Adaboost-DSNN: an adaptive boosting algorithm based on deep self normalized neural network for pulsar identification

Tariq, Irfan; Meng, Qiao; Yao, SM; Liu, Wei; Zhou, Caihua; Ahmed, Adnan; Spanakis-Misirlis, Apostolos

doi:10.1093/mnras/stac086

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…In recent years, with the rapid development of neural networks, especially the emergence of deep learning technology that relies on large-scale data training methods, major breakthroughs have been made in natural language processing, which has led to a new dawn and direction for machine understanding of text [ 6 ]. Bengio first proposed that the use of deep neural networks to build pretrained language models can learn semantic information in text data.…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Hung

Hsiao

2022

Computational Intelligence and Neuroscience

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Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model’s ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers’ composition correction.

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Section: Introductionmentioning

confidence: 99%

Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Hung

Hsiao

2022

Computational Intelligence and Neuroscience

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“…Additionally, we define the true positive rate (TPR) and false positive rate (FPR) as described in equations ( 13) and (14), respectively. e TPR and FPR are used to plot the receiver operating characteristic (ROC) curve, which was used to evaluate the trained models on both datasets [30].…”

Section: Performance Metricsmentioning

confidence: 99%

“…As a result, more information is gathered about nodules. Similarly, AdaBoost is an ensemble learning method [13,14] datasets, and a detailed explanation of the AdaBoost-SNMV-CNN model upon which our manuscript is based. Section 3 comprises a detailed explanation of the results.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, more information is gathered about nodules. Similarly, AdaBoost is an ensemble learning method [ 13 , 14 ] and the incorporation of self-normalized MV-CNN (SNMV-CNN) in AdaBoost (AdaBoost-SNMV-CNN) enriches the model with superior learning, analyzing, and finding pattern capabilities of MV-CNN for nodules identification and the AdaBoost characteristic of dealing with large datasets; for instance, Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset and Early Lung Cancer Action Program (ELCAP) dataset. The reported method AdaBoost-SNMV-CNN has the transfer learning property of deep learning methods that reduces the computational cost and time making it superior to existing methods.…”

Section: Introductionmentioning

confidence: 99%

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Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network

Khan

Tariq

Khan

et al. 2022

Journal of Oncology

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Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model’s good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.

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“…CNNs have also been used to classify transient RFI in time domain signals [14]. Supervised deep learning of neural networks have been used for candidate selection in pulsar searches [15,16]; however, to date there has been no published attempt to use CNNs for RFI flagging in pulsar data.…”

Section: Introductionmentioning

confidence: 99%

PSRFINET: Radio Frequency Interference Detection in Pulsar Data with Deep Residual Networks

2022

RFI 2022

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Radio Frequency Interference (RFI) is a hindrance to highprecision pulsar timing experiments aimed at detecting the stochastic gravitational wave background. Thresholds set by linear combinations of statistical quantities are among the most common approaches to RFI flagging of folded pulse profiles. We propose a deep convolutional neural network approach to RFI flagging called PSRFINET that treats two-dimensional arrays of pulse profiles (rotational phase versus radio frequency) as images and performs feature learning on labelled RFI samples. We train and validate multiple deep residual neural networks on many hours of pulsar observations (thousands of 8 second subintegrations) of MeerKAT L-band data where the ground truth is generated from Clfd and Coastguard software packages for RFI mitigation. A method of combining the separate ground truths aimed at enhancing the RFI mitigation capabilities of the networks is also explored. The performance of the networks was evaluated by examining the classification metrics of area under the curve of the receiver operating characteristic (AUROC), Precision-Recall (PR) and F1 scores. Our preliminary results show an AUROC of more than 0.91 and PR of 0.67 which indicates that although the neural networks are capable of distinguishing between clean and corrupted frequency channels, precision and recall scores are limited by a class imbalance of a small amount of RFI with respect to clean channels. We also discuss our approach to develop a statistical objective figure of merit for evaluating and comparing the effectiveness of different RFI flagging approaches in the data.

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Adaboost-DSNN: an adaptive boosting algorithm based on deep self normalized neural network for pulsar identification

Cited by 9 publications

References 25 publications

Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network

PSRFINET: Radio Frequency Interference Detection in Pulsar Data with Deep Residual Networks

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