Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset

2021 ◽  
pp. 44-51
Author(s):  
Khishigsuren Davagdorj ◽  
Jong Seol Lee ◽  
Kwang Ho Park ◽  
Keun Ho Ryu
Keyword(s):  
Neural Network ◽  
Class Imbalance ◽  
Imbalance Dataset

scholarly journals A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

2021 ◽  
Vol 11 (22) ◽  
pp. 10970
Author(s):  
Naif Radi Aljohani ◽  
Ayman Fayoumi ◽  
Saeed-Ul Hassan
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Binary Classification ◽  
Class Imbalance ◽  
Citation Network ◽  
Graph Visualization ◽  
Scholarly Research ◽  
Research Dissemination ◽  
Reference Corpus ◽  
Feature Based

We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

scholarly journals DATA IMBALANCE IN LANDSLIDE SUSCEPTIBILITY ZONATION: UNDER-SAMPLING FOR CLASS-IMBALANCE LEARNING

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 51-57
Author(s):  
S. K. Gupta ◽  
M. Jhunjhunwalla ◽  
A. Bhardwaj ◽  
D. P. Shukla
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Training Data ◽  
Support Vector ◽  
Fisher Discriminant Analysis ◽  
Minority Class ◽  
Data Imbalance ◽  
Artificial Neural

Abstract. Machine learning methods such as artificial neural network, support vector machine etc. require a large amount of training data, however, the number of landslide occurrences are limited in a study area. The limited number of landslides leads to a small number of positive class pixels in the training data. On contrary, the number of non-landslide pixels (negative class pixels) are enormous in numbers. This under-represented data and severe class distribution skew create a data imbalance for learning algorithms and suboptimal models, which are biased towards the majority class (non-landslide pixels) and have low performance on the minority class (landslide pixels).In this work, we have used two algorithms namely EasyEnsemble and BalanceCascade for balancing the data. This balanced data is used with feature selection methods such as fisher discriminant analysis (FDA), logistic regression (LR) and artificial neural network (ANN) to generate LSZ maps The results of the study show that ANN with balanced data has major improvements in preparation of susceptibility maps over imbalanced data, where as the LR method is ill-effected by data balancing algorithms. The FDA does not show significant changes between balanced and imbalanced data.

scholarly journals AlertNet: Deep convolutional-recurrent neural network model for driving alertness detection

2021 ◽  
Vol 11 (4) ◽  
pp. 3529
Author(s):  
P. C. Nissimagoudar ◽  
A. V. Nandi ◽  
Aakanksha Patil ◽  
Gireesha H. M.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Sleep Stage ◽  
Class Imbalance ◽  
Drowsy Driving ◽  
Kappa Score ◽  
Unequal Distribution ◽  
Training Error ◽  
Complex Features ◽  
Distribution Class

Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively.

A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation

2019 ◽  
Vol 9 (8) ◽  
pp. 1705-1716
Author(s):  
Shidu Dong ◽  
Zhi Liu ◽  
Huaqiu Wang ◽  
Yihao Zhang ◽  
Shaoguo Cui
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Network Architecture ◽  
Medical Image ◽  
Negative Impact ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Memory Space

To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy-plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z-axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structure. As an example, we extend the deep convolutional encoder–decoder architecture (SegNet) to S3D-SegNet for brain tumor image segmentation. We also propose a method based on priority queues and the dice loss function to address the class imbalance for medical image segmentation. The experimental results show the following: (1) S3D-SegNet extended from SegNet can improve brain tumor image segmentation. (2) The proposed imbalance accommodation method can increase the speed of training convergence and reduce the negative impact of the imbalance. (3) S3D-SegNet with the proposed imbalance accommodation method offers performance comparable to that of some state-of-the-art 3D-CNNs and experts in brain tumor image segmentation.

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