Machine Learning Algorithms and Applications. Группа авторов
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target="_blank" rel="nofollow" href="#ulink_eb182e99-db9f-588e-9ee5-d044ce499715">Figure 1.8 Heat map for ozone O3 for day and night in December, 2017.
Figure 1.9 Heat map for ozone O3 for day and night in June, 2020.
Figure 1.10 Heat map for all parameters for 3 days and nights in December, 2017.
Figure 1.11 Heat map for all parameters for 3 days and nights in June, 2020.
Figure 1.12 Predicted values for O3 for Anand Vihar, New Delhi.
Figure 1.13 Predicted values for PM10 for Sector 62, Noida.
Figure 1.14 Pollution levels in major Indian cities.
1.5 Conclusion
After applying K-means clustering using Silhouette coefficient, the data is divided into seven clusters. The SVM is successfully able to classify the data into its respective air quality class with accuracy of 99%. The LSTM models for different places have been tuned accordingly to minimize MAE and RMSE. The proposed model could be used for various purposes like predicting future trends of air quality, assessing past trends of air quality, visualizing data in an effective way, issuing health advisory, and providing health effects (if any) based on the current air quality. Various parameters can be compared and it could be determined which pollutant is affecting more in a particular area and accordingly actions could be taken beforehand. Anyone could get inference from the data easily which is tough to analyze numerically and could take certain actions to control air pollution in any area.
References
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1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
2
Automatic Counting and Classification of Silkworm Eggs Using Deep Learning
Shreedhar Rangappa1*, Ajay A.1 and G. S. Rajanna2
1Intelligent Vision Technology, Bengaluru, India
2Maharani Cluster University, Sheshadri Road, Bengaluru, India
Abstract
The method of using convolutional neural networks to identify and quantify the silkworm eggs that are laid on a sheet of paper by female silk moth. The method is also capable of segmenting individual egg and classifying them into hatched egg class and unhatched egg class, thus outperforming image processing techniques used earlier. Fewer limitations of the techniques employed earlier are described and attempt to increase accuracy using uniform illumination of a digital scanner is illustrated. The use of a standard key marker that helps to transform any silkworm egg sheet into a standard image, which can be used as input to a trained convolution neural network model to get predictions, is discussed briefly. The deep learning model is trained on silkworm datasets of over 100K images for each category. The experimental results on test image sets show that our approach yields an accuracy of above 97% coupled with high repeatability.
Keywords: Deep learning, convolution neural network, datasets, accuracy, silkworms, fecundity, hatching percentage
2.1 Introduction
In