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"Neural network-based models versus empirical models for the prediction" by Shehroze Ali, Junaid Ahmad et al.

This study presents new neural-network (NN)-based models to predict the axial load-carrying capacities of fiber-reinforced polymer (FRP) bar reinforced-concrete (RC) circular columns. A database of FRP-reinforced concrete (RC) circular columns having outside diameter and height ranged between 160–305 and 640–2500 mm, respectively was established from the literature. The axial load-carrying capacities of FRP-RC columns were first predicted using the empirical models developed in the literature and then predicted using deep neural-network (DNN) and convolutional neural-network (CNN)-based models. The developed DNN and CNN models were calibrated using various neurons integrated in the hidden layers for the accurate predictions. Based on the results, the proposed DNN and CNN models accurately predicted the axial load-carrying capacities of FRP-RC circular columns with R2 = 0.943 and R2 = 0.936, respectively. Further, a comparative analysis showed that the proposed DNN and CNN models ar ....

Axial Load Carrying Capacity , Circular Concrete Columns , Convolutional Neural Network Cnn , Deep Neural Network Dnn , Empirical Models , Iber Reinforced Polymer Frp Reinforcement ,

"An attention-based CNN for automatic whole-body postural assessment" by Zewei Ding, Wanqing Li et al.

Fully automatic postural assessment is highly useful, but has been challenging. Conventional methods either require manual assessment by ergonomists or depend on special devices that are intrusive, thus being hardly feasible in daily activities and workplaces. In this work, an attention-based convolutional neural network (CNN) is developed for automatic whole-body postural assessment. The proposed network learns to identify highly relevant regions (or body parts) and extract features automatically. Risk of the posture is estimated from the extracted features accordingly. To evaluate the proposed method, a postural dataset, referred to as pH36M, is created by re-targeting Human3.6M, one of the largest publicly available datasets for pose estimation using the Rapid Entire Body Assessment (REBA) criteria. Experimental results on pH36M demonstrate that proposed method achieves promising performance in comparison to baselines and the average assessment scores are substantially aligned with ....

Rapid Entire Body Assessment , Convolutional Neural Network Cnn , Usculoskeletal Disorder Msd , Ostural Assessment , Apid Entire Body Assessment Reba ,

"A time-series Wasserstein GAN method for state-of-charge estimation of" by Xinyu Gu, K. W. See et al.

Estimating the state-of-charge (SOC) of lithium-ion batteries is essential for maintaining secure and reliable battery operation while minimizing long-term service and maintenance expenses. In this work, we present a novel Time-Series Wasserstein Generative Adversarial Network (TS-WGAN) approach for SOC estimation of lithium-ion batteries, characterized by a well-designed data preprocessing process and a distinctive WGAN-GP architecture. In the data preprocessing stage, we employ the Pearson correlation coefficient (PCC) to identify strongly associated features and apply feature scaling techniques for data normalization. Moreover, we leverage polynomial regression to expand the original features and utilize principal component analysis (PCA) to reduce the computational load and retain essential information by projecting features into a lower-dimensional subspace. Within the WGAN-GP architecture, we originally devise a Transformer as the generator and a Convolution Neural Network (CNN) ....

Series Wasserstein Generative Adversarial Network , Convolution Neural Network , Convolutional Neural Network Cnn , Lithium Ion Battery , Tate Of Charge Soc , Time Series Forecasting , Asserstein Generative Adversarial Network With Gradient Penalty Wgan Gp ,

Autonomous Surveillance of Infants' Needs Using CNN Model for Audio Cry Classification

Infants portray suggestive unique cries while sick, having belly pain, discomfort, tiredness, attention
and desire for a change of diapers among other needs. There exists limited knowledge in accessing
the infants’ needs as they only relay
information through suggestive cries. Many teenagers tend to give birth at an early
age, thereby exposing them to be the key monitors of their own babies. They
tend not to have sufficient skills in monitoring the infant’s dire needs, more so during the early stages of infant development.
Artificial intelligence has shown promising efficient predictive analytics
from supervised, and unsupervised to reinforcement learning models. This study, therefore, seeks to develop an android app that could be used to discriminate
the infant audio cries by leveraging the strength of convolution neural networks
as a classifier model. Audio analytics from many kinds of literature is an untapped area
by researchers as it’s ....

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