REVIEW | doi:10.20944/preprints201908.0203.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: machine learning; deep learning; ensemble models
Online: 20 August 2019 (08:41:28 CEST)
The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.
ARTICLE | doi:10.20944/preprints201908.0180.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: machine learning; smart cities; IoT; deep learning; big data; soft computing; sustainable urban development; building energy; energy demand and consumption; sustainable cities
Online: 17 August 2019 (04:11:44 CEST)
Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.
REVIEW | doi:10.20944/preprints201908.0179.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: biofuels; deep learning; big data; machine learning models; biodiesel
Online: 17 August 2019 (03:48:28 CEST)
Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.
REVIEW | doi:10.20944/preprints201908.0152.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: deep learning; machine learning model; convolutional neural networks (CNN); recurrent neural networks (RNN); denoising autoencoder (DAE); deep belief networks (DBNs); long short-term memory (LSTM); review; survey; state of the art
Online: 13 August 2019 (09:32:09 CEST)
Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.
ARTICLE | doi:10.20944/preprints201908.0202.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: combine harvester; hybrid machine learning; ANFIS; response surface methodology (RSM); artificial intelligence in agriculture; radial basis function (RBF)
Online: 20 August 2019 (08:03:38 CEST)
Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.
REVIEW | doi:10.20944/preprints201908.0166.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: machine learning; deep learning; big data; hydrology; climate change; global warming; hydrological model; earth systems
Online: 15 August 2019 (05:50:48 CEST)
Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.
ARTICLE | doi:10.20944/preprints201908.0162.v1
Subject: Engineering, Civil Engineering Keywords: Urban mobility, urban train lines, modeling, soil mass-structure, soil-structure interaction, PLAXIS, computational mechanics, simulation, smart cities, urban sustainable devel-opment, urban rail transportation
Online: 14 August 2019 (09:27:56 CEST)
Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.
ARTICLE | doi:10.20944/preprints201905.0124.v3
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: air pollution prediction; flue gas; mercury emissions; adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); ANFIS-PSO; hybrid machine learning model; data science; particulate matter; health hazards of air pollution; air quality
Online: 12 August 2019 (05:19:37 CEST)
Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.
ARTICLE | doi:10.20944/preprints201906.0055.v2
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: supercritical carbon dioxide; machine learning modeling; acid; artificial intelligence; solubility; artificial neural networks (ANN); adaptive neuro-fuzzy inference system (ANFIS); least-squares support-vector machine (LSSVM); multi-layer perceptron (MLP); engineering applications of artificial intelligence
Online: 31 July 2019 (04:35:26 CEST)
In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.
ARTICLE | doi:10.20944/preprints202001.0227.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: transportation; mobility; prediction model; pavement management; pavement condition index; falling weight deflectometer; multilayer perceptron; radial basis function; artificial neural network; intelligent machine system committee
Online: 20 January 2020 (11:08:32 CET)
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
ARTICLE | doi:10.20944/preprints201907.0351.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: evaporation; meteorological parameters; Gaussian process regression; support vector regression; machine learning modeling; hydrology; prediction; data science; hydroinformatics
Online: 31 July 2019 (10:58:29 CEST)
Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.
ARTICLE | doi:10.20944/preprints201908.0019.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: emotion classification; machine learning classifiers; ISEAR dataset; data mining; performance evaluation; data science; opinion-mining
Online: 2 August 2019 (08:49:27 CEST)
Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.