The present study emphasizes the forecast of Andhra Pradesh's total marine fish production and catch commercially important fishes, viz., Indian Mackerel, Oil Sardine, Horse Lesser Sardines for next 5 years by different statistical machine learning approaches under climate change scenario. Forecasting is done with without inclusion climatic environmental parameters in models. Exogenous variable...

This paper presents a self-organizing power system stabilizer (SOPSS) which use the fuzzy Auto-Regressive Moving Average (FARMA) model. The control rules and the membership functions of proposed the fuzzy logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show t...

The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which later declared as a global pandemic view its exponential rise and spread around the world. Resultantly, scientific medical research communities globe geared up to curb spread. In this manuscript, authors claim competence AI-mediated methods predict mortality rate. Efficient prediction model enables he...

Air pollution is a worldwide issue that affects the lives of many people in urban areas. It considered air may lead to heart and lung diseases. A careful timely forecast quality could help reduce exposure risk for affected people. In this paper, we use data-driven approach predict based on historical data. We compare three popular methods time series prediction: Exponential Smoothing (ES), Auto...

Horizontal displacement of Hoa Binh dam in operation phase is analyzed and then forecasted by using three methods: the multi-regression model (MTR), Seasonal Integrated Auto-regressive Moving Average (SARIMA) Back-propagation Neural Network (BPNN) model. The monitoring data Dam 137 periods, including horizontal displacement, time, reservoir water level air temperature, are used for experiments....

BACKGROUND Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic....

Predicting stock price is a trend yet very challenging task. It because the prices depend upon several internal and external factors. Stock prediction can be useful for financial sectors government help in informed decision-making. This paper analyzes market of K-Electric Karachi. found that K-electric on refinery sector. The data two sectors. Also, compares based moving average, auto-regressiv...

Abstract Life expectancy at birth (LEB) is a major factor for decision-making bodies when developing new healthcare policies or improving existing ones. This paper, with the help of R language, processes and examine LEB data in Saudi Arabia from 1960 to 2012 using time-series analysis. To test validity model, 2013 2018 are used. The performance selected auto regressive integrated moving average...

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observatio...

In this paper, we want to examine how unemployment impacts social life, and, by using datasets from six European countries, analyze the effect of on two main aspects life: exclusion and life satisfaction. First, predict rates Auto Regressive Integrated Moving Average (ARIMA) model results are further used in a linear regression alongside satisfaction data, thus obtaining hybrid model. With help...