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The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar...
Since the introduction of artificial neural networks (ANN) the numerous investigations of concrete systems had been proposed. But further development of the theory and applications of networks follows to the investigations of new examples of dependent of time systems with anticipatory property. New special class of anticipatory systems had been introduced by D, Dubois — namely the system with strong...
Machining parameters influence the energy consumed during machining processes. A reliable prediction model for energy consumption will enable industry to achieving energy saving by optimizing the machining parameters during process planning stage. This paper presents a two-level optimization artificial neural network modelling method to characterizing the relationship between energy consumption and...
Various governments and stakeholders are established across the globe to respond to various energy challenges that has led to one or more energy policy development. A proper analysis of what contributes to energy consumption will assist in the development of policies needed for the conservation of energy consumption. This study made use of the connection weight approach as an instrument of the Artificial...
Floods are the most common natural disasters, and cause significant damage to life, agriculture and economy. Research has moved on from mathematical modeling or physical parameter based flood forecasting schemes, to methodologies focused around algorithmic approaches. The Internet of Things (IoT) is a field of applied electronics and computer science where a system of devices collects data in real...
Predicting flood disasters are good potential research areas due to its impact to publics and economics of the affected country. With rapid economic growth and urbanization, flash floods in cities are frequent and annoying to publics. Thus, accurate and reliable prediction model of respective rivers that causing flood to highly dense populated area is needed so that the public can be warn of the possible...
The investigation and forecasting network traffic usage is an essential concern in the academic activities of university. This paper reports how to apply and compare SARIMA, NARX, and BPNN by using short-term time series datasets. The network traffic datasets are obtained from the ICT Universitas Mulawarman. As a result, the determination of several prediction models will continue to be an alternative...
Seawater chlorophyll-a (Chla) represents algal biomass in ocean and is a major index of eutrophication. In this paper, bootstrapped artificial neural network (BANN) model is developed for predicting the seawater Chla concentration around the north Pacific Rim. Three-layer ANN structure is applied and the modeling is based on comprehensive five-minute interval datasets of water temperature, depth,...
Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can be faced using standard...
This study investigates an application of nonlinear autoregressive (NAR) models to the prediction of the most likely time series of emotional state transitions of speakers engaged in dyadic conversations. While, previous methods analyzed each speaker in separation, the new approach proposes to couple both speakers into a nonlinear recursive predictive neural network system (NARX-NN). The NARX-NN system...
Accurate prediction of electricity demand is essential for planning, policy making and resource allocation in national level. In this manuscript, we applied a number of artificial intelligence methods to predict macro-scale electricity consumption rates in Iran. To this end, three socio-economic and three environmental factors were considered as inputs to the prediction models. We used data for the...
This study sought to investigate the effect of the number of input variables on both the accuracy and the robustness of the artificial neural network (ANN) method for predicting the performance parameters of a solar energy system. Tests were conducted on a solar energy system in Ottawa, Canada during summer under different weather conditions. Three different ANN models, i.e., one each with nine, eight...
The time series analysis and forecasting is an essential tool which can be widely applied for identifying the meaningful characteristics for making future ad-judgements; especially making decisions in finance under the numerous type of economic policies and reforms have been regarding as the one of the biggest challenge in the modern economy today.
The past decade has witnessed an increasing interest in applying internet and internet of things for problems involved behavioural intervention, and using these technologies to supervise the collection, analysis, and guidance of optimised interventions. In this paper, we presented a web-based multipurpose behaviour intervention investigation solution. This multipurpose behaviour intervention system...
Electrical energy consumption is affected by many parameters. These includes the variables related to power system itself, weather and climatic factors and socio-economic being of the energy consumers. In this paper, two components of load forecasting are classified. The parameters that influence the energy consumption and the methods used to forecast the energy consumption are reviewed. It is observed...
In the planning process of a supply chain, demand forecast have an important role in planning process of a company. The forecasts have to be as accurate as possible in order to allow the optimization of production, avoiding extra stocking costs or lost sales. In the case of spare parts, the challenge arises as the demand presents intermittent behavior. Nowadays, many forecast techniques, namely ARIMA...
This research explores the dynamic relation between price, temperature and humidity; and its effect on electricity consumption of electric appliances. It develops prediction models for electricity consumption based on these variables. It is important that reliable methods are employed in modelling and prediction of energy needs otherwise inappropriate models and poor forecasts may occur. In this research,...
In recent years, water quality prediction has attracted many attentions of governments and researchers. The safety of water quality seriously affects the human health, fishery economy and agricultural activities. If an early prediction to the water quality with an acceptable accuracy can be achieved, the negative impacts will be minimized or even be avoided. Many researchers have applied artificial...
Online Peer-to-Peer (P2P) lending has achieved explosive development recently, which could be beneficial to both sides of individual lending. In this study, a data mining (DM) approach to predict the performance of P2P loan before funded is proposed. Using data from the Lending Club, we explore the characteristics of loan and its applicant and use random forest to do the feature selection in the modeling...
Rainfall forecasting is one of the most imperative and demanding operational responsibilities carried out by meteorological services all over the world. The task is complicated since all decisions are to be taken in the visage of uncertainty. In this article, the traditional data pre-processing technique, moving average is coupled with Artificial Neural Network as MA - ANN to improve the prediction...
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