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Journal articles on the topic 'XG boost'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kumavat,, Aditya. "Phishing URL and Website Detection using MI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41473.

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Phishing attacks remain a significant threat in the digital landscape, with cybercriminals constantly developing sophisticated techniques to deceive users into revealing sensitive information. This study presents a robust framework for phishing URLs and website detection utilizing the XG-Boost (Extreme Gradient Boosting) algorithm, known for its superior performance and efficiency in classification tasks. The proposed system focuses on analyzing various features extracted from URLs and webpage content, including lexical, structural, and host-based attributes, to distinguish between legitimate

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Nisha Gurung, MD Rokibul Hasan, Md Sumon Gazi, and Md Zahidul Islam. "Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market." Journal of Business and Management Studies 6, no. 2 (2024): 132–43. http://dx.doi.org/10.32996/jbms.2024.6.2.13.

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In the recent past, algorithmic trading has become exponentially predominant in the American stock market. The principal objective of this research was to explore the employment of machine learning frameworks in formulating algorithmic trading strategies tailored for the US stock market. For this investigation, an array of software tools was employed, comprising the Pandas library for data manipulation and analysis, the Python programming language, the Scikit-learn library for machine learning algorithms and analysis metrics, and the LIME library for explainable AI. In this study, the research

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Zheng, Ziwei, Yuanyu Chen, Yongzhong Yang, et al. "A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers." International Journal of Environmental Research and Public Health 19, no. 15 (2022): 9165. http://dx.doi.org/10.3390/ijerph19159165.

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The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boo

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Zheng, Ziwei, Zhikang Si, Xuelin Wang, et al. "Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset." International Journal of Environmental Research and Public Health 20, no. 4 (2023): 3411. http://dx.doi.org/10.3390/ijerph20043411.

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OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discriminat

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Vemulapalli, Saritha, M. Sushma Sri, P. Varshitha, P. Pranay Kumar, and T. Vinay. "An experimental analysis of machine learning techniques for crop recommendation." Nigerian Journal of Technology 43, no. 2 (2024): 301–8. http://dx.doi.org/10.4314/njt.v43i2.13.

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Taking a country into consideration where agriculture remains the primary occupation and farming still happens using conventional methods, the farmers are not able to produce anticipated yields. Modern farming strategies called precision farming play a vital role in improving crop yield and generating more profit for the farmers. This includes recommendations of crops that are suitable for specific fields based on soil conditions, temperature, rainfall, and humidity. To solve this problem, crop recommendation systems play an important role. In this research work, a crop recommendation system (

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I., Gede Pajar Bahari. "Hyperparameter Optimization in XG Boost for Insurance Claim Prediction." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1510–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201630.

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Pavithraa, G. "Analysis and Comparison of Prediction of Heart Disease Using Novel Genetic Algorithm and XGBoost Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 778–82. http://dx.doi.org/10.18137/cardiometry.2022.25.778782.

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Aim: Prediction of coronary sickness utilizing novel genetic algorithm and contrasting its accuracy with XG boost algorithm. Materials and methods: Two models are proposed for foreseeing the accuracy (%) of coronary infection. To be unequivocal, a novel genetic algorithm and XG boost algorithm. Here we take 20 samples each for evaluation and analysis. Result: The novel genetic algorithm gives better accuracy (88.35%) than the XG boost accuracy (81.88%). Along these lines the genuine meaning of novel genetic algorithms is superior to XGBoost calculation with significance value of 0.115 Conclusi

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Shen, Siyin. "A Review of Risks Associated with Machine Learning in Application to Quantitative Investment." Academic Journal of Science and Technology 3, no. 3 (2022): 35–38. http://dx.doi.org/10.54097/ajst.v3i3.2538.

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Based on inspirations and ideas from relevant literatures, this paper evaluated the risks associated with using random forest, XG Boost and logistic regression for quantitative investment from the perspective of its accuracy, adaptability, efficiency, simplicity and interpretability. Overall, the random forest and the XG Boost contains better accuracy and have higher adaptability than the logistic regression as they are susceptible to different data types. The XG Boost have the fastest processing speed which gives it higher efficiency over the other two, however it is also the most difficult t

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Kumar, Hemant, Soumyabrata Chakravarty, Nitesh Kuamr, and Nikhil Kumar. "Prediction of laser welding qualities of Al alloys using regression and machine learning techniques." Materials Research Express 12, no. 6 (2025): 066501. https://doi.org/10.1088/2053-1591/addd68.

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Abstract This work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. The key weld quality parameters are ultimate load, weld width and penetration depth. Each model was trained and validated based on data experimentally collected by varying laser power, scanning speed and offset distance to compare them. Quantitative results are shown to prove that XG-Boost produces a better predictive accuracy, as it gives a root mean square error (RMSE) of 0.05 for ultimate load, 0.03 for

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P. K., Rajani, Kalyani Patil, Bhagyashree Marathe, Prerna Mhaisane, and Atharva Tundalwar. "Heart Disease Prediction using Different Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 354–59. http://dx.doi.org/10.17762/ijritcc.v11i9s.7430.

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Identifying a person's potential for developing heart disease is one of the most challenging tasks medical professionals faces today. With nearly one death from heart disease every minute, it is the leading cause of death in the modern era [4]. The database is taken from Kaggle. Various machine learning algorithms are used for prediction of heart disease detection here are Random Forest, XG-Boost, K- Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM). All these algorithms are implemented using Python programming with Google collab. The performance evaluation parameters

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naik, Dr Anima. "Convolutional Neural Network using Social Group Optimization for Electrocardiogram." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32261.

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Electrocardiogram (ECG) analysis plays a pivotal role in diagnosing various cardiac conditions, making it essential to develop accurate and robust classification models. This project proposes a novel approach to optimize ECG analysis by considering social group factors, leveraging the combined power of XG Boost classifier and Convolutional Neural Networks (CNNs). The methodology involves collecting a diverse dataset of ECG recordings, spanning various demographic groups. Preprocessing techniques are applied to standardize and clean the data, followed by feature extraction using CNNs to capture

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Mhaske, Disha, Riya Satam, Snehal Londhe, Tanushree Kohad, and Sonali Kadam. "AN EFFICIENT ELECTRICITY THEFT DETECTION USING XG BOOST." International Journal of Engineering Applied Sciences and Technology 6, no. 10 (2022): 282–87. http://dx.doi.org/10.33564/ijeast.2022.v06i10.037.

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Electricity theft is a significant occurrence that is happening about all nations from one side of the planet to the other. This adversely affects the nation's economy. Theft identification in the power sector is a difficult task for power distribution organizations overall, since this power theft prompts to monetary losses just as loss of electric energy. There are numerous ways by which power is stolen like controlling energy meters or tapping cables at the consumer's end, and so on. Since this robbery is going on in enormous amount, manual examination of such burglary is a hectic task. So,

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Arifur Rahman, Pravakar Debnath, Adib Ahmed, et al. "Machine learning and network analysis for financial crime detection: Mapping and identifying illicit transaction patterns in global black money transactions." Gulf Journal of Advance Business Research 2, no. 6 (2024): 250–72. https://doi.org/10.51594/gjabr.v2i6.49.

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The detection and prevention of malicious financial activities should be paramount for organizations in the US. Global economic integration, online banking, and increasing cases of cryptocurrency transactions have just increased the complexity of tracing illegal transactions. This research project examines the combined application and deployment of machine learning and network analysis in detecting black money transactions in the USA and globally. Machine learning and network analysis have emerged as a powerful mechanism in the fight against financial crime. Machine learning techniques, whereb

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Akbar, Teuku Alif Rafi, and Catur Apriono. "Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate." Green Intelligent Systems and Applications 3, no. 1 (2023): 22–34. http://dx.doi.org/10.53623/gisa.v3i1.249.

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Customer churn frequently occurs in the telecommunications industry, which provides services and can be detrimental to companies. A predictive model can be useful in determining and analyzing the causes of churn actions taken by customers. This paper aims to analyze and implement machine learning models to predict churn actions using Kaggle data on customer churn. The models considered for this research include the XG Boost Classifier algorithm, Bernoulli Naïve Bayes, and Decision Tree algorithms. The research covers the steps of data preparation, cleaning, and transformation, exploratory data

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Pavithra, J., and S. Selvakumara Samy. "An Adaptive Feature Centric XG Boost Ensemble Classifier Model for Improved Malware Detection and Classification." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (2022): 208–17. http://dx.doi.org/10.17762/ijritcc.v10i2s.5930.

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Machine learning (ML) is often used to solve the problem of malware detection and classification and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To manage the issue, an efficient Adaptive Feature Centric XG Boost Ensemble Learner Classifier “AFC-XG Boost” novel algorithm is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the process of XG Boost classifier

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A, Mrs NANDHINI. "Online Payment Fraud Detection Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42092.

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Online payment fraud has become a critical challenge in the era of digital transactions, affecting businesses and customers globally. Traditional rule-based fraud detection systems often fail to adapt to the evolving nature of fraudulent activities. This study explores the application of machine learning techniques to detect online payment fraud effectively. This study investigates the application of machine learning algorithms, including Random Forest, k-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost), to effectively detect online payment fraud.

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Shukla, Pushkal Kumar, Sarika Jain, and Siddharth Kalra. "Leveraging Machine Learning for Early Detection of Asthmatic Children in Healthcare." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 09 (2025): 110–24. https://doi.org/10.3991/ijoe.v21i09.55037.

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Breathing problems are common and often transient in early childhood, making it challenging to predict which children will develop persistent asthma. Early and accurate diagnosis is important to ensure appropriate medical treatment. Current prediction models, based on small and specific sample groups, demonstrate limited precision. Machine learning (ML) techniques, however, show promise for providing more accurate and generalizable predictions compared to traditional models. Method: In this study, we developed ML-based prediction models for childhood asthma using a health dataset. Dimensionali

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Poornima, T. "Fraud Detection in Credit Card Data Using Supervised Machine Learning Based Scheme." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 512–19. https://doi.org/10.22214/ijraset.2025.68242.

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bstract: In fraud detection, Decision Trees, Random Forest, and XG Boost are utilized for their effectiveness in classifying transactions. Decision Trees create a model that splits data based on featurevalues, forminganintuitivetreestructure thatleadstofinalclassifications.RandomForest improves uponthis byusing multiple DecisionTrees withrandomdata subsets, aggregating their predictions to enhance accuracy and reduce overfitting. XG Boost employs a gradient boosting approach, building trees sequentially and optimizing performance through techniques like regularizationandparallelprocessing. Tog

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Deshmukh, Varun, Sunil Pathak, and Pronaya Bhattacharya. "Bl-Boost: A blockchain-based XG-Boost EHR scheme in Healthcare 5.0 ecosystems." International Journal of Computing and Digital Systems 17, no. 1 (2025): 1–15. https://doi.org/10.12785/ijcds/1571033989.

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Karthicsonia, B., and M. Vanitha. "Multilayer grid XG Boost architecture based automatic osteosarcoma classification." Biomedical Signal Processing and Control 90 (April 2024): 105782. http://dx.doi.org/10.1016/j.bspc.2023.105782.

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Karale, Dr Ankita, Ayushi Narlawar, Bhushan Bhujba, and Sakshi Bharit. "Student Performance Prediction using AI and ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1644–50. http://dx.doi.org/10.22214/ijraset.2022.44032.

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Abstract: As we all know that due to this pandemic situation many problems had been faced in the education stream. As we saw that the most of the students got good marks and on the other hand most students got lesser or average marks. In Critical case the good students got average marks or lesser then there expectations. As a result, all students got the admission but if the person is not that capable but taking admission to that college due to good result .So it seems unfair so to resolve this loophole, we decided to build a software for student performance prediction. It is an important desi

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Nair, Akira A., Mihir Velagapudi, Lakshmana Behara, et al. "Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients." Methods of Information in Medicine 58, no. 02/03 (2019): 079–85. http://dx.doi.org/10.1055/s-0039-1693731.

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Abstract Background Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose management plan. Objective To develop predictive models to estimate peak glucose levels in surgical patients and to implement the best performing model as a point-of-care clinical tool to assist the surgical team to optimally manage glucose levels. Methods Using a large perioperative dataset (6,579 patients) of patient- and surgery-specific parame

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Chandrahas, N. Sri, Bhanwar Singh Choudhary, M. Vishnu Teja, M. S. Venkataramayya, and N. S. R. Krishna Prasad. "XG Boost Algorithm to Simultaneous Prediction of Rock Fragmentation and Induced Ground Vibration Using Unique Blast Data." Applied Sciences 12, no. 10 (2022): 5269. http://dx.doi.org/10.3390/app12105269.

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The two most frequently heard terms in the mining industry are safety and production. These two terms put a lot of pressure on blasting engineers and crew to give more while consuming less. The key to achieving the optimum blasting results is sophisticated bench analysis, which must be combined with design blast parameters for good fragmentation and safe ground vibration. Thus, a unique solution for forecasting both optimum fragmentation and reduced ground vibration using rock mass joint angle and blast design parameters will aid the blasting operations in terms of cost savings. To arrive at a

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Singh, Pragati, Ashok Kumar Yadav, and Sanjeev Gangwar. "Forecasting Liver Disorders with Machine Learning Models." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 237–43. http://dx.doi.org/10.17762/ijritcc.v11i9.8339.

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Liver disorders encompass a spectrum of ailments that impact the liver, a crucial organ responsible for a variety of vital bodily functions. These functions encompass metabolic processes, detoxification, protein synthesis, and the production of bile. Liver maladies can arise from various sources, such as viral infections (e.g., hepatitis), excessive alcohol consumption, conditions related to obesity (like non-alcoholic fatty liver disease), autoimmune conditions, genetic predisposition, or exposure to toxins. Common signs and symptoms may encompass fatigue, jaundice, abdominal discomfort, and

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Anandan, Meena, Pandiyarasan Veluswamy, and Rohini Palanisamy. "Quantitative ECG based emotion state recognition using Detrended Fluctuation Analysis." Current Directions in Biomedical Engineering 9, no. 1 (2023): 702–5. http://dx.doi.org/10.1515/cdbme-2023-1176.

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Abstract Wearable emotion recogniton system is essential in identifying mental health disorders by early detection and continuous monitoring of human emotions to provide proper treatment care. Electrocardiogram (ECG) signals can be used for emotion recognition for its noninvasiveness and easy usability. In this study, Detrended Fluctuation Analysis (DFA) and Extreme Gradient Boost (XG Boost) classifier is used to classify the scary and boring emotion from the ECG signals. For this, ECG signal corresponding to these emotions are obtained from public database. The preprocessing is performed by a

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Solanki, Shital. "COMPARATIVE ASSESSMENT OF MACHINE LEARNING MODELS IN RICE CROP STAGE CLASSIFICATION." International Journal of Advanced Research 12, no. 01 (2024): 632–37. http://dx.doi.org/10.21474/ijar01/18164.

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The effective classification of rice crops plays a crucial role in optimizing agricultural management and enhancing yield forecasts. In this paper, we explored the efficacy of various machine learning (ML) techniques in advancing the classification of rice crops. Four machine learning classification algorithms, namely k-nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forests (RF), Decision Trees (DT), and XG Boost, are assessed using a dataset comprising rice crop images and environmental parameters. The studys findings reveal that XG Boost significantly outperforms other models,

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Gao, Adian. "Prediction of Heart Failure Using Random Forest and XG Boost." International Journal of High School Research 6, no. 5 (2024): 1–4. https://doi.org/10.36838/v6i5.1.

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Amit, Talele, Pawale Sarthak, Sonawane Adarsh, Salunkhe Yogesh, Pratiksha Gadakh Mrs., and Mahesh Bhandakkar Mr. "CUSTOMER CHURN PREDICTION MODEL USING MACHINE LEARNING." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 197–206. https://doi.org/10.5281/zenodo.15172087.

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ABSTRACTCustomer attrition poses a major issue for the telecommunications sector, resulting in revenue declineand higher expenses for gaining new customers. Predicting churn accurately enables telecomcompanies to take instant measures for retention of customers. This study leverages specificallyRandom Forest, Decision Tree, and XG-Boost, to develop a robust and accurate churn predictionmodel. These algorithms analyse customer behavioural data, including call duration, internet usage,billing history, and complaints, to identify potential churners. Decision Tree provides an interpretablemodel, R

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Kullarkar, Shubham D., and N. R. Thote. "TBM Performance Prediction Using Statistical and Machine Learning Algorithms: Case Study of Slurry TBM Used in Mumbai Metro Rail Tunnel Line–3 (UGC-03), India." Journal Of The Geological Society Of India 101, no. 2 (2025): 149–62. https://doi.org/10.17491/jgsi/2025/174077.

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ABSTRACT Estimating the performance of a tunnel boring machine (TBM) is vital for enhancing mining productivity and reducing tunnelling operations-related risks. Penetration rate (PR) is the most prominent TBM performance indicator, which majorly influences the project completion time, contractual activities, and overall cost of the project. Prediction of PR considering varying geotechnical parameters needs to be addressed. This study comprises 235 data points collected from a 3.572 km long twin-tunnel bored using two refurbished articulated single-shield slurry TBM, employed in a metro projec

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Nainar, M. Asan. "Predictive Modeling for Brain Stroke Detection Using Machine Learning M. Asan Nainar." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35392.

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Brain strokes often resulting in severe health complications and mortality are a significant global health concern. Early detection and brain stroke prediction involves assessing risk factors, medical history, diagnostic tests, and predictive models. Aims to identify individuals at risk before stroke occurrence, enabling timely interventions and lifestyle modifications to mitigate the risk. In this research, an in-depth exploration of predictive modeling for brain stroke detection utilizing machine learning algorithms specifically XG Boost, Decision Tree, and K-Nearest Neighbors (KNN) is prese

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Md Sumon Gazi, Md Rokibul Hasan, Nisha Gurung, and Anik Mitra. "Ethical Considerations in AI-driven Dynamic Pricing in the USA: Balancing Profit Maximization with Consumer Fairness and Transparency." Journal of Economics, Finance and Accounting Studies 6, no. 2 (2024): 100–111. http://dx.doi.org/10.32996/jefas.2024.6.2.8.

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Organizations in the USA are progressively employing AI-driven dynamic pricing as a strategic intervention to flexibly modify their prices based on competition, market demand, and various other factors. This research paper focused on the ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness. The recommended models for dynamic pricing solutions entailed ensemble learning methods, notably, XG-Boost, Light-GBM, Cat-Boost, and X-NGBoost models. Particularly, the proposed model consolida

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de-Prado-Gil, Jesús, Covadonga Palencia, P. Jagadesh, and Rebeca Martínez-García. "A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete." Materials 15, no. 12 (2022): 4164. http://dx.doi.org/10.3390/ma15124164.

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Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly

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A, Prof Ajil, Tanvi Jain, T. M. Namratha, Vismaya S, and Thummaluru Ganga Lakshmi. "Detection of PCOS using Ensemble Models." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 420–25. http://dx.doi.org/10.22214/ijraset.2023.51426.

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Abstract: Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that affects women of childbearing age. It is characterized by a range of symptoms, including irregular monthly cycles, hirsutism, and childlessness. Early diagnosis and detection of PCOS is vital for successful management of this condition. In the last few years, machine learning algorithms have shown great results in the diagnosis of various medical conditions. The proposed model is an ensemble model consisting of XG Boost and Random Forest to detect PCOS in women by analysing a dataset of 541 women, including 177 pat

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BKV, Vismayaa Yadav. "Prediction of Stock Price Using XG Boost: A Machine Learning Technique." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 1562–70. http://dx.doi.org/10.22214/ijraset.2024.63695.

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Abstract: Forecasting stock prices is a very difficult task due to the sudden and volatile nature of financial markets. This paper reviews recent developments in the use of the XGBoost algorithm for stock price forecasting. XGBoost, a robust and efficient gradient enhancement implementation, has demonstrated excellent performance in a variety of predictive modeling environments. The analysis uses various experimental methods, including data generation, feature engineering, model training, and validation procedures. It also compares the performance of XGBoost with other machine learning algorit

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Prasher, Shikha, Leema Nelson, and Manal Gafar. "NIPP: Non-Invasive PCOS Prediction using XG-boost Machine Learning Model." International Journal of Information Technology and Computer Science 17, no. 1 (2025): 82–95. https://doi.org/10.5815/ijitcs.2025.01.06.

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Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder that affects women of reproductive age, leading to hormonal imbalances and ovarian dysfunction. Early detection and intervention are vital for effective management and prevention of complications. This study compares PCOS prediction using the XGBoost machine learning model against four traditional models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees (DT), and Random Forests (RF). LR and SVM achieve accuracies of 95% and 96%, respectively, demonstrating strong predictive capabilities. In contrast, DT had a

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Radhakrishna, Laishetty, V. S. Hariharan, Banothu Srinivas, et al. "Performance Evaluation of ML-Based Algorithm and Taguchi Algorithm of the Hardness Value of the Friction Stir Welded AA6262 Joints at a Nugget Joint." E3S Web of Conferences 430 (2023): 01249. http://dx.doi.org/10.1051/e3sconf/202343001249.

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Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min),

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Ren, Yiting. "Application of Machine Learning Algorithms in Detecting Credit Card Fraud: A Comparative Analysis." Highlights in Business, Economics and Management 21 (December 12, 2023): 733–39. http://dx.doi.org/10.54097/hbem.v21i.14753.

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Credit card transaction have grown increasingly prevalent in the digital era, and along with them, so have incidents of associated fraud. Hence, identification and prevention of such frauds are critically crucial. Machine learning algorithms are predominantly employed in the realm of credit fraud detection. According to current literature, class imbalance of data, a great disparity in ratio between normal and fraudulent transactions, could severely affect the result in detection. In this paper, a combination of imbalanced classification methods, specifically the Synthetic Minority Random Overs

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Ahmed, Adib, Tanaya Jakir, Md Nazmul Hossain Mir, et al. "Predicting Energy Consumption in Hospitals Using Machine Learning: A Data-Driven Approach to Energy Efficiency in the USA." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 199–219. https://doi.org/10.32996/jcsts.2025.7.1.15.

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In the USA, hospitals are confronted with significant challenges regarding energy consumption, which not only impacts operational costs but also contributes to environmental concerns. The primary objective of this research was to develop and evaluate machine learning models that are capable of accurately predicting energy consumption in U.S. hospitals. This study will be focused on United States hospital energy consumption data, recognizing the unique difficulties and opportunities present in the U.S. healthcare setting. The data used for this hospital energy consumption analysis has been care

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Yadav, Dhyan Chandra, and Saurabh Pal. "Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques." International Journal of Big Data and Analytics in Healthcare 6, no. 1 (2021): 40–56. http://dx.doi.org/10.4018/ijbdah.20210101.oa4.

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This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algori

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Alharbi, Amal H., Aravinda C. V, Meng Lin, B. Ashwini, Mohamed Yaseen Jabarulla, and Mohd Asif Shah. "Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models." Computational Intelligence and Neuroscience 2022 (May 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/3922763.

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Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether

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Destiny, Agboro. "English Premier League Football Predictions." International Journal of Research and Innovation in Applied Science IX, no. XII (2025): 247–53. https://doi.org/10.51584/ijrias.2024.912024.

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This research project utilized advanced computer algorithms to predict the outcomes of Premier League soccer matches. The dataset containing match data and odds from seasons was processed to handle missing information, select features anzd reduce complexity using Principal Component Analysis. To address imbalances, in the target variable Synthetic Minority Over sampling Technique (SMOTE) was employed. Various machine learning models such as Random Forest, Decision Tree, SVM, XG Boost and Light GBM were evaluated. Model performance was fine tuned using Grid Search CV by adjusting hyperparameter

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MD Azam Khan, Pravakar Debnath, Abdullah Al Sayeed, et al. "Explainable AI and Machine Learning Model for California House Price Predictions: Intelligent Model for Homebuyers and Policymakers." Journal of Business and Management Studies 6, no. 5 (2024): 73–84. http://dx.doi.org/10.32996/jbms.2024.6.5.9.

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California's housing and real estate market is one of the most valuable markets in the USA. Many shareholders such as individual homebuyers, home sellers, real estate agents, lenders, and policymakers depend on high-volume information regarding the dynamics at work and their correct estimation. The research project aimed at developing an Explainable AI machine-learning model for California house price predictions. Data on house prices were collected from reliable sources such as California home estate websites, land sites, and public datasets. Features of the data included location, size, numb

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Ayub, Muhammad Yaseen, Usman Haider, Ali Haider, Muhammad Tehmasib Ali Tashfeen, Hina Shoukat, and Abdul Basit. "An Intelligent Machine Learning based Intrusion Detection System (IDS) for Smart cities networks." EAI Endorsed Transactions on Smart Cities 7, no. 1 (2023): e4. http://dx.doi.org/10.4108/eetsc.v7i1.2825.

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INTRODUCTION: Internet of Things (IoT) along with Cloud based systems are opening a new domain of development. They have several applications from smart homes, Smart farming, Smart cities, smart grid etc. Due to IoT sensors operating in such close proximity to humans and critical infrastructure, there arises privacy and security issues. Securing an IoT network is very essential and is a hot research topic. Different types of Intrusion Detection Systems (IDS) have been developed to detect and prevent an unauthorized intrusion into the network.OBJECTIVES: The paper presents a Machine Learning ba

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Sarkar, Himangshu, Chandra Shekhar Prasad Ojha, and Sanjay Kumar Ghosh. "AI-Driven Spatial Data Analysis of Groundwater Level and Gravimetric Data in Roorkee Region, India." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 12, 2025): 763–70. https://doi.org/10.5194/isprs-annals-x-g-2025-763-2025.

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Abstract. Excessive consumption of groundwater can lead to a significant imbalance between groundwater recharge rates and water demand. This disparity underscores the importance of accurately estimating future groundwater storage to ensure global water and food security, in line with sustainable development goals (SDGs) related to clean water and sanitation and sustainable cities and communities. However, traditional methods face challenges in predicting groundwater storage due to their inherent complexity. To address this gap and align with SDGs, this study aims to develop a regression-based

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Wibowo, Jati Sasongko, Budi Hartono, and Veronica Lusiana. "Online Payment Fraud Detection Optimization with XG Boost and Recursive Feature Elimination." Journal of Software Engineering and Simulation 10, no. 8 (2024): 35–42. http://dx.doi.org/10.35629/3795-10083542.

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Online payment fraud is an increasingly pressing issue as the volume of digital transactions grows. Accurate and fast detection is essential to minimize financial losses. This paper presents an approach to optimize fraud detection using the XGBoost algorithm and the Recursive Feature Elimination (RFE) feature selection technique. In this research, we use an online payment fraud dataset to train a model that can distinguish between legitimate and fraudulent transactions. The main contribution of this research is to demonstrate the effectiveness of the combination of XGBoost and RFE in improving

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S.P. Valli, Sharmila Sankar, C. Hema, and Mohammad Munzir. "Enhancement of XG-Boost Using Custom Hyper Parameter Tuning for Bank Churning." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 07 (2024): 1909–14. http://dx.doi.org/10.47392/irjaeh.2024.0261.

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Bank is an important component of our society which deals with money transaction i.e., lending and deposit of money. Customer churn is termination of business of a customer with the company. Bank customer churn creates an impact on revenue and operational efficiencies of banks, where a customer switches or leaves availing the services of bank. Bank is an important part of our society since it makes money by lending money to others. To understand customer churning behavior it is necessary to retain customers and increase the number of customers. In order to predict the bank customer churning be

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Khan, Azaz Hassan, Abdullah Shah, Abbas Ali, et al. "A performance comparison of machine learning models for stock market prediction with novel investment strategy." PLOS ONE 18, no. 9 (2023): e0286362. http://dx.doi.org/10.1371/journal.pone.0286362.

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Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured

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Bijalwan, Priya, Ashulekha Gupta, Anubhav Mendiratta, Amar Johri, and Mohammad Asif. "Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms." Economies 12, no. 1 (2024): 16. http://dx.doi.org/10.3390/economies12010016.

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One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important fac

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Sihare, Muskan. "Evaluation of Machine Learning Methods for Prediction Student Performance." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 534–44. http://dx.doi.org/10.22214/ijraset.2024.58001.

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Abstract: Significant findings and useful insights have emerged from the study of using machine learning techniques to predict student performance. used large datasets with a wide variety of demographic, socioeconomic, and academic performance data to conduct in-depth evaluations of several machine learning methods. Our study highlighted the importance of careful data preprocessing, which involves fundamental steps like classifying student performance and doing in-depth exploratory data analysis (EDA). To ensure the validity of our model assessments, we meticulously split the dataset into trai

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Nutipalli, Preeti. "Model Construction Using ML for Prediction of Student Placement." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2213–19. http://dx.doi.org/10.22214/ijraset.2022.44273.

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Abstract: “Model construction using ML for prediction of student placement” aims to predict the placement of a student using various performance metrics on the Machine Learning algorithms. Early prediction makes the institutional growth as well as the student to get placed. It helps the student to prepare all the company requirements at early stage and monitors the student performance. Existed work was done on the algorithms like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes. In the proposed work to predict the student placement considered dataset and applied data preproc

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