scholarly journals An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine and neural networks

2021 ◽  
Vol 24 (2) ◽  
pp. 243-254
Author(s):  
Yamina Ammi ◽  
Salah Hanini ◽  
Latifa Khaouane
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Reverse Osmosis ◽  
Support Vector ◽  
Kernel Support Vector Machine ◽  
Inflammatory Drugs ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach ◽  
Reverse Osmosis Membranes

scholarly journals An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hong-Hai Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Soil Improvement ◽  
Function Parameter ◽  
Support Vector ◽  
Established Method ◽  
In Situ Data ◽  
Svm Algorithm ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and  failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

scholarly journals A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2444 ◽  
Author(s):  
Dieu Tien Bui ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ebrahim Omidavr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Kernel Functions ◽  
Gully Erosion ◽  
Support Vector ◽  
Base Function ◽  
Conditioning Factors ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

An Overview of Artificial Intelligence-Based Methods for Building Energy Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Moncef Krarti
Keyword(s):  
Heat Transfer ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Use ◽  
Energy Systems ◽  
Building Energy ◽  
Building Envelope ◽  
Building Energy Systems ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

An overview of commonly used methodologies based on the artificial intelligence approach is provided with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. A description of selected applications to building energy systems of AI approaches is outlined. In particular, methods using the artificial intelligence approach for the following applications are discussed: Prediction energy use for one building or a set of buildings (served by one utility), Modeling of building envelope heat transfer, Controlling central plants in buildings, and Fault detection and diagnostics for building energy systems.

Artificial Intelligence

2017 ◽  
pp. 454-474 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Polynomial Regression ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Complex Data ◽  
Evolutionary Polynomial Regression

This chapter explains the Artificial Intelligence (AI) techniques in terms of Artificial Neural Networks (ANNs), fuzzy logic, expert systems, machine learning, Genetic Programming (GP), Evolutionary Polynomial Regression (EPR), and Support Vector Machine (SVM); the AI applications in modern education; the AI applications in software engineering development; the AI applications in Content-Based Image Retrieval (CBIR); and the multifaceted applications of AI in the digital age. AI is a branch of science which deals with helping machines find the suitable solutions to complex problems in a more human-like manner. AI technologies bring more complex data-analysis features to the existing applications in various industries and greatly contribute to management's organization, planning, and controlling operations, and will continue to do so with more frequency as programs are refined.

scholarly journals Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

2021 ◽  
Vol 11 (11) ◽  
pp. 4725
Author(s):  
Kashif Nisar ◽  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Ag. Asri Ag. Ibrahim ◽  
Joel J. P. C. Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Active Set ◽  
Active Set Method ◽  
Hybrid Heuristics ◽  
Hidden Layer ◽  
The Stability ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach ◽  
Search Capability

In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

scholarly journals AN ARTIFICIAL INTELLIGENCE APPROACH TO PREDICTION OF EXTREME EVENTS: THE CASE OF STORMS IN WESTERN FRANCE

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 115-122
Author(s):  
A. Frifra ◽  
M. Maanan ◽  
H. Rhinane ◽  
M. Maanan
Keyword(s):  
Artificial Intelligence ◽  
Extreme Events ◽  
Human Life ◽  
Low Frequency ◽  
Support Vector ◽  
Storm Characteristics ◽  
Buoy Data ◽  
Gated Recurrent Unit ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Abstract. Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures.

Comparative Study of Support Vector Machines and Neural Networks Used in Pattern Recognition

2012 ◽  
Vol 562-564 ◽  
pp. 2026-2029
Author(s):  
Shu Xian Zhu ◽  
Xue Li Zhu ◽  
Sheng Hui Guo
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Theoretical Analysis ◽  
Comparative Study ◽  
Support Vector ◽  
Vector Machines

Artificial neural networks and support vector machine (SVM), as two important tools, have widely applied in artificial intelligence and pattern recognition. In this paper, a comparative study has been done for making an analysis on their performances, when they are used in pattern recognition. Through theoretical analysis and confirmed by experimental results, a conclusion can be drawn that support vector machines have obvious advantages over those of traditional neural networks.

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