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Particle swarm optimization model to predict scour depth around bridge pier
To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data.
Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task.
Sensitivity study of ANFIS model parameters to predict the pressure gradient with combined input and outputs hydrodynamics parameters in the bubble column reactor
This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.
Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression
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.
Multi-label Classification for Fault Diagnosis of Rotating Electrical Machines
The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions.
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
Emotion detection from the text is an important and challenging problem in text analytics.
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive.
Flood Prediction Using Machine Learning Models: Literature Review
Floods are among the most destructive natural disasters, which are highly complex to model.
Developing an ANFIS PSO Model to Estimate Mercury Emission in Combustion Flue Gases
Accurate prediction of mercury content emitted from fossil fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation.
Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System
The performance of the proposed model is further compared with a linear-SVM model.
Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO$_2$
In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide.
Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Models
Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.
Simulation of Turbulent Flow around a Generic High-Speed Train using Hybrid Models of RANS Numerical Method with Machine Learning
So, drag, lift, and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters.
Extreme learning machine-based model for Solubility estimation of hydrocarbon gases in electrolyte solutions
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering.
Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System
The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea.
Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants
This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain.
A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy
We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels.
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model
Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate.
Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming
This paper proposes a model based on gene expression programming for predicting the discharge coefficient of triangular labyrinth weirs.
Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods.
Evaluation of electrical efficiency of photovoltaic thermal solar collector
Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies.
Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014).
Comparative analysis of machine learning models for Ammonia Capture of Ionic Liquids
Industry uses various solvents in the processes of refrigeration and ventilation.
Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System
Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models.
Performance Analysis of Combine Harvester using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization
Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace.
Application of ERA5 and MENA simulations to predict offshore wind energy potential
In this regard, EC-EARTH near surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy.
Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and B-Thalassemia with CBC Indices Based on Dynamic Harmony Search
In recent decades, attention has been directed at anemia classification for various medical purposes, such as thalassemia screening and predicting iron deficiency anemia (IDA).
Hybrid Machine Learning Models for Crop Yield Prediction
Prediction of crop yield is essential for food security policymaking, planning, and trade.
Sustainable Banking; Evaluation of the European Business Models
The proposed business model components of this study were ranked in terms of their impact on achieving sustainability goals.
Data Science in Economics
The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models.
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased.
Deep learning for Stock Market Prediction
This paper concentrates on the future prediction of stock market groups.
Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks
Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique.
A Mobile Cloud-Based eHealth Scheme
Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage.
Generation expansion planning in the presence of wind power plants using a genetic algorithm model
The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants.
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran
The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively.
Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction
The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research.
Early detection of the advanced persistent threat attack using performance analysis of deep learning
One of the most common and important destructive attacks on the victim system is Advanced Persistent Threat (APT)-attack.
Synthesizing multi-layer perceptron network with ant lion, biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings
The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models.
Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet
In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance.
Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer
Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology.
Machine Learning versus Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel
The results of the sensitivity analysis show that the most influential parameter for the SSD in a smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one.
Driving Factors Behind the Social Role of Retail Centers on Recreational Activities
Accordingly, two hypotheses were raised illustrating that the travel time (i. e., the time it takes for a customer to reach the retail center) and the variety of shops (in a retail center) increase the percentage of people who spend their leisure time and recreational activities retail centers.
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series.
The Effect of Marketing Investment on Firm Value and Systematic Risk
Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk.
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS
First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models.
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals.
GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset
One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis.
Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature
Whilst the ANFIS model is extremely stable for almost all numbers of membership functions, the BNN model is highly sensitive to this scale factor to predict DPT.
Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate
A recent study derived the combined heat transfers and their temperature profiles for the PMMA sample.
Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit
Streamlined weirs which are a nature-inspired type of weir have gained tremendous attention among hydraulic engineers, mainly owing to their established performance with high discharge coefficients.
Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors
Detection of the relationship between two time series is so important in environmental and hydrological studies.
Energy-Exergy Analysis and Optimal Design of a Hydrogen Turbofan Engine
In on design conditions, entropy generation rate, nitrogen oxide production rate, and TSFC for the chosen cycle based on the economic approach +18. 89 percent, +10. 01 percent, and -0. 21percent, respectively, and based on the exero-environmental approach -54. 03percent, -42. 02percent, and +21. 44percent change compared to the base engine, respectively.
Prediction of the energy and exergy performance of F135 PW100 turbofan engine via deep learning
Based on the results obtained in the first phase, to model the thermodynamic performance of the aforementioned engine cycle, Flight-Mach number and flight altitude are considered to be 2. 5 and 30, 000 m, respectively; due to the operational advantage of supersonic flying at high altitude flight conditions, and the higher thrust of hydrogen fuel.
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique
The purpose of this study is to construct a secure health monitoring system in healthcare 5. 0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases.
Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover
Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90 percent.
Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark
Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method.
A Bayesian Framework on Asymmetric Mixture of Factor Analyser
In this paper, an MFA model with a rich and flexible class of skew normal (unrestricted) generalized hyperbolic (called SUNGH) distributions along with a Bayesian structure with several computational benefits have been introduced.
