Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

Nisar, Kashif; Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Ibrahim, Ag Asri Ag; Rodrigues, Joel J. P. C.; Khan, Adnan Shahid; Gupta, Manoj; Kamal, Aldawoud; Rawat, Danda B.

doi:10.3390/app11114725

Cited by 30 publications

(16 citation statements)

References 59 publications

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“…To compute spatial attention, we first use average pooling and max pooling processes along the channel axis and concatenate them to build an efficient feature descriptor. To produce a spatial attention map that encodes where to highlight or suppress, we apply a convolution layer to the concatenated feature descriptor [44][45][46][47][48].…”

Section: Spatial Attentionmentioning

confidence: 99%

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Singh¹,

Rawat²,

Gupta³

et al. 2023

Computers, Materials &Amp; Continua

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In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attentionaware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.

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Section: Spatial Attentionmentioning

confidence: 99%

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Singh¹,

Rawat²,

Gupta³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Deep learning is being implemented in many disease identification processes, and is improving machine learning performance in the field [16]. Multilayer perceptron (MLP) is a modern technology known as a feed-forward neural network used in deep learning to identify and classify different types of tumors [17,18]. A previous study lists instances in which deep learning has been used as stacked denoising autoencoders (SDAE) to transfer high dimensional noisy data to low dimensional data for the classification of breast cancer [8].…”

Section: Introductionmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

et al. 2022

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Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.

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“…Matrix lling is one of research hotspots in the elds of matrix analysis, optimization, image processing, means lling the missing elements accurately through known elements in the case of missing elements in the sampling matrix, and nally completing the sampling matrix [1]. In practice, the sampling matrix sometimes has special structures, such as symmetric matrix and Toeplitz matrix, which play an important role in communication engineering and power system, especially in the eld of signal and image processing [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Filling High Rank Matrix of Network Big Data Based on Density Peak Clustering

Liu

2022

Mathematical Problems in Engineering

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The traditional filling method for network big data matrix has poor filling effect and suffers from noise. Therefore, a filling algorithm for network big data high rank matrix based on density peak clustering is proposed. The missing data are replaced by small-interval data, the information entropy of the high rank matrix of network big data is calculated, the density peak clustering algorithm is optimized through the cluster center selection strategy, the block data set is obtained through the unknown block method, and the block filling is realized by the host filling algorithm. Experimental results show that the filling accuracy of the proposed algorithm is as high as 0.895, and the loss rate is between 2% and 12%.

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Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

Cited by 30 publications

References 59 publications

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Algorithm for Filling High Rank Matrix of Network Big Data Based on Density Peak Clustering

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