Relevant bibliographies by topics / Random Forest Regression and ANN Algorithm

Contents

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

Academic literature 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 lists of relevant articles, books, theses, conference reports, and other scholarly sources 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.

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

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
More sources

Dissertations / Theses on the topic "Random Forest Regression and ANN Algorithm"

1

Das, Abhishek. "Analyses of Crash Occurence and Injury Severities on Multi Lane Highways Using Machine Learning Algorithms." Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2576.

Full text
Abstract:
Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; adverse weather and environmental conditions; and roadway geometric and traffic factors. The objective of
APA, Harvard, Vancouver, ISO, and other styles
2

Bellmann, Hector G. "Categorization of Tonal Music Styles: A Quantitative Investigation." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/366215.

Full text
Abstract:
The objective of this study is to identify the main conceptual dimensions of tonal musical style in order to provide a theoretical understanding of the phenomenon which could constitute the basis for a scientic taxonomy of styles. Music style is a substantial quality of the musical material that can be readily recognized as belonging to individual composers or associated with their epoch. I was initially attracted to the topic through the knowledge of the successes of literary stylometry, in the expectation that similar accomplishments could have been achieved in the realm of music. I was surp
APA, Harvard, Vancouver, ISO, and other styles
3

Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.

Full text
Abstract:
It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model
APA, Harvard, Vancouver, ISO, and other styles
4

Ekeberg, Lukas, and Alexander Fahnehjelm. "Maskininlärning som verktyg för att extrahera information om attribut kring bostadsannonser i syfte att maximera försäljningspris." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240401.

Full text
Abstract:
The Swedish real estate market has been digitalized over the past decade with the current practice being to post your real estate advertisement online. A question that has arisen is how a seller can optimize their public listing to maximize the selling premium. This paper analyzes the use of three machine learning methods to solve this problem: Linear Regression, Decision Tree Regressor and Random Forest Regressor. The aim is to retrieve information regarding how certain attributes contribute to the premium value. The dataset used contains apartments sold within the years of 2014-2018 in the Ö
APA, Harvard, Vancouver, ISO, and other styles
5

Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.

Full text
Abstract:
With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predict
APA, Harvard, Vancouver, ISO, and other styles
6

Johansson, David. "Price Prediction of Vinyl Records Using Machine Learning Algorithms." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96464.

Full text
Abstract:
Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in g
APA, Harvard, Vancouver, ISO, and other styles
7

Duncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.

Full text
Abstract:
Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warni
APA, Harvard, Vancouver, ISO, and other styles
8

Chen, Carla Chia-Ming. "Bayesian methodology for genetics of complex diseases." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf.

Full text
Abstract:
Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interacti
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Random Forest Regression and ANN Algorithm"

1

Yadav, Tushar, Nabha Varshey, and Shanu Khare. "Random Forest Algorithm, Support Vector Machine for Regression Analysis." In Computational Intelligence and Mathematical Applications. CRC Press, 2024. http://dx.doi.org/10.1201/9781003534112-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Anwar, Suzan, Arthur Rahming, Mikea Fernander, Otito Udedibor, and Shereen Ali. "Breast Cancer Diagnosing System: Using a Rough Set-Ensemble Classifier Approach." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88220-3_2.

Full text
Abstract:
Abstract Breast cancer occurs when normal breast cells turn cancerous, grow abnormally and form tumors. The most common cancer impacting women worldwide is breast cancer. Diagnosing breast cancer early and accurately is crucial for giving the correct treatment and ensuring patients receive the best care possible. Due to human error, misdiagnosis is a possibility in the medical field. Over-diagnosis can cause patients to go through unnecessary treatments. Under-diagnosis can allow malignant tumors to become more aggressive and life-threatening. The aim of our research is to create a dependable model to correctly diagnose breast cancer. We propose to use a rough set ensemble classifier approach to assist doctors in making more accurate diagnosis. The rough set reduct algorithm will be used for feature reductions and the model will be built with logistic regression algorithm, Support Vector Machine (SVM) algorithms and random forest algorithm. The proposed model produced an accuracy of 93% for logistic regression algorithm, 97% for SVM, and 92% for Random Forest when classifying the image data and overall produced a 96% accuracy.
APA, Harvard, Vancouver, ISO, and other styles
3

Yang, Kaifeng, and Michael Affenzeller. "Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_13.

Full text
Abstract:
AbstractSurrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.
APA, Harvard, Vancouver, ISO, and other styles
4

Mao, Mohan. "A Comparative Study of Random Forest Regression for Predicting House Prices Using." In Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023). Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-370-2_63.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Priya, Nallabothu Vamsi, and S. TamilSelvan. "Comparison of improved XGBoost algorithm with random forest regression to determine the prediction of the mobile price." In Applications of Mathematics in Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003606659-120.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Girish, Maturi Sai, and P. Sriramya. "Effective prediction of IPL outcome matches to improve accuracy using novel random forest algorithm compared over logistic regression." In Applications of Mathematics in Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003606659-117.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Rakesh, G., and J. Chenni Kumaran. "A novel logistic regression algorithm compared with the random forest algorithm to improve the accuracy of predicting crashes in automated driving system cars." In Applications of Mathematics in Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003606659-109.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Arslantas, Mustafa Kemal, Tunc Asuroglu, Reyhan Arslantas, et al. "Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_1.

Full text
Abstract:
AbstractSerum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.
APA, Harvard, Vancouver, ISO, and other styles
9

El Fallah, Saad, Jaouad Kharbach, Abdellah Rezzouk, and Mohammed Ouazzani Jamil. "Robust State of Charge Estimation and Simulation of Lithium-Ion Batteries Using Deep Neural Network and Optimized Random Forest Regression Algorithm." In Artificial Intelligence and Industrial Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43520-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Pandya, Mayur, and Jayaraman Valadi. "Random Forest Classification and Regression Models for Literacy Data." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0332-8_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Random Forest Regression and ANN Algorithm"

1

Setiyanti, Michelle, Genrawan Hoendarto, and Jimmy Tjen. "Enhancing Water Potability Identification through Random Forest Regression and Genetic Algorithm Optimization." In INTERNATIONAL CONFERENCE ON APPLIED TECHNOLOGY 2024. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-2fikqf.

Full text
Abstract:
Water quality is important for both environmental sustainability and public health. This research introduces an innovative method for forecasting water quality using Random Forest Regression, optimized through Genetic Algorithm (GA) techniques. The goal is to enhance prediction accuracy and offer meaningful insights for better water resource management. The study employed the “Water Quality Data” dataset, encompassing 11 essential water quality parameters from different locations. After thorough data preprocessing, the Random Forest model, refined with GA optimization, achieved a Mean Squared
APA, Harvard, Vancouver, ISO, and other styles
2

Deng, Yawen. "Predicting and Analyzing Match Fluctuations Based on Random Forest Regression Algorithm." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709310.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Charan, K. Sai, Supraja Eduru, Jaishree R. Devaru, and H. B. Divyashree. "Diabetes Prediction with Random Forest - Logistic Regression algorithm using voting classifier." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11035945.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Pengbo. "Prediction of the Groundwater Levels Based on Random Forest Regression Algorithm." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10869277.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Rastogi, Stuti, Simmi Deol, Anju Gera, Pawan Kumar Singh, Rupa Rani Sharma, and Kuldeep Singh. "Prediction of diabetes disease using Adaboost, Random Forest Algorithm and Logistic Regression." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911780.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ruuhwan, Rikza Fauzan Nuradiah, Aso Sudiarjo, Dhema Yunautama, Gunawansyah, and Edi Andriansyah. "A Comparative Analysis of Logistic Regression and Random Forest Algorithm for Cyber Attacks Classification." In 2024 18th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2024. https://doi.org/10.1109/tssa63730.2024.10863823.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yang, Xingliang, Yujie Wang, Zhendong Sun, and Zonghai Chen. "Degradation Prediction for Fuel Cell Based on Random Forest Regression and Grey Wolf Optimizer Algorithm." In 2024 The 9th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2024. https://doi.org/10.1109/icpre62586.2024.10768312.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Srivastava, Shraddha, Ishita Sharma, Harshita Negi, and Khushi Singh. "Advancements in Heart Disease Prediction: A Comprehensive Survey of Logistic Regression and Feature Engineering Techniques Enhanced by Random Forest Algorithm." In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP). IEEE, 2024. http://dx.doi.org/10.1109/innocomp63224.2024.00052.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Rosine, Germanicus, and El-Hassani Othman. "Machine Learning for Predicting DataCube Atomic Force Microscope (AFM)—MultiDAT-AFM." In ISTFA 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.istfa2024p0351.

Full text
Abstract:
Abstract In nanoscience, techniques based on Atomic Force Microscope (AFM) stand as a cornerstone for exploring local electrical, electrochemical and magnetic properties of microelectronic devices at the nanoscale. As AFM's capabilities evolve, so do the challenges of data analysis. With the aim of developing a prediction model for AFM mappings, based on Machine Learning, this work presents a step towards the analysis and benefit of Big Data recorded in the hyperspectral modes: AFM DataCube. The MultiDAT-AFM solution is an advanced 2000-line Python-based tool designed to tackle the complexitie
APA, Harvard, Vancouver, ISO, and other styles
10

Pandey, Ritesh, Maneesh Upadhya, and Manvendra Singh. "Rainfall Prediction Using Logistic Regression and Random Forest Algorithm." In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT). IEEE, 2024. http://dx.doi.org/10.1109/ic2pct60090.2024.10486681.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Random Forest Regression and ANN Algorithm"

1

Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

Full text
Abstract:
Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
APA, Harvard, Vancouver, ISO, and other styles
2

Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004488.

Full text
Abstract:
We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider on
APA, Harvard, Vancouver, ISO, and other styles
3

Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

Full text
Abstract:
In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Random Forest Regression and ANN Algorithm' for particular source types:
Journal articles
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!

To the bibliography