scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

Prediction of the optimal FSW process parameters for joints using machine learning techniques

2021 ◽  
Vol 63 (12) ◽  
pp. 1104-1111
Author(s):  
Furkan Sarsilmaz ◽  
Gürkan Kavuran
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Friction Stir ◽  
Learning Techniques ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Fsw Parameters

Abstract In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.

scholarly journals Designing a Compact Microstrip Antenna Using the Machine Learning Approach

2020 ◽  
Vol 4 ◽  
pp. 44-52
Author(s):  
Kanhaiya Sharma ◽  
Ganga Prasad Pandey
Keyword(s):  
Machine Learning ◽  
Microstrip Antenna ◽  
Machine Learning Techniques ◽  
Dual Band ◽  
Ann Model ◽  
Shape Prediction ◽  
Learning Techniques ◽  
Notch Length ◽  
Artificial Neural Network Ann ◽  
Rectangular Microstrip Antenna

This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results

Applications of Machine Learning Techniques in Disease Classification From Medical Images

2018 ◽  
pp. 318-337 ◽  
Author(s):  
Bikesh Kumar Singh ◽  
Satya Eswari Jujjavarapu
Keyword(s):  
Machine Learning ◽  
Medical Images ◽  
Radial Basis Function Network ◽  
Objective Evaluation ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Future Challenges ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning

Machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), radial basis function network (RBFN), random forest (RF), naive Bayes classifier, etc. have gained much attention in recent years due to their widespread applications in diverse fields. This chapter is focused on providing a comprehensive insight of various techniques employed for key areas of medical image processing and analysis. Different applications covered in this chapter include feature extraction, feature selection, and cancer classification in medical images. The authors present current practices and evaluation measures used for objective evaluation of different machine learning methods in context to above-mentioned applications. Various factors associated with acceptance/rejection of such automated systems by medical research community are discussed. The authors also discuss how the interaction between automated analysis systems and medical professionals can be improved for its acceptance in clinical practice. They conclude the chapter by presenting research gaps and future challenges.

Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization

2021 ◽  
Author(s):  
Asad Mustafa Elmgerbi ◽  
Clemens Peter Ettinger ◽  
Peter Mbah Tekum ◽  
Gerhard Thonhauser ◽  
Andreas Nascimento
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Real Time ◽  
Predictive Models ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Time Rate ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Learning Techniques

Abstract Over the past decade, several models have been generated to predict Rate of Penetration (ROP) in real-time. In general, these models can be classified into two categories, model-driven (analytical models) and data-driven models (based on machine learning techniques), which is considered as cutting-edge technology in terms of predictive accuracy and minimal human interfering. Nevertheless, most existing machine learning models are mainly used for prediction, not optimization. The ROP ahead of the bit for a certain formation layer can be predicted with such methods, but the limitation of the applications of these techniques is to find an optimum set of operating parameters for the optimization of ROP. In this regard, two data-driven models for ROP prediction have been developed and thereafter have been merged into an optimizer model. The purpose of the optimization process is to seek the ideal combinations of drilling parameters that would lead to an improvement in the ROP in real-time for a given formation. This paper is mainly focused on describing the process of development to create smart data-driven models (built on MATLAB software environment) for real-time rate of penetration prediction and optimization within a sufficient time span and without disturbing the drilling process, as it is typically required by a drill-off test. The used models here can be classified into two groups: two predictive models, Artificial Neural Network (ANN) and Random Forest (RF), in addition to one optimizer, namely genetic algorithm. The process started by developing, optimizing, and validation of the predictive models, which subsequently were linked to the genetic algorithm (GA) for real-time optimization. Automated optimization algorithms were integrated into the process of developing the productive models to improve the model efficiency and to reduce the errors. In order to validate the functionalities of the developed ROP optimization model, two different cases were studied. For the first case, historical drilling data from different wells were used, and the results confirmed that for the three known controllable surface drilling parameters, weight on bit (WOB) has the highest impact on ROP, followed by flow rate (FR) and finally rotation per minute (RPM), which has the least impact. In the second case, a laboratory scaled drilling rig "CDC miniRig" was utilized to validate the developed model, during the validation only the previous named parameters were used. Several meters were drilled through sandstone cubes at different weights on bit, rotations per minute, and flow rates to develop the productive models; then the optimizer was activated to propose the optimal set of the used parameters, which likely maximize the ROP. The proposed parameters were implemented, and the results showed that ROP improved as expected.

scholarly journals Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data

2015 ◽  
Vol 9s3 ◽  
pp. BBI.S29469 ◽  
Author(s):  
Lucas J. Adams ◽  
Ghalib Bello ◽  
Gerard G. Dumancas
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Predictive Modeling ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Questionnaire Data ◽  
Special Equipment ◽  
Learning Techniques ◽  
Specific Outcome ◽  
Performance Area

The problem of selecting important variables for predictive modeling of a specific outcome of interest using questionnaire data has rarely been addressed in clinical settings. In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality. We examined 123 questions (variables) answered by 5,444 individuals in the National Health and Nutrition Examination Survey. The GA iterations selected the top 24 variables, including questions related to stroke, emphysema, and general health problems requiring the use of special equipment, for use in predictive modeling by various parametric and nonparametric machine learning techniques. Using these top 24 variables, gradient boosting yielded the nominally highest performance (area under curve [AUC] = 0.7654), although there were other techniques with lower but not significantly different AUC. This study shows how GA in conjunction with various machine learning techniques could be used to examine questionnaire data to predict a binary outcome.

scholarly journals Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3113 ◽  
Author(s):  
Silvia Liberata Ullo ◽  
G. R. Sinha
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Sustainable Growth ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Smart Environment ◽  
Environment Monitoring ◽  
Learning Techniques ◽  
Quality Water ◽  
Major Factors

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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