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A review of studies involving machine learning-based modeling of nanofluid flows with a specific focus on their utilization in compact heat transfer devices for electronic device cooling is reported here. This paper includes separate discussions on studies involving mono-disperse and hybrid nanofluids whenever feasible. The emphasis is on studies published within the last three to five years, revealing a significant increase in research on nanofluid flow modeling using machine learning methods during this time frame. Various machine learning models have been extensively employed to model various nanofluid properties. A significant number of studies have also predicted flow and thermal characteristics for different flow geometries relevant to the cooling of compact electronic devices. It has been observed that a majority of these studies have utilized the data collected from previously published experimental studies, while a few have incorporated accurate theoretical models for data generation. Given the substantial variations in dataset sizes and input parameters considered across such studies, selecting the most accurate machine learning algorithm out of all is highly unlikely. A few studies have also utilized physics-informed machine learning methods for flow field prediction, and those are also covered in this review. It is observed that the investigations utilizing data-driven methods to accelerate or substitute the conventional CFD-based modeling methods for multiphase flows in general, and nanofluid flows in particular, are very scarce in the literature. Insights derived from the review are also presented, and potential future research directions in this field are outlined. [ABSTRACT FROM AUTHOR]

Around the world, lung disease is a prevalent cause of death and illness. In this article, we propose a lung disease detection system for the automated identification of critical lung diseases: 1. Tuberculosis 2. Regular Pneumonia 3. COVID Pneumonia and healthy individuals from chest X-ray (CXR) images. The proposed system incorporates a two-stage classification method using an ensemble of convolutional neural networks (CNNs). We evaluated the performance of the proposed system using various evaluation metrics with a test set containing 200 normal, 100 Tuberculosis, 200 Regular Pneumonia, and 100 COVID Pneumonia (a total of 600) CXR images. The proposed system achieves an average macro precision, recall, and F1-score of 0.99 and an accuracy of 0.99. The performance of the proposed system based on F1-Score (0.99) for three classes, namely Tuberculosis, Pneumonia, and COVID, is better than the following existing classification frameworks: ensemble of K-ResNets for the same three classes (F1-Score: 0.84), modified ResNet50 (F1-Score:0.89), KNN classifier with fractional multi-channel exponent moments (F1-Score: 0.89), VGG-16 (F1-Score: 0.89), and convolutional sparse support estimator network (F1-Score for COVID: 0.78). The proposed system can also determine the confidence associated with the challenging classification of Regular Vs. COVID Pneumonia. We also utilized visualization techniques like Grad-CAM and Saliency maps to analyze the features learned by the CNNs, to create transparency in the prediction. [ABSTRACT FROM AUTHOR]

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia. Delayed diagnosis and overpriced tests are the prime reason for the high mortality rate, especially in underdeveloped countries. A time and cost-efficient diagnosis tool: Chest X-rays, was thus accepted as the standard diagnostic test for pediatric pneumonia. However, the lower radiation levels for diagnosis in children make the task much more onerous and time-consuming. The mentioned challenges initiate the need for a computer-aided detection model that is instantaneous and accurate. Our work proposes a stacked ensemble learning of deep learning-based features for pediatric pneumonia classification. The extracted features from the global average pooling layer of the fine-tuned Xception model pretrained on ImageNet weights are sent to the Kernel Principal Component Analysis for dimensionality reduction. The dimensionally reduced features are further trained and validated on the stacking classifier. The stacking classifier consists of two stages; the first stage uses the Random-Forest classifier, K-Nearest Neighbors, Logistic Regression, XGB classifier, Support Vector Classifier (SVC), Nu-SVC, and MLP classifier. The second stage operates on Logistic Regression using the first stage predictions for the final classification with Stratified K-fold cross-validation to prevent overfitting. The model was tested on the publicly available pediatric pneumonia dataset, achieving an accuracy of 98.3%, precision of 99.29%, recall of 98.36%, F1-score of 98.83%, and an AUC score of 98.24%. The performance shows its reliability for real-time deployment in assisting radiologists and physicians. [ABSTRACT FROM AUTHOR]

The article titled "Deep learning architectures for land cover classification using red and near-infrared satellite images" has been retracted by the Editor-in-Chief and the publisher due to concerns identified during an investigation. These concerns include unsupported citations, non-standard phrasing, and image irregularities. The Editor-in-Chief no longer has confidence in the results and conclusions of the article. The authors Anju Unnikrishnan and V. Sowmya disagree with the retraction, while author K.P. Soman has not responded to correspondence regarding the retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

Intracranial Haemorrhage (ICH) occurring due to any injury to the brain is a fatal condition and its timely diagnosis is critically important. In this work, we propose a complete one-stop model for the identification of Intracranial Haemorrhage (ICH) and for the segmentation of ICH regions in Computerized Tomography (CT) images. The proposed method incorporates Residual Neural Network (ResNet) architecture for ICH identification and further segments the ICH region using an Adversarial Network called SegAN. This work incorporates a data augmentation method using CycleGAN, to solve the problem of class imbalance in the ICH dataset, leading to improved performance in the ICH identification task. CycleGAN is trained to convert a non-ICH CT slice into a synthetic CT slice with ICH, thereby augmenting the ICH sub-class, where there is a lack of data points. The proposed method achieved a macro average F1-score of 0.91 and a specificity of 0.99 and a sensitivity of 0.80 in the ICH identification task. Also, the proposed method works as a segmentation tool for all the five ICH sub-types and achieved a dice score of 0.32 and a mean Intersection Over Union (IOU) of 0.22. Thus, our proposed ICH identification and segmentation model can aid doctors in the accurate and timely diagnosis of ICH. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Effect of Legendre-Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification." The article has been retracted by the Editor-in-Chief and the publisher due to concerns about compromised editorial handling and peer review process, inappropriate references, and not being in scope of the journal or guest-edited issue. The authors R. Reshma and V. Sowmya disagree with the retraction, while K. P. Soman has not responded to correspondence regarding the retraction. The publisher, Springer Nature, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

A novel approach of preprocessing EEG signals by generating spectrum image for effective Convolutional Neural Network (CNN) based classification for Motor Imaginary (MI) recognition is proposed. The approach involves extracting the Variational Mode Decomposition (VMD) modes of EEG signals, from which the Short Time Fourier Transform (STFT) of all the modes are arranged to form EEG spectrum images. The EEG spectrum images generated are provided as input image to CNN. The two generic CNN architectures for MI classification (EEGNet and DeepConvNet) and the architectures for pattern recognition (AlexNet and LeNet) are used in this study. Among the four architectures, EEGNet provides average accuracies of 91.37%, 94.41%, 85.67% and 90.21% for the four datasets used to validate the proposed approach. Consistently better results in comparison with results in recent literature demonstrate that the EEG spectrum image generation using VMD-STFT is a promising method for the time frequency analysis of EEG signals. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems. [ABSTRACT FROM AUTHOR]

Detection of salient region in an image is a crucial problem in many cognition and computer vision applications like object detection, adaptive image compression, automatic image cropping, video and image analysis. A part of an image is considered as salient, if the set of pixels under consideration protrudes from the rest, in terms of features such as color, contrast and local orientations. Generally, computational models for saliency assume that the image under observation is clean and fails to deal with visual disturbances. This paper presents a robust method for the detection of salient regions, using the multi-resolution dynamic mode decomposition (MRDMD approach). Effectiveness of the proffered method for the detection of salient region within clean and noisy images was examined and successfully verified for a wide range of noise strengths. [ABSTRACT FROM AUTHOR]

Human visual system is endowed with an innate capability of distinguishing the salient regions of an image. It do so even in the presence of noise and other natural disturbances. Conventional8 computational saliency models in the literature assume that the input images are clean, though an explicit treatment of noise is missing. In this paper, we propose a coupled data-driven approach for estimating saliency map for a noisy input using Variational Mode Decomposition (VMD) and Dynamic Mode Decomposition(DMD. Variational Mode Decomposition (VMD) is a well received technique explored for denoising in the literature. VMD modes with high entropy (randomness) are removed and the residual modes are employed to generate a scalar valued saliency map. The proposed method is compared against seven state-of-the-art methods over a wide range of noise strengths. The submitted approach furnished comparable results with respect to state-of-the art methods for clean and noisy images in terms of various benchmark performance measures. [ABSTRACT FROM AUTHOR]

Segmentation of fundamental heart sounds–S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This pa-per proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation. [ABSTRACT FROM AUTHOR]

Classification of remotely sensed data requires the modelling of suitable image processing algorithms. The rise of machine learning systems upgraded the viability of satellite image applications. Using Convolutional Neural Networks (CNN), benchmark classification exactness can be accomplished for land cover grouping. Motivated by the concept of Normalized Difference Vegetation Index (NDVI), this paper utilizes only the red and near infrared (NIR) band information for classifying the publicly available SAT-4 and SAT-6 datasets. This is done, since NDVI computation requires only the two band (red and NIR) information and the classes involved in the dataset are types of vegetation. In this work, new deep learning architectures for three different networks (AlexNet, ConvNet, VGG) were proposed by hypertuning the network and the input as two band data. The modified architectures with the two band information along with reduced number of filters were trained and tested model manages to classify the images into different classes. The proposed architectures are compared against the existing architectures in terms of accuracy, precision and trainable parameters. The proposed architecture is found to perform equally efficient by retaining high accuracy with less number of trainable parameters, when compared against the the performance of benchmark deep learning architectures for satellite image classification. [ABSTRACT FROM AUTHOR]

Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods. [ABSTRACT FROM AUTHOR]

In this work, a frequency-based dimensionality reduction technique using variational mode decomposition (VMD) is proposed. Dimensionality reduction is a very important aspect of preprocessing in case of hyperspectral image (HSI) analysis where this step helps in elimination of the lesser informative bands, thereby reducing the size of the data and making its processing computationally less challenging. In contrast to the standard dimensionality reduction methods such as inter-band block correlation (IBBC) where bands are eliminated based on their similarity with the consecutive bands, the proposed method uses frequency information of each band to categorize it as a less or more informative band. In this way, only the topmost informative bands of HSI are selected to form the reduced dataset. In our experiment, in order to verify the efficiency of VMD as a dimensionality reduction technique, the hyperspectral unmixed results obtained for IBBC reduced dataset is compared with those obtained for VMD reduced dataset. From the parametric measures such as classification accuracy, root-mean-square error (RMSE) and visual results obtained after unmixing for both IBBC and VMD reduced datasets, it is noticed that the VMD reduced dataset performs better by achieving higher classification accuracy and lower RMSE than that of the existing IBBC method. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based deep learning architectures are the state-of-the-art in image-based pattern recognition applications. The receptive filter fields in convolutional layers are learned from training data patterns automatically during classifier learning. There are number of well-defined, well-studied and proven filters in the literature that can extract informative content from the input patterns. This paper focuses on utilizing scattering transform-based wavelet filters as the first-layer convolutional filters in CNN architecture. The scattering networks are generated by a series of scattering transform operations. The scattering coefficients generated in first few layers are effective in capturing the dominant energy contained in the input data patterns. The present work aims at replacing the first-layer convolutional feature maps in CNN architecture with scattering feature maps. This architecture is equivalent to utilizing scattering wavelet filters as the first-layer receptive fields in CNN architecture. The proposed hybrid CNN architecture experiments the Malayalam handwritten character recognition which is one of the challenging multi-class classification problems. The initial studies confirm that the proposed hybrid CNN architecture based on scattering feature maps could perform better than the equivalent self-learning architecture of CNN on handwritten character recognition problems. [ABSTRACT FROM AUTHOR]

Scattering convolution network generates stable feature representation by applying a sequence wavelet decomposition operation on input signals. The feature representation in higher layers of the network builds a large-dimensional feature vector, which is often undesirable in most of the applications. Dimension reduction techniques can be applied on these higher-dimensional feature descriptors to produce an informative representation. In this paper, singular value decomposition is applied to the higher-layer scattering representation to generate informative feature descriptors. The effectiveness of the reduced scattering representation is evaluated on Malayalam printed and handwritten character recognition using support vector machine classifier. The reduced scattering representation improves the recognition performance when combining with lower-layer scattering network features. [ABSTRACT FROM AUTHOR]

This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre-Fenchel transformation denoises each band in the data while preserving the edge information. To overcome the issue of high data volume, inter-band block correlation coefficient technique followed by singular value decomposition and QR decomposition is utilized to reduce the dimension of hyperspectral image without affecting the critical information. The preprocessed data are classified using kernel-based libraries, namely GURLS and LibSVM. Performance of these techniques is evaluated with accuracy assessment measures. The experiment was performed on five datasets. Experimental analysis shows that the proposed denoising technique increases the classification accuracy. In the case of Indian Pines data, with 10% of the training data, the classification accuracy is improved from 83.5 to 97.3%. And also, dimensionality reduction technique gives good classification accuracy even with 50% reduction in the number of bands. The classification accuracy of the Salinas-A and Pavia University data is 99.4 and 94.6% with the 50% dimensionally reduced (100 and 50 bands, respectively) number of bands. The bands extracted by the dimensionality reduction technique using the denoised hyperspectral data differ from that of the hyperspectral data without denoising. This emphasizes the importance of denoising the dataset before applying dimensionality reduction technique. In case of Pavia University, the band numbers above 50 (out of 100 bands) which were not informative bands before denoising are selected as informative bands by dimension reduction technique after denoising. [ABSTRACT FROM AUTHOR]