scholarly journals An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

2021 ◽  
Vol 11 (22) ◽  
pp. 11050
Author(s):  
Hye-Jin Lee ◽  
Sun-Young Ihm ◽  
So-Hyun Park ◽  
Young-Ho Park
Keyword(s):  
Feature Maps ◽  
Companion Dogs ◽  
Spatiotemporal Information ◽  
Proposed Model ◽  
Tensor Network ◽  
Tightly Coupled ◽  
Net Method ◽  
Behavioral Classification ◽  
Conventional Models ◽  
Improved Accuracy

Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

A Patch Antenna Design for Giga Frequency Unmanned Aerial Vehicles Application

2021 ◽  
Vol 13 (1) ◽  
pp. 01-07
Author(s):  
Dr.M.D. Javeed Ahammed ◽  
Dr.G. Srinivasa Rao
Keyword(s):  
Patch Antenna ◽  
Return Loss ◽  
Surface Current ◽  
Vital Role ◽  
Frequency Range ◽  
Gain Bandwidth ◽  
Proposed Model ◽  
Major Parameter ◽  
Aerospace Technology ◽  
Conventional Models

In this paper a present time developing application is used that is a UAV Antenna in aerospace technology. These antennas play a vital role in this WIMAX technology. A patch antenna is designed such that all the dimensions should be shrinked yet efficient in radiation in comb shape and this proposed antenna is used at 4.2GHz frequency range. A CST tool is used for designing and simulating our antenna all the dimensions taken for proposed antenna are comparatively less when compared to conventional models. Low return loss, gain, bandwidth and VSWR are optimized by using this design the efficiency is also enhanced by 95% which makes our antenna suitable to the UAV WIMAX applications. Surface current is also one of the major parameter which is reduced by our proposed model.

Bayesian Semi-Parametric Realized Conditional Autoregressive Expectile Models for Tail Risk Forecasting

2020 ◽  
Author(s):  
Richard Gerlach ◽  
Chao Wang
Keyword(s):  
Value At Risk ◽  
Model Parameters ◽  
Model Framework ◽  
Measurement Equation ◽  
Asset Return ◽  
Proposed Model ◽  
Asymmetric Least Squares ◽  
Least Squares Optimization ◽  
Conditional Autoregressive ◽  
Improved Accuracy

Abstract A new model framework called Realized Conditional Autoregressive Expectile is proposed, whereby a measurement equation is added to the conventional Conditional Autoregressive Expectile model. A realized measure acts as the dependent variable in the measurement equation, capturing the contemporaneous dependence between it and the latent conditional expectile; it also drives the expectile dynamics. The usual grid search and asymmetric least squares optimization, to estimate the expectile level and parameters, suffers from convergence issues leading to inefficient estimation. This article develops an alternative random walk Metropolis stochastic target search method, incorporating an adaptive Markov Chain Monte Carlo sampler, which leads to improved accuracy in estimation of the expectile level and model parameters. The sampling properties of this method are assessed via a simulation study. In a forecast study applied to several market indices and asset return series, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed model class.

scholarly journals Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Nachatchapong Kaewsompong ◽  
Paravee Maneejuk ◽  
Woraphon Yamaka
Keyword(s):  
Bayesian Estimation ◽  
Bayesian Method ◽  
Real Data ◽  
Copula Function ◽  
Dependence Structure ◽  
Simulation Studies ◽  
Multivariate Normal ◽  
Empirical Comparison ◽  
Proposed Model ◽  
Conventional Models

We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable copula function to relax this assumption. As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. The real data analysis is also provided, and the empirical comparison indicates our proposed model outperforms the conventional models in all considered quantile levels.

scholarly journals Monaural Singing Voice and Accompaniment Separation Based on Gated Nested U-Net Architecture

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1051
Author(s):  
Haibo Geng ◽  
Ying Hu ◽  
Hao Huang
Keyword(s):  
Time Domain ◽  
Singing Voice ◽  
Feature Maps ◽  
Time Frequency ◽  
Convolutional Networks ◽  
Full Resolution ◽  
Mask Layer ◽  
Proposed Model ◽  
Phase Spectra ◽  
Separation Model

This paper proposes a separation model adopting gated nested U-Net (GNU-Net) architecture, which is essentially a deeply supervised symmetric encoder–decoder network that can generate full-resolution feature maps. Through a series of nested skip pathways, it can reduce the semantic gap between the feature maps of encoder and decoder subnetworks. In the GNU-Net architecture, only the backbone not including nested part is applied with gated linear units (GLUs) instead of conventional convolutional networks. The outputs of GNU-Net are further fed into a time-frequency (T-F) mask layer to generate two masks of singing voice and accompaniment. Then, those two estimated masks along with the magnitude and phase spectra of mixture can be transformed into time-domain signals. We explored two types of T-F mask layer, discriminative training network and difference mask layer. The experiment results show the latter to be better. We evaluated our proposed model by comparing with three models, and also with ideal T-F masks. The results demonstrate that our proposed model outperforms compared models, and it’s performance comes near to ideal ratio mask (IRM). More importantly, our proposed model can output separated singing voice and accompaniment simultaneously, while the three compared models can only separate one source with trained model.

A Multilevel Bayesian Item Response Theory Method for Scaling Socioeconomic Status in International Studies of Education

2006 ◽  
Vol 31 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Henry May
Keyword(s):  
Socioeconomic Status ◽  
Item Response Theory ◽  
Item Response ◽  
International Studies ◽  
Response Theory ◽  
Irt Model ◽  
National Differences ◽  
Proposed Model ◽  
Conventional Models ◽  
Imputation Approach

A new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores that are internationally comparable, (b) reduce the influence of irrelevant national differences in culture on the SES scores, and (c) effectively and efficiently deal with the problem of missing data in a manner similar to Rubin’s (1987) multiple imputation approach. The results suggest that this model is superior to conventional models in terms of its fit to the data and its ability to use information collected via international surveys.

A Saliency Detection Method Based on Wavelet Transform and Simple Priors

2014 ◽  
Vol 602-605 ◽  
pp. 2238-2241
Author(s):  
Jian Kun Chen ◽  
Zhi Wei Kang
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Feature Maps ◽  
Multi Scale ◽  
Saliency Model ◽  
Proposed Model ◽  
Visual Saliency Model ◽  
Better Than

In this paper, we present a new visual saliency model, which based on Wavelet Transform and simple Priors. Firstly, we create multi-scale feature maps to represent different features from edge to texture in wavelet transform. Then we modulate local saliency at a location and its global saliency, combine the local saliency and global saliency to generate a new saliency .Finally, the final saliency is generated by combining the new saliency and two simple priors (color prior an location prior). Experimental evaluation shows the proposed model can achieve state-of-the-art results and better than the other models on a public available benchmark dataset.

scholarly journals AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

2021 ◽  
Author(s):  
Bambang Tutuko ◽  
Siti Nurmaini ◽  
Alexander Edo Tondas ◽  
Muhammad Naufal Rachmatullah ◽  
Annisa Darmawahyuni ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
Learning System ◽  
Single Frequency ◽  
Feature Maps ◽  
Proposed Model ◽  
Unseen Data ◽  
Specific Device ◽  
Model Formation

Abstract Background: Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R-R intervals to determine the Heart Rate Variability (HRV). An accurate HRV is the gold standard for predicting the AF condition. Hence, we propose a DL approach to analyze massive amounts of ECG raw data in a broad range of devices to overcome a current challenge.Results: This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8,232 records based on three datasets and tested with 3,610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. When tested with unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity in 0.02 seconds for one instance when processed in theDL-Cloud System.Conclusions: These findings demonstrate that the proposed model approach can used in a broad range of devices and validated to unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment.

scholarly journals Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

2020 ◽  
Vol 8 (5) ◽  
pp. 1335-1340
Keyword(s):  
Fuzzy Rule ◽  
Vital Role ◽  
Rule Based ◽  
Rule Based System ◽  
Proposed Model ◽  
Human Decision ◽  
Fuzzy Partitions ◽  
Accuracy Assessments ◽  
Improved Accuracy ◽  
Rule Based Systems

Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.

scholarly journals Super-resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

2021 ◽  
Author(s):  
Lakpa Dorje Tamang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Super Resolution ◽  
Input Image ◽  
Feature Maps ◽  
Proposed Model ◽  
Reconstruction Performance

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves an exquisite reconstruction performance of ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), while providing compelling SR reconstruction time.

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