1,677 research outputs found
Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion
In order to increase the prediction accuracy of the online vehicle velocity
prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused
with traffic information was presented for the multiple scenarios. Initially,
traffic scenarios were established inside the co-simulation environment. In
addition, the algorithm of a general regressive neural network (GRNN) paired
with datasets of the ego-vehicle, the front vehicle, and traffic lights was
used in traffic scenarios, which increasingly improved the prediction accuracy.
To ameliorate the robustness of the algorithm, then the strategy was optimized
by particle swarm optimization (PSO) and k-fold cross-validation to find the
optimal parameters of the neural network in real-time, which constructed a
self-adaptive online PSO-GRNN VVP strategy with multi-information fusion to
adapt with different operating situations. The self-adaptive online PSO-GRNN
VVP strategy was then deployed to a variety of simulated scenarios to test its
efficacy under various operating situations. Finally, the simulation results
reveal that in urban and highway scenarios, the prediction accuracy is
separately increased by 27.8% and 54.5% when compared to the traditional GRNN
VVP strategy with fixed parameters utilizing only the historical ego-vehicle
velocity dataset.Comment: 9 pages, 7 figure
A PSO-GRNN model for railway freight volume prediction: empirical study from China
Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate the limited resources and organize transport more reasonably.
Design/methodology/approach: In this paper, a PSO-GRNN model is proposed to predict the railway freight volume. In this model, GRNN is applied to carry out the nonlinear regression analysis and output the prediction value, PSO algorithm is applied to optimize the GRNN model by searching the best smoothing parameter. In order to improve the performance of PSO algorithm, time linear decreasing inertia weight algorithm and time varying acceleration coefficient algorithm are applied in the paper.
Originality/value: A railway freight volume prediction index system containing seventeen indexes from five aspects is established in this paper. And PSO-GRNN model constructed in this paper are applied to predict the railway freight volume from 2007 to 2011. Finally, an empirical study is given to verify the feasibility and accuracy of the PSO-GRNN model by comparing with RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good performance in reducing the prediction error, and can be applied in actual production easilyPeer Reviewe
GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications
Active queue control aims to improve the overall communication network
throughput while providing lower delay and small packet loss rate. The basic
idea is to actively trigger packet dropping (or marking provided by explicit
congestion notification (ECN)) before buffer overflow. In this paper, two
artificial neural networks (ANN)-based control schemes are proposed for
adaptive queue control in TCP communication networks. The structure of these
controllers is optimized using genetic algorithm (GA) and the output weights of
ANNs are optimized using particle swarm optimization (PSO) algorithm. The
controllers are radial bias function (RBF)-based, but to improve the robustness
of RBF controller, an error-integral term is added to RBF equation in the
second scheme. Experimental results show that GA- PSO-optimized improved RBF
(I-RBF) model controls network congestion effectively in terms of link
utilization with a low packet loss rate and outperform Drop Tail,
proportional-integral (PI), random exponential marking (REM), and adaptive
random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635
A Prediction Modeling Framework For Noisy Welding Quality Data
Numerous and various research projects have been conducted to utilize historical manufacturing process data in product design. These manufacturing process data often contain data inconsistencies, and it causes challenges in extracting useful information from the data. In resistance spot welding (RSW), data inconsistency is a well-known issue. In general, such inconsistent data are treated as noise data and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for every design and manufacturing applications since every data can contain important information to further explain the process. In this research, we propose a prediction modeling framework, which employs bootstrap aggregating (bagging) with support vector regression (SVR) as the base learning algorithm to improve the prediction accuracy on such noisy data. Optimal hyper-parameters for SVR are selected by particle swarm optimization (PSO) with meta-modeling. Constructing bagging models require
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more computational costs than a single model. Also, evolutionary computation algorithms, such as PSO, generally require a large number of candidate solution evaluations to achieve quality solutions. These two requirements greatly increase the overall computational cost in constructing effective bagging SVR models. Meta-modeling can be employed to reduce the computational cost when the fitness or constraints functions are associated with computationally expensive tasks or analyses. In our case, the objective function is associated with constructing bagging SVR models with candidate sets of hyper-parameters. Therefore, in regards to PSO, a large number of bagging SVR models have to be constructed and evaluated, which is computationally expensive. The meta-modeling approach, called MUGPSO, developed in this research assists PSO in evaluating these candidate solutions (i.e., sets of hyper-parameters). MUGPSO approximates the fitness function of candidate solutions. Through this method, the numbers of real fitness function evaluations (i.e., constructing bagging SVR models) are reduced, which also reduces the overall computational costs. Using the Meta2 framework, one can expect an improvement in the prediction accuracy with reduced computational time. Experiments are conducted on three artificially generated noisy datasets and a real RSW quality dataset. The results indicate that Meta2 is capable of providing promising solutions with noticeably reduced computational costs
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction
Internet traffic volume estimation has a significant impact on the business
policies of the ISP (Internet Service Provider) industry and business
successions. Forecasting the internet traffic demand helps to shed light on the
future traffic trend, which is often helpful for ISPs decision-making in
network planning activities and investments. Besides, the capability to
understand future trend contributes to managing regular and long-term
operations. This study aims to predict the network traffic volume demand using
deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based
noise reduction, Empirical rule based outlier detection, and -Nearest
Neighbour (KNN) based outlier mitigation. In contrast to the former studies,
the proposed model does not rely on a particular EMD decomposed component
called Intrinsic Mode Function (IMF) for signal denoising. In our proposed
traffic prediction model, we used an average of all IMFs components for signal
denoising. Moreover, the abnormal data points are replaced by nearest data
points average, and the value for has been optimized based on the KNN
regressor prediction error measured in Root Mean Squared Error (RMSE). Finally,
we selected the best time-lagged feature subset for our prediction model based
on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information
Criterion (AIC) value. Our experiments are conducted on real-world internet
traffic datasets from industry, and the proposed method is compared with
various traditional deep sequence baseline models. Our results show that the
proposed EMD-KNN integrated prediction models outperform comparative models.Comment: 13 pages, 9 figure
Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction
Regional logistics prediction is the key step in regional logistics planning and logistics resources rationalization. Since regional economy is the inherent and determinative factor of regional logistics demand, it is feasible to forecast regional logistics demand by investigating economic indicators which can accelerate the harmonious development of regional logistics industry and regional economy. In this paper, the PSO-RBFNN model, a radial basis function neural network (RBFNN) combined with particle swarm optimization (PSO) algorithm, is studied. The PSO-RBFNN model is trained by indicators data in a region to predict the regional logistics demand. And the corresponding results indicate the model’s applicability and potential advantages
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