A New Predictive Model for Uniaxial Compressive Strength of Rock Using Machine Learning Method: Artificial Intelligence-Based Age-Layered Population Structure Genetic Programming (ALPS-GP)

2021 ◽  
Author(s):  
Engin Özdemir
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Compressive Strength ◽  
Population Structure ◽  
Genetic Programming ◽  
Predictive Model ◽  
Uniaxial Compressive Strength ◽  
Machine Learning Method ◽  
Learning Method

scholarly journals Application of Sensors in Intelligent Clothing Design

2018 ◽  
Vol 14 (06) ◽  
pp. 4
Author(s):  
Shali Jiang ◽  
Qiong Ren
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Clothing Design ◽  
Artificial Intelligence Technology ◽  
Contact Sensor ◽  
Intelligent Clothing ◽  
Contact Sensors

<p class="0abstract"><span lang="EN-US">In order to study the application of sensors in intelligent clothing design, the artificially intelligent cutting-edge technology -machine learning method was proposed to combine a variety of signals of non-contact sensors in several different positions. Higher accuracy was achieved, while maintaining the comfort brought by a non-contact sensor. The experimental results showed that the proposed strategy focused on the combination of clothing design technology and artificial intelligence technology. As a result, without changing the sensor materials, it enhances the comfort and precision of clothing, eliminates the comfort reduced by sensor close to the skin, and transforms inaccurate measurement into accurate measurement. </span></p>

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

scholarly journals Study of Population Structure and Genetic Prediction of Buffalo from Different Provinces of Iran using Machine Learning Method

2020 ◽  
Vol 9 ◽  
pp. 48-59
Author(s):  
Zahra Azizi ◽  
Hossein Moradi Shahrbabak ◽  
Seyed Abbas Rafat ◽  
Mohammad Moradi Shahrbabak ◽  
Jalil Shodja
Keyword(s):  
Machine Learning ◽  
Population Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
Genetic Prediction

scholarly journals Simulation of liquid flow with a combination artificial intelligence flow field and Adams–Bashforth method

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fluid Flow ◽  
Flow Field ◽  
Computational Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Particle Movement ◽  
Lagrangian Framework ◽  
Cfd Method

Abstract Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high-resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams–Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams–Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the square-shaped cavity. AI model can mimic the vortex structure in the cavity, where there is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls.

scholarly journals Clustering Analysis using an Unsupervised Machine Learning Method

2021 ◽  
pp. 602-609
Author(s):  
Tashfin Ansari ◽  
Dr. Almas Siddiqui ◽  
Awasthi G. K
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Complexity ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Experimental Result ◽  
Machine Learning Method ◽  
Learning Method ◽  
Science Study ◽  
Data Object

Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.

scholarly journals Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method

2019 ◽  
Vol 15 (2) ◽  
pp. 1-7
Author(s):  
Nabeel Shakeel ◽  
Farrukh Baig ◽  
Muhammad Abubakar Saddiq
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Survey Data ◽  
Public Transport ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Transport Services ◽  
Usage Frequency

Abstract Predictive modeling is the key fundamental method to study passengers’ behavior in transportation research. One of the limited studied topic is modeling of public transport usage frequency, which can be used to estimate present and future demand and users’ trend toward public transport services. The artificial intelligence and machine learning methods are promising to be better substitute to statistical techniques. No doubt, traditionally been used econometrics models are better for causal relationship studies among variables, but they made rigid assumptions and unable to recognize the pattern in data. This paper aims to build a predictive model to solve passengers’ classification, and public transport usage frequency using socio-demographic survey data. The supervised machine learning algorithm, K-Nearest Neighbor (KNN) applied to build a predictive model, which is the better machine learning method for dealing with small datasets, because of its ability of having less parameter tuning. Survey data has been used to train and validate the model performance, which is able to predict public transport usage frequency of future users of public transport. This model can practically be used by public transport agencies and relevant government organizations to predict the public transport demand for new commuters before introducing any new transportation projects.

scholarly journals Study of Population Structure and Genetic Prediction of Buffalo from Different Provinces of Iran using Machine Learning Method

2020 ◽  
Vol 9 ◽  
pp. 48-59
Author(s):  
Zahra Azizi ◽  
Hossein Moradi Shahrbabak ◽  
Seyed Abbas Rafat ◽  
Mohammad Moradi Shahrbabak ◽  
Jalil Shodja
Keyword(s):  
Machine Learning ◽  
Population Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
Genetic Prediction

scholarly journals Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach

2021 ◽  
Author(s):  
Rania M. Hathout ◽  
Orchid A Mahmoud ◽  
Dalia S Ali ◽  
Marina Mamdouh ◽  
Abdelkader A Metwally
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gaussian Processes ◽  
Plga Nanoparticles ◽  
Binding Energies ◽  
Machine Learning Method ◽  
Learning Method ◽  
Physico Chemical ◽  
Chemical Descriptors ◽  
Delta G

The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68 % &plusmn; 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (&Delta;G) can easily be projected to the drugs loading as was previously proven. Adopting the &ldquo;Pharmaceutics Informatics&rdquo; approach can save the pharmaceutical industry and the drug delivery scientists a lot of exerted resources, efforts and time.

Sign in / Sign up

Export Citation Format

Share Document