scholarly journals Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4369 ◽  
Author(s):  
Ratiranjan Jena ◽  
Biswajeet Pradhan ◽  
Abdullah Al-Amri ◽  
Chang Wook Lee ◽  
Hyuck-jin Park
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Indian Subcontinent ◽  
Slope Angle ◽  
Classification Model ◽  
Probability Assessment ◽  
Earthquake Parameters ◽  
Earthquake Probability ◽  
Statistical Measures ◽  
Proposed Model

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals A multi-label classification model for full slice brain computerised tomography image

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Jianqiang Li ◽  
Guanghui Fu ◽  
Yueda Chen ◽  
Pengzhi Li ◽  
Bo Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Image Features ◽  
Classification Model ◽  
Brain Diseases ◽  
Ct Scans ◽  
Computerised Tomography ◽  
Brain Ct ◽  
Proposed Model ◽  
The Brain

Abstract Background Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

scholarly journals Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification

2019 ◽  
Vol 9 (18) ◽  
pp. 3717 ◽  
Author(s):  
Wenkuan Li ◽  
Dongyuan Li ◽  
Hongxia Yin ◽  
Lindong Zhang ◽  
Zhenfang Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Linguistic Knowledge ◽  
Great Success ◽  
Text Representation ◽  
Attention Network ◽  
Sentiment Lexicon

Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method

2020 ◽  
Author(s):  
Hao Lv ◽  
Fu-Ying Dao ◽  
Zheng-Xing Guan ◽  
Hui Yang ◽  
Yan-Wen Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Information Gain ◽  
Independent Set ◽  
Cost Effective ◽  
Cellular Regulation ◽  
Proposed Model ◽  
Experimental Approaches ◽  
Memory Network

Abstract As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been developed to detect Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and labor-intensive, which is difficult to widely popularize in large-scale species. Computational approaches are cost-effective and can be used in a high-throughput manner to generate relatively precise identification. In this study, we develop a deep learning-based method termed as Deep-Kcr for Kcr sites prediction by combining sequence-based features, physicochemical property-based features and numerical space-derived information with information gain feature selection. We investigate the performances of convolutional neural network (CNN) and five commonly used classifiers (long short-term memory network, random forest, LogitBoost, naive Bayes and logistic regression) using 10-fold cross-validation and independent set test. Results show that CNN could always display the best performance with high computational efficiency on large dataset. We also compare the Deep-Kcr with other existing tools to demonstrate the excellent predictive power and robustness of our method. Based on the proposed model, a webserver called Deep-Kcr was established and is freely accessible at http://lin-group.cn/server/Deep-Kcr.

scholarly journals Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Chin-Fu Liu ◽  
Johnny Hsu ◽  
Xin Xu ◽  
Sandhya Ramachandran ◽  
Victor Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
False Positive Rate ◽  
Learning Networks ◽  
Large Dataset ◽  
Lower False Positive Rate ◽  
Proposed Model ◽  
Positive Rate ◽  
Total Agreement ◽  
Clinical And Translational Research

Abstract Background Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

scholarly journals A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

2017 ◽  
Vol 12 (5) ◽  
pp. 677 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Amin Namadchian
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Approach ◽  
Detection Systems ◽  
Video Mining ◽  
Proposed Model ◽  
Evaluation Algorithm

A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.

scholarly journals Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6839
Author(s):  
Aisha Al-Mohannadi ◽  
Somaya Al-Maadeed ◽  
Omar Elharrouss ◽  
Kishor Kumar Sadasivuni
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Large Scale ◽  
Intima Media Thickness ◽  
Semantic Segmentation ◽  
Learning Techniques ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Huge Impact ◽  
Good Learning

Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

scholarly journals Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1364
Author(s):  
Beomjoo Park ◽  
Muhammad Afzal ◽  
Jamil Hussain ◽  
Asim Abbas ◽  
Sungyoung Lee
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
High Impact ◽  
Biomedical Literature ◽  
Classification Model ◽  
Evidence Based ◽  
Proposed Model

To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.

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