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In this work, objects from Sloan Digital Sky Survey Data Release 17 (SDSS DR17) [1] were classified as dwarfs or giants using learning methods. The classification was created by supervised learning. Machine learning algorithms like K Nearest Neighbors, Naive Bayes Classifier, Support Vector Classification and Random Forest were developed in addition to Decision Trees. The algorithm that performed the worst was naive Bayes. Random Forest, which performed the best, was able to successfully classify the cases in the dataset that were marked as stars. [ABSTRACT FROM AUTHOR]

Mineralogy plays an important role in the early stages of earth resource exploration. In the past decades, geologists started to use hyperspectral in various scales of mineral identification, from hand specimen samples to airborne geological mapping surveys. Performing conventional mineral classification with hyperspectral methods is time-consuming and requires significant human intervention, from the endmember selection for each mineral to validating the classes manually in laboratory analysis. In this study, we introduced supervised machine learning algorithms to stimulate the mineral mapping process of a large dataset of core data. Three hyperspectral imageries of milled pebbled samples were used where one of the samples was pre-identified and used for training machine learning models to identify the mineralogy of the other two samples. The samples contain four minerals; namely Muscovite, Tourmaline, Illite and High-Crystallinity Illite, that will be auto-identified by the machine learning algorithms. In this study, Random Forest and Convolutional Neural Network were the selected algorithms to perform the mineral identification. Both algorithms produce high-accuracy mineral maps compared to the existing mineral maps from the previous study. The Convolutional Neural Network struggled to identify High-Crystallinity Illite, whilst Random Forest succeeded in separating High-Crystallinity Illite from other minerals. Thus, the Random Forest algorithm produces higher accuracy results. The proposed workflow provides a time-efficient alternative methodology for further mineral mapping process on a larger scale dataset. [ABSTRACT FROM AUTHOR]

Survival analysis is a branch of statistics deals to analyze and model the time to event data. To tackle this censoring problem, statistical strategies have been widely explored in the literature. To deal with survival data, traditionally many methods including Nonparametric, Semiparametric, Parametric and Bayesian Models were incorporated. But these methods require some assumptions, but many machine learning techniques (Survival Decision Tree, Survival Random Forest, Naive Bayes and Logistic Regression) are designed to handle survival data and other challenges that may emerge in real-time data. In this paper, the author elaborates significance of these methods for survival data and identified important variables for the specified event for three real time data. Important variables were prioritized using random forest's variable selection methods, and the results were compared to survival approaches. Supervised Machine Learning Methods were applied, and survival analysis was used to validate them. [ABSTRACT FROM AUTHOR]

Assessing type of soil and its required nutrients is an important domain in modern agriculture. So moving towards the same vision, this research work addresses this issue of forecasting suitable crops on the basis of environmental factors and its yield based on previous data sets available on the net. The study not only discusses yield and crops, but also stresses on the amounts and types of nutrients present in the soil beforehand by using supervised machine learning algorithms. Among different models used, random forest generates the best performance. Further in the paper we will see how random forest provides us an accuracy of 93% and the least error rate of only 0.3% among all other algorithms using rainfall as a parameter to predict our desired crops. [ABSTRACT FROM AUTHOR]

Within the banking industry, requests for credit cards are growing tremendously, and manually reviewing each application is frequently a tiresome task that is also prone to human error. In this situation, banks and other big financial institutions can use a machine learning model to forecast whether or not to grant the customer a credit card. Banks utilize machine learning techniques to process their financial data, extract knowledge from it, and use it for risk management and decision-making. This study has created, trained, and evaluated three classification models utilizing authentic Kaggle datasets. The main research goal is to assess and contrast the models based on how accurately they project the composition of the typical class. In this work, we examine the accuracy, F1 Score, Precision, and confusion matrix of different supervised machine learning models to estimate the probability that a credit card request would be approved. After testing three classifiers, it is discovered that Random Forest outperformed Decision Tree and Logistic Regression. Random Forest's accuracy is 94.67%, precision is 0.85, recall is 0.980, and F1 Score is 2.940. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is one of the common classification problems that has a lot of interest. In HAR, different techniques are explored to identity the physical activity of humans, given data coming from various sensors. This paper explored the use of different supervised learning techniques and algorithms such as Gaussian Bayes Theory, Logistics Regression, SVM Classifier, Quadratic Discriminant Analysis, k-Nearest Neighbor and random Forest Classifier. Accuracy measurement of how each algorithm successfully predict the correct classification is done with the Random Forest Classifier giving the highest user accuracy of 87% without cross validation and 84.9% with 5-fold cross validation. For class accuracy, Random Forest Classifier gives an accuracy of 90.5% without cross validation while k-nearest neighbor gives an accuracy of 89.2% with k=3 and 5-fold cross validation. [ABSTRACT FROM AUTHOR]

Crime has become a big and dominating problem in our society so preventing it has become an important task. Currently, the number of crimes that have occurred is increasing and in various forms, this has led to demands for the relevant institutions to develop more efficient methods of taking preventive actions. In line with the current situation, traditional crime prevention methods are considered less effective and produce less optimal results. Every day several crimes occur, therefore technology is needed for the prevention of crime cases more quickly. Maintenance of crime data and data analysis processes can help predict and solve crimes that will occur in the future. The existence of these problems makes the researcher conduct research on how to solve crime cases more easily. Like, if we can find a way to predict crime with the data we already have. This study aims to compare the accuracy of several supervised learning algorithm techniques in predicting crime data in Chicago. 4 different type of algorithm, K-Nearest Neighbor (KNN) classification, Logistic Regression, Decision Trees and Random Forest is used for comparing the different level of accuracy in predicting the type of crime. As the result successfully proved that the KNN, Decision tree and Random Forest have better accuracy than Logistic Regression because the accuracy result are more than 95%. In the last stage, data visualization will be carried out in the form of graphical representations of various insights obtained during the data analysis process. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is one among a major reason for a lot of no of deaths in our world. With reference to World Health Organization 17.9 million death happens a year due to heart connected illness. In our project we implemented machine learning techniques with the assistance of MATLAB to predict heart related diseases. In our proposed system we have used machine learning that facilitates us to collect information and help the longer term predictions of the given information and it provides us with higher accuracy than existing one that our preferred base paper. we have handled MATLAB so as we've got support of image processing and in signal processing and an enormous support to use information visual image. data mining plays a very important role in medical management to correctly use the information and detect to spot impotence that uplifts care with cut back prices. one among machine learning techniques is supervised learning which helps to precisely forecast the target class for every case within the information. Logistics regression is comparatively stable with relevancy little variation or changes within the feature normalization. Then the RANDOM FOREST, NAVIE BAYES is developed that selects the foremost optimum options that contribute a lot of to the result that decreases the calculating time and will increase the accuracy. Experimental result gives that the projected model with logistics regression and RANDOM FOREST, NAVIE BAYES provides accuracy to classify heart diseases. [ABSTRACT FROM AUTHOR]

This paper presents a study on the utility of supervised machine learning models in estimating the remaining useful life (RUL) of industrial equipment through health condition-based classification approach. A comparison of three main supervised learning models was made in term of accuracy as well as performance using the PHM08 Challenge Dataset from NASA Ames Intelligent Systems Division Diagnostics and Prognostics Group, as the training and test data. This dataset includes synthetic run-to-failure of turbojet engines. The data represents degradation trajectories on a small fleet that includes nine different engines with different initial health conditions. The real flight conditions recorded onboard a commercial jet were taken as inputs to the C-MAPSS model to support engine degradation simulation. The health condition-based classification approach for RUL estimation involves pre-processing of the input dataset and classification of RUL into three health indicative categories which are "red", "amber" and "green". The three machine learning models being compared are random forest, decision tree, and bagging classifier. The results show that random forest is the best among the three models. It has 91.4% accuracy in predicting the remaining useful life based on the proposed classification approach. Future works shall include adapting the model with real feed data related to predictive maintenance of equipment in other domains such as oil and gas industry. [ABSTRACT FROM AUTHOR]

To anticipate the choice dependent on recorded information is conceivable through Predictive Analytics. Machine learning calculations are arranged into four classifications: supervised, unsupervised, semi-supervised, and reinforcement learning. Be that, as it may, in this paper, we will consider supervised learning. The working of supervised learning algorithms is to prepare informational collection as information, which comprises features and class names for learning. Prior to learning, descriptive analytics is applied to authentic information to comprehend the highlights and their effect on preparing. When learning is finished, the classifier is applied on informational test collection or constant informational index, which comprises just features. The classifier needs to foresee the class name, which is obscure in the informational test index. Here we examine a portion of the famous supervised learning algorithms like SMO, Naïve Bayes, J48, Random Forest, k-NN and the performance of these algorithms on different datasets based on time and accuracy. ML algorithms can be applied on different applications, such as email messages as spam or non-spam, an estimate of client purchasing conduct dependent on recorded deals information, oddity location, misrepresentation disclosure, cancer detection, diabetes prediction, etc. Our outcomes anticipated that SMO is best at exactness and IBk is best at preparing rapidly. [ABSTRACT FROM AUTHOR]

Occupational Health Hazard is prevalently on the rise as industrialization increases in the global world. In the fireworks industry, manufacturing procedures for fireworks utilize hazardous chemicals for producing light, color, and sound. Fireworks manufacturing often necessitates human handling of dangerous chemicals, for all manufacturing processes where employees come into close contact with dangerous substances that cause illness to heath. It produces health implications that could result in diseases and sicknesses. Machine learning supervised algorithms were a dominating approach in the field of data mining. Recently, predictions on the health effects associated with hazardous chemicals have proven that these strategies may be applied. Each algorithm's experimental outcomes on the dataset have been examined. The purpose of this study is to discover major trends between different types of supervised machine learning algorithms and their performance and use to prevent ailment risk based on the work process. In comparison between the Support Vector Machine with Random Forest algorithm predicts that the severity level of disease would have the highest accuracy (90.7%), Sensitivity (88.8%) and specificity (92.6%). [ABSTRACT FROM AUTHOR]

The implementation of high-speed broadband has resulted in a huge rise in internet use, which has skyrocketed as more people use remote access. Despite the fact that this rise in use allows for more online activities, the rapid growth of the Internet provides attackers with a wealth of network knowledge, and a large number of network threats has a negative effect on the network's development. Machine learning approaches are good at detecting unknown attacks. Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, and Gradient boosting Classifier are among the Supervised Machine Learning classification algorithms evaluated against the NSL-KDD dataset in this paper. Random Forest Classifier outperforms other methods in detecting attacks, according to the results of the experiments. [ABSTRACT FROM AUTHOR]