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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 ,

"Health Monitoring of Old Buildings in Bangladesh: Detection of Cracks " by Rafiul Bari Angan, Md Safaiat Hossain et al.

Numerous buildings in Bangladesh were constructed without following standard building codes. As a result, those are vulnerable to increased earthquake frequency and variable loads. To address this issue, old buildings need to be monitored frequently by non-destructive testing (NDT) to avoid any failures. The identification of cracks and dampness is one of the most important parts of this test. Generally, this detection part is costly and time-consuming to conduct it manually. To resolve this, the current study has been performed for intelligent structural damage identification based on deep learning techniques. A state-of-the-art Convolutional Neural Network (CNN)-based object detection model YOLOv4-tiny has been used to detect cracks and dampness and repair cost analysis of damages. The study outcome suggests that using our proposed deep model, it is possible to detect building cracks with a mean Average Precision (%mAP) of 72.46%. In addition to traditional structural health monitori ....

Convolutional Neural Network , Average Precision , Crack And Dampness , Deep Learning , Deep Neural Network Dnn , Object Detection , Epair Cost Estimation , Olov4 Tiny ,

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 ....

Google Gpus , Convolution Neural Networks , Summary Of Research Gaps , Convolutional Neural Network Model Training , Reservoir Network , Convolution Neural Network Deep Learning Model , Artificial Neural Network , Random Forest , Support Vector Machine , Multi Layer Perceptron , Research Gaps , Data Preprocessing , Child Maltreat , Graphic Processing Unit , Scale Augmentation , Neural Network Deep Learning , Convolution Layer , Pooling Layer , Fully Connected Layers , Non Linearity Layers , Activation Functions , Rectified Linear Unit , Linear Unit , Neural Network Model , Naive Bayesian , Precision Score ,

"Modeling of Correlated Complex Sea Clutter Using Unsupervised Phase Re" by Liwu Wen, Jinshan Ding et al.

Abstract
The spatially and temporally correlated sea clutter with phase information is valuable for marine radar applications. The major difficulty of coherent sea clutter modeling is the generation of the continuous phases. This article presents a new phase retrieval approach for modeling the correlated complex sea clutter based on unsupervised neural networks. The unsupervised short-term and long-term neural networks have been developed for the phase retrieval on different term scales. Both these networks have the same input layer and feature extraction module, and however, the number of output neurons is different. The amplitude sea clutter series and the desired Doppler spectrum are fed into the network in parallel, and their features are extracted by two parallel bidirectional long short-term memory (Bi-LSTM) networks which sufficiently utilize the correlations of sea clutter data. These features are concatenated and fused by a residual network (ResNet). The phases can be ....

Ice Multiparameter Imagingx Band , Deep Neural Network Dnn , Ce Multiparameter Imagingx Band Ipix Data , Phase Retrieval , Ea Clutter Simulation ,