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"Multi-Layer Efficient Data Classification Methods for Enterprise Busin" by Yazeed Alzahrani, Jun Shen et al.

The efficient maintenance and classification of huge amounts of data is a big challenge for the websites which provide services for online businesses. Many of the websites provide multiple services for the customer. In the present work, we have compared various machine learning-based classification methods for the efficient distribution of data. To effectively categorize the data, the Enterprise Interface (El) layer is suggested between the application layer and the physical layer. Methods based on global and local clustering are proposed for the effective distribution of the data in the El layer. For the effective classification of the merchandise as per the various client classes, we have collected four parameters/features from the mall's customers. We have utilized the K-Means clustering approach to efficiently divide classes (Global Clustering). Additionally, we have examined seven categories for the proper group selection and prediction of recently arrived customers. The perf ....

Enterprise Interface , Global Clustering , Naive Bayesian , Decision Tree , Random Forest , Support Vector Machine , K Nearest Neighbor , Random Forest Classification , Enterprise Architecture , Nternet Of Things ,

Korean scientists work to prevent water leaks from district heating pipes

Using machine learning tools trained with information from acoustic emission and accelerometer sensors, the research team aimed to prevent dangerous leaks. ....

South Korea , Kangwon Do , Lee Hongcheol , Korea Institute Of Civil Engineering , National Research Council Of Science , Building Technology , Korea Institute , Civil Engineering , District Heating Hot Water Production Facility , Baekseok Subway Station , Insulated Pipes , National Research Council , Support Vector Machine ,

AI model provides a way for rapid COVID-19 testing and detection

COVID-19 and its latest Omicron strains continue to cause infections across the country as well as globally. Serology (blood) and molecular tests are the two most commonly used methods for rapid COVID-19 testing. ....

Stella Batalama , Xingquan Hill Zhu , Magdalyne Elkin , College Of Engineering , Florida Atlantic University College Of Engineering , National Science Foundation , Neural Network , Department Of Electrical Engineering , Smart Health , Florida Atlantic University , Computer Science , Electrical Engineering , Random Forest , Logistic Regression , Support Vector Machine , National Science , Covid 19 , Machine Learning , Ars Cov 2 ,

AI model proactively predicts if a COVID-19 test might be positive or not

AI model proactively predicts if a COVID-19 test might be positive or not
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Stella Batalama , Xingquan Hill Zhu , Magdalyne Elkin , College Of Engineering , Florida Atlantic University College Of Engineering , National Science Foundation , Neural Network , Department Of Electrical Engineering , Smart Health , Florida Atlantic University , Computer Science , Electrical Engineering , Random Forest , Logistic Regression , Support Vector Machine , National Science ,

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 ,