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The rate of penetration (ROP) prediction is carried out using a recorded drilling dataset from 02-Well. The prediction approaches use machine learning random forest regressor model and artificial neural network, especially the MLP regressor. The aim is to make the best machine learning model accurately predict the ROP parameter at 02-Well. The method used is designing the architecture of the machine learning model, which is divided into five stages: exploratory data analysis, data pre-processing, prediction and modeling using the selected algorithm, hyper-parameter tuning, and model evaluation. The 02-Well dataset would be divided into a 70% training set and a 30% test set as the base case. The model evaluation results show that modeling using a random forest regressor has a mean absolute percentage error (MAPE) score of 19.81%, which belongs to the "Good Forecasting" criteria. Meanwhile, modeling using MLP regressors has a MAPE score of 22.84% with the "Reasonable Forecasting" criteria. [ABSTRACT FROM AUTHOR]

Now-a-days, Health care industries are literally playing a major role in curing the diseases that are suffering the people. And this will be one kind of help to health care industries. In present days, people are facing lot of issues related to their health due to their life style and their livelihood. Due to their busy schedule in their lives, people are not at all taking care of their health. They are not having the time to consult doctors and know what they are going through and it may lead to severe risk for them. So, People should be aware of what they are going through at early stage will reduce the high risk. In our proposed system we used logistic regression, Random Forest Classifier and Decision tree classifier in prediction of the disease. Disease Prediction is a supervised model that is used for prediction of diseases from the symptoms or the information provides by the user. This proposed system will process the symptoms entered by the user and provide the predicted disease as an output. [ABSTRACT FROM AUTHOR]

The aim of the work is to detect the problems for housing prices to estimate the relationship by Random Forest (RF) algorithm for predictions using supervised techniques. Materials and Methods: Data collection was carried out and the analysis was done in the Google Collab for the execution of results and to estimate the relationship of given algorithms. Proposed work involves two groups for detection of problems for housing prices. Group 1 was the predicted price and Group 2 was the actual price performed by the Random Forest (RF) algorithm. The sample size was calculated. It was identified that 10 samples/group and 20 samples were taken totally. The improved Random Forest (RF) machine learning technique is used for predicting the accuracy based on Coefficient of Determination (CD), Mean Square Error (MSE), Mean Absolute Error (MAE). Results and Discussion: The significant difference of Random Forest (RF) algorithm (p <0.05), in is 0.0372, MSE is 0.0332 and MAE is 0.0355. Conclusion: The data was collected from various resources for the detection of problems for housing prices to estimate the relationship. The Novel Random Forest (RF) algorithm obtains better performance when analyzed with other existing algorithms in detection of problems for housing prices. [ABSTRACT FROM AUTHOR]

The study's primary goal is to identify accuracy in auto loan eligibility utilising machine learning techniques, namely the Logistic Regression Algorithm and the Novel Random Forest Algorithm. The Logistic Regression and Random Forest algorithms were iterated 180 times with a sample size of N=90 to determine the accuracy of automobile loan eligibility. The novel Random Forest method outperforms the Random Forest algorithm in terms of accuracy (87.02%). The loan prediction independent samples T-test has a high significance (p<0.05). When the results were compared, the Random Forest algorithm outperformed the Logistic Regression approach in terms of discovering accuracy in automobile loan eligibility. [ABSTRACT FROM AUTHOR]

Due to the environmental condition people face various health hazards and it affects their living habits. So predicting the disease at earlier stage becomes important. As a specific symptom can be responsible for various diseases, it is time consuming to predict the perfect one for the doctors. Data mining plays a significant role in this most challenging task i.e. the accurate prediction of diseases. Due to increase in data growth in every year in healthcare field the precise analysis of data helps patients to take early care. Using the disease data, hidden pattern information was found by data mining in the enormous amount of medical data. Here in this study based on the symptoms general disease prediction techniques have been proposed. For the disease prediction, K-Nearest Neighbor (KNN), decision tree and random forest machine learning algorithm were used. [ABSTRACT FROM AUTHOR]

Software is becoming more complex, and lengthier in size, and needs to be updated on a time basis. But the constant change in codes has a high chance of arising faults that adversely affect the system performance. To avoid this interruption, and to improve the overall performance of software, a fault prediction system is required. The nature of the fault depends upon various parameters, but it is also important to identify the role of parameters in fault generation. In this paper, four feature selection techniques (Correlation Coefficient, Fisher Score, LASSO, Recursive Feature Selection) are applied on three machine learning methods individually (Random Forest, Logistic Regression, SVM) using Python 3.8 to compare the result for highest performance. The result clearly shows that Random Forest gives the highest accuracy of 95% when combined with the Fisher Score feature selection technique. The outcome shows that rather than using all features of any data set, a selected feature should be used for better fault-free software performance. [ABSTRACT FROM AUTHOR]

Over 17.3 million people are dying because of cardio-vascular disease. In past, predicting heart failure (HF) disease was a challenging task. In the modern era, we have relevant training data for HF prediction. Using state-of-the-art machine learning (ML) models, the HF can be predicted with high precision. In this paper, by employment of different ML algorithms, we predict whether a person has cardio-vascular disease (CVD) or not using relevant symptoms of the person. This research predicts the heart failure chances using discriminative attributes that are collected from the patients. A standard dataset from the university of California at Irvine (UCI) that contains 14 parameters related to heart disease has been examined in this study. Our machine learning models are trained using five different classification techniques. The algorithms are logistic regression, k-nearest neighbours (KNN), support vector machines (SVM), random forest, and gradient boosting. The SVM classifier has shown the highest accuracy of 86.84%. The accuracy of predictions has also been enhanced by suitable data pre-processing and cross validation techniques. [ABSTRACT FROM AUTHOR]

The prediction by the machine learning using molecular orbital energies as an explanatory variable is attempted to predict the strength of anxiolytics, anti-anxiety, and muscle relaxant of benzodiazepine anxiolytics. We also attempt to predict half-life of concentration in the body T1/2, and time to reach maximum body concentration Tmax of benzodiazepine anxiolytics with the same procedure. The molecular orbital calculations are performed at 6-31G(d, p) level and random forest is used as regression method. The number of molecular orbitals is varied from 2 to 20 and it is found that 4 or 6 is almost sufficient for the prediction of these 5 objective variables. Finally, the predictions of five properties in the present study are fairly well agreed with the experiments by machine learning employing the molecular orbital energies as the only explanatory variables. [ABSTRACT FROM AUTHOR]

There is a tremendous increment of liver sickness patients as a result of exorbitant utilization of liquor, breathe in of hurtful gases, admission of polluted food, pickles and medications. Therefore lot of burden is put on the doctors to identify a patient whether he is having any liver disease are not.This paper helps to reduce the burden on the doctor by analyzing patients conditions using machine learning techniques[1]. We also make a comparison of few machine learning algorithms like random forest,logistic regression and SVM and compare their accuracy levels in predicting the liver disease. [ABSTRACT FROM AUTHOR]