Train Delay Estimation in Indian Railways by Including Weather Factors through Machine Learning Techniques

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

Expert Annotation ◽

Natural Scientists

The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

Adoption of machine learning techniques in Ecology and Earth Science

10.7287/peerj.preprints.1720 ◽

2016 ◽

Cited By ~ 1

Author(s):

Anne E Thessen

Keyword(s):

Machine Learning ◽

Earth Science ◽

Collaborative Work ◽

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

Expert Annotation ◽

Natural Scientists

The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

Prediction of Ubiquitination Sites Using UbiNets

Advances in Fuzzy Systems ◽

10.1155/2018/5125103 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Sarthak Yadav ◽

Manoj Gupta ◽

Ankur Singh Bist

Keyword(s):

Machine Learning ◽

Posttranslational Modification ◽

Protein Sequences ◽

Random Forest Classifier ◽

Gradient Boosting ◽

Multilayer Perceptrons ◽

Learning Techniques ◽

Proposed Model ◽

Main Target

Ubiquitination controls the activity of various proteins and belongs to posttranslational modification. Various machine learning techniques are taken for prediction of ubiquitination sites in protein sequences. The paper proposes a new MLP architecture, named UbiNets, which is based on Densely Connected Convolutional Neural Networks (DenseNet). Computational machine learning techniques, such as Random Forest Classifier, Gradient Boosting Machines, and Multilayer Perceptrons (MLP), are taken for analysis. The main target of this paper is to explore the significance of deep learning techniques for the prediction of ubiquitination sites in protein sequences. Furthermore, the results obtained show that the newly proposed model provides significant accuracy. Satisfactory experimental results show the efficiency of proposed method for the prediction of ubiquitination sites in protein sequences. Further, it has been recommended that this method can be used to sort out real time problems in concerned domain.

Automated Amharic News Categorization Using Deep Learning Models

Computational Intelligence and Neuroscience ◽

10.1155/2021/3774607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Demeke Endalie ◽

Getamesay Haile

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Document Classification ◽

Machine Learning Algorithms ◽

Support Vector ◽

Language Resources ◽

Data Set ◽

Learning Techniques ◽

Proposed Model

For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep learning model for Amharic news document classification. The proposed model uses fastText to generate text vectors to represent semantic meaning of texts and solve the problem of traditional methods. The text vectors matrix is then fed into the embedding layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost (XGB), and random forest (RF) and achieved good results.

Prediction of Survivors in the Titanic Cruise

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4408.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1268-1271

Keyword(s):

Machine Learning ◽

Data Science ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Learning Techniques ◽

Gradient Boosting Machine ◽

Individual Survival ◽

Long Time

On the 15th of April, 1912 the titanic witnessed a disaster resulting in the sinking of her passengers on the maiden voyage near North Atlantic. Even though it is a very long time since this maritime disaster took place, the idea behind what impacts each individual survival is still a great research attracting researcher’s attention. The approach taken in this paper is to utilize the publically available data set from website called Kaggle. Kaggle is a popular data science webpage that put together information of people in the titanic into a data set for the data mining competition: “Titanic: Machine Learning from Disaster”. The research and comparisons in this paper uses a few machine learning techniques and algorithms to analyse the data for classification and prediction of survivors. The prediction and efficiency of these algorithms depend greatly on data analysis and model. The techniques used to do so are Random Forest, Support Vector Machine, Gradient Boosting Machine.

Do Data Mining Methods Matter? : A Wolfcamp “Shale” Case Study

10.2118/spe-173334-ms ◽

2015 ◽

Author(s):

Ming Zhong ◽

Jared Schuetter ◽

Srikanta Mishra ◽

Randy F. LaFollette

Keyword(s):

Machine Learning ◽

Data Mining ◽

Production Optimization ◽

Support Vector ◽

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

Study Results

Abstract Data mining for production optimization in unconventional reservoirs brings together data from multiple sources with varying levels of aggregation, detail, and quality. Tens of variables are typically included in data sets to be analyzed. There are many statistical and machine learning techniques that can be used to analyze data and summarize the results. These methods were developed to work extremely well in certain scenarios but can be terrible choices in others. The analyst may or may not be trained and experienced in using those methods. The question for both the analyst and the consumer of data mining analyses is, “What difference does the method make in the final interpreted result of an analysis?” The objective of this study was to compare and review the relative utility of several univariate and multivariate statistical and machine learning methods in predicting the production quality of Permian Basin Wolfcamp Shale wells. The data set for the study was restricted to wells completed in and producing from the Wolfcamp. Data categories used in the study included the well location and assorted metrics capturing various aspects of the well architecture, well completion, stimulation, and production. All of this information was publicly available. Data variables were scrutinized and corrected for inconsistent units and were sanity checked for out-of-bounds and other “bad data” problems. After the quality control effort was completed, the test data set was distributed among the statistical team for application of an agreed upon set of statistical and machine learning methods. Methods included standard univariate and multivariate linear regression as well as advanced machine learning techniques such as Support Vector Machine, Random Forests, and Boosted Regression Trees. The strengths, limitations, implementation, and study results of each of the methods tested are discussed and compared to those of the other methods. Consistent with other data mining studies, univariate linear methods are shown to be much less robust than multivariate non-linear methods, which tend to produce more reliable results. The practical importance is that when tens to hundreds of millions of dollars are at stake in the development of shale reservoirs, operators should have the confidence that their decisions are statistically sound. The work presented here shows that methods do matter, and useful insights can be derived regarding complex geosystem behavior by geoscientists, engineers, and statisticians working together.

Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques

Applied Sciences ◽

10.3390/app10165628 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5628 ◽

Cited By ~ 1

Author(s):

Kangjae Lee ◽

Alexis Richard C. Claridades ◽

Jiyeong Lee

Keyword(s):

Machine Learning ◽

Training Data ◽

Gradient Boosting ◽

Similarity Metrics ◽

Matching Process ◽

Learning Techniques ◽

Extreme Gradient Boosting ◽

Address Matching

Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on street-based addresses. Three different kinds of machine learning methods are tested to find the best method showing the highest accuracy. The performance of address matching using machine learning models is compared to multiple text similarity metrics, which are generally used for the word matching. It was proved that extreme gradient boosting with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of extreme gradient boosting outperformed similarity metrics when using training data or input data. The address matching process using machine learning achieved high accuracy and can be applied to any geocoding systems to precisely convert addresses into geographic coordinates for various research and applications, including car navigation.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Predictive modeling for peri-implantitis by using machine learning techniques

Scientific Reports ◽

10.1038/s41598-021-90642-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoaki Mameno ◽

Masahiro Wada ◽

Kazunori Nozaki ◽

Toshihito Takahashi ◽

Yoshitaka Tsujioka ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

Risk Indicators ◽

Support Vector ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Machine Learning Algorithms ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.