Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

Download Full-text

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

Applied Sciences ◽

10.3390/app10082820 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2820 ◽

Cited By ~ 1

Author(s):

In Hyuk Choi ◽

Ja Bin Koo ◽

Ju Am Son ◽

Jun Sin Yi ◽

Young Geun Yoon ◽

...

Keyword(s):

Machine Learning ◽

Damage Detection ◽

Frequency Response ◽

Dynamic Characteristics ◽

Machine Learning Techniques ◽

Support Vector ◽

Physical Damage ◽

Long Distance ◽

Porcelain Insulators ◽

The Impact

The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.

Download Full-text

Modeling of the Mechanical Properties of Carbon-Black Reinforced Rubber Blends by Machine Learning Techniques

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.627.97 ◽

2014 ◽

Vol 627 ◽

pp. 97-100 ◽

Cited By ~ 1

Author(s):

R. Fernandez-Martinez ◽

R. Hernandez ◽

J. Ibarretxe ◽

Pello Jimbert ◽

M. Iturrondobeitia ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Carbon Black ◽

Machine Learning Techniques ◽

Support Vector ◽

Efficient Design ◽

Rubber Blends ◽

Taguchi Design Of Experiments ◽

Physical Attributes ◽

Input Variables

Mastering the relationship between the final mechanical properties of carbon black reinforced rubber blends and their composition is a key advantage for an efficient design of the composition of the blend. In this work, some models to predict three relevant physical attributes of rubber blends — modulus at 100% deformation, Shore A hardness, and tensile strength — are built by machine learning methods and subsequently evaluated. Linear regression, artificial neural networks, support vector machine, and regression trees are used to generate the models. The number of used samples and the values for the input variables is determined by a Taguchi design of experiments, and prior to the modeling the uncertainty of the experimental data was analyzed.

Download Full-text

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Modern Physics Letters B ◽

10.1142/s0217984918501324 ◽

2018 ◽

Vol 32 (11) ◽

pp. 1850132 ◽

Cited By ~ 9

Author(s):

Harpreet Singh ◽

Prashant Singh Rana ◽

Urvinder Singh

Keyword(s):

Machine Learning ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Machine Learning Techniques ◽

Support Vector ◽

Prediction Errors ◽

Learning Approaches ◽

Inference System ◽

Drug Synergy ◽

Learning Techniques

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug–drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Download Full-text

Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i2.pp1140-1150 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1140

Author(s):

Md Abul Kalam Azad ◽

Anup Majumder ◽

Jugal Krishna Das ◽

Md Imdadul Islam

Keyword(s):

Machine Learning ◽

Signal Detection ◽

Fuzzy Inference ◽

Fuzzy Rule ◽

Energy Detection ◽

Decision Time ◽

Machine Learning Techniques ◽

Support Vector ◽

Detection Accuracy ◽

Inference System

<span>The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy <em>c</em>-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.</span>

Download Full-text

Computational Statistics and Machine Learning Techniques for Effective Decision Making on Student’s Employment for Real-Time

Mathematics ◽

10.3390/math9111166 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1166

Author(s):

Deepak Kumar ◽

Chaman Verma ◽

Pradeep Kumar Singh ◽

Maria Simona Raboaca ◽

Raluca-Andreea Felseghi ◽

...

Keyword(s):

Machine Learning ◽

Hybrid Approach ◽

T Test ◽

Job Placement ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Computational Statistics ◽

The Impact

The present study accentuated a hybrid approach to evaluate the impact, association and discrepancies of demographic characteristics on a student’s job placement. The present study extracted several significant academic features that determine the Master of Business Administration (MBA) student placement and confirm the placed gender. This paper recommended a novel futuristic roadmap for students, parents, guardians, institutions, and companies to benefit at a certain level. Out of seven experiments, the first five experiments were conducted with deep statistical computations, and the last two experiments were performed with supervised machine learning approaches. On the one hand, the Support Vector Machine (SVM) outperformed others with the uppermost accuracy of 90% to predict the employment status. On the other hand, the Random Forest (RF) attained a maximum accuracy of 88% to recognize the gender of placed students. Further, several significant features are also recommended to identify the placement of gender and placement status. A statistical t-test at 0.05 significance level proved that the student’s gender did not influence their offered salary during job placement and MBA specializations Marketing and Finance (Mkt&Fin) and Marketing and Human Resource (Mkt&HR) (p > 0.05). Additionally, the result of the t-test also showed that gender did not affect student’s placement test percentage scores (p > 0.05) and degree streams such as Science and Technology (Sci&Tech), Commerce and Management (Comm&Mgmt). Others did not affect the offered salary (p > 0.05). Further, the χ2 test revealed a significant association between a student’s course specialization and student’s placement status (p < 0.05). It also proved that there is no significant association between a student’s degree and placement status (p > 0.05). The current study recommended automatic placement prediction with demographic impact identification for the higher educational universities and institutions that will help human communities (students, teachers, parents, institutions) to prepare for the future accordingly.

Download Full-text

Using Machine Learning Techniques In Multi -Hazards Assessment of Golestan National Park, Iran

10.21203/rs.3.rs-814390/v1 ◽

2021 ◽

Author(s):

Hassan Faramarzi ◽

Seyed Mohsen Hosseini ◽

Hamid Reza Pourghasemi ◽

Mahdi Farnaghi

Keyword(s):

Machine Learning ◽

National Park ◽

Regression Tree ◽

Environmental Hazards ◽

High Potential ◽

Machine Learning Techniques ◽

Support Vector ◽

Boosted Regression Tree ◽

Learning Techniques ◽

The Impact

Abstract Golestan National Park is one of the oldest biosphere reserves exposed to environmental hazards due to growing demand, geographical location of the park, mountainous conditions, and developments in the last five decades. The purpose of this study was to evaluate potential environmental hazards using machine-learning techniques. In this study, maximum entropy, random forest, boosted regression tree, generalized additive model, and support vector machine methods were applied to model environmental hazards and evaluate the impact of affecting agents, their area of influence, and interactions. After data collection and preprocessing, the models were implemented, tuned, and trained, and their accuracies were determined using the receiver operating characteristic curve. The results indicate the high importance of climatic and human variables, including rainfall, temperature, presence of shepherds, and villagers for fire hazards, elevation, transit roads, temperature, and rainfall for the formation of floodplains, and elevation, transit roads, rainfall, and topographic wetness index in the occurrence of landslides in the national park. The boosted regression tree model with a ROC value of 0.98 for flooding, 0.97 for fire, and 0.93 for landslide hazards, had the best performance. The modeling estimated that, on average, 16.2% of the area of Golestan National Park has a high potential for landslides, 14% has a high potential for fire, and 7.2% has a high potential for flooding.

Download Full-text

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

Sensors ◽

10.3390/s20247087 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7087

Author(s):

Waldemar Mucha

Keyword(s):

Machine Learning ◽

Fuzzy Inference ◽

Machine Learning Algorithms ◽

Computational Time ◽

Support Vector ◽

Numerical Comparison ◽

Operational Load ◽

Load Monitoring ◽

Using Data ◽

Inference Systems

The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

Download Full-text

Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Download Full-text

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Download Full-text

Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

10.26434/chemrxiv.11886702.v1 ◽

2020 ◽

Author(s):

Azhagiya Singam Ettayapuram Ramaprasad ◽

Phum Tachachartvanich ◽

Denis Fourches ◽

Anatoly Soshilov ◽

Jennifer C.Y. Hsieh ◽

...

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

Androgen Receptor ◽

Endocrine Disruptors ◽

Hormone Receptors ◽

Steroid Hormone Receptors ◽

Machine Learning Techniques ◽

Support Vector ◽

Polyfluoroalkyl Substances ◽

Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

Download Full-text