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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]

Online social media platforms such as YouTube have grown in popularity in recent years, providing as critical routes for content creators, advertisers, and platform administrators to access a large worldwide audience. Accurate video popularity prediction is critical for improving content strategy, increasing user engagement, and increasing video visibility. This study intends to improve video popularity prediction accuracy by presenting a fresh methodology that goes beyond traditional analysis. To construct a more effective prediction model, the suggested method combines examining metadata, such as title, description, and channel title, with thumbnail analysis. The project aims to dramatically increase prediction accuracy by extracting relevant information from both metadata and thumbnails. To do this, multiple classification techniques, including Support Vector Machine, Gradient Boosting, Random Forest, Extreme Gradient Boosting, and K-Nearest Neighbors, were used to train and forecast the model. Notably, when it came to feature extraction, the Gradient Boosting and Random Forest algorithms achieved amazing accuracy of 97% when using all extracted and original data, and 82% and 92% when using only extracted features. In comparison, previous studies employing simply extracted features obtained only 74% accuracy. These findings demonstrate the potential benefit of integrating metadata and thumbnail data research to get insights into the elements that drive video popularity and lead to more accurate predictions. [ABSTRACT FROM AUTHOR]

The most widely used and dominant operating system for smartphones is Android. Over a million Android mobile applications are available in the Google Play store, which users download and use for various purposes. In the meantime, they have become a prominent target for immoral incursions as a result of their open-source platform and popularity. Due to their inability to recognize and predict malware with varying features, conventional techniques in this investigation failed to detect sophisticated malware. In recent years, machine learning classification methods have been used to tackle these problems, and this study discovers that ensemble learning yields the best outcomes. Consequently, this study suggested an Ensemble learning classification model trained on the (Malgenome, CIC-MalDroid2020, and Drebin) mobile malware dataset using Random Forest with Binary Grasshopper Optimization Algorithm. The model's performance is assessed. We achieved a maximum accuracy of 97% for the Drebin dataset, the highest accuracy of 98.70% for the Malgenom dataset, and the highest accuracy of 97.70% for the CIC-MalDroid2020 dataset. [ABSTRACT FROM AUTHOR]

Traffic collisions occur at an alarming rate in many parts of the world. More than 151,000 people were killed on India's roads in 2019, for example. Finding out what factors contribute to traffic collisions is crucial in lowering financial losses. We explore two models using the US accident cluster dataset: logistic regression and random forest. Random Forest achieves higher accuracy ratings and Auc curve results. We use them for forecasting and incorporate them into our web app to display the likelihood of each accident hotspot. [ABSTRACT FROM AUTHOR]

Cybercrime has been on a massive increase for several years. As more individuals conduct business and live their lives online, more criminals are turning to the internet to steal. With the developments in cybersecurity, adoption of best practices, cybersecurity awareness initiatives, and increased regulations and partnerships between businesses and governments to resolve the problems, cybercriminals may be feeling the pressure. In this paper, we are going to predict operating system vulnerabilities using machine learning algorithms. And evaluate their accuracies through training them. The aim of the paper is to use Machine Learning algorithms to predict with accuracy operating systems vulnerabilities. Different ML algorithms have been analyzed, such as Logistic Regression, Support Vector Machine and Random Forest. And, out of all of these algorithms, Random Forest had the highest accuracy of 99.33%. [ABSTRACT FROM AUTHOR]

The accuracy and precision with which Logistic Regression (LR) and Random Forest can predict forest fires are compared in this study (RF). The List Consists Of The Random Forest method outperforms other machine learning techniques in fire detection speed and accuracy. To better detect forest fires, a system that compares Random Forest with Logistic Regression was developed. Using G power, we determined that 28 people were needed for each experiment. The sample size for the pilot study was calculated to be 95 percent confident. The dataset shows that the Random Forest (RF) model is 95 percent accurate in predicting forest fires, whereas the Logistic Regression (LR) model is 62 percent accurate. The p-value for Logistic Regression is 0.005, but Random Forest is a superior classifier. In terms of accuracy and precision, Random Forest is superior to Logistic Regression. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the accuracy of cybercrime predictions provided using Novel Random Forest and Support Vector Machine. Material and Procedure: Cybercrime predictions were made using the new random forest (N=10) and the support vector machine (N=10), which were then analysed. Random forest trumps SVM in terms of precision (by a margin of 84 percent to 85 percent). 81 percent is the percentage. The advanced random forest classifier performed better than the conventional SVM classifier. There is a significant difference in accuracy between the two approaches (p>0.005). The development of the new cybercrime prediction system used machine learning. This novel random forest technique outperformed SVM. [ABSTRACT FROM AUTHOR]

The aim of the study is to diminish the power loss with reference to sensitivity and precision in power transmissions and consumptions. As power theft is considered as non-technical loss, it is hard to track the total transmission. So the Random forest algorithm is proposed over the K-NN Algorithm to compare accuracy in power theft detection. Materials and Methods: There are two groups in this study each with a sample size of 21400 per group. Analysis is done with the pretest power 0.8. Results: The mean value for accuracy in the Random Forest algorithm is 90.6215 which is high when compared to K-NN algorithm whose accuracy is 82.2200. It has an insignificant value of 0.07 (p>0.05). These values are evaluated using SPSS for statistical analysis. Conclusion: This analysis shows that the novel Random Forest algorithm has more reliable accuracy and sensitivity of power theft detection compared to the K-NN algorithm. [ABSTRACT FROM AUTHOR]

The fundamental objective of this research is to investigate several approaches that may be used to enhance the precision of recent machine learning algorithms that are used to anticipate road accidents, in particular the Random Forest Algorithm and the Naive Bayes Algorithm. Both the Materials and the Procedures are: Both the Random Forest and the Naive Bayes algorithms were run through 20 iterations in order to see how well they could predict future traffic incidents. The results show that in terms of accuracy, the Random Forest approach performs better than the Naive Bayes algorithm (89.95 percent). The significant independent samples T-test for the Random Forest approach is 0.001, as stated in the previous sentence (p0.05). When the data were compared, it was evident that the Random Forest approach surpassed the Naive Bayes algorithm in terms of the prediction of traffic accidents. This was the conclusion reached after analysing the data. [ABSTRACT FROM AUTHOR]

This study aims to classify human Coronary Artery Heart Disease using the cutting-edge Random Forest Algorithm and to compare its performance to that of the more traditional Naive Bayes algorithm. Forty human subjects were used to create a dataset on kaggle (Framingham dataset). Naive Bayes (NB) is in Group 1, and Random Forest (RF) is in Group 2. (RF). The G power was determined using a 0.05 alpha and 80% confidence. The innovative Random Forest approach outperformed the Naive Bayes algorithm by a wide margin, with 84% accuracy and 78% precision, respectively. The significance levels for Novel Random Forest's accuracy and precision were 0.025 and 0.038, respectively (p 0.05). The results of this study show that the Novel Random Forest algorithm outperforms the Naive Bayes algorithm by a wide margin. [ABSTRACT FROM AUTHOR]

The purpose is to use attenuation measurements of dynamic tuning parameters to classify and predict floods in advance using rainfall data patterns in India. This may be done by using rainfall data patterns in India. We use two distinct categorization approaches in order to achieve the best possible outcomes. Each of these approaches has a sample size of five, a G power of eighty percent, a threshold of five percent, a confidence interval (CI) of ninety-five percent, as well as a mean and standard deviation. Using information on previous rainfall, we conducted this investigation in which we examined the accuracy of forecasts generated by two distinct methods: Random Forest and K-Nearest Neighbor. The findings demonstrated that Random Forest had a performance that was 51.47 percentage points higher than that of the K-Nearest Neighbor algorithm (50.35 percent). It has been demonstrated that Rainfall-based Random Forests are superior to K-Nearest Neighbor algorithms when it comes to flood prediction. [ABSTRACT FROM AUTHOR]

The study's overarching goal is to enhance the accuracy with which the healthcare dataset's Random forest classifier can predict disease from symptoms in the face of climate change. There are two groups in this research. A random forest classifier is first created, then compared to the Gradient boosting classifier. With a sample size of 25, we may achieve a significance level of 0.001 when comparing the models' accuracies to those of these algorithms. The purpose of this research was to determine whether or not the more accurate Random forest classifier (97.16 percent) or the less accurate Gradient boosting classifier (97.1 percent) could be used to predict diseases based on symptoms (75 percent). When using an independent sample test, the Random forest classifier consistently achieves a high level of statistical significance (p0.05). In a head-to-head comparison with a Gradient boosting classifier, the suggested model's results were shown to be more accurate. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to compare the performance of the Novel Random Forest (RF) algorithm, Decision Tree (DT), and Logistic Regression in forecasting loan default (LR). The 346-record loan dataset that Novel Random Forest is associated with. It has been suggested and assessed how well the revolutionary methods of Random Forest, Decision Tree, and Logistic Regression can forecast loan defaults in the banking and finance industry. There were a total of 17 participants in each study group. The classifier's efficacy in terms of accuracy and precision is measured and documented. On this dataset, the Logistic Regression model predicts loan default with an accuracy of 81%, while the Decision Tree model achieves 93% and the Random Forest model achieves 95%. (p 0.031) is statistically significant. That's why it's clear that Novel Random Forest outperforms both Decision Tree and Logistic Regression. When compared to Decision Tree and Logistic Regression, Novel Random forest has superior accuracy and precision. [ABSTRACT FROM AUTHOR]

The fundamental objective of this study is to evaluate and contrast the performance of two innovative classification algorithms, namely Random Forest and Decision Tree. Each of the random forest and choice tree exercises had 20 participants. The academic success of a student in the past might serve as an indication of the possible future achievements the student will have. The purpose of this research is to come up with strategies that can accurately forecast how well a kid will do academically. The use of decision trees and random forests as methods for forecasting student success are now both considered to be well-established methodologies. Each of the research groups had a total of 72 participants. The accuracy of predictions made using this dataset ranges from 88.2 percent when utilising the time-tested Decision tree to 91.7 percent when utilising the innovative Random forest classifier. 0.03 is the most significant part of the fraction. The Random Forest algorithm is better to the decision tree approach for this reason. The innovative decision tree known as the random forest performs far better than its predecessor, the tried-and-true decision tree. [ABSTRACT FROM AUTHOR]

The prediction rate of social vulnerability in a web application based on a comparison of the F1-Measure of novel Reinforcement learning and the Random forest method. Components and Techniques: Outcome F1-measure and the Random Forest Algorithm with a New Reinforcement Learning Algorithm (83.85 percent). There are a total of 55,336 samples to be analysed, split evenly between two groups. Discussion and Results With an F1-measure score of 83.85 percent, the novel Reinforcement learning algorithm outperforms the more traditional Random forest approach for detecting social vulnerability (74.58 percent). In this study's final analysis, the Novel Reinforcement Learning algorithm was shown to be more accurate in predicting social vulnerability than the Random Forest technique. [ABSTRACT FROM AUTHOR]

The study's primary objective is to compare the efficacy of the state-of-the-art SVM method for image prediction with that of KNN, Logistic Regression, Random Forest, and Decision Tree. The UCI Machine Learning Laboratory provides a total of 569 samples. Groups like SVM, KNN, Decision Tree, Random Forest, and Logistic Regression are used to the samples after they have been separated into benign and malignant cells so that their respective performances may be compared. G power calculation is used to determine how many samples are needed for this analysis. The maximum acceptable error is set at 0.5, and the minimum power of analysis is set at 0.8. Predictions made using Logistic Regression appear to have a higher accuracy(95%) than those made using SVM, KNN, Decision Tree, or Random Forest(92%,90%,85%, and 91%). This proposed system has a probability importance of 0.55. The Wisconsin dataset was used to compare Logistic Regression against SVM, KNN, Decision Tree, and Random Forest for the detection of breast cancer. [ABSTRACT FROM AUTHOR]

The purpose of this work is to contrast the effectiveness of the Logistic Regression algorithm with that of Novel Random Forest when predicting device failures. Two groups, alpha (0.05), power (80%), and the environment ratio are utilised in the G power analysis to determine the necessary sample size. Twenty thousand datasets are taken from Kaggle as a representative sample. There is a 75% training dataset and a 25% test dataset created from the collected samples (25 percent). For the purpose of measuring Novel Random Forest's efficacy, accuracy, precision, and sensitivity scores are computed. The model is significant at the (p 0.05) level. The Novel Random Forest model outperformed the Logistic Regression method by a wide margin, with an accuracy of 88.23%, precision of 87.43%, and sensitivity of 88.95%. The Logistic Regression method only managed 86.33% accuracy, 96.10% precision, and 83.30% sensitivity. It is determined that G power is 0. In this study, the Random Forest classifier outperforms the Logistic Regression classifier in terms of reducing costs for datasets that include failed devices. [ABSTRACT FROM AUTHOR]

An IDS built using the Information Gain Algorithm and an unique One Class Support Vector Machine with Random Forest Feature Selection (Group 1) for detecting denial-of-service attacks in wireless ad hoc networks are compared for accuracy and detection rate (Group 2). Substances and Methods: An IDS model was created using CIC-IDS 2017. The IDSs were modelled using the above methods and analysed using SPSS with 19 samples per group. The innovative One Class SVM with Random Forest Feature Selection IDS had a detection rate of 90% and an accuracy of 99.0%, whereas the Information Gain Algorithm IDS had 82.0 percent and 80.0 percent. The significance threshold was 0.05. (Accuracy - 0.00; Detection rate - 0.00). Finally, this data shows that the innovative One Class SVM with Random Forest Feature Selection IDS beats the Information Gain Algorithm IDS by a large margin. [ABSTRACT FROM AUTHOR]

The purpose of this study is to apply machine learning methods to the problem of classifying musical genres. Here, signal processing is used to extract the rhythm pattern feature, which is then used in conjunction with machine learning algorithms to provide a multiclass categorization of musical genres. A preexisting internet music collection is used to generate two distinct datasets, one with undersampling and the other with oversampling to achieve class balance. Two types of models were compared for their efficacy in this research. The first model is an enhanced support vector machine that is fully trained to identify the musical genre of an input signal from its spectrogram. The Random Forest Classifier is the second model. G power for this research is 80%; there are two groups in total, and each has a sample size of 20. Experiments performed on the audio dataset show that an assembler classifier that takes use of both techniques achieves an AUC of 0.894. According to the findings, the support vector machine method provides a higher accuracy (69.24%) than the Random Forest Classifier (64.26%). Independent sample T-test significance level of P=0 (P 0.05, 2-tailed). The findings showed that the accuracy of the music genre classification using the enhanced support vector machine with an unique Rhythm pattern feature method was higher than using the Random Forest Classifier. [ABSTRACT FROM AUTHOR]

Water, in its many forms, is essential to the survival of all forms of life. The recommended maximum levels for water contaminants are specified in the drinking water recommendations. Taking into account two classes, for example, the cutting-edge Support vector regression technique and the tried-and-true Random forest approach. A dataset of 1993 samples has been used to construct and test the methods. Each group underwent a total of 5 iterations with a larger sample size to ensure precision. About 80% of the time, G-power is utilised. The experiments showed that the support vector regression method improved the accuracy of water quality monitoring by a mean of.9440 percentage points, while the Random forest approach improved accuracy by.8700 percentage points. Using independent samples t-tests, we find a statistically significant difference in accuracy between the two methods, with a p-value of 0.885. The purpose of this research is to apply cutting-edge methods to unique image processing classifiers in order to enhance the precision of water quality monitoring. The novel support vector regression approach outperforms the conventional Random forest technique, as shown by the comparative findings. [ABSTRACT FROM AUTHOR]

Aim: The primary objective of this research is to increase the accuracy of detecting brain tumors using the Random Forest algorithm and to compare it to the thresholding method. Methods: There are two separate studies that are conducted in this research, the first is the Random Forest algorithm, and the second is a thresholding method. Analysis is conducted with the 36 magnetic resonance images (MRI) that are available, of which 90 % are dedicated to training and 10 % are used to test the effectiveness of a brain tumor. The total number of samples is 36 (18 samples per group), and the pre-test probability of the total number is 80 %, alpha is 0.05, and 95 % confidence intervals. Results : The accuracy of 81.69% and 75.81% are attained using the random forest and thresholding methods respectively. The two classes have a significant difference, with p<0.05 Conclusion: From the study, it is evident that the degree of success in recognizing brain disorders of neurological origin using the Random Forest classifier is greater than the thresholding method. [ABSTRACT FROM AUTHOR]

The purpose of this research is to compare and contrast how well the random forest algorithm and the decision tree method perform in classifying fire and smoke photographs. With a g power of 0.8 and an alpha of 0.05, we find that out of a total of 4000 photos, 2000 depict fire and the remaining 2000 depict smoke. In this case, we split the dataset into a training set (n=3200, or 80%) and a validation set (n=800, or 20%). Classification tasks are executed with the help of the Sklearn machine learning package in Python. Precision, recall, f score, and accuracy numbers are some of the metrics used to measure an algorithm's effectiveness. When compared to the Decision Tree algorithm's 90.54, 90.00, 90.9, and 89.70 percent accuracy, F-score, recall, and precision, Random Forest achieved 97.42, 97.28, 97.43, and 97.15 percent values, respectively (p0.001(2-tailed)). The results of this research show that the random forest method outperforms the decision tree algorithm by a wide margin. [ABSTRACT FROM AUTHOR]

This research evaluates the Logistic Regression (LR) and Random Forest (RF) algorithms, two popular statistical approaches for long-term pollution forecasting. The Parts and Methods: Logistic Regression may effectively predict air pollution better than other machine learning methods. Logistic Regression and Random Forest were used to create a framework for diagnosing air pollution to reduce its impacts. G power indicated that each group required 96 participants. Pretest power was 92%, and the sample size was 2 groups of 48 samples. The dataset showed that Logistic Regression predicted air pollution with 92% accuracy, outperforming Random Forest with a significance of 0.001(p=0.005). Logistic Regression trumps Random Forest in accuracy and precision. [ABSTRACT FROM AUTHOR]

In this study, we compare Naive Bayes (NB) and Novel Tree Specific Random Forest for their ability to forecast the downstream impacts of air pollution (RF). Naive Bayes has a greater recognition rate than other machine learning algorithms and is thus more effective in detecting air pollution. Naive Bayes over Novel Tree Specific Random Forest is presented and developed as a framework for identifying air pollution to mitigate its aftereffects. Using G power, we determined that we needed 96 participants in each group. The sample size, consisting of 48 individuals per group, was determined using a pretest power of 95%. The goal of this study is to enhance the performance of the algorithm throughout the harvesting process. Average detection accuracy is within one standard deviation, suggesting that the suggested model is both effective and quicker than the current approach. The level of significance is 0.006 (p0.05). Findings highlight Naive Bayes' superior accuracy and precision compared to Random Forest. [ABSTRACT FROM AUTHOR]

The goal of this study is to utilise machine learning classifiers to categorise people's income levels, namely those with and without a household income of $50,000 or more. We examine two common algorithms in this study: Random Forest and Logistic Regression Algorithm. This experimental inquiry makes use of the Adult Income dataset, which has 32516 items. An experiment with N=10 repetitions was carried out in order to discriminate between income groups of more than $500,000 and less than $40,000. For statistical reasons, the G-power test is run at 80%. The trials demonstrate that the Random Forest Algorithm has an average accuracy of 84.1840, whereas the Logistic Regression Algorithm has an average accuracy of 79.6410. Statistical analysis reveals a substantial difference in accuracy between the two techniques, with a p-value of 0.032 for the t-test on independent samples. The results reveal that the Random Forest Algorithm outperforms the Logistic Regression Algorithm. [ABSTRACT FROM AUTHOR]

To use tweets about finance to help decide on a financial choice. To forecast if financial tweets are positive or negative, we use a new Decision Tree that is better at getting the right results compared to Random Forest. The proposed way of doing things looks at 10 examples in each of two groups. We want to make sure we have enough power to detect any differences between the groups, so we aim for an 80% chance of finding a difference if one exists. We also set a significance level of 0. 05, which means we want to be fairly confident in our findings. The Novel Decision Tree is more accurate (96. 4%) than the Random Forest (92. 7%) in predicting the sentiments of financial tweets. The Novel Decision Tree Algorithm is much better than the Random Forest at guessing the feelings in financial tweets. [ABSTRACT FROM AUTHOR]

To evaluate the performance of the Music Genre Prediction System using a novel Convolutional Neural Network and Random Forest Algorithm. Materials and Methods: The dataset used in this study is the standard dataset called GTZAN, which contains 1000 music files, each 100 of 10 different genres. Spectrograms and Mel-frequency cepstral coefficients (MFCC) are derived from the music files for training the model. In both algorithms,20 iterations are done with different sample sizes. The pretest power obtained is 0.08. Comparative analysis of mean accuracies is made between two algorithms using SPSS software. Result: From the experimental data analysis it is observed that the Convolutional Neural Network gives an accuracy of 79.23% while the Random Forest gives an accuracy of 64.67%. Conclusion: This study shows that the novel Convolutional Neural Network performs better when compared to the Random Forest for predicting the music genre accurately. [ABSTRACT FROM AUTHOR]

The goal of this research is to catalogue all of the undesirable materials available on rival and popular social media platforms. Tools and methods: The dataset for training and testing the proposed prediction models was constructed using the messages from a number of popular social media messaging platforms, with a minimum of 5 attributes and 150 messages. Random Forest, a machine learning algorithm, was used to design the framework, and it was compared favourably to the Support Vector Machine algorithm. Discussion and Remarks: Accuracy in retrieval is 90.30 percent for the Random forest algorithm and 94.36 percent for the Support vector machine learning algorithm (countvectorizer). The significance level between the two algorithms is high at p=0.02 (p0.05). This study confirms that the SVM (countvectorizer) Machine Learning algorithm has a higher accuracy rate than the Random Forest algorithm and creates a novel framework to identify spam words. [ABSTRACT FROM AUTHOR]

Improved prediction of SQL injection vulnerabilities using a comparison of F1-Measure in a Random Forest model and Novel Reinforcement learning. Accuracy in Outcomes and Random Forest's Reinforcement Learning Innovations (83.8580 percent). There are a total of 55,336 samples to be analysed, split evenly between two groups. With an accuracy of 83.8580 percent, the reinforcement learning algorithm outperforms the support Random Forest algorithm's 74.5890 percent accuracy by a statistically significant amount (P0.004). The results of this research show that when it comes to predicting SQL injection vulnerabilities, the Novel Reinforcement learning Algorithm performs significantly better than the Random Forest algorithm. [ABSTRACT FROM AUTHOR]

This study's goal is to estimate the price range for mobile devices using the Deep Neural Network algorithm and the Random Forest model. When N = 10 sample iterations were used to test the accuracy of the model for the validation of the mobile price range, two groups—the Deep Neural Network algorithm and the Random Forest model— with G power (value = 0.8) were studied. The Deep Neural Network model's accuracy results have a possible range of 84.183 percent, and the Random Forest model has an accuracy of (74.80 percent). The Deep Neural Network and Random Forest had statistically significant differences (p = 0.005). The Deep Neural Network technique outperforms the Random Forest approach in terms of performance and significance, according to the study findings for validating the pricing range for mobile devices. [ABSTRACT FROM AUTHOR]

Early Parkinson's disease (PD) diagnosis is essential for preventing the illness's development and providing patients with the opportunity to take disease-specific therapy. Speech signal processing is widely used and has gained attention recently. In this project, we carry out a comparative analysis for effective dysphonia (a speech disorder) and Parkinson's disease spotting using Machine Learning methods. To distinguish between PD patients and normal individuals. We used Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LREG), and K-Nearest Neighbor (KNN) algorithms to demonstrate a robust detection process. According to experimental findings, the KNN outperformed other ML algorithms. The results of the trial conducted at UCI contain data from 31 patients, 23 of whom were diagnosed with PD. After implementing these algorithms, we found that our accuracy rates for these algorithms are NB (86.95%), RF (85.77%), SVM (79.18%), LREG (91.56%), DT (87.16%), and KNN (97.67%). [ABSTRACT FROM AUTHOR]

One of the main causes of death in the world is caused by cancer. And one of them is Lung Cancer. According to World Health Organization, in 2014 the death rate caused by lung cancer in Indonesia was 21.8% in men and 9.1% in women with 30,865 cases of death caused by lung cancer each year in men and women. Many studies have been carried out on computational lung cancer. One of them is by implementing machine learning in detecting lung cancer. However, there are obstacles, namely the imbalance in the amount of data between patients and non-patients. So it takes an approach to overcome this imbalance. One of these methods is ADASYN which is then combined with the Random Forest classification algorithm. In this study, we will compare the results of the classification model performance in Random Forest before and after the ADASYN sampling method was used in the training process. The results of this study showed an increase in the performance of the Random Forest classification model with an AUC value of 0.859 after the ADASYN sampling method was carried out with an error rate of 4.9%. [ABSTRACT FROM AUTHOR]

The increasing population, accompanied by the development of technological advances, especially in the world of transportation, has led to the creation of vehicles of various types and the increase in CO2 gas emissions resulting from the combustion of vehicle fuels. The need for estimation efforts to determine the estimated CO2 gas emissions produced by vehicles with different engine sizes, cylinders, and fuel consumption. Machine learning-based estimation using a regression model. There are seven regression models, including Linear Regression, Support Vector Regression – Linear, Support Vector Regression – RBF, Decision Tree Regression, Random Forest Regressor, Gradient Boosting Regression, and NLP Regressors were compared in this study. We will look for the best model with the best accuracy value. The Gradient Boosting Regression model has a better accuracy increase in 4 accuracy tests with training and test data ratios of 90:10, 80:20,70:30 and 60:40. As a result, the Gradient Boosting Regression method has an accuracy rate of 98% with an RMSE of 5.89426278 at a data test ratio of 90:10 and 96% at a data test ratio of 70:30 with an RMSE of 11.29584732. [ABSTRACT FROM AUTHOR]