scholarly journals Hand Gesture Vocalizer for Dumb and Deaf People

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 22-29
Author(s):  
Sanish Manandhar ◽  
Sushana Bajracharya ◽  
Sanjeev Karki ◽  
Ashish Kumar Jha
Keyword(s):  
Random Forest ◽  
Hand Movement ◽  
Training Model ◽  
Hand Gesture ◽  
Deaf People ◽  
Random Forest Algorithm ◽  
The Disabled ◽  
Flex Sensors ◽  
Is Design ◽  
Pictorial Form

The main purpose of this paper is to confer the system that converts a given sign used by disabled person into its appropriate textual, audio, and pictorial form using components such as Arduino Mega, Flex sensors, Accelerometer, which could be under standby a common person. A wearable glove controller is design with fl ex sensors attached on each finger, which allows the system to sense the finger movements, and aGy-61 accelerometer, which are uses to sense the hand movement of the disabled person. The wearable input glove controller sends the collected input signal to the system for processing. The system uses Random forest algorithm to predict the correct output to an accuracy of 85% on current training model.

scholarly journals Design of Hand Movement based Smart Automation System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1402-1405
Keyword(s):  
Gesture Recognition ◽  
Hand Movement ◽  
Automation System ◽  
Deaf People ◽  
Home Appliances ◽  
The World ◽  
Speech Feature ◽  
Flex Sensors

There are millions deaf people in the world and around many people in India who are affected by ALS and paralysis, they face difficulty in communicating with others. Therefore in this paper we propose a system that converts simple gesture to speech and also use these gesture to control the home appliances he project will be in the form of glove which makes it handy and reliable. It uses flex sensors for gesture recognition and TTS module for the gesture to speech feature which makes it slender and much viable.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

2021 ◽  
Vol 252 ◽  
pp. 106906
Author(s):  
Guomin Shao ◽  
Wenting Han ◽  
Huihui Zhang ◽  
Shouyang Liu ◽  
Yi Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Crop Coefficient ◽  
Random Forest Algorithm ◽  
Maize Crop ◽  
Area Index

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

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