Academic literature on the topic 'DT (Decision Tree) and RF (Random Forest)'
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Journal articles on the topic "DT (Decision Tree) and RF (Random Forest)"
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Purwanto, Anang Dwi, Ketut Wikantika, Albertus Deliar, and Soni Darmawan. "Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia." Remote Sensing 15, no. 1 (2022): 16. http://dx.doi.org/10.3390/rs15010016.
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Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI+NDSI+DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI+NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI+NDSI+DEM parameters with the number of trees = 100, mtry = all variables (, and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.
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Sinambela, Dewi Pusparani, Husni Naparin, Muhammad Zulfadhilah, and Nurul Hidayah. "Implementasi Algoritma Decision Tree dan Random Forest dalam Prediksi Perdarahan Pascasalin." Jurnal Informasi dan Teknologi 5, no. 3 (2023): 58–64. http://dx.doi.org/10.60083/jidt.v5i3.393.
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Perdarahan Postpartum (PPP) merupakan salah satu kegawatdaruatan pada persalinan yang dapat menyebabkan kematian di negara maju dan negara berkembang. Salah satu pencegahan terjadiya PPP dengan melakukan prediksi pada ibu bersalin dengan mempertimbangkan faktor faktor risiko menggunakan pendekatan model Machine Learning (ML). Algoritma Random Forest (RF) dan Decision Tree (DT) merupakan algoritma yang digunakan dalam prediksi kejadian PPP. Tujuan dari penelitian ini adalah mengembangkan kinerja dari Algoritma RF dan Algoritma RF untuk mengklasifikasi kejadian PPP. Hasil analisis Berdasarkan hasil analisis univariat yang ditunjukkan pada tabel 1 didapatkan ibu yang memiliki paritas > 4 sebanyak 102 orang (20,4%), jarak kehamilan ibu yang ≤ 2 tahun sebanyak 310 orang (62%), ibu pasca bersalin yang mengalami anemia sebanyak 124 orang (24,8%), ibu yang melahirkan bayi makrosomia sebanyak 60 orang (12 %), ibu yang mengalami komplikasi persalinan sebanyak 229 orang (45,8 %),ibu yang mengalami kehamilan ganda sebanyak 16 orang (3,2%), umur ibu yang berisiko sebanyak 132 orang (26,4%). Perbandingan tingkat akurasi algoritma RF mencapai 0,830 dibandingkan dengan algoritma DT sebesar 0.820, AUC RF 0.74. Hal ini menunjukan bahwa Algoritma RF mempunya perfomance metric lebih naik dibandingkan dengan algoritma DT. Algoritma Random Forest dapat dianggap sebagai salah satu algoritma representatif ML, yang dikenal karena kemudahannya dan efektivitasnya
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Kaunang, Fergie Joanda, Bhustomy Hakim, Fedelis Fraderic, Sherren Hartono, and Andrew Kristanto Mulyanto. "Breast Cancer Detection using Decision Tree and Random Forest." Journal of Applied Informatics and Computing 9, no. 2 (2025): 302–9. https://doi.org/10.30871/jaic.v9i2.9073.
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Cancer is one of the most challenging diseases to cure and is a chronic condition that contributes significantly to global mortality. With advancements in artificial intelligence (AI) technology, AI-integrated systems can provide quick and accurate diagnoses based on collected medical data. By leveraging machine learning techniques, this study aims to compare the performance of two models using the Decision Tree (DT) and Random Forest (RF) algorithms on routine blood test data. The research process involves data preprocessing techniques such as handling missing values, detecting outliers, and feature selection, followed by applying the bootstrap aggregating technique to enhance model performance. Feature selection is used to identify the most significant features in the data that contribute to cancer detection. Using the KBest feature selection technique, the study found that the features age, BMI, leptin, adiponectin, and MCP-1 had the highest correlation with the target variable. The resulting models were evaluated to compare the performance of each algorithm. The evaluation results showed that the RF algorithm outperformed DT, achieving an accuracy of 89.65% on the processed dataset using the bootstrap technique, compared to DT's accuracy of 80.17%. Additionally, the RF algorithm demonstrated superior metric values, including a precision of 91.66% and an F1-score of 87.12%. This study concludes that the RF algorithm is more effective than DT for detecting cancer in limited datasets, especially when used with the bootstrap technique. The findings are expected to support the development of decision support systems in healthcare services for more accurate early cancer detection.
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Prasanna, S. T. P., and T. Veeramani. "Comparing the Efficiency of Heart Disease Prediction using Novel Random Forest, Logistic Regression and Decision Tree And SVM Algorithms." CARDIOMETRY, no. 25 (February 14, 2023): 1491–99. http://dx.doi.org/10.18137/cardiometry.2022.25.14911499.
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Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p<0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p>0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.
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Prasanna, S. T. P., and T. Veeramani. "Supervised study of Novel Random Forest Algorithm for prediction of heart disease in Comparison With The Decision Tree Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 1483–90. http://dx.doi.org/10.18137/cardiometry.2022.25.14831490.
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Aim: The aim of this work is to evaluate the accuracy and precision in predicting heart disease using Decision Tree (DT) and Novel Random forest (RF) Classification algorithms. Materials and Methods: Novel Random forest is appealed on a heart dataset which consists of 150 records. A framework for predicting heart disease in the medical field comparing the proposed and developed RF and DT classifiers. Sample Size Calculated as 55 in every group by using 80% G power. Sample Size Calculated using clinical analysis, with Alpha and Beta values of 0.05 and 0.5, the confidence level. confidence is 95%, nicest strength is 80% and registration rate is 1. Results: The Decision Tree classifier produces 96.42% accuracy in predicting the heart disease on the data set, whereas the Random forest classifier predicts the same at the rate of 78.45% of the time with a statistically significant difference between the two groups (p=0.004;p<0.05)with confidence interval 95%. Hence Novel Random forest is better than the Decision Tree. Conclusion: The results show that the performance of Random forest is better compared with Decision Tree in terms of both precision and accuracy.
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Leny Margaretha Huizen and Roy Rudolf Huizen. "Optimalisasi Keamanan IoT dan Edge Computing Menggunakan Model Machine Learning." Jurnal Sistem dan Informatika (JSI) 17, no. 2 (2024): 89–94. http://dx.doi.org/10.30864/jsi.v17i2.543.
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Penggunaan teknologi berbasis Internet of Things (IoT) telah meningkat pesat berkat revolusi digital dan membawa tantangan keamanan yang signifikan. Pengoptimalan keamanan IoT pada edge computing dengan menerapkan model berbasis machine learning, untuk deteksi dan identifikasi. Metodologi yang digunakan meliputi pengumpulan data dari sensor IoT dan log aktifitas sebagai data, pra-pemrosesan data, serta pelatihan dan validasi model machine learning. Pada penelitian ini, deteksi dan identifikasi serangan menggunakan empat algoritma, yaitu K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), dan Decision Trees (DT). Hasil penelitian menunjukkan bahwa model Random Forest (RF) dan Decision Tree (DT) memiliki kinerja terbaik dalam mendeteksi serangan siber, dengan nilai True Positive (TP) yang tinggi dan tingkat kesalahan yang rendah. Evaluasi kinerja berdasarkan metrik Akurasi, Presisi, Recall, dan F1-Score mengonfirmasi bahwa RF dan DT mampu memberikan hasil yang akurat dan andal dalam mendeteksi ancaman. Model Random Forest menunjukkan Akurasi 98,4%, Presisi 98,4%, Recall 83,9%, dan F1-Score 90,5%, sedangkan Decision Tree menunjukkan Akurasi 98,1%, Presisi 90,5%, Recall 83,9%, dan F1-Score 87,1%. Implementasi model machine learning dalam sistem keamanan IoT dan edge computing terbukti tidak hanya meningkatkan keamanan data dan perangkat, tetapi juga memaksimalkan efisiensi operasional dengan kemampuan untuk mempelajari dan beradaptasi dengan pola serangan baru.
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Owusu-Ansah, Dominic, Joaquim Tinoco, Faramarzi Lohrasb, Francisco Martins, and José Matos. "A Decision Tree for Rockburst Conditions Prediction." Applied Sciences 13, no. 11 (2023): 6655. http://dx.doi.org/10.3390/app13116655.
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This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.
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Reddy, Patlolla Varshini, Mr Y. Manohar Reddy, Rathod Praveen, and Mohammad Asif. "INNOVATIVE APPROACHES TO MALICIOUS URL DETECTION: USING MACHINE LEARNING UNLEASHED." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39918.
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The proliferation of malicious URLs presents significant challenges to cyber security, necessitating the development of advanced detection techniques. Using the capabilities of Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) models, this study investigates novel machine learning techniques for identifying dangerous URLs. The effectiveness of each model in differentiating between benign and malicious URLs is assessed, taking into account a range of performance indicators including accuracy, precision, recall, and F1-score. The integration of feature extraction techniques and robust data preprocessing enhances the models' ability to generalize across diverse URL data sets. This study demonstrates how machine learning may be used to strengthen defenses against cyber attacks and lays the groundwork for future developments in the detection of dangerous URLs. Keywords: Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).
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Maher, Rand Mohanad, Saba Hussein Rashid, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, and Fatimah N. Ameen. "Predictive Modeling and Analysis of Monkeypox Outbreaks Using Machine Learning Techniques." Applied Data Science and Analysis 2025 (April 12, 2025): 94–111. https://doi.org/10.58496/adsa/2025/006.
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As Monkeypox becomes a prevalent public health issue, it is important to develop advanced detection and prediction methods that will inform public health strategies that govern Monkeypox prevention. This study employs machine learning methods to analyze and predict Monkeypox case trends. In particular, features on new cases and deaths were applied to regression and classification models to predict the total number of Monkeypox cases and new case probablity. The regression models that were applied included Linear regression (LR), Decision Tree Regression (DT), Random Forest Regression (RF), Support Vector Regression (SVR), and K-Nearest Neighbor Regression (KNN), with total cases as the outcome. Among regression methods, the Random Forest Regression model performed the best with a Mean Squared Error (MSE) of 92,425,437.81 and R-squared of 0.06, indeicating moderate predictive ability. The methods were also similar to predict new cases, and once again the same algorithms were applied to classification methods, including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) classification, and each model achieved an accuracy score of one (1.00), indicating no new cases would be missed. These results provide evidence that these are effective machine learning methods, and random forests in particular provides the best predictive capability for Monkeypox case trend analysis. The results illustrate how these models can assist data-driven decisions in public health, and evidence-based preparedness and response for future Monkeypox outbreaks.
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Park, Soyoung, Se-Yeong Hamm, and Jinsoo Kim. "Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling." Sustainability 11, no. 20 (2019): 5659. http://dx.doi.org/10.3390/su11205659.
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This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.
Book chapters on the topic "DT (Decision Tree) and RF (Random Forest)"
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Bartz-Beielstein, Thomas, and Martin Zaefferer. "Models." In Hyperparameter Tuning for Machine and Deep Learning with R. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_3.
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AbstractThis chapter presents a unique overview and a comprehensive explanation of Machine Learning (ML) and Deep Learning (DL) methods. Frequently used ML and DL methods; their hyperparameter configurations; and their features such as types, their sensitivity, and robustness, as well as heuristics for their determination, constraints, and possible interactions are presented. In particular, we cover the following methods: $$k$$ k -Nearest Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and DL. This chapter in itself might serve as a stand-alone handbook already. It contains years of experience in transferring theoretical knowledge into a practical guide.
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Poorahad Anzabi, Pooria, Mahmoud R. Shiravand, and Shima Mahboubi. "Machine Learning-Aided Prediction of Seismic Response of RC Bridge Piers Exposed to Chloride-Induced Corrosion." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_118.
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AbstractDifferent environmental issues such as carbonation and corrosion due to chloride threaten aging reinforced concrete (RC) bridges that are in service in areas highly prone to corrosion and earthquakes. Significant experimental and numerical efforts have been put into scrutinizing the effect of corrosion on nonlinear behavior of structural elements. With the rapid development of artificial intelligence, useful methods are now provided to allow for the assessment of such bridges without the drawbacks and limitations of the experimental and numerical methods. In this paper, four machine learning (ML) algorithms are employed; linear regression (LR), decision tree (DT), random forest (RF), and XGBoost for data fitting of the models, and Bayesian search is used for optimization of hyperparameters. Numerical models of RC piers with stochastic parameters defining geometry, loading, and materials are built, and the degradation due to corrosion is applied with a randomly determined level of corrosion. Then, the corroded models are nonlinearly analyzed with random ground motions scaled to design-based and maximum credible earthquake spectra, and maximum drift ratios are stored. Using the created database, different ML models are compared to find the most accurate one. R-squared, mean absolute error, mean squared error, and root mean squared error metrics are considered as the criteria for the selection of the most accurate model. LR model with R2 = 0.53, MAE = 0.0026, mean squared error (MSE) = 1.4 × 10−5, and root mean squared error (RMSE) = 0.0036 has the lowest accuracy while XGBoost with R2 = 0.8, MAE = 0.0015, MSE = 5 × 10−6, and RMSE = 0.0028 is the most accurate model. DT and RF models with R2 = 0.7 and R2 = 0.73, respectively, are in between.
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Xin, Cun, Dangfeng Yang, Xiaodong Liu, Yong Huang, and Xueming Qian. "Research on Dam Crack Identification Method Based on Multi-source Information Fusion." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9184-2_1.
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AbstractCracks as the main safety concern of dams, high-precision identification of dam cracks is of great application value and scientific significance to ensure the safety of dams. The paper proposes a dam crack identification method based on multi-source information fusion. Specifically, image gray scale and geometric features are extracted based on the image information. And then a single crack identification model based on Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), XGBoost, and BP Neural Network are established based on the features, respectively. Finally, a multi-classifier fusion algorithm based on D-S evidence theory is established to identify the presence of cracks by fusing single identification models. Experiments are carried out to compare the proposed method with the existing identification methods based on the evaluation metrics such as accuracy, precision, F1-score, and recall. The results show that the accuracy of crack identification of the proposed method in this paper reaches 98.9%, and the crack identification results are better than the existing methods.
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Azeez, Nassr, Wafa Yahya, Inas Al-Taie, Arwa Basbrain, and Adrian Clark. "Regional Agricultural Land Classification Based on Random Forest (RF), Decision Tree, and SVMs Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0637-6_6.
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Kadyan, Sunil, Yogita Sharma, Atul Kumar Agnihotri, Veer Bhadra Pratap Singh, Rakshit Kothari, and Fateh Bahadur Kunwar. "Human-Centric AI Applications for Remote Patient Monitoring." In Advances in Healthcare Information Systems and Administration. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1662-7.ch006.
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This research analyses the deployment of a human-centric IoT gadget for remote impacted person monitoring, employing sophisticated technology to beautify healthcare operations. The suggested approach incorporates a community of sensors, together with temperature, stress, coronary heart charge, and oxygen sensors, strategically situated at the afflicted person's frame. These sensors capture actual-time physiological information, which is processed via a signal converter, delivered to character controllers, and consolidated within the cloud for complete analysis. Subsequently, machine studying styles, including artificial neural network (ANN), decision tree (DT), random forest (RF), and naive bayes (NB), are used to anticipate impacted person fitness outcomes based at the accumulated dataset. The analysis assesses each version's performance using a dataset of 3233 items, of which 70% are designated for learning and 30% for experimentation. Results suggest that the proposed ANN model achieves an outstanding accuracy of 97.5%, outperforming DT, RF, and NB. Decision tree and random forest comply closely with accuracies of 92.33% and 91.22%, correspondingly, while naive bayes demonstrates a superb accuracy of 86.5%. These outcomes underline the potential of sophisticated machine learning models, notably ANN, within the field of remote affected person monitoring, giving a transformational method to healthcare. The merger of human-centric layout ideas, IoT technologies, and device learning contributes to the continuous dialogue on improving affected person care, opening the way for extra proactive, customized, and successful healthcare treatments. This investigation suggests a leap forward in utilising generation to alter healthcare practices, highlighting the crucial significance of facts-driven decision-making in making sure best patient impacts.
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Brindha, V., and A. Muthukumaravel. "Improved Classification of Thyroid Diseases With Greater Accuracy Using Random Forest Over Decision Tree in Machine Learning Approaches." In Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9420-5.ch008.
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Since rapid diagnosis for a lifestyle condition is rarely available in rural locations, it becomes vital to create intelligent prediction systems using modern computing techniques. Conventional diagnostic techniques frequently depend on imaging studies, laboratory testing, and clinical evaluations, all of which can be laborious and unpredictable. ML can potentially revolutionize processes such as classifying thyroid illnesses in medical diagnostics. These models can categorize thyroid disorders and help medical professionals make decisions by being trained on various data sources, including patient demographics, hormone levels, imaging results, and clinical symptoms. This research provided a more accurate classification model to help identify thyroid disorders. The Random Forest(RF) and Decision Tree(DT), is compared in this study to establish the optimum training model for detecting thyroid disorders. Accuracy and Precision are performance measurements used to evaluate the models' performance.
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Marreiros, Marcelo, Diana Ferreira, Cristiana Neto, Deden Witarsyah, and José Machado. "Classification of Polycystic Ovary Syndrome Based on Correlation Weight Using Machine Learning." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9172-7.ch006.
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Polycystic ovarian syndrome (PCOS) is the most common endocrine pathology in reproductive-age women worldwide. Research has shown that the application of machine learning (ML) and data mining (DM) can have a positive impact in this condition's diagnosis. This study aims to develop a model to identify patients with PCOS using different scenarios based on correlation weights. Five DM techniques were applied, namely random forest (RF), decision tree (DT), naive bayes (NB), logistic regression (LR), and artificial neural network (ANN), to determine the best model, which was the RF classifier. Additionally, the results show that the model was able to predict PCOS with 93.06% of accuracy, 92.66% of precision, 93.52% of sensitivity, and 92.59% of specificity. Compared with a previous work conducted by the authors, the feature selection-based solo on the correlation weight decreased the accuracy values by 1.9%, precision by 3.7%, sensitivity by 0.3%, and specificity by 3.6%.
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Mohanchandra, Kusuma, and Snehanshu Saha. "Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch081.
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Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.
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Berutu, Sunneng Sandino, Stephen Anugerah Wau, Haeni Budiati, and Jatmika Jatmika. "Sentiment Analysis in Transportation Apps." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9846-3.ch010.
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This research investigates the application of Machine Learning in the context of sentiment classification of user reviews on online transportation applications in Indonesia. This research aims to develop a model to accurately classify positive, negative, or neutral sentiments from customer reviews. Review data collected from the Gojek, Grab, and Maxim applications is utilized to train and test Machine Learning models, including natural language processing (NLP) techniques to extract significant features. Sentiment analysis results show that approximately 63.1% have negative sentiment reviews, 27.7% have positive sentiments, and 9.2% have neutral sentiments. Furthermore, the Support Vector Machine (SVM) algorithm achieved the highest level of accuracy at 92%, followed by Random Forest (RF) with an accuracy level of 91%, Naive Bayes (NB) at 79%, Decision Tree (DT) at 80%, and Logistic Regression (LR) with an accuracy level of 83%. In general, the SVM model performs better than others.
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Majumder, Jeet, Suman Ghosh, Alex Khang, Tridibesh Debnath, and Avijit Kumar Chaudhuri. "Hepatitis C Prediction Using Feature Selection by Machine Learning Technique." In Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2105-8.ch013.
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This study suggests a prediction framework for the Hepatitis C virus that is based on machine learning techniques. The authors made use of a dataset available on Kaggle. In this dataset, 564 patients with 12 distinct features are present. They tested two cases, the first one without feature selection and with feature selection based on gain ratio attribute evaluation (GRAE), to guarantee the strength and dependability of the suggested framework. Additionally, an evaluation is conducted on the feature subset that was chosen using the GRAE-generated features. For model evaluation, induction methods and classifiers such as logistic regression (LR), naive bayes (NB), decision tree (DT), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) are used. According to the experimental findings, the suggested framework outperformed the others in terms of all accuracy matrices following GRAE selection. According to the experimental findings, the suggested framework outperformed the unfeatured one in terms of accuracy after GRAE selection.
Conference papers on the topic "DT (Decision Tree) and RF (Random Forest)"
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Azevedo, Karolayne, Luísa Souza, Matheus Dalmolin, and Marcelo Fernandes. "IA explicável aplicada para identificar genes influentes na classificação do câncer por meio de dados de expressão gênica de RNA-Seq." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-096.
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Este artigo faz uso de três técnicas de aprendizagem de máquina (Machine Learnig – ML) para classificar os cinco tipos de câncer mais recorrentes em mulheres, a partir de dados de expressão gênica de RNA-Seq. Os desafios incluem: alta dimensionalidade do conjunto de dados e a falta de transparência dos modelos de ML. Para mitigar esses problemas, foi utilizado a técnica SHAP (SHapley Additive exPlanations) que e uma técnica de inteligência artificial explicável (Explainable artificial intelligence – XAI) utilizada para compreender como esses modelos tomam decisões podendo ser usada como uma estratégia para a seleção de recursos. Como entrada, foram utilizadas 2.105 amostras, sendo 421 amostras referentes a cada tumor, processadas pelos modelos Arvore de Decisão (Decision Tree- DT), Floresta Aleatoria (Random Forest-RF) e Aumento de Gradiente Extremo (eXtreme Gradient Boosting-XGB) treinadas e validadas por meio da técnica de validação cruzada. Os modelos RF, DT e XGB alcançaram precisões de 99, 40%, 97, 60% e 99, 34%. Posteriormente, a técnica SHAP foi utilizada para obter uma lista de recursos visando compreender quais características influenciaram nas tomadas de decisões dos modelos e consequentemente, nos resultados de predição dos cinco tumores. 122, 90 e 11 genes foram obtidos nos modelos RF, XGB e DT, totalizando 223 resultando em 194 genes únicos.
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Pir Mohammadiani, Rojiar, Zaniar Pir Mohammadiani, and Sogand Dehghan. "Provision a generalizable approach to the ranking of credible Twitter users." In The 3rd International Conference on Engineering and Innovative Technology. Salahaddin University-Erbil, 2025. https://doi.org/10.31972/iceit2024.043.
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The abundance of information shared on social networks presents valuable opportunities, such as timely news coverage and user needs forecasting. However, the lack of oversight facilitates the spread of fake content across various fields. Therefore, evaluating user credibility is crucial for responsible social media usage. This paper proposes a topic-based user ranking system on Twitter. The system leverages machine learning algorithms to prioritize user credibility based on specific topics and introduces new features for comprehensive evaluation. Finally, users are rated with 5 models of machine learning, Linear Regression (LR), Support Vector Regression (SVR), k-nearest neighbors (KNN), Random Forest (RF) and Decision Tree (DT) that, DT has achieved the highest accuracy of 82%. This approach offers a generalizable solution for various user credibility assessment needs.
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Kocher, Geeta, and Gulshan Kumar. "Performance Analysis of Machine Learning Classifiers for Intrusion Detection using UNSW-NB15 Dataset." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102004.
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With the advancement of internet technology, the numbers of threats are also rising exponentially. To reduce the impact of these threats, researchers have proposed many solutions for intrusion detection. In the literature, various machine learning classifiers are trained on older datasets for intrusion detection which limits their detection accuracy. So, there is a need to train the machine learning classifiers on latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. On the basis of theoretical analysis, taxonomy is proposed in terms of lazy and eager learners. From this proposed taxonomy, KNearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Naïve Bayes (NB) classifiers are selected for training. The performance of these classifiers is tested in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) on UNSW-NB15 dataset and comparative analysis of these machine learning classifiers is carried out. The experimental results show that RF classifier outperforms other classifiers.
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Shirmarz, Alireza, Carlos Henrique de França Marques, Fábio Luciano Verdi, Roberto Silva Netto, Suneet Kumar Singh, and Christian Esteve Rothenberg. "DCTPQ: Dynamic Cloud Gaming Traffic Prioritization Using Machine Learning and Multi-Queueing for QoE Enhancement." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2025. https://doi.org/10.5753/sbrc.2025.6266.
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Cloud gaming (CG) traffic requires high bandwidth and low latency to ensure Quality of Experience (QoE). We propose DCTPQ, an ML-based edge solution that dynamically identifies and prioritizes CG traffic on-the-fly, achieving 97.6% classification accuracy using packet-based and RTP frame-based features with Decision Tree (DT) and Random Forest (RF) models. DCTPQ employs separate queues for CG, UDP (Non-CG), and TCP traffic, with varied lengths and rates, implemented using P4 on the data plane. Leveraging Inband Network Telemetry (INT) and Device-in-the-Loop (DIL) techniques, we evaluate QoS (throughput, latency, packet sojourn time) and QoE (VMAF score) under congestion. The system is tested with three distinct CG games (Fortnite, Forza, Mortal Kombat) on the Xbox platform, while users play online, ensuring a realistic assessment of the deployed model’s impact on QoS and QoE.
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Harrasi, Mohammed Talib Said Al, Alireza Kazemi, Rami Al-Hmouz, Abdulrahman Aal Abdulsalaam, and Rashid Al Hajri. "Machine Learning Techniques for Inorganic Scale Precipitation Prediction: A Real Field Data from a Carbonate Reservoir." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2024. http://dx.doi.org/10.2118/218796-ms.
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Abstract The precipitation of inorganic scales in the oil and gas industry has been identified as a major issue for flow assurance and the optimization of oil and gas fields due to the damage that these precipitations can cause in reservoirs, well completions, and surface facilities. On the other hand, predicting these precipitations has always been challenging for engineers of petroleum, production, and production facilities. Although many commercial computer programs in the industry can predict inorganic scale precipitations with some accuracy, the majority have many limitations that can negatively impact prediction performance. Machine learning (ML) has received substantial attention in the oil and gas industry in recent years. The purpose of this study is to investigate the use of machine learning algorithms as a new approach to predicting inorganic scale precipitations in oil and gas carbonate formations. The methodology of the current study consists of gathering input and output data, such as pressure, temperature, artificial lifting type, target formation, water ionic composition, pH, TDS, and whether or not each well tends to precipitate the inorganic scale. The algorithms chosen for prediction are Naive Bayes (NA), Neural Network classifier (NN), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and K- Nearest Neighbors (KNN), and they will be evaluated based on accuracy and other classification performance metrics. The results of the models show that SVM, DT, and KNN are the best classifiers in terms of prediction accuracy scores with around 83%. Furthermore, a decision tree chart was created based on the Decision Tree (DT) model and can be used to examine the scale precipitation tendency for any future water sample. The chart is validated using real well cases from the same field, demonstrating a match between the predicted class (the well possesses or does not possess a high potential to precipitate inorganic scale) and the data collected in the well's interventions history reports. Based on the DT model, the artificial lifting method, target formation, pressure at the pump depth, and SO42-, HCO3- ionic compositions are found to be the strongest features that play a significant role in the scale precipitations in the studied field. Implementing the proposed model will lead to many benefits, including properly employed well intervention resources, reduced oil deferment due to pump failures caused by scale precipitation, and reduced budget overspending entailed by unexpected failures in pumps, valves, or even surface facilities.
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Pontes Júnior, Armando, and Roberta Fagundes. "Aplicação de Técnicas de Otimização de Hiperparâmetros em Modelos de Machine Learning na Tarefa de Classificar Bons e Maus Clientes." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-141.
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A indústria financeira necessita de soluções rápidas, de menor custo e mais assertivas na classificação de risco de crédito. Métodos de machine learning estão cada vez mais sendo incorporados para executar essa tarefa. Além disso, técnicas de otimização de hiperparâmetros podem melhorar o desempenho de classificadores. Neste trabalho foram aplicadas duas importantes técnicas de otimização com cinco classificadores distintos, são eles: Decision Tree (DT), Random Forest (RF), MultiLayer Perceptron (MLP), eXtreme Gradient Boost (XGBoost) e Light Gradient Boost Machine (LGBM). Para medir o desempenho dos classificadores foram utilizadas as seguintes métricas: Accuracy, Precison, Recall e F1-Score. Os modelos foram treinados e testados com informações de duas bases de dados. Inicialmente foram utilizadas as configurações padrões dos hiperparâmetros, e, posteriormente, foram aplicadas as otimizações bayesiana e por Particle Swarm Optimization (PSO) para alcançar melhores resultados. A otimização bayesiana apresentou melhorias nas métricas de todos os modelos, com destaque para o Recall, chegando a uma assertividade de 91,4% no classificador MLP. Porém, a otimização por PSO não apresentou melhoria no desempenho.
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Anifowose, Fatai, Mokhles Mezghani, Saleh Badawood, and Javed Ismail. "A Field-Scale Real-Time Prediction of Reservoir Porosity from Advanced Mud Gas Data." In SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214398-ms.
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Abstract In our previous study, we presented the preliminary results of the first attempt to predict reservoir rock porosity from advanced mud gas (AMG) data within the wellbore. The objective was to investigate the feasibility of generating a porosity log while drilling prior to wireline logging and core description processes. Knowing that porosity remains a critical property of petroleum reservoirs, this work improves on the previous research to predict porosity within a field. The methodology leveraged the machine learning (ML) paradigm in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 15,000 data points collected from representative wells in a field were used to prove the possibility of predicting the missing porosity in a well within the field. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The dataset was randomly split into training and validation subsets in 70:30 ratio simulating the complete and missing sections respectively. Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model consistently outperformed the others. In one of the test cases, the RF model gave a correlation coefficient (R-Squared) value of 0.84 compared to 0.46, and 0.78 for ANN and DT models respectively. The RF model also has a mean squared error (MSE) of 0.001 compared to 0.02 and 0.01 respectively for ANN and DT models. Having showed in a previous publication that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. It can be deduced that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research. This study has confirmed the feasibility of predicting porosity at the field scale and the huge benefit in utilizing AMG data beyond the traditional fluid typing and petrophysical correlation processes. The presented approach has the capability to complement existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.
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Panggabean, D. A. "The Machine Learning's Classification Methods Comparison to Estimate Electrofacies Type, Lithology and Hydrocarbon Fluids from Geophysical Well Log Data." In Indonesian Petroleum Association 44th Annual Convention and Exhibition. Indonesian Petroleum Association, 2021. http://dx.doi.org/10.29118/ipa21-sg-196.
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Supervised learning methods from machine learning are starting to be widely used in oil & gas data management. The usage of the method is adjusted to the purpose of data processing, including data classification and regression. In this research, there are six classification methods to estimate the electrofacies shape, lithology type, and fluids, namely Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGB). This research compared those six methods qualitatively and quantitatively to obtain the best method. This research was conducted in the Maju Royal Field using one oil well data for training data and another one well as testing data. For validation purposes, 85% of the data was split for training and 15% for validation, aiming to evaluate the machine learning model through the correlation coefficient value. In the test data, qualitative and quantitative analyzes were also conducted. Qualitative analysis was performed by comparing the results of the electrofacies shape prediction with the original interpretation, lithology prediction with shale volume data, and prognosis of fluids with test zone data. Meanwhile, quantitatively, it is done by comparing the correct predictive data with the actual amount of data on each parameter. The training data evaluation result shows that KNN and XGB are suitable for electrofacies shape prediction. Meanwhile, lithology and fluid estimation are good with DT, KNN, and XGB methods. The qualitative and quantitative analysis result from the test data shows that the DT and GNB methods are suitable for estimating the electrofacies shape. In contrast, all methods are considered good at predicting and have good correlation values for calculating the lithology and fluids. Hence, both training and test data evaluation result has good correlation values
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Anifowose, Fatai, Mokhles Mezghani, Saleh Badawood, and Javed Ismail. "From Well to Field: Reservoir Rock Porosity Prediction from Advanced Mud Gas Data Using Machine Learning Methodology." In Middle East Oil, Gas and Geosciences Show. SPE, 2023. http://dx.doi.org/10.2118/213339-ms.
Full textAbstract:
Abstract The utility of advanced mud gas (AMG) data has been limited to fluid typing and petrophysical correlations. There is the need to extend the utility to real-time reservoir characterization prior to wireline logging and geological core description. Our first attempt to predict reservoir rock porosity within a well yielded good result. This study improves on the previous effort by utilizing big data obtained from combining various wells in the study area. We used machine learning (ML) methodology in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 20,000 data points collected from representative wells were used to prove the concept of predicting the porosity in an interval or section of any well within the study area. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The combined dataset was randomly split into training and validation subsets in 70:30 ratio. The 30% validation subset simulates a missing well interval or section. Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model outperformed the others. The RF model gave a training and validation correlation coefficient (R-Squared) values of 0.94 and 0.83 respectively compared to 0.36 and 0.35 for the ANN and 0.84 and 0.73 for the DT models respectively. However, the p-value and mean errors show that the models are statistically acceptable. Having showed in a previous research that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. We conclude that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research. This study has confirmed the feasibility of predicting porosity based on a dataset of combined wells and the huge benefit in extending the utility of AMG data beyond the traditional workflows. This approach is capable of complementing existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.
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Huang, Jinhui, and Tingru Zhang. "EEG-based Prediction of Driver Takeover Performance." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005233.
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In the context of conditional autonomous driving, ensuring a safe takeover is of paramount importance. While previous studies have delved into factors influencing drivers’ takeover performance, there remains a gap in research concerning the development of performance models capable of predicting takeover quality. To address this challenge, this study focuses on predicting driver takeover performance before the issuance of a takeover request based on Electroencephalogram (EEG) features. For this purpose, 72 subjects were recruited to participate in a driving simulation experiment, responding to a total of eight takeover events. Both their EEG signals and driving performance data were recorded. The takeover performance was subsequently categorized as high, medium, or low quality through a subjective review of the takeover process videos. A total of 480 EEG features, such as the power of α band, were extracted. Five machine learning models: Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Multi-layer Perceptron (MLP), were utilized to develop the takeover performance prediction models. The results showed that the LightGBM model outperformed others, achieving an accuracy of 84.2% and an F1 score of 83.0%. In contrast, the DT model demonstrated the lowest performance, with an accuracy of 59.4% and an F1 score of 57.8%. This study underscores the potential of machine learning models in predicting driver takeover performance, thereby contributing to the advancement of machine learning applications in the field of autonomous driving.
Reports on the topic "DT (Decision Tree) and RF (Random Forest)"
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.
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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.