Relevant bibliographies by topics / K-Nearest Neighbors Classification and Regression Tree Logistic Regression Support Vector Machine Random Forest and Bogura

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'K-Nearest Neighbors Classification and Regression Tree Logistic Regression Support Vector Machine Random Forest and Bogura'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "K-Nearest Neighbors Classification and Regression Tree Logistic Regression Support Vector Machine Random Forest and Bogura"

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Mostafizur Rahman, Md, and M. Sayedur Rahman. "PREDICTING RAINFALL BASED ON MACHINE LEARNING ALGORITHM: AN EVIDENCE FROM BOGURA DISTRICT, BANGLADESH." International Journal of Advanced Research 10, no. 08 (2022): 850–58. http://dx.doi.org/10.21474/ijar01/15243.

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Accurately and timely predicting climatic variables are most challenging task for the researchers. Scientists have been trying numerous methods for forecasting environmental data with different methods and found confusing performance of different methods. Recently machine learning tools are considering as a robust technique for predicting climatic variables because these tools extracted hidden relationship from the data and can predict more correctly than existing methods. In this paperwe compare the forecasting performance of various machine learning algorithms such as Classification and Regr
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Ehsan, Muhmammad. "Comparison of the Predictive Models of Human Activity Recognition (HAR) in Smartphones." UMT Artificial Intelligence Review 1, no. 2 (2021): 27–35. http://dx.doi.org/10.32350/air.0102.03.

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This report compared the performance of different classification algorithms such as decision tree, K-Nearest Neighbour (KNN), logistic regression, Support Vector Machine (SVM) and random forest. The dataset comprised smartphones’ accelerometer and gyroscope readings of the participants while performing different activities, such as walking, walking downstairs, walking upstairs, standing, sitting, and laying. Different machine learning algorithms were applied to this dataset for classification and their accuracy rates were compared. KNN and SVM were found to be the most accurate of all.
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Angula, Taapopi John, and Valerianus Hashiyana. "Detection of Structured Query Language Injection Attacks Using Machine Learning Techniques." International Journal of Computer Science and Information Technology 15, no. 4 (2023): 13–26. http://dx.doi.org/10.5121/ijcsit.2023.15402.

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This paper presents a comparative analysis of various machine learning classification models for structured query language injection prevention. The objective is to identify the best-performing model in terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, fol
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Azeez, N. A., S. S. Oladele, and O. Ologe. "Identification of pharming in communication networks using ensemble learning." Nigerian Journal of Technological Development 19, no. 2 (2022): 172–80. http://dx.doi.org/10.4314/njtd.v19i2.10.

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Pharming scams are carried out by exploiting the DNS as the main weapon while phishing attacks employ spoofed websites that appear to be legitimate to internet users. Phishing makes use of baits such as fake links but pharming leverages and negotiates on the DNS server to move and redirect internet users to a fake and simulated website.Having seen several challenges through pharming resulting into vulnerable websites, personal emails and accounts on social media, the usage and reliability on internet calls for caution. Against this backdrop, this work aims at enhancing pharming detection strat
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Vardhan, L. VN Sasi, and Mrs G. Kumari. "Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1220–28. http://dx.doi.org/10.22214/ijraset.2022.47384.

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Abstract: A myocardial infarction, indigestion, or even death can take place as a result of several illnesses known as heart disease, including restricted or blocked veins. Depending on the extent of the patient's side effects, the condition is anticipated by the supervised classification classifier. This research intends to investigate how Machine Learning Tree Classifiers depict Heart Disease Prediction. Pattern recognition tree classifiers are analyzed using Random Forest, Decision Tree, Logistic Regression, Support Vector Machine (SVM), and K-nearest Neighbors (KNN) based on their correctn
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Yao, Jian-Rong, and Jia-Rui Chen. "A New Hybrid Support Vector Machine Ensemble Classification Model for Credit Scoring." Journal of Information Technology Research 12, no. 1 (2019): 77–88. http://dx.doi.org/10.4018/jitr.2019010106.

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Credit scoring plays important role in the financial industry. There are different ways employed in the field of credit scoring, such as the traditional logistic regression, discriminant analysis, and linear regression; methods used in the field of machine learning include neural network, k-nearest neighbors, genetic algorithm, support vector machines (SVM), decision tree, and so on. SVM has been demonstrated with good performance in classification. This paper proposes a new hybrid RF-SVM ensemble model, which uses random forest to select important variables, and employs ensemble methods (bagg
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Al-Imran, Md, Salma Akter, Md Abu Sufian Mozumder, et al. "EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE." American Journal of Engineering and Technology 6, no. 9 (2024): 22–33. http://dx.doi.org/10.37547/tajet/volume06issue09-04.

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This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and k-Nearest Neighbors (KNN)—for breast cancer detection using the Breast Cancer Wisconsin Diagnostic dataset. We implemented comprehensive pre-processing and model evaluation with Scikit-learn in Python. Our findings show that SVM achieved the highest accuracy, with 99.9% on the training set and 98.50% on the testing set, indicating superior performance in handling high-dimensional data. Random Forest also performed well, with accuracies of 98.5% an
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Siraj, Mohammed Siddiq, Mohammed Wahaj haqqani, and Dr Khaja Mizbahuddin Quadry. "A novel credit card fraud detection using supervised machine learning model." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 313–24. http://dx.doi.org/10.54660/.ijmrge.2024.5.1.313-324.

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Financial fraud, especially in credit card transactions, is a growing concern. To tackle this, data mining techniques are used to automatically analyze large and complex financial datasets. Detecting credit card fraud is tricky because the patterns of normal and fraudulent behavior keep changing, and the data about fraud is much less common compared to legitimate transactions Several techniques were tried on a dataset from European cardholders, including Decision Tree, Random Forest, SVC, XGBoost, K-Nearest Neighbors, and Logistic Regress The dataset had information from 284,786 credit card tr
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Mishra, Ashwani, and Sanjeev Gangwar. "Lung Cancer Detection and Classification using Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 277–82. http://dx.doi.org/10.17762/ijritcc.v11i6s.6920.

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Lung cancer is a clump of cells in the lung that are multiplying uncontrollably and improperly. Lung cancer is the deadliest disease, and its cure should be the primary focus of all scientific research. Although it cannot be prevented, we can lessen the danger. Thus, a patient's chance of life depends on the early identification of lung cancer. Several machine learning methods, such as Support Vector Machine, Logistic Regression, Artificial Neural Networks, and Naive Bayes, have been used for the investigation and prognosis of lung cancer. In this paper, Lung cancer prediction is finished by g
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Sudhan Reddy, K. Madhu. "Comparative Analysis Of Liver Diseases By Using Machine Learning." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03486.

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ABSTRACT: Liver diseases constitute a major public health concern worldwide, often leading to life- threatening conditions if not diagnosed and treated in time. Conventional diagnostic methods rely heavily on clinical expertise and laboratory tests, which can be time-consuming and may not always yield accurate early detection. With the growing availability of healthcare data, machine learning (ML) techniques have emerged as powerful tools for disease prediction and classification. This paper presents a comparative analysis of liver disease prediction using multiple ML algorithms, including Log
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Dissertations / Theses on the topic "K-Nearest Neighbors Classification and Regression Tree Logistic Regression Support Vector Machine Random Forest and Bogura"

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Elmasry, Mohamed Hani Abdelhamid Mohamed Tawfik. "Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults." Master's thesis, 2019. http://hdl.handle.net/10362/62427.

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Dissertation submitted in partial fulfilment of the requirements for the degree of Statistics and Information Management specialized in Risk Analysis and Management<br>To effectively manage credit score analysis, financial institutions instigated techniques and models that are mainly designed for the purpose of improving the process assessing creditworthiness during the credit evaluation process. The foremost objective is to discriminate their clients – borrowers – to fall either in the non-defaulter group, that is more likely to pay their financial obligations, or the defaulter one which has
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Book chapters on the topic "K-Nearest Neighbors Classification and Regression Tree Logistic Regression Support Vector Machine Random Forest and Bogura"

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Vijaya Lakshmi, Adluri, Sowmya Gudipati Sri, Ponnuru Sowjanya, and K. Vedavathi. "Prediction using Machine Learning." In Handbook of Artificial Intelligence. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124514123010005.

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This chapter begins with a concise introduction to machine learning and the classification of machine learning systems (supervised learning, unsupervised learning, and reinforcement learning). ‘Breast Cancer Prediction Using ML Techniques’ is the topic of Chapter 2. This chapter describes various breast cancer prediction algorithms, including convolutional neural networks (CNN), support vector machines, Nave Bayesian classification, and weighted Nave Bayesian classification. Prediction of Heart Disease Using Machine Learning Techniques is the topic of Chapter 3. This chapter describes the numerous heart disease prediction algorithms, including Support Vector Machines (SVM), Logistic Regression, KNN, Random Forest Classifier, and Deep Neural Networks. Prediction of IPL Data Using Machine Learning Techniques is the topic of Chapter 4. The following algorithms are covered in this chapter: decision trees, naive bayes, K-Nearest Neighbour Random Forest, data mining techniques, fuzzy clustering logic, support vector machines, reinforcement learning algorithms, data analytics approaches and Bayesian prediction techniques. Chapter Five: Software Error Prediction by means of machine learning- The AR model and the Known Power Model (POWM), as well as artificial neural networks (ANNs), particle swarm optimisation (PSO), decision trees (DT), Nave Bayes (NB), and linear classifiers, are among the approaches (K-nearest neighbours, Nave Bayes, C-4.5, and decision trees) Prediction of Rainfall Using Machine Learning Techniques, Chapter 6: The following are discussed: LASSO (Least Absolute Shrinkage and Selection Operator) Regression, ANN (Artificial Neural Network), Support Vector Machine, Multi-Layer Perception, Decision Tree, Adaptive Neuro-Fuzzy Inference System, Wavelet Neural Network, Ensemble Prediction Systems, ARIMA model, PCA and KMeans algorithms, Recurrent Neural Network (RNN), statistical KNN classifier, and neural SOM Weather Prediction Using Machine Learning Techniques that includes Bayesian Networks, Linear Regression, Logistic Regression, KNN Decision Tree, Random Forest, K-Means, and Apriori's Algorithm, as well as Linear Regression, Polynomial Regression, Random Forest Regression, Artificial Neural Networks, and Recurrent Neural Networks.
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Rajalingam, Rajani, Dr Madhusudhana Reddy Barusu, G. Prathibha Priyadarshini, and Pulagouni Priyanka. "MACHINE LEARNING ALGORITHMS." In Futuristic Trends in Computing Technologies and Data Sciences Volume 2 Book 18. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs18p4ch4.

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Now-a-days, everyone is familiar with the term “data” and it is everywhere. But, this is huge in size and may be generated by people or devices. The problem with data is that, it could be in different forms like text, audio, video, and image etc., Due to this the data can be categorized as structured or unstructured. Analyzing and producing results out of this unstructured data is a time-consuming process. However, it would be easy to derive output from unbalanced data if it could be converted into balanced data. Here comes the role of Machine Learning, which is a subset of Artificial Intelligence (AI) that enables machines or other systems to learn on their own without any kind of explicit programming. These systems are designed in such a way that, they use knowledge to extract information from the unbalanced data. To deal with these data problems, various techniques have been supported by machine learning. For instance, to develop decision–making insights, many data-intensive problems require implementation of regression or classification techniques. This falls within the area of machine learning. Machine learning algorithms can be categorized as supervised, unsupervised and reinforcement learning strategies based on the desired outcome of the algorithm. Examples of various Machine learning algorithms include Linear Regression, Logistic regression, k-nearest neighbors, k-means, Naïve Bayes, Support Vector Machine (SVM), Random forest, Decision tree, Dimensionality reduction, Gradient boosting and Ada Boosting algorithm etc., could be applied on data for future predictions
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Gowroju, Swathi, Shilpa Choudhary, Arpit Jain, and R. Srilakshmi. "Classification of Moderate and Advanced Dementia Patients Using Gradient Boosting Machine Technique." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6303-4.ch011.

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In the twenty-first century, caring for persons with Dementia's has become extremely difficult due to the prevalence of dementia cases. Using data from the OASIS (Open Access Series of Imaging Studies) program provided by the University of Washington Dementia's Disease Research Center, the study presents a new predictive model for Dementia's. Dementia, a chronic condition and it's become a serious health concern in adults. Various methods of data imputation, preprocessing, and transformation were used to prepare the data for model training. Machine learning algorithms, including AdaBoost (AB), Decision Tree (DT), Exclusion Tree (ET), Gradient Boost (GB), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB;, Random Forest (RF), and Support Vector Machine (SVM), were used in this field. These algorithms were evaluated on both the complete feature set and a subset of features selected via the Least Absolute Shrinkage and Selection Operator (LASSO) method. Comparative analysis based on accuracy, precision, and other metrics showed that the proposed method achieved the highest accuracy of 96.77% using Support Vector Machine (SVM) with all feature sets, further refined and applied, has great potential for the diagnosis of early Dementia's disease (AD) disease.
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Abiramasundari, S., and P. Umamaheswari. "Liver Disease Prediction Using Classification and Feature Selection Techniques." In Predictive Algorithms for Rehabilitation and Assistive Systems. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0194-5.ch013.

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Liver disease is one of the most prominent causes of the increase in the death rate worldwide. Early detection can help in better management and treatment of the disease. Machine learning can help in accurate and efficient diagnosis of Liver disease. This paper aims to develop a machine learning model for the detection of Liver disease. The main focus is to predict liver disease based on a software engineering approach using classification and feature selection techniques. The model has a good accuracy and can be used for early detection of Liver disease. This proposed methodology has the potential to contribute to improved patient outcomes and quality of life. In this paper, different machine learning algorithms such as Logistic Regression, Multi-Layer Perceptron, Decision Tree Random Forest, Support Vector Machine, Extreme Gradient Boosting and K-Nearest Neighbours have been used to detect the liver disease. In this proposed work, the accuracy of the algorithms is compared and the best algorithm is chosen that gives the highest accuracy rate in predicting the Liver disease.
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Kantagba, Yves M. K., Seydou G. Barro, Serge L. W. Nikiema, and Pascal Staccini. "Artificial Intelligence for Drug-Induced Liver Injury Prediction: A Systematic Review." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250698.

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Drug toxicity and side effects are the cause of clinical trials failure, resulting in wasted investment and time. Liver injury is one of the leading causes of drug rejection in the clinical phase. Machine learning models have been developed to predict induced liver damage with significant results. We carried out a systematic review to identify and describe the AI strategies reported in the scientific literature for drug induced liver injury prediction. Using combinations of keywords, we retrieved articles from Google Scholar and PubMed database and analyzed them according to defined criteria. PRISMA guidelines were used in the articles selection process. The main algorithms reported were Random Forest, Support Vector Machine, Decision Tree, Neural network, K-Nearest Neighbors, XGBoosting and Logistic Regression. Random Forest seemed to perform better than others in same study. The US FDA DILI rank database was the main source of drugs and the AI models integrated diverse features as physicochemical properties, molecular fingerprints and genes expression. The diversity of training data ensures that the training data can provide more discriminating information for the model. Voting combinations-based ensemble is one of innovative strategy used for improving accuracy. Artificial intelligence remains a tool of choice for risk assessment and a definite ally in drug research and development.
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Veziroğlu, Merve, Erkan Eziroğlu, and İhsan Ömür Bucak. "PERFORMANCE COMPARISON BETWEEN NAIVE BAYES AND MACHINE LEARNING ALGORITHMS FOR NEWS CLASSIFICATION." In Bayesian Inference - Recent Trends. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1002778.

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The surge in digital content has fueled the need for automated text classification methods, particularly in news categorization using natural language processing (NLP). This work introduces a Python-based news classification system, focusing on Naive Bayes algorithms for sorting news headlines into predefined categories. Naive Bayes is favored for its simplicity and effectiveness in text classification. Our objective includes exploring the creation of a news classification system and evaluating various Naive Bayes algorithms. The dataset comprises BBC News headlines spanning technology, business, sports, entertainment, and politics. Analyzing category distribution and headline length provided dataset insights. Data preprocessing involved text cleaning, stop word removal, and feature extraction with Count Vectorization to convert text into machine-readable numerical data. Four Naive Bayes variants were evaluated: Gaussian, Multinomial, Complement, and Bernoulli. Performance metrics such as accuracy, precision, recall, and F1 score were employed, and Naive Bayes algorithms were compared to other classifiers like Logistic Regression, Random Forest, Linear Support Vector Classification (SVC), Multi-Layer Perceptron (MLP) Classifier, Decision Trees, and K-Nearest Neighbors. The MLP Classifier achieved the highest accuracy, underscoring its effectiveness, while Multinomial and Complement Naive Bayes proved robust in news classification. Effective data preprocessing played a pivotal role in accurate categorization. This work contributes insights into Naive Bayes algorithm performance in news classification, benefiting NLP and news categorization systems.
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Otero, Marta, Luisa Fernanda Velasquez, Boris Basile, Jordi Ricard Onrubia, Alex Josep Pujol, and Josep Pijuan. "Data Driven Predictive Models Based on Artificial Intelligence to Anticipate the Presence of Plasmopara Viticola and Uncinula Necator in Southern European Winegrowing Regions." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220333.

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Downy and powdery mildews are two of the main diseases threatening grapevine cultivation worldwide caused by the phytopathogens Plasmopara viticola and Uncinula necator, respectively. These diseases may cause severe damage to grapevines by inducing wilting of plant organs, including bunches, especially when vines are untreated. This fact, together with the widespread of these pathogens due to the large extensions of land dedicated to grapevine monoculture, makes necessary to develop new predictive modeling tools that allow anticipating disease appearance in the vineyard, minimizing the losses in fruit yield and quality, and helping farmers in defining appropriate and more sustainable disease management strategies (fungicides applied at the right time and dose). For this purpose, farms located in three countries (Portugal, Spain, and Italy) were selected to study the relationship between the microclimatic characteristics of the plots, the phenological stage of the plants throughout the annual cycle, and the presence of both pathogens using different Machine and Deep Learning classification algorithms: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, K-Nearest Neighbors, Naïve Bayes, Support Vector Machines, and Deep Neural Networks. The results showed that, after an entire annual grapevine cycle, the best performing models were Support Vector Machines for downy mildew and Random Forest for powdery mildew, providing a prediction accuracy of more than 90% for the infection risk and more than 80% for the treatment recommendation. These models will be fine-tuned during two additional vegetative seasons to ensure their robustness and will receive short- and medium-term climatological and phenological forecasts to make recommendations. The preliminary results obtained show that these models are a promising tool in the field of plant disease prevention and resource saving.
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Conference papers on the topic "K-Nearest Neighbors Classification and Regression Tree Logistic Regression Support Vector Machine Random Forest and Bogura"

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Atencia Mondragon, Lelis Raquel, Melany Cristina Huarcaya Carbajal, and Rosario Guzmán Jiménez. "Exploring Stroke Risk Identification by Machine Learning: A Systematic Review." In Congreso Internacional de Ingeniería de Sistemas. Universidad de Lima, 2024. http://dx.doi.org/10.26439/ciis2023.7081.

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This work aims to systematize previous studies on stroke risk identification and its relationship with machine learning. A systematic review was conducted using the Web of Science and Scopus databases. The information was organized into three sections: stroke risk factors, data preprocessing techniques and techniques for identifying stroke risk with an emphasis on the most important features. The main results are as follows: risk factors are divided into modifiable (work environment and air pollution) and non-modifiable (sex, family history). The most commonly used data preprocessing technique
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Asedegbega, Jerome, Oladayo Ayinde, and Alexander Nwakanma. "Application of Machine Learniing For Reservoir Facies Classification in Port Field, Offshore Niger Delta." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207163-ms.

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Abstract Several computer-aided techniques have been developed in recent past to improve interpretational accuracy of subsurface geology. This paradigm shift has provided tremendous success in variety of Machine Learning Application domains and help for better feasibility study in reservoir evaluation using multiple classification techniques. Facies classification is an essential subsurface exploration task as sedimentary facies reflect associated physical, chemical, and biological conditions that formation unit experienced during sedimentation activity. This study however, employed formation
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Hassan Hussen, Skala, and Gullanar M Hadi. "Utilizing Various Machine-Learning Techniques in Breast Cancer Detection." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1531.

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Worldwide, cancer is the most frequent cause of passing away for women. Any development in predicting and diagnosing cancer is crucial for a healthy life. As such, vital cancer accuracy in predicting patients' survival parameters and treatment aspects is necessary. Machine learning methods significantly impact breast cancer diagnosis and early diagnosis. This study aims to increase prediction accuracy using a novel statistical feature selection technique. This article examines the classification test accuracy, standard data precision, and the process performance of multiple machine learning (M
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PARE, Vikas. "Role of machine learning in air pollution control and monitoring: A recent review." In Mechanical Engineering for Sustainable Development. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903438-7.

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Abstract. Air pollution is a significant environmental and societal issue, causing significant health risks. Researchers are exploring machine learning techniques to control pollution, focusing on potential sources and mitigation strategies. The main causes of air pollution include emissions, transportation dispersion, transformation, and immisions. The study aims to understand and mitigate these issues to improve air quality and safety.Pollution in air comes from both exhaust and non-exhaust emission sources, affecting both indoor and outdoor environments. Exhaust emission pollutants include
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Hamada, G., A. M. Al-Khudafi, A. T. Al-Yazidi, H. A. Al-Sharifi, T. Al-Qadhi, and A. A. Al-Gathe. "Enhanced Machine Learning Modelling Techniques for Better Classification of Carbonate Reservoir Rocks." In Mediterranean Offshore Conference. SPE, 2024. http://dx.doi.org/10.2118/223312-ms.

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Abstract This study aims to enhance machine learning models for classifying carbonate rocks into limestone and dolomite using well logging and core analysis data. The research evaluates various machine learning algorithms' performance and identifies effective techniques to improve model accuracy for geological and environmental applications. The study employed several strategies to improve classification models, including grid search, random search, Bayesian optimization, SMOTE, and ensemble techniques (boosting and bagging). A dataset of 4290 points was used to train eight different classific
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Garba, Muhammad, Muhammad Abdurrahman Usman, and Anas Muhammad Gulumbe. "Improving Breast Cancer Detection with Naive Bayes: A Predictive Analytics Approach." In 12th International Conference of Security, Privacy and Trust Management. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141116.

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The study focuses on predicting breast cancer survival using naïve bayes techniques and compares several machine learning models across large dataset of 310,000 patient records. The survival and non-survival classes were the two main categories. The objective of the study was to assess the effectiveness of the Naïve Bayes classifier in the data mining area and to attain noteworthy outcomes for survival classification that were consistent with the body of existing literature. Naive Bayes achieved an average accuracy of 91.08%, indicating reliable performance but with some variability across fol
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Thumpati, Asitha, and Yan Zhang. "Towards Optimizing Performance of Machine Learning Algorithms on Unbalanced Dataset." In 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131914.

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Imbalanced data, a common occurrence in real-world datasets, presents a challenge for machine learning classification models. These models are typically designed with the assumption of balanced class distributions, leading to lower predictive performance when faced with imbalanced data. To address this issue, this paper employs data preprocessing techniques, including Synthetic Minority Oversampling Technique (SMOTE) for oversampling and random undersampling, on unbalanced datasets. Additionally, genetic programming is utilized for feature selection to enhance both performance and efficiency.
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Albuquerque, Adriano, Maria Beatriz Lima, Renata Neves, and Danilo Ricardo Araújo. "Algoritmos de Aprendizado de Máquina Aplicados na Avaliação do Volume de Chuvas na Cidade do Recife." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2024. http://dx.doi.org/10.21528/cbic2023-090.

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A crise climática tem cada vez mais relevância devido aos seus eventos extremos e constantes, com diversos impactos sociais e economicos. A previsibilidade destes eventos, por sua vez, e cada vez mais desafiante. Os avanços em Inteligência Artificial ( IA) possibilitam a extração e classificação de informaçoes capazes de serem usadas na modelagem de dados meteorológicos, e com isto, auxiliam na mitigação dos impactos destes eventos extremos. Este artigo apresenta um comparativo entre modelos de aprendizado de máquina para a classificação do volume de chuvas na cidade do Recife, por meio da aná
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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 &amp; 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
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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 neg
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