We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starti...

A deep learning network is introduced to predict concentrations of gases in the underground coal mine enclosed region using various IoT-enabled gas sensors installed a metallic chamber. The air sucked automatically at specific intervals from sealed-off site utilizing solenoid valve, suction pump, and programmed microprocessor. monitor content communicate concentration surface server room throug...

Sri Lanka is mainly an agricultural country and about 40 per cent of its working population engaged in agriculture island-wide. Rice cultivated during two seasons; Maha season (October-March) usually accounts for 65% annual production with the remaining 35% coming from Yala (April-September). The objectives this study are to investigate present trend paddy develop most appropriate time series m...

Groundwater resource plays a crucial role for agricultural crop production and socio-economic development in some parts of the world including Bangladesh. Joypurhat district, northwest part Bangladesh, hub, is entirely dependent on groundwater irrigation. A precise assessment prediction level (GWL) can assist long-term resources (GWR) management, especially drought-prone regions. Therefore, thi...

During the COVID-19 outbreak, governments, scientists, health workers, and numerous people worked on strategies or solutions for halting disease propagation. Unfortunately, need monitoring is steeply increasing, taking necessary restrictive actions currently unavoidable. Due to lack of epidemiological data constantly changing numbers, constructing less error-prone predictive models reliable mat...

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable cand...

The main obstacle against the penetration of wind power into grid is its high variability in terms speed fluctuations. Accurate forecasting, while making maintenance more efficient, leads to profit maximisation traders, whether for a turbine or farm. Machine learning (ML) models are recognised as an accurate and fast method prediction, but their accuracy depends on selection correct hyperparame...

Markov chains (MC) are statistical models used to predict very short short-term wind speed accurately. Such generally trained with a single moving window. However, time series do not possess an equal length of behavior for all horizons. Therefore, window can provide reasonable estimates but is optimal choice. In this study, forecasting model proposed that integrates MCs adjusting dynamic The se...

Speech synthesizers based on paramedic methods, still have not achieved the expected naturalness. This is due to less consideration on linear time variant nature between the neighbor phonemes. This paper presents a study to model the phoneme transitions between neighbor phonemes with lesser number of parameters using Auto Regressive Moving Average (ARMA) model, where Steiglitz-McBride algorithm...