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The time-dependent origin-destination (TDOD) demand estimation problem aims at estimating dynamic demand that represents the observed traffic flow patterns in a transportation network. Errors in TDOD demand are often propagated into the network outputs causing unreliable planning and operational policies. In this study, a bi-level optimization problem is proposed where the upper level is an Ordinary...
Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time-series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic...
The aim of this work is to create a decision support system based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which will be used for objective classification of employees in the employment process by analyzing available information about the candidates. Information about the candidates is extracted from the relevant documents on one company during the advertising for job in Information Technology...
Nowadays there are numerous user-generated restaurant reviews available on the Internet, of which they are considered valuable resources for decision making to customers. In reality, not every reviews available online are helpful to users, so the need for filtering unqualified reviews is realized. There have been several studies on spam review detection that attempt to detect unqualified reviews using...
This paper builds on previous work involving different Bayesian Neural Networks namely Partial Logistic Artificial Neural Network with Automatic Relevance Determination (PLANN-ARD) for Single Risk (SR) and Competing Risk (CR) [1, 6, 15, 16] and applied in the medical survival studies. The results obtained with these PLANN-ARD/PLANN-CR-ARD models are compared with the results obtained with a different...
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...
Space relays are affected by many nonlinear elements during storage, and the reason for predicting time series is to achieve nonlinear mapping. Combining artificial neural networks and grey system theory, we built a grey artificial neural network (GANN) model. The model effectively combined the characteristics of artificial-neural-network nonlinear adaptability and the characteristics of grey theory...
Air pollution has become health hazard. With the growth of industries, the air quality has now become an issue both for the environment as well to the society over the last few years. Due to the rising degradation of air quality, the need for control has risen. Artificial neural network have been applied to many environmental engineering problems and have demonstrated good degree of success in processing...
Bus Transportation plays an important role in modern society and has been developed in many parts of the world. It reduces the private vehicle usage; fuel consumption and more over reduce traffic congestion, if the arrival time of the buses is accurate. In this paper, various literatures have been surveyed which is used for prediction of bus arrival time. Real time prediction of arrival time is so...
Modeling of dynamic systems using system identification became an important discipline as it overrides the errors that may be introduced by traditional modelling techniques. There are two methodologies for identification of systems' models; statistical and deterministic methods. Identification algorithms are proposed in this paper using deterministic neural network and compare the results with regression...
This paper proposes an ensemble neural network (ENN) framework for robust automatic speech recognition (ASR). The proposed ENN framework can be divided into offline and online phases. In the offline phase, the ENN framework first applies an environment clustering technique to partition the training data into several subsets, where each subset characterizes specific local information of the entire...
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...
Flood prediction modelling is one of the most popular research areas among researcher around the world. This is due to negative impacts to the economy and society that were caused by flood. The dynamic behaviours of river water level that causing flood were commonly modelled by researcher either by equations using physic theories or by black-box model. River water level prediction model that could...
Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. In this paper, we aim...
Given the difficulty of developing physics-based degradation process models in practice, data-driven prognostics approaches are preferred in several industrial applications. Among data-driven approaches, one can distinguish between (i) degradation-based approaches that predict the future evolution of the equipment degradation and (ii) direct Remaining Useful Life (RUL) prediction approaches which...
Solar forecasting is a pivotal factor in a viable solar energy deployment to support reliable and cost-effective grid operation and control. This paper proposes a new approach to overcome one of the most significant challenges in solar generation forecasting, i.e., the limited availability of the stationary data sets. This challenge is addressed by converting the non-stationary historical solar irradiance...
The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of...
With the advent of restructuring electricity sector and smart grids, combined with the increased variability and uncertainty associated with electricity market prices (EMP) signals and players' behavior, together with the growing integration of renewable energy sources, enhancing prediction tools are required for players and different regulators agents to face the non-stationarity and stochastic nature...
The paper contains selected results of studies and research of hybrid modeling which is comprised from neural modeling and evolutionary modeling. Neural modeling was focused on designing and teaching the Artificial Neural Network (ANN) of the Polish Electricity Power Exchange (PEPE) on the example of the data for the period from 01.01.2015 to 06.30.2015 of the Next Day Market, evolutionary modeling...
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