Log in

Relevant bibliographies by topics / Random Forest Regression and ANN Algorithm / Journal articles

To see the other types of publications on this topic, follow the link: Random Forest Regression and ANN Algorithm.

Journal articles on the topic 'Random Forest Regression and ANN Algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Random Forest Regression and ANN Algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Kaur, Amanpreet, Vansh Sachdeva, Abhijot Singh, Ayush Jaiswal, Niyati Aggrawal, and Archana Purwar. "Performance Analysis of Different Machine Learning Algorithms on Credit Card Fraud Detection." JOURNAL OF INTERNATIONAL ACADEMY OF PHYSICAL SCIENCES 27, no. 03 (2023): 295–303. http://dx.doi.org/10.61294/jiaps2023.2739.

Full text

Abstract:

Machine learning (ML) is a logical investigation of various algorithms and factual models that PCs utilize to carry out particular operations that are not clearly programmed. This paper aims to statistically analyze different machine learning algorithms, and compare and contrast their performance for credit card fraud detection. Algorithms used are Artificial Neural Networks(ANN), Sample Vector Machine (SVM), and Kth Nearest Neighbour (KNN), Decision Tree, Logistic Regression and Random Forest. All these above mentioned algorithms are compared on basis of performance measures. It is deduced th

APA, Harvard, Vancouver, ISO, and other styles

2

Han, Nam-Gyu, and Bong-Hyun Kim. "Configuration of Efficient Returning Farmers Data Set for Algorithms Validation based on ANN and Random Forest." Webology 19, no. 1 (2022): 4428–43. http://dx.doi.org/10.14704/web/v19i1/web19292.

Full text

Abstract:

Since 2010, as the number of urban residents returning to farming and returning to rural areas has increased, various policies and service models such as education have been supported. However, as the number of failures and dissatisfaction cases for returning to farming and returning home increases, it is urgent to prepare a support service model. After all, in addition to farming technology, it is necessary to collect and prepare a lot of information, such as selecting competitive crops, needing to check how to secure housing/farmland, and recognizing legal process information such as registr

APA, Harvard, Vancouver, ISO, and other styles

3

Tran, Toai Kim, Roman Senkerik, Hahn Thi Xuan Vo, et al. "Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge." MENDEL 29, no. 2 (2023): 283–94. http://dx.doi.org/10.13164/mendel.2023.2.283.

Full text

Abstract:

   Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR), Artificial neural network (ANN), Random forest regression (RFR), and a hybrid ANN-Ridge regression are compared in terms of accuracy metrics to predict ICO value after six months. We use a dataset collected from 109 ICOs that were obtained from the cryptocurrency websites after data preprocessing. The dataset consists of 12 field

APA, Harvard, Vancouver, ISO, and other styles

4

Sena, I. Gede Wiarta, and Andi W. R. Emanuel. "MOBILE LEGEND GAME PREDICTION USING MACHINE LEARNING REGRESSION METHOD." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, no. 2 (2023): 221–30. http://dx.doi.org/10.33330/jurteksi.v9i2.1866.

Full text

Abstract:

Abstract: A research institute explains that with 83.7 million people using the Internet, Indonesia is among the top 20 internet users globally. Various individual or group activities require an internet network, one of which is playing games, for developments in the gaming sector, especially the MOBA (Massive Online Battle Arena) genre game, is being hotly discussed. There are various kinds of MOBA genre games, one of which is the Mobile Legends game. Many E-Sport Mobile Legends teams, especially in Asia, make this phenomenon a business space to generate large profits. In this study, the rese

APA, Harvard, Vancouver, ISO, and other styles

5

Fondaj, Jakup, Mentor Hamiti, Samedin Krrabaj, Xhemal Zenuni, and Jaumin Ajdari. "Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 21 (2023): 65–78. http://dx.doi.org/10.3991/ijim.v17i21.44143.

Full text

Abstract:

This paper compares predictive algorithms for smart agriculture sensor data in Internet of Things (IoT) applications. The main objective of IoT in agriculture is to improve productivity and reduce production costs using advanced technology and artificial intelligence. In this study, we compared various predictive algorithms for analyzing IoT smart agriculture sensor data. Specifically, we evaluated the performance of NeuralProphet, Random Forest Regression, SARIMA, and Artificial Neural Networks (ANN) by KERAS algorithms on a dataset containing temperature, humidity, and soil moisture data. Th

APA, Harvard, Vancouver, ISO, and other styles

6

Shi, Yuanyuan, Junyu Zhao, Xianchong Song, et al. "Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm." PLOS ONE 16, no. 6 (2021): e0253385. http://dx.doi.org/10.1371/journal.pone.0253385.

Full text

Abstract:

Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling acc

APA, Harvard, Vancouver, ISO, and other styles

7

Harshit, Mathur, and Surana Aditya. "Glass Classification based on Machine Learning Algorithms." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, no. 11 (2020): 139–42. https://doi.org/10.35940/ijitee.H6819.0991120.

Full text

Abstract:

Glass Industry is considered one of the most important industries in the world. The Glass is used everywhere, from water bottles to X-Ray and Gamma Rays protection. This is a non-crystalline, amorphous solid that is most often transparent. There are lots of uses of glass, and during investigation in a crime scene, the investigators need to know what is type of glass in a scene. To find out the type of glass, we will use the online dataset and machine learning to solve the above problem. We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm,

APA, Harvard, Vancouver, ISO, and other styles

8

Vishnuvardhan, T., and A. Rama. "Comparison of Accuracy Rate in Prediction of Cardiovascular Disease using Random Forest with Logistic Regression." CARDIOMETRY, no. 25 (February 14, 2023): 1526–31. http://dx.doi.org/10.18137/cardiometry.2022.25.15261531.

Full text

Abstract:

Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Novel Random Forest with Logistic Regression. Materials and Methods: The Novel Random forest (N=20) and Novel Logistic Regression Algorithm (N=20) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work.The sample size is determined using the G power Calculator and it’s found to be 10. Results: The Random Forest exhibited 89.06% accuracy whilst a Logistic Regression has shown 92.18%. accuracy. Statistical significance difference between Random forest al

APA, Harvard, Vancouver, ISO, and other styles

9

Kerdprasop, Kittisak, Nittaya Kerdprasop, and Paradee Chuaybamroong. "Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry." International Journal of Machine Learning 13, no. 4 (2023): 142–45. http://dx.doi.org/10.18178/ijml.2023.13.4.1142.

Full text

Abstract:

This paper present the study results of predicting energy consumption in the steel industry using modeling methods based on machine learning and deep learning techniques. Machine learning algorithms used in this work include artificial neural network (ANN), k-nearest neighbors (kNN), random forest (RF), and gradient boosting (GB). Deep learning technique is long short-term memory (LSTM). Linear regression, which is the statistical-based learning algorithm, is also applied to be the baseline of this comparative study. The modeling results reveal that among the statistical-based and machine lear

APA, Harvard, Vancouver, ISO, and other styles

10

Faurina, Ruvita, M. Jumli Gazali, and Icha Dwi Aprilia Herani. "OPTIMIZATION OF DISEASE PREDICTION ACCURACY THROUGH ARTIFICIAL NEURAL NETWORK (ANN) ALGORITHMS IN DIAGNESE APPLICATION." Jurnal Teknik Informatika (Jutif) 5, no. 2 (2024): 339–47. https://doi.org/10.52436/1.jutif.2024.5.2.1182.

Full text

Abstract:

This research aims to enhance the accuracy and speed of diagnoses in the Diagnese application by implementing the ANN algorithm for disease prediction. The dataset used for experimentation was featuring binary data types, containing 131 symptoms used to predict 41 types of diseases. The Diagnese application assists patients in identifying diseases and finding suitable specialist doctors based on reported symptoms. To achieve this goal, researchers explored various machine learning algorithms, such as decision trees, SVM, Random Forest, Logistic Regression, and ANN. Through comprehensive analys

APA, Harvard, Vancouver, ISO, and other styles

11

Note, Johan, and Maaruf Ali. "Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms." Annals of Emerging Technologies in Computing 6, no. 3 (2022): 19–36. http://dx.doi.org/10.33166/aetic.2022.03.003.

Full text

Abstract:

Attacks against computer networks, “cyber-attacks”, are now common place affecting almost every Internet connected device on a daily basis. Organisations are now using machine learning and deep learning to thwart these types of attacks for their effectiveness without the need for human intervention. Machine learning offers the biggest advantage in their ability to detect, curtail, prevent, recover and even deal with untrained types of attacks without being explicitly programmed. This research will show the many different types of algorithms that are employed to fight against the different type

APA, Harvard, Vancouver, ISO, and other styles

12

G, Manoj Kumar. "Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 1228–40. http://dx.doi.org/10.47059/revistageintec.v11i4.2182.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Dalvi, Shraddhesh, Shubham Gholap, Piyush Jadhav, and Prof Pallavi Baviskar. "SOCIAL MEDIA FAKE ACCOUNT IDENTIFICATION USING ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem38546.

Full text

Abstract:

The proliferation of social media platforms has led to an increase in the creation of fake accounts. These accounts are used for various malicious activities, such as spreading false information, phishing, and identity theft. As a result, there is a growing need for effective methods to identify and eliminate fake accounts. This paper proposes a machine learning-based approach for social media fake account identification. This paper proposes a machine learning-based approach to identify fake accounts on social media platforms. Our method leverages a combination of feature extraction techniques

APA, Harvard, Vancouver, ISO, and other styles

14

Ye, Pengchao, Guoli Ji, Lei-Ming Yuan, et al. "A Sparse Classification Based on a Linear Regression Method for Spectral Recognition." Applied Sciences 9, no. 10 (2019): 2053. http://dx.doi.org/10.3390/app9102053.

Full text

Abstract:

This study introduces a spectral-recognition method based on sparse representation. The proposed method, the linear regression sparse classification (LRSC) algorithm, uses different classes of training samples to linearly represent the prediction samples and to further classify them according to residuals in a linear regression model. Two kinds of spectral data with completely different physical properties were used in this study. These included infrared spectral data and laser-induced breakdown spectral (LIBS) data for Tegillarca granosa samples polluted by heavy metals. LRSC algorithm was em

APA, Harvard, Vancouver, ISO, and other styles

15

Tanuwijaya, Padmavati Darma, Jhonatan Laurensius Tjahjadi, and Yosefina Finsensia Riti. "Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting the Number of New Students." JISA(Jurnal Informatika dan Sains) 6, no. 2 (2023): 161–66. http://dx.doi.org/10.31326/jisa.v6i2.1789.

Full text

Abstract:

Higher education institutions are educational units located at a higher level after high school or vocational school. Catholic University Darma Cendika Surabaya (UKDC) faces challenges in managing the admission of new students due to variations in the number of prospective students applying to each department, which is also influenced by changing trends in interests and job needs in Indonesia. The use of Artificial Neural Network with Backpropagation and Random Forest Regression algorithms for comparing the prediction of new student admissions in the following year will be beneficial for the a

APA, Harvard, Vancouver, ISO, and other styles

16

Fitri, Evita, and Siti Nurhasanah Nugraha. "OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN." INTI Nusa Mandiri 18, no. 2 (2024): 210–17. http://dx.doi.org/10.33480/inti.v18i2.5269.

Full text

Abstract:

Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the

APA, Harvard, Vancouver, ISO, and other styles

17

Yang, Yanhua, Guiyong Liu, Haihong Zhang, Yan Zhang, and Xiaolong Yang. "Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms." Buildings 14, no. 1 (2024): 190. http://dx.doi.org/10.3390/buildings14010190.

Full text

Abstract:

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS)

APA, Harvard, Vancouver, ISO, and other styles

18

Liang, Guanglin, Linchong Huang, and Chengyong Cao. "Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics." Mathematics 13, no. 2 (2025): 264. https://doi.org/10.3390/math13020264.

Full text

Abstract:

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction met

APA, Harvard, Vancouver, ISO, and other styles

19

Oleiwi, Zahraa chaffat, Ebtesam N. AlShemmary, and Salam Al-augby. "Identify Best Learning Method for Heart Diseases Prediction Under impact of Different Datasets Characteristics." Journal of Kufa for Mathematics and Computer 10, no. 1 (2023): 27–41. http://dx.doi.org/10.31642/jokmc/2018/100104.

Full text

Abstract:

This paper introduces an experimental study of the heart disease datasets characteristics impact on the performance of classification algorithms in the aim of identifying the best algorithm for each dataset under its characteristics. The performance of five machine learning algorithms (logistic regression (LR), K-Nearest Neighbor (KNN), Decision tree (DT), Random Forest (RF), and support vector machine (SVM)), single layer neural network (ANN), and deep neural network (DNN), has been evaluated using five heart disease datasets under four data complexity measurement: number of samples (dataset

APA, Harvard, Vancouver, ISO, and other styles

20

Adya M D, Dr. S Ajitha, and Ms Reshma K J. "Predictive Analysis of Stock market return using Machine Learning Algorithms." International Journal of All Research Education and Scientific Methods 13, no. 02 (2025): 269–79. https://doi.org/10.56025/ijaresm.2025.130225269.

Full text

Abstract:

Stock market return is inherently volatile and uncertain, driving investors toconstantly seek methods to forecast future trends in order to minimize losses andmaximize profits. However, it remains a fact that no technique can predict marketmovements with absolute accuracy. Despite this, various approaches are beingexplored to enhance the predictive performance of models as much as possible.With the rapid progress in Machine Learning (ML) over recent years, numerous algorithms are now being applied to improve stock price prediction. This paperresearches 5 algorithms namely Linear Regression, Ra

APA, Harvard, Vancouver, ISO, and other styles

21

Han, Sunwoo, and Hyunjoong Kim. "Optimal Feature Set Size in Random Forest Regression." Applied Sciences 11, no. 8 (2021): 3428. http://dx.doi.org/10.3390/app11083428.

Full text

Abstract:

One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focus on classification problems. We studied the effect of feature set size in the context of regression. Through experimental studies using many datasets, we first investigated whether the RF regression predictions are affected by the feature set size. Then, we found a rule associated with the optimal size based on the characteristics of each data. La

APA, Harvard, Vancouver, ISO, and other styles

22

El-Amin, Mohamed F., Budoor Alwated, and Hussein A. Hoteit. "Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media." Energies 16, no. 2 (2023): 678. http://dx.doi.org/10.3390/en16020678.

Full text

Abstract:

Reservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, t

APA, Harvard, Vancouver, ISO, and other styles

23

Kassa, Semachew Molla, and Betelhem Zewdu Wubineh. "Use of Machine Learning to Predict California Bearing Ratio of Soils." Advances in Civil Engineering 2023 (January 25, 2023): 1–11. http://dx.doi.org/10.1155/2023/8198648.

Full text

Abstract:

CBR is a crucial metric used to assess the durability of base course materials and subgrade soils in various types of pavements. In this research, the machine learning (ML) approach has been implemented using random forest (RF), decision tree (DT), linear regression (LR), and artificial neural network (ANN) models to estimate CBR (California bearing ratio) values of the soil based on seven predictors such as maximum dry density, soil classification, optimum moisture content, liquid limit, plastic limit, plastic index, and swell, which can be easily determined from the laboratory. AASHTO M 145

APA, Harvard, Vancouver, ISO, and other styles

24

Hadianto, Agus, and Wiranto Herry Utomo. "CatBoost Optimization Using Recursive Feature Elimination." Jurnal Online Informatika 9, no. 2 (2024): 169–78. http://dx.doi.org/10.15575/join.v9i2.1324.

Full text

Abstract:

CatBoost is a powerful machine learning algorithm capable of classification and regression application. There are many studies focusing on its application but are still lacking on how to enhance its performance, especially when using RFE as a feature selection. This study examines the CatBoost optimization for regression tasks by using Recursive Feature Elimination (RFE) for feature selection in combination with several regression algorithm. Furthermore, an Isolation Forest algorithm is employed at preprocessing to identify and eliminate outliers from the dataset. The experiment is conducted b

APA, Harvard, Vancouver, ISO, and other styles

25

Dong, Luofan, Huaqiang Du, Ning Han, et al. "Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2." Remote Sensing 12, no. 6 (2020): 958. http://dx.doi.org/10.3390/rs12060958.

Full text

Abstract:

Above-ground biomass (AGB) directly relates to the productivity of forests. Precisely, AGB mapping for regional forests based on very high resolution (VHR) imagery is widely needed for evaluation of productivity. However, the diversity of variables and algorithms and the difficulties inherent in high resolution optical imagery make it complex. In this paper, we explored the potentials of the state-of-art algorithm convolutional neural networks (CNNs), which are widely used for its high-level representation, but rarely applied for AGB estimation. Four experiments were carried out to compare the

APA, Harvard, Vancouver, ISO, and other styles

26

Sonsare, Pravinkumar, Ashtavinayak Pande, Akshay Kurve, Sudhanshu Kumar, and Chinmay Shanbhag. "A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION." JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH 5, no. 6 (2023): 1–12. http://dx.doi.org/10.46947/joaasr562023621.

Full text

Abstract:

Initial Public Offerings (IPOs) are a popular way for companies to raise capital and enter the public markets. However, many IPOs underperform and fail to meet the expectations of investors. In this research paper, we explore the use of different machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANN and SVM, for predicting IPO underperformance. We collect and pre-process a dataset of IPOs from the past few years, and use it to train and evaluate the performance of each model. Our results show that Artificial Neural Network model is better suited for predicting IPO u

APA, Harvard, Vancouver, ISO, and other styles

27

Abbas, Muhammad Adeel, and Zeshan Iqbal. "Double Auction used Artificial Neural Network in Cloud Computing." Vol 4 Issue 5 4, no. 5 (2022): 65–76. http://dx.doi.org/10.33411/ijist/2022040506.

Full text

Abstract:

Double auction (DA) algorithm is widely used for trading systems in cloud computing. Distinct buyers request different attributes for virtual machines. On the other hand, different sellers offer several types of virtual machines according to their correspondence bids. In DA, getting multiple equilibrium prices from distinct cloud providers is a difficult task, and one of the major problems is bidding prices for virtual machines, so we cannot make decisions with inconsistent data. To solve this problem, we need to find the best machine learning algorithm that anticipates the bid cost for virtua

APA, Harvard, Vancouver, ISO, and other styles

28

Ogundero, Micheal Ayodeji, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, and Kingsley Abhulimen. "Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models." ABUAD Journal of Engineering Research and Development (AJERD) 8, no. 1 (2025): 292–306. https://doi.org/10.53982/ajerd.2025.0801.30-j.

Full text

Abstract:

Sand production is one of the major challenges in the oil and gas industry, impacting the operational integrity and economic efficiency of oil extraction activities. This study focuses on predicting Reservoir Flow Capacity (RFC) in sandstone formations by analyzing geological and petrophysical properties critical to reservoir performance and mechanical stability. It also identified key factors that impact the mechanical stability of formations during production. Given a large number of input variables that enclose geological and environmental factors, the study set the correlation of these con

APA, Harvard, Vancouver, ISO, and other styles

29

Wang, Fangyi, Yongchao Wang, Xiaokang Ji, and Zhiping Wang. "Effective Macrosomia Prediction Using Random Forest Algorithm." International Journal of Environmental Research and Public Health 19, no. 6 (2022): 3245. http://dx.doi.org/10.3390/ijerph19063245.

Full text

Abstract:

(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predictin

APA, Harvard, Vancouver, ISO, and other styles

30

Almaliki, Abdulrazak H. "Estimating Tidal Sea levels along the Central Coast of the Western Arabian Gulf using Machine Learning Algorithms (MLAs)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 1–6. http://dx.doi.org/10.35940/ijrte.b8073.13020724.

Full text

Abstract:

Precise tidal forecasting is an academic exercise and a crucial tool for designing and constructing coastal and marine infrastructure. Machine learning algorithms (MLAs) like Random Forest Regression (RF), K-Nearest Neighbors Regression (KN), Gradient Boosting Machines (GBM), and artificial neural networks (ANNs) are powerful data-driven techniques that can be harnessed for this practical purpose. This study utilizes four machine learning algorithms (MLAs), namely (RF), (KN), (GBM), and the Artificial Neural Network - Multilayer Perceptron (ANN-MLP) model, to accurately estimate the tidal leve

APA, Harvard, Vancouver, ISO, and other styles

31

Almaliki, Abdulrazak H. "Estimating Tidal Sea levels along the Central Coast of the Western Arabian Gulf using Machine Learning Algorithms (MLAs)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 1–6. http://dx.doi.org/10.35940/ijrte.b8073.1302072.

Full text

Abstract:

Precise tidal forecasting is an academic exercise and a crucial tool for designing and constructing coastal and marine infrastructure. Machine learning algorithms (MLAs) like Random Forest Regression (RF), K-Nearest Neighbors Regression (KN), Gradient Boosting Machines (GBM), and artificial neural networks (ANNs) are powerful data-driven techniques that can be harnessed for this practical purpose. This study utilizes four machine learning algorithms (MLAs), namely (RF), (KN), (GBM), and the Artificial Neural Network - Multilayer Perceptron (ANN-MLP) model, to accurately estimate the tidal leve

APA, Harvard, Vancouver, ISO, and other styles

32

Abdulrazak, H. Almaliki. "Estimating Tidal Sea levels along the Central Coast of the Western Arabian Gulf using Machine Learning Algorithms (MLAs)." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 2 (2024): 1–6. https://doi.org/10.35940/ijrte.B8073.13020724.

Full text

Abstract:

<strong>Abstract:</strong> Precise tidal forecasting is an academic exercise and a crucial tool for designing and constructing coastal and marine infrastructure. Machine learning algorithms (MLAs) like Random Forest Regression (RF), K-Nearest Neighbors Regression (KN), Gradient Boosting Machines (GBM), and artificial neural networks (ANNs) are powerful data-driven techniques that can be harnessed for this practical purpose. This study utilizes four machine learning algorithms (MLAs), namely (RF), (KN), (GBM), and the Artificial Neural Network - Multilayer Perceptron (ANN-MLP) model, to accurat

APA, Harvard, Vancouver, ISO, and other styles

33

Rohini, Ashok Gamane, and Dabhade Dr.Vaibhav. "FINDING FAKE SOCIAL MEDIA ACCOUNT USING MACHINE LEARNING." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 284–303. https://doi.org/10.5281/zenodo.15251857.

Full text

Abstract:

Abstract: The widespread use of social media has resulted in a surge of fake accounts, posing seriousrisks to individuals, organizations, and society at large. Identifying fake accounts effectively isessential to preserving the integrity and credibility of social media platforms. This study introduces amachine learning-based approach to detect fake social media accounts.We employed five machinelearning algorithms—Support Vector Machines (SVM), K-Nearest Neighbors (KNN), RandomForest, Logistic Regression, and Artificial Neural Networks (ANN)—to classify accounts as fake orgenuine. T

APA, Harvard, Vancouver, ISO, and other styles

34

Thohari, Afandi Nur Aziz, and Rima Dias Ramadhani. "Performance Comparison Supervised Machine Learning Models to Predict Customer Transaction Through Social Media Ads." Journal of Computer Networks, Architecture and High Performance Computing 4, no. 2 (2022): 116–26. http://dx.doi.org/10.47709/cnahpc.v4i2.1488.

Full text

Abstract:

The application of machine learning has been used in various sectors, one of which is digital marketing. This research compares the performance of six machine learning algorithms to predict customer transaction decisions. The six algorithms used for comparison are Perceptron, Linear Regression, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. The dataset is obtained from Facebook ads transaction data in 2020. The goal is to get a model that has the best performance so that it can be deployed to the web. The method that is used to compare the results is a confusion matrix and

APA, Harvard, Vancouver, ISO, and other styles

35

Luo, Jianxiang, and Yonggang Fu. "Predicting China's Economic Running State Using Machine Learning." MATEC Web of Conferences 232 (2018): 03036. http://dx.doi.org/10.1051/matecconf/201823203036.

Full text

Abstract:

China's business index of macro-economic includes early warning index, coincidence index, leading index and lagging index, among which early warning index reflects the economic running state. However, obtaining these indexes is a complex and daunting task. To simplify the task, this article mainly explores how to use machine learning algorithms including multiple linear regression (MLR), support vector machine regression (SVM), random forest (RF), artificial neural network (ANN) and extreme learning machine (ELM) to accurately predict early warning index. Finally, it can be found that the warn

APA, Harvard, Vancouver, ISO, and other styles

36

M, Angulakshmi, Deepa M, Mala Serene I, Thilagavathi M, and Aarthi P. "House Price Prediction using Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 69–75. http://dx.doi.org/10.17762/ijritcc.v11i9.8321.

Full text

Abstract:

House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decis

APA, Harvard, Vancouver, ISO, and other styles

37

Are, M. T. Okorie P. U. Olarinoye G. A. "A Review of Short-Term Electrical Load Forecasting Using Ensemble Stacking Generalization with Artificial Neural Network." Journal of Materials Engineering, Structures and Computation 2, no. 1 (2023): 36–52. https://doi.org/10.5281/zenodo.7760014.

Full text

Abstract:

<em>Electric load forecasting has gained much attention in electricity production due to its important role in electric power system management. Short-term load forecasting (STLF) uses the perception of ensemble learning approaches as a general scheme for educating the prognostic skill of a machine learning model (MLM). STLF is subjected to numerous errors /problems like high bias and variance. This prompts the need for the employment of ensemble stacking generalization with artificial neural networks (ANN) to ensure an improved performance with accurate results. This approach combined four mo

APA, Harvard, Vancouver, ISO, and other styles

38

Pham, Tuan Anh, Hai-Bang Ly, Van Quan Tran, Loi Van Giap, Huong-Lan Thi Vu, and Hong-Anh Thi Duong. "Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest." Applied Sciences 10, no. 5 (2020): 1871. http://dx.doi.org/10.3390/app10051871.

Full text

Abstract:

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average

APA, Harvard, Vancouver, ISO, and other styles

39

Vaishnavi, Ramathirtham. "Smart Crop Recommendation Using Machine Learning for Precision Agriculture." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49536.

Full text

Abstract:

Abstract - Agriculture and its allied sectors are undoubtedly the largest providers of livelihoods in rural India. The agriculture sector is also a significant contributor factor to the country's Gross Domestic Product (GDP). Blessing to the country is the overwhelming size of the agricultural sector. However, regrettable is the yield per hectare of crops in comparison to international standards. This is one of the possible causes for a higher suicide rate among marginal farmers in India. This paper proposes a viable and user-friendly yield prediction system for the farmers. The proposed syste

APA, Harvard, Vancouver, ISO, and other styles

40

Jisha, G., T. Bhuvan Nikhila, and Jerrard Ritta. "Time series prediction of personalized insulin dosage for type 2 diabetics." Time series prediction of personalized insulin dosage for type 2 diabetics 31, no. 2 (2023): 1080–87. https://doi.org/10.11591/ijeecs.v31.i2.pp1080-1087.

Full text

Abstract:

Careful blood glucose monitoring and consistent insulin administration are necessary for managing diabetes. People with demanding schedules or little access to medical personnel may find this difficult. Fortunately, without having to visit a doctor every day, daily insulin dosage may now be customized to a person’s unique needs using technology and customised algorithms based on their food intake, exercise routines, and blood glucose levels. This information can be entered into a diabetes management app or device, where an algorithm will determine the proper insulin dosage and offer real

APA, Harvard, Vancouver, ISO, and other styles

41

Rahman, Senjuti, Mehedi Hasan, and Ajay Krishno Sarkar. "Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques." European Journal of Electrical Engineering and Computer Science 7, no. 1 (2023): 23–30. http://dx.doi.org/10.24018/ejece.2023.7.1.483.

Full text

Abstract:

The brain is the human body's primary upper organ. Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. The World Health Organization (WHO) claims that stroke is the leading cause of death and disability worldwide. Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. The main obj

APA, Harvard, Vancouver, ISO, and other styles

42

Rajković, Dragana, Ana Marjanović Jeromela, Lato Pezo, et al. "Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models." Agronomy 12, no. 1 (2021): 58. http://dx.doi.org/10.3390/agronomy12010058.

Full text

Abstract:

As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carr

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Yuzhen, Jun Ma, Shunlin Liang, Xisheng Li, and Manyao Li. "An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products." Remote Sensing 12, no. 24 (2020): 4015. http://dx.doi.org/10.3390/rs12244015.

Full text

Abstract:

This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, as well as climatic and topographical data. Some of these algorithms have not been commonly used for forest AGB estimation such as the extremely randomized trees, stochastic gradient boosting, and categorical boosting (CatBoost) regression. For each algorithm, its hyperparameters were optimized using grid search with cross-validation, and the optima

APA, Harvard, Vancouver, ISO, and other styles

44

Akbari, Elahe, Ali Darvishi Boloorani, Jochem Verrelst, et al. "Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms." Remote Sensing 15, no. 14 (2023): 3690. http://dx.doi.org/10.3390/rs15143690.

Full text

Abstract:

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic a

APA, Harvard, Vancouver, ISO, and other styles

45

Cao, Yanmei, Boyang Li, Qi Xiang, and Yuxian Zhang. "Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization." Sustainability 15, no. 17 (2023): 12772. http://dx.doi.org/10.3390/su151712772.

Full text

Abstract:

With the aim of predicting the environmental vibrations induced by an elevated high-speed railway, a machine learning method was developed by combining a random forest algorithm and Bayesian optimization, using a dataset from on-site experiments. When it comes to achieving a rapid and effective prediction of environmental vibrations, there is little research on comparisons between and verifications of different algorithms, and none on the parameter tuning and optimization of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations c

APA, Harvard, Vancouver, ISO, and other styles

46

Kalpana, G., Dr A. Kanaka Durga, Anoop Reddy, and Dr G. Karuna. "Predicting the Price of Pre-Owned Cars Using Machine Learning and Data Science." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1468–76. http://dx.doi.org/10.22214/ijraset.2022.45469.

Full text

Abstract:

Abstract: Storm Motors Is An E-Commerce Company Who Act As Mediators Between Parties Interested In Selling And Buying Pre-Owned Cars. They Have Recorded Data About The Seller And Car Details, Registration Details, Web Advertisement Details, Make And Model Information And Price. The Company Wishes To Develop An Algorithm To Predict The Price Of Pre-Owned Cars Based On Various Attributes Associated With The Car To Make A Sale Quickly, If The Price Is Reasonable And Satisfies Both The Seller And Buyer, By Comparing The Price Of Various Car Models Based On Car Features To Improve Their Business. I

APA, Harvard, Vancouver, ISO, and other styles

47

Tırınk, Cem, Dariusz Piwczyński, Magdalena Kolenda, and Hasan Önder. "Estimation of Body Weight Based on Biometric Measurements by Using Random Forest Regression, Support Vector Regression and CART Algorithms." Animals 13, no. 5 (2023): 798. http://dx.doi.org/10.3390/ani13050798.

Full text

Abstract:

The study’s main goal was to compare several data mining and machine learning algorithms to estimate body weight based on body measurements at a different share of Polish Merino in the genotype of crossbreds (share of Suffolk and Polish Merino genotypes). The study estimated the capabilities of CART, support vector regression and random forest regression algorithms. To compare the estimation performances of the evaluated algorithms and determine the best model for estimating body weight, various body measurements and sex and birth type characteristics were assessed. Data from 344 sheep were us

APA, Harvard, Vancouver, ISO, and other styles

48

G., Jisha, Nikhila T. Bhuvan, and Ritta Jerrard. "Time series prediction of personalized insulin dosage for type 2 diabetics." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (2023): 1080. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp1080-1087.

Full text

Abstract:

Careful blood glucose monitoring and consistent insulin administration are necessary for managing diabetes. People with demanding schedules or little access to medical personnel may find this difficult. Fortunately, without having to visit a doctor every day, daily insulin dosage may now be customized to a person’s unique needs using technology and customised algorithms based on their food intake, exercise routines, and blood glucose levels. This information can be entered into a diabetes management app or device, where an algorithm will determine the proper insulin dosage and offer real-time

APA, Harvard, Vancouver, ISO, and other styles

49

Langenberger, Benedikt, Timo Schulte, and Oliver Groene. "The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data." PLOS ONE 18, no. 1 (2023): e0279540. http://dx.doi.org/10.1371/journal.pone.0279540.

Full text

Abstract:

Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications

APA, Harvard, Vancouver, ISO, and other styles

50

Zahoor, Muhammad Farhan, Arshad Hussain, and Afaq Khattak. "Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures." Infrastructures 10, no. 6 (2025): 142. https://doi.org/10.3390/infrastructures10060142.

Full text

Abstract:

The longevity and safety of asphalt pavements, which form the foundation of our transportation infrastructure, are directly impacted by their performance. Pavement performance has traditionally been measured using the Marshall Mix Design method, which is a time- and resource-intensive laboratory procedure. Machine learning algorithms (MLAs) are increasingly popular today and are being utilized in various fields. Their performances vary; therefore, evaluating different MLAs and comparing them is important. The potential of various machine learning (ML) algorithms to predict Marshall Stability (

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!