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1
Yi, Siming. "Walmart Sales Prediction Based on Machine Learning." Highlights in Science, Engineering and Technology 47 (May 11, 2023): 87–94. http://dx.doi.org/10.54097/hset.v47i.8170.
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Accurate sales forecasting can improve a company's profitability while minimizing expenditures. The use of machine learning algorithms to predict product sales has become a hot topic for researchers and companies over the past few years. This report features the machine learning sales prediction model that combines the ML algorithm and meticulous feature engineering processing to predict Walmart sales. The following regressions are analyzed in this paper: linear regression, random forest regression, and XGBoost regression. The regression analysis has been tested for the same time period every year for three years from 2010 to 2012 on a continuous time basis. The experiments show that XGBoost algorithm overperforms the other machine learning methods by examining the same evaluation metric (WAME). The findings can contribute to a better understanding of the development of new decision support for the retail industry e.g., Walmart retail stores. Moreover, this paper also represents a detailed procedure to rank the feature importance for the dataset. Within the next few years, the ML algorithm is destined to become an important approach for business forecasting. However, this strategy largely ignores the time series method for accuracy.
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Zuo, Xiaonan. "Prediction of Facebook and GOOG Prices based on Linear Regression and LSTM Regression." BCP Business & Management 44 (April 27, 2023): 688–95. http://dx.doi.org/10.54691/bcpbm.v44i.4919.
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Stock market analysis is a very difficult task, and stock markets are very complex and constantly changing environments. More and more stock investors are now becoming aware of the prominence of machine learning in the field of stocks and finance, and over the last decade or so machine learning has driven advances in the stock market, such as the ability to use different machine learning methods to predict stock movements in order to arrive at the best decisions and algorithmic trades. The problem that this project wants to investigate is the use of machine learning methods for stock prediction. Two stocks, Facebook and GOOG, were chosen as the datasets for the study. The datasets consisted of stock information from the last decade or so and two machine learning methods, namely long and short term memory and linear regression, were used to make predictions. The results obtained from these two models were analyzing and different results were obtained. The results present the conclusion that the linear regression model is more suitable than the LSTM model for predicting these two groups of stocks. Some error analysis was also carried out and some improvements were given for the two different models.
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Li, Guoqiang, and Peifeng Niu. "An enhanced extreme learning machine based on ridge regression for regression." Neural Computing and Applications 22, no. 3-4 (2011): 803–10. http://dx.doi.org/10.1007/s00521-011-0771-7.
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Bodunde.O., Akinyemi, Aladesanmi Temitope.A., Oyebade Adedoyin.I., Aderounmu Ganiyu.A., and Kamagaté Beman.H. "Evaluation of a Bayesian Machine Learning –Based and Regression Analysis -Based Performance Prediction Model for Computer Networks." International Journal of Future Computer and Communication 8, no. 4 (2019): 134–41. http://dx.doi.org/10.18178/ijfcc.2019.8.4.555.
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Surendar, S., and M. Elangovan. "Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions During Boring Operation." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (2017): 887. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp887-892.
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Modern manufacturing methods permit the study and prediction of surface roughness since the acquisition of signals and its processing is made instantaneously. With the availability of better computing facilities and newer algorithms in the machine learning domain, online surface roughness prediction will lead to the manufacture of intelligent machines that alert the operator when the process crosses the specified range of roughness. Prediction of surface roughness by multiple linear regression, regression tree and M5P tree methods using multivariable predictors and a single response dependent variable Ra (surface roughness) is attempted. Vibration signal from the boring operation has been acquired for the study that predicts the surface roughness on the inner face of the workpiece. A machine learning approach was used to extract the statistical features and analyzed by four different cases to achieve higher predictability, higher accuracy, low computing effort and reduction of the root mean square error. One case among them was carried out upon feature reduction using Principle Component Analysis (PCA) to examine the effect of feature reduction.
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S., Surendar, and Elangovan M. "Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions during Boring Operation." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 887–92. https://doi.org/10.11591/ijeecs.v7.i3.pp887-892.
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Modern manufacturing methods permit the study and prediction of surface roughness since the acquisition of signals and its processing is made instantaneously. With the availability of better computing facilities and newer algorithms in the machine learning domain, online surface roughness prediction will lead to the manufacture of intelligent machines that alert the operator when the process crosses the specified range of roughness. Prediction of surface roughness by multiple linear regression, regression tree and M5P tree methods using multivariable predictors and a single response dependent variable Ra (surface roughness) is attempted. Vibration signal from the boring operation has been acquired for the study that predicts the surface roughness on the inner face of the workpiece. A machine learning approach was used to extract the statistical features and analyzed by four different cases to achieve higher predictability, higher accuracy, low computing effort and reduction of the root mean square error. One case among them was carried out upon feature reduction using Principle Component Analysis (PCA) to examine the effect of feature reduction.
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Hafsa, Fathima, Juveria Soha, Fathima Rida, and Hifsa Naaz Syeda. "An Analysis of Car Price Prediction Using Machine Learning." Research and Reviews: Advancement in Cyber Security 2, no. 2 (2025): 33–40. https://doi.org/10.5281/zenodo.15308198.
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<em>Car price prediction is a critical task in the automotive industry, enabling buyers, sellers, and financial institutions to make informed and objective decisions. This research focuses on applying machine learning techniques, specifically Linear Regression and Lasso Regression to predict used car prices based on multiple factors including fuel type, transmission, seller type, vehicle age, and kilometers driven. The dataset was carefully preprocessed to handle missing values and encode categorical variables, ensuring the data was suitable for model training. Both models were evaluated using R² scores, with Linear Regression achieving high accuracy and Lasso Regression providing a more simplified model by reducing overfitting. The findings demonstrate that even basic regression models can deliver reliable predictions, highlighting the potential of machine learning to improve transparency and efficiency in car pricing within real-world applications.</em>
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Avhishek, Biswas, Talukder Ananya, Bhattacharjee Deep, Chowdhury Arijit, and Sanyal Judhajit. "Machine Learning Based Prediction of Suicide Probability." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 1 (2020): 94–97. https://doi.org/10.35940/ijeat.A1701.1010120.
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Many factors have led to the increase of suicide-proneness in the present era. As a consequence, many novel methods have been proposed in recent times for prediction of the probability of suicides, using different metrics. The current work reviews a number of models and techniques proposed recently, and offers a novel Bayesian machine learning (ML) model for prediction of suicides, involving classification of the data into separate categories. The proposed model is contrasted against similar computationally-inexpensive techniques such as spline regression. The model is found to generate appreciably accurate results for the dataset considered in this work. The application of Bayesian estimation allows the prediction of causation to a greater degree than the standard spline regression models, which is reflected by the comparatively low root mean square error (RMSE) for all estimates obtained by the proposed model.
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Iman, Youssif Ibrahim, and Sedqi Kareem Omar. "Email Spam Classification Based on Logistics Regression." Engineering and Technology Journal 10, no. 05 (2025): 4847–54. https://doi.org/10.5281/zenodo.15378605.
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Email is a common communication tool used by both individuals and organizations. It involves a variety of interactions, including file sharing. In addition to the advantages it provides, there is the uninvited email sharing. This unsolicited email is referred to as "spam." Malicious content like viruses, phishing scams, and unsolicited ads can be found in spam. It is well known that communication security is crucial. In order to filter email systems for malicious tools or software, it is essential to classify them based on a variety of criteria. In these kinds of classification studies, machine learning algorithms work well. The objective of this research is to solve the problem at hand and compare the logistic regression, random forest, naive Bayes decision tree, and support vector machine (SVM) algorithms. The effects of various methods and approaches on the issue were thoroughly examined. A comparison of the various performance outcomes using the various approaches is provided. With an accuracy of 98%, logistic regression was the most accurate, followed by random forest with 97%.
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Vishal, Raja S. V., Kiran Reddy S, B. H. Tippesh, R. Udhand Rahul, Deepak NR Dr., and B. Omprakash. "Machine Learning -Based Stroke Prediction." Journal of Advancement in Parallel Computing 8, no. 2 (2025): 35–42. https://doi.org/10.5281/zenodo.15314873.
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<em>This study emphasizes the importance of early stroke detection and prevention, highlighting challenges like missing and unbalanced data. It evaluates various machine learning models, including Random Forest and Logistic Regression, using k-fold cross-validation on balanced and imbalanced datasets. Key predictors identified are age, BMI, average blood sugar, and marital status. The Random Forest model achieved the highest accuracy (95.5%), demonstrating the potential of machine learning to enhance stroke prediction and improve patient outcomes.</em>
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Zhang, Huanhuan, Jigeng Li, and Mengna Hong. "Machine Learning-Based Energy System Model for Tissue Paper Machines." Processes 9, no. 4 (2021): 655. http://dx.doi.org/10.3390/pr9040655.
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With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.
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Han, Yingjie. "Machine Learning Based Portfolio Analysis." Highlights in Business, Economics and Management 44 (November 25, 2024): 135–47. http://dx.doi.org/10.54097/wc6epk11.
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This paper investigates the effectiveness of machine learning methods in predicting stock returns in China. In this paper, we use 16 liquidity, momentum, market risk and macro fundamentals indicators, as well as 112 interaction terms between indicators and macro variables with monthly stock excess returns for the lagged period from January 2000 to November 2015 as the sample set, train elasticity networks, gradient boosting, random forests, neural networks, and simple linear regression models, and based on the data of December 2015, predict the January 2016 returns, form portfolios based on the predictions and hold them for 12 months, and thereafter roll over the extended sample period to form new portfolios, and evaluate the performance of the portfolios based on out-of-sample alpha versus the estimated shap value to assess the significance of the metrics. The results show that the machine learning approach significantly outperforms the simple linear regression model and performs better in terms of weighted portfolio returns. The portfolio constructed based on the neural network model (NN1) achieves the lowest mean square error and the highest model fit, and NN3 achieves the highest out-of-sample alpha, suggesting that the model is able to achieve higher risk-adjusted returns when risk factors are taken into account. Further analysis shows that the NN3 model is able to effectively identify high-yielding stocks and control portfolio risk, making it a preferred strategy for investment portfolios in the Chinese equity market. This paper still supports Leippold's (2022) view that liquidity indicators are most important after excluding fundamental indicators.
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Yuhong Wu, Yuhong Wu, and Xiangdong Hu Yuhong Wu. "AMS Intrusion Detection Method Based on Improved Generalized Regression Neural Network." 網際網路技術學刊 24, no. 2 (2023): 549–63. http://dx.doi.org/10.53106/160792642023032402029.
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<p>The smart grid integrates the computer network with the traditional power system and realizes the intelligentization of the power grid. The Advanced Measurement System (AMS) interconnects the power system with the user, realizes the two-way interaction of data and information between the power supplier and the user, and promotes the development of the smart grid. Therefore, the safe operation of AMS is the key to the development of the smart grid. As smart grids and computer networks become more and more closely connected, the number of cyberattacks on AMS continues to increase. Currently, AMS intrusion detection algorithms based on machine learning are constantly being proposed. Machine learning algorithms have better learning and classification capabilities for small sample data, but when faced with a large amount of high-dimensional data information, the learning ability of machine learning algorithms is reduced, and the generalization ability is reduced. To enhance the AMS intrusion detection algorithm, this paper uses a Generalized Regression Neural Network (GRNN) to identify attack behaviors. GRNN has strong non-linear mapping ability, is suitable for unstable data processing with small data characteristics, has good classification and prediction ability, and has been widely used in power grid systems. Aiming at the existing problems, this paper proposes an upgraded generalized regression neural network AMS intrusion detection method DBN-DOA-GRNN. Based on the feature extraction and dimensionality reduction of the data by DBN, GRNN is used for data with less feature information in learning classification. In addition, to improve the detection effect of the method, the Drosophila Optimization Algorithm (DOA) is used to optimize the parameters of GRNN to reduce the influence of random parameters on the detection results, improve the detection accuracy of this method on small-scale sample data, and thereby improve the detection performance of the AMS intrusion detection algorithm. The proposed method archives an accuracy of 87.61%, 3.10% false alarm rate, and 96.9 precision rate.</p> <p>&nbsp;</p>
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Sapkal, Kunal. "Machine Learning based Predicting House Prices using Regression Technique." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30682.
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Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Key Words: house price, lasso regression, ridge regression, R-squared.
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He, Qing, Tianfeng Shang, Fuzhen Zhuang, and Zhongzhi Shi. "Parallel extreme learning machine for regression based on MapReduce." Neurocomputing 102 (February 2013): 52–58. http://dx.doi.org/10.1016/j.neucom.2012.01.040.
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Yıldırım, Hasan, and M. Revan Özkale. "An Enhanced Extreme Learning Machine Based on Liu Regression." Neural Processing Letters 52, no. 1 (2020): 421–42. http://dx.doi.org/10.1007/s11063-020-10263-2.
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Liu, Ce, Chunyuan Zhao, Yubo Wang, and Haowei Wang. "Machine-Learning-Based Calibration of Temperature Sensors." Sensors 23, no. 17 (2023): 7347. http://dx.doi.org/10.3390/s23177347.
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Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.
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E, Çakıt. "Machine Learning-Based Sentiment Analysis of Tweets about COVID-19 Vaccines." Virology & Immunology Journal 7, no. 4 (2023): 1–14. http://dx.doi.org/10.23880/vij-16000337.
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The objectives of the study were two-fold: (1) To group mindsets related to COVID-19 vaccinations and examine their distribution by country. Then, based on this distribution, the study aimed to compare the number of vaccinations, deaths, and cases and analyze the relationship between these numbers and the mindset of the society. (2) To analyze people's tweets about the vaccine and compare them with the number of people vaccinated, in order to determine if there was a significant result. The study analyzed data from 17 countries among the top 20 countries with the highest gross national product in 2020. Machine learning methods such as multinomial logistic regression, random forest, naive Bayes, and ridge classification were used to evaluate the performance of predictive models. The accuracy achieved by these models were as follows: naive Bayes (76%), random forest (85.03%), ridge classification (85.72%), and multinomial logistic regression (86.67%). In conclusion, the study found that with increasing vaccination rates, positive interpretations of vaccines differed more than other moods. The study contributes to advancing awareness of the public's perception of COVID-19 vaccinations and supports the goal of eliminating coronavirus from the planet.
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B, Suresh. "Bigmart Sales Prediction Based on Voting Classifier Algorithm and Linear Regression." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31745.
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Machine Learning is a technology that allows machines to become more accurate in predicting outcomes without being explicitly programmed for it. The basic premise of machine learning is to build models and deploy algorithms that can receive input data and use statistical analysis to predict an output while modifying outputs as the new data becomes available. These models can be used in different areas and trained to match the expectations so that accurate steps can be taken to achieve the organization’s target. In this paper, the case of Big Mart Shopping Centre has been discussed to predict the sales of different types of items and for understanding the effects of different factors on the sales of different items. Taking various features of a dataset collected for Big Mart, and the methodology followed for building a predictive model, results with high levels of accuracy are generated, and these observations can be used to take decisions to improve sales. Key words : Machine Learning, Sales Prediction, Big Mart, Voting classifier algorithm, Linear Regression.
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Hu, Yuting. "Face Age Prediction Based on Machine Learning." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 124–28. http://dx.doi.org/10.54097/j2ezkt16.
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Age recognition, an essential attribute of human identity, plays a pivotal role in various social and medical applications. Due to the influences of photography equipment, lighting, and angles, age recognition based on facial images has always been a challenging topic. Fortunately, with the development of machine learning, especially the breakthroughs achieved by neural network models in image-related fields, researchers have applied machine learning to age recognition. From the perspective of research methodologies, age prediction can be regarded as either a regression or classification task. Therefore, this article analyzes from both regression and classification perspectives. Firstly, it introduces the commonly used datasets in the field of facial recognition. Then, it discusses the commonly used regression models, classification models, and optimization methods in age recognition tasks. Finally, the article summarizes the entire text and proposes future research ideas, providing references for researchers in related fields.
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Zhang, Wenqi, and Yixin Li. "Traffic flow prediction based on machine learning." Highlights in Science, Engineering and Technology 118 (November 23, 2024): 162–70. http://dx.doi.org/10.54097/sw3nmf68.
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The rapid development of intelligent transportation systems requires accurate prediction of short-term traffic flow. However, the nonlinearity, uncertainty, and spatiotemporal variability of traffic flow make it difficult for traditional traffic flow prediction methods to achieve ideal results. This study uses machine learning methods to improve the accuracy of prediction and expand features by evaluating the clustering effect of the k-means algorithm through data dimensionality reduction, interpolation of missing data, and the silhouette coefficient method. The 5-fold cross-validation and grid search methods were used to optimize the hyperparameters, and the Lasso regression model, ridge regression model and random forest regression model were compared. It was found that the feature expansion of the random forest had the highest fitting coefficient and the smallest error. This improves prediction accuracy, provides a valuable reference for intelligent traffic flow prediction, and has the potential for further optimization in feature selection, model design, and traffic control strategies.
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Reddy, G. Dinesh, and Dr M. Sireesh Kumar. "PCOS Detection Using Machine Learning." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 364–70. https://doi.org/10.47001/irjiet/2025.inspire59.
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Reproductive-age women worldwide suffer from metabolic problems, hormonal imbalance, and irregular menstruation due to PCOS. Complex symptoms of PCOS often lead to misdiagnosis or underdiagnosis, causing suffering and increasing the risk of obesity, diabetes, and cardiovascular disease. Treating these symptoms requires early, correct diagnosis. The project tests machine learning techniques such as Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, K-Nearest Neighbors, Decision Tree Regressor, and Support Vector Machines employing Mean Squared Error to address diagnostic issues. Since it handled noisy and non-linear data better, the Random Forest Regressor was best. Django-based internet applications and predictive algorithm help clinicians identify PCOS risk. The approach instantly assesses risk using BMI, age, blood pressure, and lifestyle factors. This simple method lets clinicians identify high-risk patients for rapid intervention and personalized treatment. Accuracy, scalability, and usability tests validated the system's clinical value. Finally, our machine learning-based solution will improve early PCOS identification, clinical resource use, and global women's health.
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Swamy, Mr K. K., Mrs SRILATHA PULI, S. SWETHA, B. SHARANYA, A. ANUHYA, and J. SHREYAS. "CRIME ANALYSIS USING MACHINE LEARNING." YMER Digital 21, no. 05 (2022): 412–16. http://dx.doi.org/10.37896/ymer21.05/44.
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Crimes are the significant threat to the humankind. as crimes are increasing at a rapid rate approach for identifying trends in crime. Our project can predict regions which have high probability for crime occurrence and can visualize crime prone areas by visual representation of data by bar graphs, pie charts etc. It speed up the classification of criminal activities by calculating the average accuracy rate .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression. The objective would be train a model for prediction using logistic regression classification algorithm. Logistic regression used for classification problems and it is a predictive analysis algorithm based on concept of probability. Crime analysis project is a systematic approach for identifying trends in crime. .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression.
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Sabrigiriraj, M., and K. Manoharan. "Teaching Machine Learning and Deep Learning Introduction: An Innovative Tutorial-Based Practical Approach." WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 21 (June 25, 2024): 54–61. http://dx.doi.org/10.37394/232010.2024.21.8.
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Machine learning and deep learning techniques have penetrated deep into the various domains of engineering, science, and technology. They are very powerful tools to solve a wide variety of complex problems in those domains. This paper presents an innovative tutorial with practical examples of teaching the introduction to machine learning and deep learning. Starting with the basic concepts, the tutorial takes the readers through the basics of linear regression, logistic regression, and deep neural networks. Then the fundamental association between linear regression, logistic regression, and deep neural network is revealed using the practical examples. This tutorial article provides a solid base for readers aspiring to learn machine learning and deep learning with a systematic and practical approach.
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Akhi, Sharmin Sultana, Sonjoy Kumar Dey, Mazharul Islam Tusher, et al. "Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach." American Journal of Engineering and Technology 07, no. 03 (2025): 88–97. https://doi.org/10.37547/tajet/volume07issue03-07.
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In this study, we propose a predictive cybersecurity framework for the banking sector by integrating ensemble-based machine learning models. Our approach leverages heterogeneous datasets—including internal firewall and intrusion detection system logs, banking transaction records, user behavior data, and external threat intelligence—to capture a comprehensive view of the cyber threat landscape. Following rigorous data preprocessing, feature selection, and feature engineering, we evaluated multiple models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Deep Neural Networks. Comparative analysis revealed that while advanced individual models demonstrated strong predictive capabilities, the Ensemble Model consistently outperformed all others, achieving an accuracy of 92% and a ROC-AUC of 94%. These results underscore the model’s superior ability to minimize false negatives, which is critical for safeguarding financial assets. Our findings advocate for the adoption of ensemble techniques in real-world banking cybersecurity applications, providing a robust, scalable solution that adapts to evolving threat patterns while significantly enhancing detection performance.
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Shantala, Devi Patil, Satheesh Sreedevi, S. M. Seema, and Mothish Chowdary R. "Linear Regression Based Demand Forecast Model in Electric Vehicles -LRDF." International Journal of Human Computations and Intelligence 2, no. 2 (2023): 82–93. https://doi.org/10.5281/zenodo.7900508.
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Machine learning is an Artificial Intelligence (AI) software application that uses algorithms to analyze data, make inferences from that data, and then use what they've learned to create well-informed conclusions. Machine learning cannot process characters or strings; in order to process them, we must transform them to numerics. Otherwise, there will be exceptions or mistakes of some kind. After pre-processing, which removes the null or empty data from the original dataset, machine learning algorithms can predict or forecast future events using the provided dataset. Machine learning is now widely used in the software industry, and as a result, many software applications now offer predictions like weather forecast, impact of covid spread, future sales, etc. So, the problem of the demand of EVs in future is investigated using novel strategies for entirely eliminating problems with forecasting models, based on machine learning approach. The proposed LRDF model for Electric Vehicle’s is based on machine learning that accepts a dataset as an input in the form of an CSV file containing electric vehicles data that are useful for predicting demand for electric vehicles. The accuracy of three distinct classification algorithms, including the SVM algorithm, the Random Forest system, and the Linear Regression algorithm, will be examined. The results demonstrate that the linear regression approach performs significantly better than the other two algorithms. The LRDF model for Electric Vehicle’s when mounted on the public cloud server- which belongs to Infrastructure as a Service (IaaS), would increases the speed of dataset processing
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Klochko, O. V., R. S. Gurevych, V. M. Nagayev, L. Yu Dudorova, and T. P. Zuziak. "Data mining of the healthcare system based on the machine learning model developed in the Microsoft azure machine learning studio." Journal of Physics: Conference Series 2288, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1742-6596/2288/1/012006.
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Abstract This article presents data mining, which is based on the methods of mathematical statistics and machine learning, describes the features of applying regression analysis methods in the machine learning systems. The developed machine learning model includes the regression analysis modules based on the Bayesian linear, artificial neural network, decision tree, decision forest, and linear regressions. In the process of applying this machine learning model, using the mentioned algorithms, the corresponding regression models were constructed and their comparative analysis was performed, the results were analyzed. The results obtained indicate the feasibility of using data mining in the medical research using machine learning systems. The presented methods can serve as a basis for strategic development of a new directions of the medical data processing and decision-making in this field. We have identified the prospects for further research aimed at applying data mining methods to the healthcare system, namely, clustering, classification, anomaly detection.
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Xu, Jiao. "Fundamental Quantitative investment research based on Machine learning." SHS Web of Conferences 170 (2023): 01019. http://dx.doi.org/10.1051/shsconf/202317001019.
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In recent years, the status of quantitative investment in China's capital market has been improving, and fundamental quantification has emerged as a promising approach that integratesfundamental analysis and quantitative investment successfully. Hence, this kind of intelligent quantitative investment method has garnered significant attention. In this paper, eight machine learning algorithms, including Lasso regression, ridge regression, partial least squares regression, elastic network regression, decision tree, random forest, support vector machine and K-nearest neighbor method, are used to construct the stock return prediction model. The empirical results show that linear machine learning algorithm outperforms nonlinear machine learning algorithm. The annual return rate of CSI 300 index in the same term is 1.47%, while the investment strategy based on OLS model has an annualized return rate of 35.96%, and the maximum withdrawal rate is only 29.61%, showing its strong return capacity. In this paper, machine learning is introduced in the field of fundamental quantitative investment, which provides investment reference for all kinds of investors and is helpful for the country to promote quantitative investment.
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Hind I. Mohammed, Sabah A. Abdulkareem, and Shaimaa Khamees Ahmed. "Prediction of breast cancer based on machine learning." Computer Science & IT Research Journal 5, no. 7 (2024): 1605–20. http://dx.doi.org/10.51594/csitrj.v5i7.1306.
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Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).
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Zhou, Yushan. "Stock Forecasting Based on Linear Regression Model and Nonlinear Machine Learning Regression Model." Advances in Economics, Management and Political Sciences 57, no. 1 (2024): 7–13. http://dx.doi.org/10.54254/2754-1169/57/20230364.
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To enhance the accuracy of stock price prediction for Netflix and provide individuals with a comprehensive understanding of stock trading prices, this study constructs a predictive model by employing three distinct approaches: a linear regression model, a Long Short-term Memory (LSTM) artificial neural network, and a Gated Recursive Unit (GRU) which serves as a component of the LSTM architecture. A prediction scheme is devised, utilizing historical stock data spanning from 2002 to 2022 for Netflix. The primary objective is to forecast the stock price of Netflix for the subsequent 20-day period. To evaluate the efficacy of the three models, a rigorous assessment is conducted employing robust evaluation indices. The outcomes of this analysis will enable a determination of the fitting adequacy of each model, thereby facilitating the identification of the most suitable approach for accurate stock price prediction in the context of Netflix. This research endeavors to contribute to the field of stock market analysis by leveraging advanced predictive modeling techniques for enhanced forecasting accuracy and insightful decision-making.
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Zhang, Pengbo, and Zhixin Yang. "A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/260970.
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Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.
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Chang, Yuqing, Shu Wang, Huixin Tian, and Zhen Zhao. "Multiple Regression Machine System Based on Ensemble Extreme Learning Machine for Soft Sensor." Sensor Letters 11, no. 4 (2013): 710–14. http://dx.doi.org/10.1166/sl.2013.2513.
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Mestiri, Sami. "Credit scoring using machine learning and deep Learning-Based models." Data Science in Finance and Economics 4, no. 2 (2024): 236–48. http://dx.doi.org/10.3934/dsfe.2024009.
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<abstract><p>Credit scoring is a useful tool for assessing the capability of customers repayments. The purpose of this paper is to compare the predictive abilities of six credit scoring models: Linear Discriminant Analysis (LDA), Random Forests (RF), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM) and Deep Neural Network (DNN). To compare these models, an empirical study was conducted using a sample of 688 observations and twelve variables. The performance of this model was analyzed using three measures: Accuracy rate, F1 score, and Area Under Curve (AUC). In summary, machine learning techniques exhibited greater accuracy in predicting loan defaults compared to other traditional statistical models.</p></abstract>
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Tanone, Radius, and Arnold B. Emmanuel. "Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang." AITI 17, no. 1 (2020): 42–55. http://dx.doi.org/10.24246/aiti.v17i1.42-55.
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Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.
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Koppula, Manasa. "PREDICTIVE MAINTENANCE TO REDUCE MACHINE DOWNTIME IN FACTORIES USING MACHINE LEARNING ALGORITHMS." international journal of advanced research in computer science 16, no. 2 (2025): 71–77. https://doi.org/10.26483/ijarcs.v16i2.7224.
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Accurate machine failure detection allows manufacturers to estimate potential machine deterioration and avoid machine downtime caused by unexpected performance issues. Predictive maintenance with the use of machine learning algorithms may anticipate machine faults and maximize maintenance efforts to solve machine downtime problems. To anticipate machine breakdowns and minimize downtime, this work applies a variety of machine learning methods, such as Random Forest, Decision Tree, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, and Logistic Regression. Based on the performance measurement values, Random Forest model has shown high levels of accuracy, precision, recall, and F-score. The sequence of order for accuracy of machine learning models follows as: Random Forest > Decision Tree> Gradient Booster Classifier and SVM > Logistic Regression and KVM. This work emphasizes that, through various machine learning models, machine manufacturers could optimize the machine maintenance and prolong the life of machines
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Peng, Luna. "Stock Price Prediction of “Google” based on Machine Learning." BCP Business & Management 34 (December 14, 2022): 912–18. http://dx.doi.org/10.54691/bcpbm.v34i.3111.
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By 2022, many countries have declared the epidemic's end, both an opportunity and a challenge for many investors. More and more investors are manipulating prices to influence the stock market. So investors want to predict the price of stocks to make suitable investments. The author wants to start with the platform YouTube to study the price trend of this stock and make predictions to analyze whether there are traces of the factors affecting the stock price based on linear regression and random forest regression models. The author first backtested the price of this stock and analyzed the data according to the highest and lowest day. Then, the author used the method of Linear Regression and Random Forest Regression to predict the price. The error of the Linear Regression prediction results was within 5%, within the normal range, but the Random Forest Regression 5 days prediction's accuracy is much lower (65%). It shows that the stock price prediction model--Linear Regression is more credible and is worthy of reference for investors.
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K., Ashfaq Ahmed, and Dr Shaheda Akthar. "Ridge Regression based Missing Data Estimation with Dimensionality Reduction: Microarray Gene Expression Data." Webology 19, no. 1 (2022): 4113–28. http://dx.doi.org/10.14704/web/v19i1/web19271.
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Data is considered to be the important element in the field of Data Science and Machine Learning. Performance of Machine Learning and Data Mining algorithms greatly influenced by the characteristics of data and data with missing values. Performance of all these Machine Learning algorithms greatly improved and they can give accurate results when the data is in full without missing values. So before applying these algorithms; dataset and its missing values are completely filled. To impute these missing values in the dataset there are numerous methods were proposed. In this paper we used micro array gene expression dataset; by introducing various percentages of missing values a new methodology is proposed to impute these missing values in the data set. The nature of micro array gene expression dataset is huge in dimensionality, so at first, we used recursive feature elimination method to select the best features which contributes much for model was selected then we apply the Ridge Regression for imputation. Imputations with other methods are compared. We evaluate the performance of all models by using the metrics like MSE, MAE, R-square. To select the best model in the set of models we used Normalized Criteria Distance (NCD) to rank the models under proposed metrics. The model with least NCD rank selected as the best model among other models, in our paper proposed model has got the lowest value among other models and considered to be the best model among other models.
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Dong, Yiran. "Regression-based Analysis of Ozone Layer via Machine Learning Models." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1356–63. http://dx.doi.org/10.54097/hset.v39i.6768.
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Ozone protects the livings on the earth from ultraviolet radiation. The emission of ozone depleting substance causes the Antarctic ozone hole and reduces the ultraviolet radiation absorption rate by ozone layer. Researchers find that ozone depleting substances (ODS) accounts for ozone concentration between 9 km and 25km, by chemical-climate models and ozone concentration will increase by 15% until 2050. In this work, we used six major ODS consumption worldwide and mean stratospheric ozone concentration each year. Seven regression models are implemented to make prediction and k-fold cross validation is used for avoiding overfitting. Root mean squared error (RMSE), and standard deviation are two performance metrics of regression models. The results indicate that the prediction from support vector regression achieved the lowest RMSE. Random forester and k-nearest neighbor are also appropriate for make prediction. We also concluded that linear, polynomial, ridge, and lasso regression methods are hardly to fit the data in this application.
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Köktürk-Güzel, Başak Esin, and Selami Beyhan. "Symbolic Regression Based Extreme Learning Machine Models for System Identification." Neural Processing Letters 53, no. 2 (2021): 1565–78. http://dx.doi.org/10.1007/s11063-021-10465-2.
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Zhou, Jing, Rui Ying Liu, Xu Zhou, and Rana Aamir Raza Ashfaq. "Comparative Analysis of Gaussian Process Regression Based Extreme Learning Machine." Journal of Software 12, no. 4 (2017): 292–302. http://dx.doi.org/10.17706/jsw.12.4.292-302.
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Kaushik, Keshav, Akashdeep Bhardwaj, Ashutosh Dhar Dwivedi, and Rajani Singh. "Machine Learning-Based Regression Framework to Predict Health Insurance Premiums." International Journal of Environmental Research and Public Health 19, no. 13 (2022): 7898. http://dx.doi.org/10.3390/ijerph19137898.
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Artificial intelligence (AI) and machine learning (ML) in healthcare are approaches to make people’s lives easier by anticipating and diagnosing diseases more swiftly than most medical experts. There is a direct link between the insurer and the policyholder when the distance between an insurance business and the consumer is reduced to zero with the use of technology, especially digital health insurance. In comparison with traditional insurance, AI and machine learning have altered the way insurers create health insurance policies and helped consumers receive services faster. Insurance businesses use ML to provide clients with accurate, quick, and efficient health insurance coverage. This research trained and evaluated an artificial intelligence network-based regression-based model to predict health insurance premiums. The authors predicted the health insurance cost incurred by individuals on the basis of their features. On the basis of various parameters, such as age, gender, body mass index, number of children, smoking habits, and geolocation, an artificial neural network model was trained and evaluated. The experimental results displayed an accuracy of 92.72%, and the authors analyzed the model’s performance using key performance metrics.
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Wendt, Karsten, Katrin Trentzsch, Rocco Haase, et al. "Transparent Quality Optimization for Machine Learning-Based Regression in Neurology." Journal of Personalized Medicine 12, no. 6 (2022): 908. http://dx.doi.org/10.3390/jpm12060908.
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The clinical monitoring of walking generates enormous amounts of data that contain extremely valuable information. Therefore, machine learning (ML) has rapidly entered the research arena to analyze and make predictions from large heterogeneous datasets. Such data-driven ML-based applications for various domains become increasingly applicable, and thus their software qualities are taken into focus. This work provides a proof of concept for applying state-of-the-art ML technology to predict the distance travelled of the 2-min walk test, an important neurological measurement which is an indicator of walking endurance. A transparent lean approach was emphasized to optimize the results in an explainable way and simultaneously meet the specified software requirements for a generic approach. It is a general-purpose strategy as a fractional–factorial design benchmark combined with standardized quality metrics based on a minimal technology build and a resulting optimized software prototype. Based on 400 training and 100 validation data, the achieved prediction yielded a relative error of 6.1% distributed over multiple experiments with an optimized configuration. The Adadelta algorithm (LR=0.000814, fModelSpread=5, nModelDepth=6, nepoch=1000) performed as the best model, with 90% of the predictions with an absolute error of <15 m. Factors such as gender, age, disease duration, or use of walking aids showed no effect on the relative error. For multiple sclerosis patients with high walking impairment (EDSS Ambulation Score ≥6), the relative difference was significant (n=30; 24.0%; p<0.050). The results show that it is possible to create a transparently working ML prototype for a given medical use case while meeting certain software qualities.
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Wei, Xiuxi, Yongquan Zhou, Qifang Luo, and Huajuan Huang. "Functional Network for Nonlinear Regression Based on Extreme Learning Machine." Journal of Computational and Theoretical Nanoscience 12, no. 10 (2015): 3662–66. http://dx.doi.org/10.1166/jctn.2015.4254.
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Pastika, Puan Bening, and Alamsyah Alamsyah. "Machine Learning-Based Malicious Website Detection Using Logistic Regression Algorithm." Engineering, MAthematics and Computer Science Journal (EMACS) 6, no. 3 (2024): 207–13. https://doi.org/10.21512/emacsjournal.v6i3.11844.
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Cybercrime is an increasing threat that occurs while exploring the internet. Cybercrime is committed by cybercriminals who exploit the web's vulnerability by inserting malicious software to access systems that belong to web service users. It is detrimental to users, therefore detecting malicious websites is necessary to minimize cybercrime. This research aims to improve the effectiveness of detecting malicious websites by applying the Logistic Regression algorithm. The selection of Logistic Regression is based on its ability to perform binary classification, which is important for distinguishing between benign and potentially malicious websites. This research emphasizes a preprocessing stage that has been deeply optimized. Data cleaning, dataset balancing, and feature mapping are enhanced to improve detection accuracy. Hybrid sampling addresses data imbalance, ensuring the model is trained with representative data from both classes. Experimental results show that the Logistic Regression implementation achieves an excellent level of accuracy. The developed model recorded an accuracy of 92.60% without cross-validation, which increased to 92.71% with 5-fold cross-validation. The novelty of this research lies in the significant increase in accuracy compared to previous methods, demonstrating the potential to improve protection against malicious website threats in an increasingly complex and risky digital environment. This research makes an important contribution to the development of digital security detection technologies to address the ever-growing challenges of cybercrime.
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SAKTHIPRIYA, DHINAKARAN, and THANGAVEL CHANDRAKUMAR. "Weather based paddy yield prediction using machine learning regression algorithms." Journal of Agrometeorology 26, no. 3 (2024): 344–48. http://dx.doi.org/10.54386/jam.v26i3.2598.
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Paddy is a major crop in India which is highly affected by the weather variables resulting in drastic reduction of its yield; adverse all the variables drastically reduce the paddy yield. In this research, machine learning model was developed for prediction of paddy yield production by linear regression (LR), random forest regression (RFR), support vector regression (SVR), cat boost regression (CBR), and hybrid machine learning with variance inflation factor (VIF) LR-VIF, RFR-VIF, SVR-VIF, and CBR-VIF techniques. The dataset consists of variables (weather) for more than 15 years collected for the study area which is Madurai district, Tamil Nadu in India. Analysis was carried out by fixing 70% of data calibration & remaining 30% for validation in Jupyter notebook (Python programming). Results showed that CBR-VIF performed having nRMSE value 1.23 to 1.40% for Madurai South, nRMSE value 0.56 to 1.40% for Melur, nRMSE value 1.10 to 1.25% for Usilampatti, and nRMSE value 0.75 to 1.10% for Thirumangalam. The hybrid model of CBR along with VIF and then CBR model has shown improvement with high influenced weather variables such as maximum temperature, minimum temperature, rainfall normal, and actual rainfall.
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Priya, Tanu, Bikash Kanti Sarkar, and Sudip Kumar Sahana. "Regression based machine learning models for forecasting preterm birth cases." Procedia Computer Science 235 (2024): 830–39. http://dx.doi.org/10.1016/j.procs.2024.04.079.
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Kärkkäinen, Tommi. "Extreme minimal learning machine: Ridge regression with distance-based basis." Neurocomputing 342 (May 2019): 33–48. http://dx.doi.org/10.1016/j.neucom.2018.12.078.
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Liu, Caifeng, Lin Feng, Huibing Wang, Shenglan Liu, and Kaiyuan Liu. "Cascade regression based on extreme learning machine for face alignment." Journal of Electronic Imaging 29, no. 04 (2020): 1. http://dx.doi.org/10.1117/1.jei.29.4.043002.
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Calix, Ricardo A., Orlando Ugarte, Tyamo Okosun, and Hong Wang. "Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation." Dynamics 3, no. 4 (2023): 636–55. http://dx.doi.org/10.3390/dynamics3040034.
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Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.
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Huang, Ziyan, Kexin Li, Chuming Wang, and Jiazhi Wang. "Guangzhou Housing Price Prediction Based On Machine Learning Regression Models." Highlights in Science, Engineering and Technology 44 (April 13, 2023): 307–17. http://dx.doi.org/10.54097/hset.v44i.7355.
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Nowadays, the fluctuation of housing prices is a key concern of homeowners, the real estate market and the government. The researchers found that it was possible to predict house prices accurately by analyzing relevant attributes and using the most effective models. However, due to the impact of the COVID-19 pandemic, there is still a research gap on which model is more suitable for predicting housing prices after the outbreak of COVID-19. Therefore, this study collected the latest real estate data of Guangzhou in China through the web crawler, trained the random forest regression model with collected data, and obtained a model that could predict the housing price by inputting corresponding attributes. The MSE of the trained regression model is 2802, MAE is 534, and the determination coefficient (R²_score) is 0.89, while the MSE of the trained XGBoost regression model is 3108, MAE is 643, and the determination coefficient (R²_score) is 0.87.The random forest regression model has been shown to be more accurate at predicting Guangzhou house prices after the COVID-19 outbreak. Our paper has great potential in house price prediction under the pandemic situation.