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Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10–3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Air is the basis for the existence of life on Earth; but in the present age of modernization the degrading quality of air year by year, due to the growth of Industrialization, urbanization, automobiles, coal-fired thermal power plants, and various other factories is the matter of real concern. To predict the future growth of air pollutants numerous prediction models have been developed by researchers. Time-series ARIMA model although quite useful for forecasting but fails to handle non-stationary problems. Among all the existing forecasting models, wavelets along with the Machine learning models have proved to be very successful and have been widely used in various fields like mathematical modeling, signal recognition, image recognition, classification, function approximation, data processing, filtering, clustering, compression, robotics, and decision making. It is also used in the field of mathematical forecasting for developing efficient prediction models. This paper aims to develop a wavelet-ANFIS conjugation model and a wavelet-ARIMA coupled model along with the time-series ARIMA model for the prediction of black carbon concentration over the Raniganj, Jharia, and Bokaro coal mines of India, by considering a long term data obtained by NASA (http://nasa.gov/) and compare the results obtained by these models for determining the best prediction model. The validity of the results is tested with the help of error measures like RMSE, MSE, MAPE, MAE, and relative error. Results over the three sample sites conclude that the Wavelet-ANFIS conjugation approach outperforms the wavelet-ARIMA coupled approach and the simple time-series ARIMA model. [ABSTRACT FROM AUTHOR]

This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients' symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69. [ABSTRACT FROM AUTHOR]

Sediment transport is one of the major challenging fields in hydrology. The tropical atmosphere, complex topography and occasional extreme precipitation are the fundamental explanations behind this challenge. Thus, the rivers in this situation contain a huge quantity of sediment, which may affect the river hydraulics. Hence, it is required to collect various parameters such as discharge, velocity, rainfall and sediment concentration to analyze the impact of sediment for river engineering practices and management. Therefore, the dataset which is collected from the river may contain outliers and noises. For improving the prediction accuracy of sediment load, we present robust regularized extreme learning machine frameworks to reduce the effect of noise by using the asymmetric Huber loss function named as AHELM and ε - insensitive Huber loss function named as ε - AHELM. Further, the problems are rewritten in the form of strongly convex minimization problems whose solutions are acquired by simple function iterative schemes. To ensure the effectiveness of the proposed approach, we have considered the real-world datasets with two types of noises. Furthermore, the proposed schemes are applied on real sediment load datasets (SLDs) which are collected from the Tawang Chu river of Arunachal Pradesh, India. The results reveal that proposed AHELM and ε - AHELM with multiquadric activation function are performed better for real-world datasets, whereas AHELM and ε - AHELM with sigmoid activation function perform efficiently and effectively for the sediment load prediction. In overall, the experimental results clearly exhibit the applicability as well as the usability of the proposed extreme learning machine with asymmetric Huber loss functions. [ABSTRACT FROM AUTHOR]