilient model. We explore two distinct approaches for calculating class centers within the R2VFL framework: the simple average of all data points in each class and the median of each feature, the later providing a robust alternative by minimizing the effect of extreme values. These approaches give rise to two novel variants of the model—R2VFL-A and R2VFL-M. We extensively evaluate the proposed models on 47 UCI datasets, encompassing both binary and multiclass datasets, and conduct rigorous statistical testing, which confirms the superiority of the proposed models. Notably, the models also demonstrate exceptional performance in classifying EEG signals, highlighting their practical applicability in real-world biomedical domain.

Restricted kernel machines (RKMs) represent a versatile and powerful framework within the kernel machine family, leveraging conjugate feature duality to address a wide range of machine learning tasks, including classification, regression, and feature learning. However, their performance can degrade significantly in the presence of noise and outliers, which compromises robustness and predictive accuracy. In this paper, we propose a novel enhancement to the RKM framework by integrating a class-informed weighted function. This weighting mechanism dynamically adjusts the contribution of individual training points based on their proximity to class centers and class-specific characteristics, thereby mitigating the adverse effects of noisy and outlier data. By incorporating weighted conjugate feature duality and leveraging the Schur complement theorem, we introduce the class-informed restricted kernel machine (CI-RKM), a robust extension of the RKM designed to improve generalization and resilience to data imperfections. Experimental evaluations on benchmark datasets demonstrate that the proposed CI-RKM consistently outperforms existing baselines, achieving superior classification accuracy and enhanced robustness against noise and outliers. Our proposed method establishes a significant advancement in the development of kernel-based learning models, addressing a core challenge in the field. Codes are available at https://github.com/mtanveer1/CI-RKM.

Multi-view learning (MVL) is an emerging field in machine learning that focuses on improving generalization performance by leveraging complementary information from multiple perspectives or views. Various multi-view support vector machine (MvSVM) approaches have been developed, demonstrating significant success. Moreover, these models face challenges in effectively capturing decision boundaries in high-dimensional spaces using the kernel trick. They are also prone to errors and struggle with view inconsistencies, which are common in multi-view datasets. In this work, we introduce the multiview twin restricted kernel machine (TMvRKM), a novel model that integrates the strengths of kernel machines with the multiview framework, addressing key computational and generalization challenges associated with traditional kernel-based approaches. Unlike traditional methods that rely on solving large quadratic programming problems (QPPs), the proposed TMvRKM efficiently determines an optimal separating hyperplane through a regularized least squares approach, enhancing both computational efficiency and classification performance. The primal objective of TMvRKM includes a coupling term designed to balance errors across multiple views effectively. By integrating early and late fusion strategies, TMvRKM leverages the collective information from all views during training while remaining flexible to variations specific to individual views. The proposed TMvRKM model is rigorously tested on UCI, KEEL, and AwA benchmark datasets. Both experimental results and statistical analyses consistently highlight its exceptional generalization performance, outperforming baseline models in every scenario. The source code of the proposed TMvRKM model is available at https://github.com/mtanveer1/TMvRKM.

Recent advancements in neural networks, supported by foundational theoretical insights, emphasize the superior representational power of complex numbers. However, their adoption in randomized neural networks (RNNs) has been limited due to the lack of effective methods for transforming real-valued tabular datasets into complex-valued representations. To address this limitation, we propose two methods for generating complex-valued representations from real-valued datasets: a natural transformation and an autoencoder-driven method. Building on these mechanisms, we propose RVFL-X, a complex-valued extension of the random vector functional link (RVFL) network. RVFL-X integrates complex transformations into real-valued datasets while maintaining the simplicity and efficiency of the original RVFL architecture. By leveraging complex components such as input, weights, and activation functions, RVFL-X processes complex representations and produces real-valued outputs. Comprehensive evaluations on 80 real-valued UCI datasets demonstrate that RVFL-X consistently outperforms both the original RVFL and state-of-the-art (SOTA) RNN variants, showcasing its robustness and effectiveness across diverse application domains.

One of the major difficulties in machine learning methods is categorizing datasets that are imbalanced. This problem may lead to biased models, where the training process is dominated by the majority class, resulting in inadequate representation of the minority class. Universum twin support vector machine (UTSVM) produces a biased model towards the majority class, as a result, its performance on the minority class is often poor as it might be mistakenly classified as noise. Moreover, UTSVM is not proficient in handling datasets that contain outliers and noises. Inspired by the concept of incorporating prior information about the data and employing an intuitionistic fuzzy membership scheme, we propose intuitionistic fuzzy universum twin support vector machines for imbalanced data (IFUTSVM-ID) by enhancing overall robustness. We use an intuitionistic fuzzy membership scheme to mitigate the impact of noise and outliers. Moreover, to tackle the problem of imbalanced class distribution, data oversampling and undersampling methods are utilized. Prior knowledge about the data is provided by universum data. This leads to better generalization performance. UTSVM is susceptible to overfitting risks due to the omission of the structural risk minimization (SRM) principle in their primal formulations. However, the proposed IFUTSVM-ID model incorporates the SRM principle through the incorporation of regularization terms, effectively addressing the issue of overfitting. We conduct a comprehensive evaluation of the proposed IFUTSVM-ID model on benchmark datasets from KEEL and compare it with existing baseline models. Furthermore, to assess the effectiveness of the proposed IFUTSVM-ID model in diagnosing Alzheimer’s disease (AD), we applied them to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experimental results showcase the superiority of the proposed IFUTSVM-ID models compared to the baseline models. The supplementary material of the paper can be accessed using the following link: https://github.com/mtanveer1/IFUTSVM-ID.

In the domain of machine learning, the significance of the loss function is paramount, especially in supervised learning tasks. It serves as a fundamental pillar that profoundly influences the behavior and efficacy of supervised learning algorithms. Traditional loss functions, though widely used, often struggle to handle outlier-prone and high-dimensional data, resulting in suboptimal outcomes and slow convergence during training. In this paper, we address the aforementioned constraints by proposing a novel robust, bounded, sparse, and smooth (RoBoSS) loss function for supervised learning. Further, we incorporate the RoBoSS loss within the framework of support vector machine (SVM) and introduce a new robust algorithm named -SVM. For the theoretical analysis, the classification-calibrated property and generalization ability are also presented. These investigations are crucial for gaining deeper insights into the robustness of the RoBoSS loss function in classification problems and its potential to generalize well to unseen data. To validate the potency of the proposed -SVM, we assess it on 88 benchmark datasets from KEEL and UCI repositories. Further, to rigorously evaluate its performance in challenging scenarios, we conducted an assessment using datasets intentionally infused with outliers and label noise. Additionally, to exemplify the effectiveness of -SVM within the biomedical domain, we evaluated it on two medical datasets: the electroencephalogram (EEG) signal dataset and the breast cancer (BreaKHis) dataset. The numerical results substantiate the superiority of the proposed -SVM model, both in terms of its remarkable generalization performance and its efficiency in training time.

Alzheimer's disease (AD) and Schizophrenia (SCZ) are prominent neurodegenerative conditions and leading causes of dementia, resulting in progressive cognitive decline and memory loss. Several studies reveal that early detection and intervention can slow the progression of AD and SCZ. Numerous machine learning algorithms including twin support vector machine (TSVM) have been proposed for the early diagnosis of AD and SCZ. However, TSVM grapples with significant challenges: 1) TSVM relies on the hinge loss function, resulting in susceptibility to noise and instability; 2) TSVM encounters challenges in effectively handling large datasets, attributed to its computational complexity and dependence on matrix inversions. Keeping in view the aforementioned challenges, in this article, we propose a novel granular ball twin support vector machine with pinball loss function (Pin-GBTSVM). Pin-GBTSVM employs granular balls, as opposed to individual data points, as inputs for constructing a classifier, while also leveraging the pinball loss function to attain a heightened level of noise insensitivity. The proposed Pin-GBTSVM persists in facing challenges associated with the absence of integration of the structural risk minimization (SRM) principle and the requirement for matrix inversions. We further propose a novel large-scale Pin-GBTSVM (Pin-LGBTSVM). Pin-LGBTSVM achieves two crucial objectives: 1) it eliminates the necessity for matrix inversions, streamlining the computational efficiency of Pin-GBTSVM; and 2) it integrates the SRM principle by incorporating regularization terms, effectively addressing the concern of overfitting. Experiments are conducted on University of California Irvine (UCI), knowledge extraction based on evolutionary learning (KEEL), and normally distributed clustered (NDC) benchmark datasets, where both the proposed Pin-GBTSVM and Pin-LGBTSVM models consistently outperform the baseline models in terms of generalization performance. Furthermore, we implemented the proposed Pin-GBTSVM and Pin-LGBTSVM models on SCZ and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, showcasing the model's efficacy in real-world applications.

The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However, since edRVFL generates features for its hidden layers through random projection, it can potentially lose intricate features or fail to capture certain nonlinear features in its base models (hidden layers). To enhance the feature learning capabilities of edRVFL, we propose a novel edRVFL based on fuzzy inference system (edRVFL-FIS). The proposed edRVFL-FIS leverages the capabilities of two emerging domains, namely deep learning and ensemble approaches, with the intrinsic IF–THEN properties of FIS and produces rich feature representation to train the ensemble model. Each base model of the proposed edRVFL-FIS encompasses the following two key feature augmentation components: 1) unsupervised fuzzy layer features and 2) supervised defuzzified features. The edRVFL-FIS model incorporates diverse clustering methods (R-means, K-means, fuzzy C-means) to establish fuzzy layer rules, resulting in the following three model variations: 1) edRVFL-FIS-R, 2) edRVFL-FIS-K, and 3) edRVFL-FIS-C with distinct fuzzified features and defuzzified features. Within the framework of edRVFL-FIS, each base model utilizes the original, hidden layer, and defuzzified features to make predictions. Experimental results, statistical tests, discussions, and analyses conducted across UCI and NDC datasets consistently demonstrate the superior performance of all variations of the proposed edRVFL-FIS model over baseline models such as fuzzy broad learning system.

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.

The deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) succeed in various tasks and achieve state-of-the-art performance compared with other randomized neural networks (NNs). However, existing edRVFL structures need more diversity and error correction ability in an independent network. Our work fills the gap by combining stacked deep blocks and residual learning with the edRVFL. Subsequently, we propose a novel dRVFL combined with residual learning, ResdRVFL, whose deep layers calibrate the wrong estimations from shallow layers. Additionally, we propose incorporating a scaling parameter to control the scaling of residuals from shallow layers, thus mitigating the risk of overfitting. Finally, we present an ensemble deep stacking network, SResdRVFL, based on ResdRVFL. SResdRVFL aggregates multiple blocks into a cohesive network, leveraging the benefits of deep learning and ensemble learning. We evaluate the proposed model on 28 datasets and compare it with the state-of-the-art methods. The comparative study demonstrates that the SResdRVFL is the best-performing approach in terms of average ranking and errors based on 28 datasets.

Twin support vector machine (TSVM) is an emerging machine learning model with versatile applicability in classification and regression endeavors. Nevertheless, TSVM confronts noteworthy challenges: 1) the imperative demand for matrix inversions presents formidable obstacles to its efficiency and applicability on large-scale datasets; 2) the omission of the structural risk minimization (SRM) principle in its primal formulation heightens the vulnerability to overfitting risks; and 3) the TSVM exhibits a high susceptibility to noise and outliers and also demonstrates instability when subjected to resampling. In view of the aforementioned challenges, we propose the granular ball TSVM (GBTSVM). GBTSVM takes granular balls (GBs), rather than individual data points, as inputs to construct a classifier. These GBs, characterized by their coarser granularity, exhibit robustness to resampling and reduced susceptibility to the impact of noise and outliers. We further propose a novel large-scale GBTSVM (LS-GBTSVM). LS-GBTSVM's optimization formulation ensures two critical facets: 1) it eliminates the need for matrix inversions, streamlining the LS-GBTSVM's computational efficiency; and 2) it incorporates the SRM principle through the incorporation of regularization terms, effectively addressing the issue of overfitting. The proposed LS-GBTSVM exemplifies efficiency, scalability for large datasets, and robustness against noise and outliers. We conduct a comprehensive evaluation of the GBTSVM and LS-GBTSVM models on benchmark datasets from UCI and KEEL, both with and without the addition of label noise, and compared with existing baseline models. Furthermore, we extend our assessment to the large-scale NDC datasets to establish the practicality of the proposed models in such contexts. Our experimental findings and rigorous statistical analyses affirm the superior generalization prowess of the proposed GBTSVM and LS-GBTSVM models compared to the baseline models. The source code of the proposed GBTSVM and LS-GBTSVM models are available at https://github.com/mtanveer1/GBTSVM.

The plane-based classifiers, support vector machine (SVM) and twin support vector machine (TWSVM), are susceptible to the negative impact of noise, outliers, and class imbalance learning (CIL). The concept of intuitionistic fuzzy (IF) addresses the impact of noise and outliers present in the data. However, IF is sensitive to the threshold distance value and calculates the distance of each data point to other points of its class which is computatinally intensive. Moreover, IF assigns the same score value to the data points that have distinct distances from the center of the other class and identical membership values. To address the aforementioned limitations, in this paper, we propose dual center-based intuitionistic fuzzy plane-based classifiers, specifically dual center-based intuitionistic fuzzy support vector machine (DC-IFSVM) and dual center-based intuitionistic fuzzy least square twin support vector machine (DC-IFLSTSVM). The proposed models, DC-IFSVM and DC-IFLSTSVM handle the CIL by associating class-specific terms in the weighting function. The optimization problem of DC-IFLSTSVM is solved using the conjugate gradient (CG) method. We conducted extensive numerical experiments of the proposed DC-IFSVM, DC-IFLSTSVM and baseline models on 54 UCI and KEEL datasets, which resulted in the superiority of the proposed DC-IFLSTSVM. Furthermore, experiments on the BreakHis dataset, focused on classifying benign tumors from malignant tumors, showcased the remarkable performance of the proposed DC-IFLSTSVM. The code for the proposed model can be found on https://github.com/mtanveer1/DC-IFLSTSVM.

Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have been proposed for the diagnosis of AD at early stage. Recently, deep learning architectures have received quite a lot attention. Most of the deep learning architectures suffer from the issues of local minima, slow convergence and sensitivity to learning rate. To overcome these issues, non-iterative learning based deep randomized models especially random vector functional link network (RVFL) with direct links have proven to be successful. However, deep RVFL and its ensemble models are trained only on normal samples. In this paper, deep RVFL and its ensembles are enabled to incorporate privileged information, as the standard RVFL model and its deep models are unable to use privileged information. To fill this gap, we have incorporated 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. As RVFL is an unstable classifier, we propose ensemble deep RVFL+ with LUPI framework (edRVFL+) which exploits the LUPI as well as the diversity among the base leaners for better classification. Unlike traditional ensemble approach wherein multiple base learners are trained, the proposed edRVFL+ model 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. We utilise different activation functions while processing the normal and privileged information in the proposed deep architectures. To the best of our knowledge, this is first time that a separate privileged information is generated. The proposed dRVFL+ and edRVFL+ models are employed for the diagnosis of Alzheimer's disease. Experimental results demonstrate the superiority of the proposed dRVFL+ and edRVFL+ models over baseline models. Thus, the proposed edRVFL+ model can be utilised in clinical setting for the diagnosis of AD.

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/.

Random vector functional link (RVFL) network has been successfully employed in diverse domains such as computer vision and machine learning, due to its universal approximation capability. Recently, the shallow RVFL architecture has been extended to deep architectures. In deep architectures, multiple hidden layers are stacked for extracting informative features from the original feature space. Therefore, having rich features, deep models are very successful compared to shallow models. In this article, we propose an extended feature RVFL (efRVFL) model that is trained over extended feature space generated analytically from the original feature space. The proposed efRVFL model has three types of features, i.e., original features, supervised randomized (newly generated) features, and unsupervised randomized features, in its feature matrix. The proposed efRVFL model with additional features has capability to capture nonlinear hidden relationships within the dataset. The proposed efRVFL model is an unstable classifier, and thus, its performance can be improved further via ensemble learning. Ensemble models are stable and accurate and have better generalization performance than single models. Therefore, we also propose an ensemble of extended feature RVFL (en-efRVFL) model. Each base model of en-efRVFL is trained over different feature spaces so that more accurate and diverse base models can be generated. The outcome of the base models is integrated via average voting scheme. Empirical evaluation over$46$UCI classification datasets demonstrates that the proposed efRVFL and en-efRVFL models have better performance than RVFL and other given deep models. Furthermore, the experimental results over$12$sparse datasets show that the proposed en-efRVFL model has a winning performance among several deep feedforward neural networks (FNNs).

Alzheimer's disease (AD) is a prominent neurodegenerative disorder, which leads to memory loss and cognitive impairment. The progression is irreversible and shows atrophies in cerebral cortex. Multiple studies revealed that the early diagnosis and early treatment can slow the progress of dementia, and hence, further atrophies can be controlled. Brain imaging data, such as magnetic resonance imaging (MRI), have been prominently used for the diagnosis of AD. Multiple approaches have been proposed for the diagnosis of AD. We propose a novel intuitionistic fuzzy random vector functional link network (IFRVFL) for the diagnosis of AD. Unlike standard random vector functional link (RVFL) network, extreme learning machine (ELM), and kernel ridge regression (KRR), which uses a uniform weighting approach for generating the optimal classifier, the proposed IFRVFL uses a fuzzy weighted approach for generating the optimal classifier. A uniform weighting scheme assumes that all the data samples are equally important; however, in real-world scenarios, this assumption may not hold true due to the presence of outliers and noise. Hence, it results in lower generalization. The proposed IFRVFL assigns each sample an intuitionistic fuzzy number (IFN), which is a function of membership and nonmembership score of a sample. The membership score is a function of the sample distance from the centroid of its corresponding class and the nonmembership score is a function of sample distance from the centroid as well as the neighborhood of the given sample. The proposed IFRVFL effectively reduces the influence of outliers. To evaluate the efficiency of the proposed IFRVFL model, we employed it for the diagnosis of AD. Experimental results demonstrate that the proposed IFRVFL model is superior in mild cognitive impairment (MCI) versus AD case. Thus, IFRVFL can be used in the clinical setting for the early diagnosis of AD. Furthermore, to check the robustness of the proposed IFRVFL model, we also evaluated it on benchmark datasets. Experimental results and the statistical tests reveal that the proposed IFRVFL is better in comparison to baseline models. The source code of the proposed IFRVFL formulation is available at https://github.com/mtanveer1/.

The data mining community has a major challenge in classifying datasets with noise, outliers and imbalanced classes. Twin support vector machine (TSVM) is a well-known plane-based learning technique for classification, however, it has poor performance on aforementioned datasets. To address the issue, in this paper, we propose a novel class probability and generalized bell fuzzy twin SVM for imbalanced data (CGFTSVM-ID). The proposed CGFTSVM-ID assigns membership value to the data points by a new membership function called class probability and generalized bell (CPGB) function. The membership function for majority class is a combination of generalized bell (gbell) function, class probability and imbalance ratio. The gbell function suppress the negative impact of outliers in the training data by assigning them less value. The less class probability of the majority class data points denotes their higher possibility to be noise. The imbalance ratio of the classes considered in the membership function tackles the imbalancing issue of the datasets. In order to ensure the importance of minority class samples in model learning, relatively high memberships are assigned to them. Thus, the proposed CPGB function handles the class imbalance learning problem having noise, and outliers. We employ successive overrelaxation technique to solve the proposed optimization problem. The extensive numerical experiments and statistical analysis carried out over imbalanced real-world UCI and KEEL datasets clearly reveal that the proposed CGFTSVM-ID has superior generalization performance in comparison to baseline models. Moreover, the experiments are also conducted on the publicly available ADNI dataset for Alzheimer's disease classification and results demonstrate the superiority of the proposed CGFTSVM-ID. The code for the proposed CGFTSVM-ID can be found onhttps://github.com/mtanveer1/CGFTSVM-ID.

In practical applications, datasets frequently encompass noise, outliers, and imbalanced classes, which can markedly affect a model's generalization performance. Support vector machine (SVM) and its twin variant i.e., TWSVM tend to be biased towards the majority class samples, leading to misclassification of the minority class samples. TWSVM suffers from this biasness as it generates hyperplanes without considering preceding data information. To address the aforementioned issues, we propose bell-shaped fuzzy least square twin support vector machine for imbalance data (BSFLSTSVM-ID). The proposed BSFLSTSVM-ID allocates weight to the majority class samples through a novel membership function, namely, “class probability and bell-shaped” (CPBS). The CPBS function is amalgamation of class probability, bell-shaped function, and imbalance ratio of the dataset. The bell-shaped function's value diminishes as data points move farther away from the class center, reducing the influence of noise or outliers in constructing hyperplanes. To underscore the importance of samples from minority class, a weight of one is assigned to them. Furthermore, the proposed BSFLSTSVM-ID utilizes the conjugate gradient method to handle the challenge of matrix inversion. To demonstrate its effectiveness, we conducted experiments on 59 UCI and KEEL datasets with imbalance ratios from 1 to 72.69. Additionally, we tested the proposed BSFLSTSVM-ID model's scalability on NDC datasets and applied it to diagnosing breast cancer and Alzheimer's disease using the BreakHis and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, respectively. The results show that the proposed BSFLSTSVM-ID outperforms baseline models, highlighting its potential for tackling classification challenges in the biomedical domain.

In the realm of data classification, broad learning system (BLS) has proven to be a potent tool that utilizes a layer-by-layer feed-forward neural network. However, the traditional BLS treats all samples as equally significant, which makes it less robust and less effective for real-world datasets with noises and outliers. To address this issue, we propose fuzzy broad learning system (F-BLS) and the intuitionistic fuzzy broad learning system (IF-BLS) models that confront challenges posed by the noise and outliers present in the dataset and enhance overall robustness. Employing a fuzzy membership technique, the proposed F-BLS model embeds sample neighborhood information based on the proximity of each class center within the inherent feature space of the BLS framework. Furthermore, the proposed IF-BLS model introduces intuitionistic fuzzy concepts encompassing membership, non-membership, and score value functions. IF-BLS strategically considers homogeneity and heterogeneity in sample neighborhoods in the kernel space. We evaluate the performance of proposed F-BLS and IF-BLS models on UCI benchmark datasets with and without Gaussian noise. As an application, we implement the proposed F-BLS and IF-BLS models to diagnose Alzheimer's disease (AD). Experimental findings and statistical analyses consistently highlight the superior generalization capabilities of the proposed F-BLS and IF-BLS models over baseline models across all scenarios. The proposed models offer a promising solution to enhance the BLS framework's ability to handle noise and outliers.

The random vector functional link (RVFL) neural network has shown the potential to overcome traditional artificial neural networks’ limitations, such as substantial time consumption and the emergence of suboptimal solutions. However, RVFL struggles to provide comprehensive insights into its decisionmaking processes. We propose the Neuro-fuzzy RVFL (NFRVFL) model by combining RVFL with neuro-fuzzy system. The proposed NF-RVFL model takes human-like decisions based on the IF-THEN approach and enhances its transparency in decision-making. Within this framework, input features undergo a fuzzification process as they traverse the fuzzy layer. The resulting fuzzified features then navigate a hidden layer through random projection as well as yielding defuzzified values via defuzzification. The defuzzified values, hidden layer outputs and original input features collectively contribute to the output prediction process. The proposed NF-RVFL model employs three distinct clustering methods to establish fuzzy layer centers: randomly initialized centers (referred to as Rmeans), K-means clustering centers, and fuzzy C-means clustering centers. This approach generates three distinct model variations, namely NF-RVFL-R, NF-RVFL-K and NF-RVFL-C, each producing a diverse set of fuzzified and defuzzified samples. Our research involves experiments on various UCI benchmark datasets, covering binary, multiclass classification, and regression tasks. These datasets are sourced from diverse domains and exhibit different sizes. We compare the proposed NF-RVFL models to the existing baseline models. The statistical tests and comprehensive experimental analyses across binary, multiclass, and regression datasets consistently show that all variations of the proposed NF-RVFL model outperform baseline models, highlighting their generalization capabilities. The proposed NF-RVFL models show the generic nature by being adeptly applicable and excelling in regression as well as classification tasks. The source code of the proposed NF-RVFL model is available athttps://github.com/mtanveer1/NeuroFuzzy-RVFL.

Alzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in constructing learning classifiers in early-stage AD diagnosis. However, they often struggle with AD diagnosis due to uncertainties stemming from unclear annotations by experts, challenges in data collection, such as data harmonization issues, and limitations in equipment resolution. These factors contribute to imprecise data, hindering accurate analysis, interpretation of obtained results, and understanding of complex symptoms. In response, the integration of fuzzy logic into DL, forming fuzzy deep learning (FDL), effectively manages imprecise data and provides interpretable insights, offering a valuable advancement in AD. Therefore, exploring recent advancements in integrating DL with fuzzy logic is crucial for improving AD diagnosis. In this review, we explore the contributions of fuzzy logic within FDL models, focusing on fuzzy-based image preprocessing, segmentation, and classification. Moreover, in exploring research directions, we discuss the possibility of the fusion of multimodal data with fuzzy logic, addressing challenges in AD diagnosis. Leveraging fuzzy logic and membership while integrating diverse datasets, such as genomics, proteomics, and metabolomics may provide an effective development of a DL classifier. In addition, fuzzy explainable DL promises more accurate and linguistically interpretable decision support systems for AD diagnosis. The primary objective of this article is to serve as a comprehensive and authoritative resource for newcomers, researchers, and clinicians interested in employing FDL models for AD diagnosis.

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.

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.

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 fascinating challenge in robotics-human interaction is imitating the emotion recognition capability of humans to robots with the aim to make human-robotics interaction natural, genuine and intuitive. To achieve the natural interaction in affective robots, human-machine interfaces, and autonomous vehicles, understanding our attitudes and opinions is very important, and it provides a practical and feasible path to realize the connection between machine and human. Multimodal interface that includes voice along with facial expression can manifest a large range of nuanced emotions compared to purely textual interfaces and provide a great value to improve the intelligence level of effective communication. Interfaces that fail to manifest or ignore user emotions may significantly impact the performance and risk being perceived as cold, socially inept, untrustworthy, and incompetent. To equip a child well for life, we need to help our children identify their feelings, manage them well, and express their needs in healthy, respectful, and direct ways. Early identification of emotional deficits can help to prevent low social functioning in children. In this work, we analyzed the child’s spontaneous behavior using multimodal facial expression and voice signal presenting multimodal transformer-based last feature fusion for facial behavior analysis in children to extract contextualized representations from RGB video sequence and Hematoxylin and eosin video sequence and then using these representations followed by pairwise concatenations of contextualized representations using cross-feature fusion technique to predict users emotions. To validate the performance of the proposed framework, we have performed experiments with the different pairwise concatenations of contextualized representations that showed significantly better performance than state-of-the-art method. Besides, we perform t-distributed stochastic neighbor embedding visualization to visualize the discriminative feature in lower dimension space and probability density estimation to visualize the prediction capability of our proposed model.

Alzheimer's disease (AD) is a neurological disorder that primarily affects the elderly and is characterized by cognitive decline and memory loss. Recent research has shown that susceptibility-weighted imaging (SWI) images are useful for diagnosing AD because they reveal abnormally high iron deposition in certain brain regions of people with the disease. Machine learning (ML) algorithms, particularly deep learning (DL) networks, are making incredible strides in AD diagnosis using imaging data to assist physicians in making decisions. The random-vector functional link network (RVFL) is an example of a single-hidden-layer feedforward network that uses a closed-form solution-based approach to offer a variety of feature mapping functions and kernels. In the proposed paper, SWI image features are extracted with a DL network, ResNet 50, and afterward classified with a kernel ridge regression-based RVFL network. To manage data with an unbalanced class distribution, we present a weighted kernel ridge regression-based RVFL network that is capable of generalizing to balanced data. We used SWI images from the publicly accessible OASIS dataset to evaluate the proposed methods for AD diagnosis. Experiment results show that the proposed model outperforms the state-of-the-art models.

A multimodal biometric recognition system on Internet of Things (IoT) with Blockchain environments has been proposed in this article. This system distributes a decentralized biometric authentication process mechanism and improves security in the IoT environment. The implementation of this system consists of five components: 1) image preprocessing; 2) feature representation; 3) cancelable biometrics; 4) classification; and 5) encryption–decryption of multimodal biometrics templates. A region of interest is segmented from each biometric trait during image preprocessing. Then, a discriminant feature extraction technique has been employed for feature computation. A cancellable biometric system (CBS) is introduced to secure and preserve the original biometric features from external hazards and misuse. The extracted cancelable features undergo classification to perform the subjects’ authentication. Then, a method of encryption–decryption of templates is performed to handle the various online authentication attacks and improve IoT-enabled authentication. Finally, the recognition scores due to iris, periocular, palmprint, and face biometrics are fused to increase the performance of the proposed IoT-enabled multimodal biometric system. The proposed system obtains identification performance 99.92%, 100% for CASIA-V4-distance (CASIA-DIST), UBIRIS-v2 iris, 100% for periocular (CASIA-DIST, UBIRIS-v2), 100% for Bosphorus palmprint, and 100% for FERET face databases using 30-D cancelable features that show the superiority of the proposed system as compared with state-of-the-art methods.

Abstract 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.

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 (0.972,0.973and0.973precision, 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

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.

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 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.

Alzheimer’s disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of the abnormal brain. Machine learning models like support vector regression (SVR) based models have been successfully employed in the estimation of brain age. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR),$\varepsilon$-TSVR, and Lagrangian TSVR (LTSVR) models have been used for the regression problems.$\varepsilon$-TSVR and LTSVR models seek a pair of$\varepsilon$-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 and hence may be prone to overfitting. 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$L_2$norm instead of$L_1$. Also, we introduce a different Lagrangian function to avoid the computation of matrix inverses. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer’s disease for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for brain-age prediction.

Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the significant causes of death in the elderly population. Many deep learning techniques have been proposed to diagnose AD using Magnetic Resonance Imaging (MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is challenging as the inter-slice information gets lost. To this end, we propose a novel and lightweight framework termed 'Biceph-net' for AD diagnosis using 2D MRI scans that models both the intra-slice and inter-slice information. 'Biceph-net' has been experimentally shown to perform similar to other Spatio-temporal neural networks while being computationally more efficient. Biceph-net is also superior in performance compared to vanilla 2D convolutional neural networks (CNN) for AD diagnosis using 2D MRI slices. Biceph-net also has an inbuilt neighbourhood-based model interpretation feature that can be exploited to understand the classification decision taken by the network. Biceph-net experimentally achieves a test accuracy of 100% in the classification of Cognitively Normal (CN) vs AD, 98.16% for Mild Cognitive Impairment (MCI) vs AD, and 97.80% for CN vs MCI vs AD.

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.

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.

The papers in this special section (Part II) focus on advanced machine learning algorithms for biomedical data and imaging. The papers in Part II aim at bringing together contributions from both academia and industry to highlight the recent progress of machine-learning algorithm specific to medical data.

In Healthcare, the Internet of Things (IoT)-enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this article, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three-step template protection techniques are proposed: (i) Cancelable biometrics , (ii) BioCrypto-Circuit , and (iii) BioCrypto-Protection . The performance of the proposed system has been tested on four benchmark face databases, CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.

A teacher in a school plays significant role in classroom while teaching the students. Similarly, learning via privileged information (LUPI) gives extra information generated by a teacher to 'teach' the learning algorithm while training. This paper proposes minimum variance embedded random vector functional link network with privileged information (MVRVFL+). The proposed MVRVFL+ minimizes the intraclass variance of the training data and uses privileged information paradigm which provides the additional knowledge during the training of the model. The proposed MVRVFL+ classification model is evaluated on 43 benchmark UCI datasets. From the experimental analysis, the proposed MVRVFL+ showed best average accuracy and emerged as the lowest average rank classifier among the baseline models.

The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly improves the performance of randomized neural networks. However, the framework’s capabilities are limited by traditional hyper-parameter selection approaches. Meanwhile, different random network architectures, such as the existence or lack of a direct link and the mapping of direct links, can also strongly affect the results. We present an automated learning pipeline for the ensemble deep randomized feed-forward neural network in this paper, which integrates hyper-parameter selection and randomized network architectural search via Bayesian optimization to ensure robust performance. Experiments on 46 UCI tabular datasets show that our strategy produces state-of-the-art performance on various tabular datasets among a range of randomized networks and feed-forward neural networks. We also conduct ablation studies to investigate the impact of various hyper-parameters and network architectures.

Analyzing unlabeled data is of prime importance in machine learning. Creating groups and identifying an underlying clustering principle is essential to many fields, such as biomedical analysis and market research. Novel unsupervised machine learning algorithms, also called clustering algorithms, are developed and utilized for this task. Inspired by twin support vector machine (TWSVM) principles, a recently introduced plane-based clustering algorithm, the twin bounded support vector clustering (TBSVC), is used in widespread clustering problems. However, TBSVC is sensitive to noise and suffers from low resampling stability due to usage of hinge loss. Pinball loss is another type of loss function that is less sensitive toward noise in the datasets and is more stable for resampling of datasets. However, the use of pinball loss negatively affects the sparsity of the solution of the problem. In this article, we present a novel plane-based clustering method, the sparse TBSVC using pinball loss (pinSTBSVC). The proposed pinSTBSVC is the sparse version of our recently proposed TBSVC using pinball loss (pinTBSVC). Sparse solutions help create better-generalized solutions to clustering problems; hence, we attempt to use the$\epsilon $-insensitive pinball loss function to propose pinSTBSVC. The loss function used to propose pinSTBSVC provides sparsity to the solution of the problem and improves the aforementioned plane-based clustering algorithms. Experimental results performed on benchmark University of California, Irvine (UCI) datasets indicate that the proposed method outperforms other existing plane-based clustering algorithms. Additionally, we also give the application of our method in biomedical image clustering and marketing science. We show that the proposed method is more accurate on real-world datasets too. The code for the proposed algorithm is also provided on the author’s Github page:https://github.com/mtanveer1.

The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.

Twin support vector machines (TSVMs) have been successfully employed for binary classification problems. With the advent of machine learning algorithms, data have proliferated and there is a need to handle or process large-scale data. TSVMs are not successful in handling large-scale data due to the following: 1) the optimization problem solved in the TSVM needs to calculate large matrix inverses, which makes it an ineffective choice for large-scale problems; 2) the empirical risk minimization principle is employed in the TSVM and, hence, may suffer due to overfitting; and 3) the Wolfe dual of TSVM formulation involves positive-semidefinite matrices, and hence, singularity issues need to be resolved manually. Keeping in view the aforementioned shortcomings, in this article, we propose a novel large-scale fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL). We formulate the LS-FLSTSVM-CIL such that the proposed optimization problem ensures that: 1) no matrix inversion is involved in the proposed LS-FLSTSVM-CIL formulation, which makes it an efficient choice for large-scale problems; 2) the structural risk minimization principle is implemented, which avoids the issues of overfitting and results in better performance; and 3) the Wolfe dual formulation of the proposed LS-FLSTSVM-CIL model involves positive-definite matrices. In addition, to resolve the issues of class imbalance, we assign fuzzy weights in the proposed LS-FLSTSVM-CIL to avoid bias in dominating the samples of class imbalance problems. To make it more feasible for large-scale problems, we use an iterative procedure known as the sequential minimization principle to solve the objective function of the proposed LS-FLSTSVM-CIL model. From the experimental results, one can see that the proposed LS-FLSTSVM-CIL demonstrates superior performance in comparison to baseline classifiers. To demonstrate the feasibility of the proposed LS-FLSTSVM-CIL on large-scale classification problems, we evaluate the classification models on the large-scale normally distributed clustered (NDC) dataset. To demonstrate the practical applications of the proposed LS-FLSTSVM-CIL model, we evaluate it for the diagnosis of Alzheimer’s disease and breast cancer disease. Evaluation on NDC datasets shows that the proposed LS-FLSTSVM-CIL has feasibility in large-scale problems as it is fast in comparison to the baseline classifiers.

Recently, distributed semisupervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate nonsensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semisupervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semisupervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost. Our code is available online.1

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.

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 patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. 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, replayed videos, and 3D masks. 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. 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 model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods.

The papers in this special section focus on advanced machine learning algorithms for biomedical data and image processing. Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance, and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation and to explore large-scale data in biomedical sciences.

Alzheimer's disease (AD) is one of the deadliest neurodegenerative diseases ailing the elderly population all over the world. An ensemble of Deep learning (DL) models can learn highly complicated patterns from MRI scans for the detection of AD by utilizing diverse solutions. In this work, we propose a computationally efficient, DL-architecture agnostic, ensemble of deep neural networks, named 'Deep Transfer Ensemble (DTE)' trained using transfer learning for the classification of AD. DTE leverages the complementary feature views and diversity introduced by many different locally optimum solutions reached by individual networks through the randomization of hyper-parameters. DTE achieves an accuracy of 99.05% and 85.27% on two independent splits of the large dataset for cognitively normal (NC) vs AD classification task. For the task of mild cognitive impairment (MCI) vs AD classification, DTE achieves 98.71% and 83.11% respectively on the two independent splits. It also performs reasonable on a small dataset consisting of only 50 samples per class. It achieved a maximum accuracy of 85% for NC vs AD on the small dataset. It also outperformed snapshot ensembles along with several other existing deep models from similar kind of previous works by other researchers.

In this paper, we propose ensemble of random vector functional link network known as co-trained random vector functional link network (coRVFL). Random vector functional link network solves the optimization problem via closed form solution and hence avoids the problems of slow convergence and local minima problems. The proposed coRVFL trains two RVFL models jointly such that each RVFL model is constructed with different feature projection matrix and hence, shows better generalization performance. We use randomly projected features and sparse-l1. norm autoencoder based features to train the proposed coRVFL model. Experimental results show that the proposed coRVFL is performing better in comparison with the baseline models. Furthermore, statistical analysis reveals that the proposed coRVFL model performs statistically better than the baseline approaches.

Ensemble learning methods, which combine several base classifiers, is a common technique to enhance the classification ability of ensemble models in the field of pattern recognition and machine learning. Rotation Forest, an ensemble algorithm, has been used widely in various fields with nice generalization performance. The main idea of Rotation Forest is to animate concurrently both diversity and individual accuracy within the ensemble. On the other hand, random vector functional link (RVFL) neural network, a randomized version of single layer feed-forward neural network (SLFN), is a successful model because of its universal approximation property. In this paper, we propose a novel ensemble method, known as rotated random vector functional link neural network (RoF-RVFL), which combines rotation forest (RoF) and RVFL classifiers. To verify the effectiveness of the proposed RoF- RVFL method, empirical comparisons are carried out among Rotation Forest (RoF), Random Forest (RaF), RVFL and the proposed RoF-RVFL method over 42 UCI benchmark datasets. The experimental results show that the proposed RoF-Rvflmethod is able to generate more robust network with better generalization performance.

Rip currents are intense and localized seaward-flowing water from shore through the surf zone, cutting through the lines of breaking ocean waves. It generally pulls a person out to sea very fast, with speeds up to eight feet per second. It is one of the most important reasons for an average of 21 confirmed fatalities each year. Fully automated Rip current detection using state-of-the-art deep learning techniques can help to monitor the coast. However, there are several challenges involved, like dealing with a small training sample size and unavailability of pretrained models in the domain of Rip current. In this work, we address these issues and propose a novel fast and lightweight RipDet framework for an efficient and accurate automated Rip current detection. The proposed model is aided by careful data augmentation, fine-tuning, and a custom learning rate schedule that helps the model adapt to Rip currents' distribution with the low number of training samples. Extensive experiments on benchmark data show that the proposed model outperforms other state-of-the-art methods for Rip current detection with mean average precision (mAP) score of 98.131 %.

Abstract Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.

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 utilizes the hinge loss function to penalize the misclassification. However, the hinge loss relies on shortest distance between different clusters, and is unstable for noise-corrupted datasets, and 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. 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.

Despite the myriad of attributes found in a single style image, existing neural style transfer methods produce outputs with limited variety–typically only a single realization of the style image. They also do not provide an easy way to control the stylization process, limiting the creative freedom of users. In this paper, we propose Neural Style Palette (NSP), a method for interactively generating a variety of stylized images from only a single style input. Our approach allows human influence in the stylization process, a design inspired by Hybrid Human-Artificial Intelligence. Like a color palette,NSPenables a meaningful interaction by presenting a collection of sub-textures, which we also refer to as anchor styles, that act as a visual guide for the users. These anchor styles capture different attributes in the single style image that the users can creatively blend to create their desired realizations. To offer a diversified selection in theNSP, we constrain the anchor styles to be distant from one another while maintaining faithfulness to the original style image. This is possible through our two proposed novel losses: a style-separation loss that encourages the sub-textures to be distinct and a unification loss to ensure that the sub-textures center around the original style while encouraging additional diversity. We perform several experiments to prove the effectiveness of our method and generalize to improve existing methods.

Despite the remarkable results and numerous advancements in neural style transfer, achieving artistic control is still a challenging feat, primarily since existing methodologies treat the style representation as a black-box model. This oversight significantly limits the range of possible artistic manipulations. In this paper, we propose a method to enable artistic control on any correlation-based style transfer models along with guiding intuitions. Our focus is on controlling two perceptual factors: Pattern Density and Stroke Strength. To achieve this, we introduce the centered Gram style representation and manipulate it with our variance-aware adaptive weighting and correlation-based selective masking. Through several experiments and comparisons with the state-of-the-art, we show that we can achieve artistic control with competitive stylization quality. Additionally, since our method involves manipulating style representation, it can easily be adapted to popular style transfer models. We analyze different style representation properties to propose rules that govern the style transfer process, which is critical towards achieving artistic control over pattern density and stroke strength.

Universum-based support vector machine incorporates prior information about the distribution of data in training of the classifier. This leads to better generalization performance but with increased computation cost. Various twin hyperplane-based models are proposed to reduce the computation cost of universum-based algorithms. In this work, we present an efficient angle-based universum least squares twin support vector machine (AULSTSVM) for classification. This is a novel approach of incorporating universum in the formulation of least squares-based twin SVM model. First, the proposed AULSTSVM constructs a universum hyperplane, which is proximal to universum data points. Then, the classifying hyperplane is constructed by minimizing the angle with the universum hyperplane. This gives prior information about data distribution to the classifier. In addition to the quadratic loss, we introduce linear loss in the optimization problem of the proposed AULSTSVM, which leads to lesser computation cost of the model. Numerical experiments are performed on several benchmark synthetic, real-world, and large-scale datasets. The results show that proposed AULSTSVM performs better than existing algorithms w.r.t. generalization performance as well as computation time. Moreover, an application to Alzheimer’s disease is presented, where AULSTSVM obtains accuracy of 95% for classification of healthy and Alzheimers subjects. The results imply that the proposed AULSTSVM is a better alternative for classification of large-scale datasets and biomedical applications.

In the last decade, twin support vector machine (TWSVM) classifiers have achieved considerable emphasis on pattern classification tasks. However, the TWSVM formulation still suffers from the following two shortcomings: (1) TWSVM deals with the inverse matrix calculation in the Wolfe-dual problems, which is intractable for large-scale datasets with numerous features and samples, and (2) TWSVM minimizes the empirical risk instead of the structural risk in its formulation. With the advent of huge amounts of data today, these disadvantages render TWSVM an ineffective choice for pattern classification tasks. In this article, we propose an efficient large-scale least squares twin support vector machine (LS-LSTSVM) for pattern classification that rectifies all the aforementioned shortcomings. The proposed LS-LSTSVM introduces different Lagrangian functions to eliminate the need for calculating inverse matrices. The proposed LS-LSTSVM also does not employ kernel-generated surfaces for the non-linear case, and thus uses the kernel trick directly. This ensures that the proposed LS-LSTSVM model is superior to the original TWSVM and LSTSVM. Lastly, the structural risk is minimized in LS-LSTSVM. This exhibits the essence of statistical learning theory, and consequently, classification accuracy on datasets can be improved due to this change. The proposed LS-LSTSVM is solved using the sequential minimal optimization (SMO) technique, making it more suitable for large-scale problems. We further proved the convergence of the proposed LS-LSTSVM. Exhaustive experiments on several real-world benchmarks and NDC-based large-scale datasets demonstrate that the proposed LS-LSTSVM is feasible for large datasets and, in most cases, performed better than existing algorithms.

Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

Twin support vector machines (TWSVM) have been successfully applied to the classification problems. TWSVM is computationally efficient model of support vector machines (SVM). However, in real world classification problems issues of class imbalance and noise provide great challenges. Due to this, models lead to the inaccurate classification either due to higher tendency towards the majority class or due to the presence of noise. We provide an improved version of robust fuzzy least squares twin support vector machine (RFLSTSVM) known as regularized robust fuzzy least squares twin support vector machine (RRFLSTSVM) to handle the imbalance problem. The advantage of RRFLSTSVM over RFLSTSVM is that the proposed RRFLSTSVM implements the structural risk minimization principle by the introduction of regularization term in the primal formulation of the objective functions. This modification leads to the improved classification as it embodies the marrow of statistical learning theory. The proposed RRFLSTSVM doesn't require any extra assumption as the matrices resulting in the dual are positive definite. However, RFLSTSVM is based on the assumption that the inverse of the matrices resulting in the dual always exist as the matrices are positive semi-definite. To subsidize the effects of class imbalance and noise, the data samples are assigned weights via fuzzy membership function. The fuzzy membership function incorporates the imbalance ratio knowledge and assigns appropriate weights to the data samples. Unlike TWSVM which solves a pair of quadratic programming problem (QPP), the proposed RRFLSTSVM method solves a pair of system of linear equations and hence is computationally efficient. Experimental and statistical analysis show the efficacy of the proposed RRFLSTSVM method.

Universum based algorithms involve universum samples in the classification problem to improve the generalization performance. In order to provide prior information about data, we utilized universum data to propose a novel classification algorithm. In this paper, a novel parametric model for universum based twin support vector machine is presented for classification problems. The proposed model is termed as universum least squares twin parametric-margin support vector machine (ULSTPMSVM). The solution of ULSTPMSVM involves a system of linear equations. This makes the ULSTPMSVM efficient w.r.t. training time. In order to verify the performance of the proposed model, various experiments are carried out on real world benchmark datasets. Statistical tests are performed to verify the significance of the proposed method. The proposed ULSTPMSVM performed better than existing algorithms in terms of classification accuracy and training time for most of the datasets. Moreover, an application of proposed ULSTPMSVM is presented for classification of Alzheimer's disease data.

SDN has provided significant convenience for network providers and operators in cloud computing. Such a great advantage is extending to the Internet of Things network. However, it also increases the risk if the security of an SDN network is compromised. For example, if the network operator's permission is illegally obtained by a hacker, he/she can control the entry of the SDN network. Therefore, an effective authentication scheme is needed to fit various application scenarios with high-security requirements. In this article, we design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time. We examined THP in the perspectives of both security and usability, with a total number of 694 participants in 63 days. Our evaluation shows that THP can provide better performance than the existing schemes in terms of security and usability.

The Semantic Internet of Things (SIoT) is the extension of the Internet of Things (IoT) and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the IoT. However, the SIoT has the characteristics of both the IoT and the Semantic Web environment, and the corresponding semantic data present many new data features. In this article, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph (UKG), allowing us to be applied to the environment of the SIoT better. Here, we design a semantic collaboration method based on a UKG. It can take the UKG as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing the semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the SIoT. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes.

Video summarization (VS) has attracted intense attention recently due to its enormous applications in various computer vision domains, such as video retrieval, indexing, and browsing. Traditional VS researches mostly target at the effectiveness of the VS algorithms by introducing the high quality of features and clusters for selecting representative visual elements. Due to the increased density of vision sensors network, there is a tradeoff between the processing time of the VS methods with reasonable and representative quality of the generated summaries. It is a challenging task to generate a video summary of significant importance while fulfilling the needs of Internet of Things (IoT) surveillance networks with constrained resources. This article addresses this problem by proposing a new computationally effective solution through designing a deep CNN framework with hierarchical weighted fusion for the summarization of surveillance videos captured in IoT settings. The first stage of our framework designs discriminative rich features extracted from deep CNNs for shot segmentation. Then, we employ image memorability predicted from a fine-tuned CNN model in the framework, along with aesthetic and entropy features to maintain the interestingness and diversity of the summary. Third, a hierarchical weighted fusion mechanism is proposed to produce an aggregated score for the effective computation of the extracted features. Finally, an attention curve is constituted using the aggregated score for deciding outstanding keyframes for the final video summary. Experiments are conducted using benchmark data sets for validating the importance and effectiveness of our framework, which outperforms the other state-of-the-art schemes.

In virtue of advances in smart networks and the cloud computing paradigm, smart healthcare is transforming. However, there are still challenges, such as storing sensitive data in untrusted and controlled infrastructure and ensuring the secure transmission of medical data, among others. The rapid development of watermarking provides opportunities for smart healthcare. In this article, we propose a new data-sharing framework and a data access control mechanism. The applications are submitted by the doctors, and the data is processed in the medical data center of the hospital, stored in semi-trusted servers to support the selective sharing of electronic medical records from different medical institutions between different doctors. Our approach ensures that privacy concerns are taken into account when processing requests for access to patients’ medical information. For accountability, after data is modified or leaked, both patients and doctors must add digital watermarks associated with their identification when uploading data. Extensive analytical and experimental results are presented that show the security and efficiency of our proposed scheme.

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.

Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.

In this paper, we propose an improved version of sparse pinball twin support vector machine (SPTSVM) [1], called improved sparse pinball twin support vector machine (ISPTSVM). SPTSVM implements empirical risk minimization principle and the matrices appearing in the formulation of SPTSVM are positive semi-definite. Here, we reformulate the primal problems of SPTSVM by introducing extra regularization term to the objective function of SPTSVM. Unlike SPTSVM, structural risk minimization (SRM) principle is implemented in the proposed ISPTSVM which embodies the marrow of statistical learning theory. Also, the matrices that appear in the dual formulation of the proposed ISPTSVM are positive definite. Results computed on multiple UCI benchmark datasets clearly indicate the effectiveness and applicability of the proposed ISPTSVM compared to pinball support vector machine (Pin-SVM), twin bounded support vector machine (TBSVM) and SPTSVM.

Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well. This paper proposes a tool by which the stage and zones of Retinopathy of Prematurity in infants can be diagnosed easily. This tool takes the input from the Retcam and detects the stage, zone, and gives a rating of 1 to 9 for classifying the severity of the disease in the infants. This is achieved by extracting the optic disc, marking the ridge, and the distance of the optic nerve. This tool can be easily used by nurses and paramedics, unlike the existing technologies which require the guidance of a specialist to come to a conclusion.

In this paper, we propose a new method for automated classification of focal (epileptic) and non-focal (non-epileptic) electroencephalogram (EEG) signals. We use bivariate EEG signals of both epileptic and non-epileptic classes as our dataset. Difference time series of bivariate EEG signals is first computed to eliminate the effect of noise. Then the difference time series EEG signals are decomposed into coefficients using Fourier-Bessel (FB) series expansion. FB series expansion is a new method for signal decomposition that decomposes the signal into a finite and unique set of coefficients. The decomposition process yields coefficients which are further divided into 5 segments which are considered for the extraction of features, where for each signal 17 different features are computed. These extracted features are used for binary classification of EEG signals into epileptic and non-epileptic classes. We have implemented least square support vector machine (LS-SVM) along with various kernel functions such as linear, polynomial, and radial basis function (RBF) in our work. Classification accuracies obtained using these kernels and 10-fold crossvalidation are compared. With the proposed methodology, we can classify the EEG signals into focal and non-focal class with a significant accuracy.

Brain-Computer Interfaces (BCIs) systems convert brain signals into outputs commands those allow to user to communicate even absence of other body nerves and muscles activities. Response to cognitive activity (mental task) grounded BCI system is one of the dominate areas of research interest. Electroencephalography (EEG) signals are utilized to characterize the brain activities in the BCI domain. Efficient feature extraction from EEG signal is the most important aspect of good per-formance of classification model. Two known feature extraction methods for non-linear and non-stationary signals are Wavelet Transform and Empirical Mode Decomposition. By exploiting both techniques, an adaptive-filter based approach was proposed earlier famous as Empirical Wavelet Transform (EWT) to de-compose such dynamic signals. But EWT failed to provide useful features for dynamic signals which has overlapping in frequency domain and time domain. To overcome this problem, we utilized fuzzy c-means algorithm along with EWT in our experiment. A well-known multivariate feature selection technique named Linear Regression is used to avoid the problem of the small ratio of samples to features. Further, the Quadratic discriminant classifier (QDC) has been utilized to develop the classification model. The experiments have been done on a publicly available task-based EEG data for comparing the proposed approach with EWT based cognitive activity (mental task) classification. The experimental results show that the proposed fuzzy-based EWT approach for EEG classification gives superior performance over the original EWT.