Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
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Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
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Construction of Efficient Smart Voting Machine with Liveness Detection Module
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An Economical Robotic Arm for Playing Chess Using Visual Servoing
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Triplet loss for Chromosome Classification
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Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
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Real Time Sign Language Recognition and Speech Generation
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Analysis of Artificial Intelligence based Image Classification Techniques
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Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
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Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
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Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 4 | Issue - 3 | september 2022
Published
01 September, 2022
Estimating water quality has existed as one of the vital factors embarked on the planet in the present eons. This paper illustrates a water quality estimate based on the Linear Discriminant Analysis (LDA) technique. Weighted arithmetic index technique is used in the computation of the Water Quality Index (WQI). At that moment, the LDA is linked to the dataset, and the ultimate principal WQI dynamics have been determined. Subsequently after predicting the WQI, Light Gradient Boosted Machine (LGBM) classification is performed in the LDA. Lastly, the LGBM classifier is activated to label the water quality. This proposed LGBM with LDA technique is demonstrated and evaluated on a Gulshan Lake-related dataset. The results show 96% forecast accuracy for the LDA and 100% categorization accuracy for the Light Gradient Boosted Machine classifier system that indicate consistent interpretation linked over the futuristic prototypes. This innovative model LDA-LGBM is aimed at enhancing the prediction of water quality and its classification through AI - ML approach.
KeywordsWater quality prediction linear discriminant analysis weighted arithmetic index water quality index light gradient boosted machine classifier Gulshan Lake-related dataset
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