Artykuły w czasopismach na temat „Modèle « Random Forest »”
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Ampuła, Dariusz. "Random Forest in the Tests of Small Caliber Ammunition". Journal of KONBiN 52, nr 1 (1.03.2022): 73–85. http://dx.doi.org/10.2478/jok-2022-0006.
Pełny tekst źródłaK, Srinivasa Reddy. "Texture Filtration Module Under Stabilization Via Random Forest Optimization Methodology". International Journal of Advanced Trends in Computer Science and Engineering 8, nr 3 (25.06.2019): 458–69. http://dx.doi.org/10.30534/ijatcse/2019/20832019.
Pełny tekst źródłaOrtiz-Reyes, Alma Delia, Efraín Velasco-Bautista, Arian Correa-Díaz i Gregorio Ángeles-Pérez. "Predicción de variables dasométricas mediante modelos lineales mixtos y datos de LiDAR aerotransportado". E-CUCBA 9, nr 17 (29.12.2021): 88–95. http://dx.doi.org/10.32870/ecucba.vi17.213.
Pełny tekst źródłaMitra, Mainak, i Soumit Roy. "Comparative Analysis of Predictive Models for Carbon Emission in Major Countries: A Focus on Linear Regression and Random Forest". International Journal of Science and Research (IJSR) 6, nr 8 (5.08.2017): 2295–302. http://dx.doi.org/10.21275/sr231205142350.
Pełny tekst źródłaAlimbayeva, Zhadyra, Chingiz Alimbayev, Kassymbek Ozhikenov, Nurlan Bayanbay i Aiman Ozhikenova. "Wearable ECG Device and Machine Learning for Heart Monitoring". Sensors 24, nr 13 (28.06.2024): 4201. http://dx.doi.org/10.3390/s24134201.
Pełny tekst źródłaGao, Quansheng. "Design and Implementation of 3D Animation Data Processing Development Platform Based on Artificial Intelligence". Computational Intelligence and Neuroscience 2022 (30.05.2022): 1–7. http://dx.doi.org/10.1155/2022/1518331.
Pełny tekst źródłaTogatorop, Parmonangan R., Megawati Sianturi, David Simamora i Desriyani Silaen. "Optimizing Random Forest using Genetic Algorithm for Heart Disease Classification". Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 13, nr 1 (10.08.2022): 60. http://dx.doi.org/10.24843/lkjiti.2022.v13.i01.p06.
Pełny tekst źródłaZhao, Lefa, Yafei Zhu i Tianyu Zhao. "Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest". Mathematics 10, nr 16 (13.08.2022): 2921. http://dx.doi.org/10.3390/math10162921.
Pełny tekst źródłaLudot-Vlasak, Ronan. "Romulus en Amérique : recyclage et récupération des modèles antiques par John Howard Payne". Recherches anglaises et nord-américaines 45, nr 1 (2012): 65–82. http://dx.doi.org/10.3406/ranam.2012.1424.
Pełny tekst źródłaZhou, Bo, i Omer Saeed. "Comparative Analysis of Volleyball Serve Action Based on Human Posture Estimation". Mobile Information Systems 2022 (30.09.2022): 1–11. http://dx.doi.org/10.1155/2022/4817463.
Pełny tekst źródłaCai, Jiaowu, Peng Liu i Liangyu Li. "Pipeline gas leakage early warning system based on wireless sensor network". Frontiers in Computing and Intelligent Systems 2, nr 2 (29.12.2022): 53–57. http://dx.doi.org/10.54097/fcis.v2i2.4085.
Pełny tekst źródłaRadivojević, Dušan, Nikola Mirkov i Slobodan Maletić. "Human activity recognition based on machine learning classification of smartwatch accelerometer dataset". FME Transactions 49, nr 1 (2021): 225–32. http://dx.doi.org/10.5937/fme2101225r.
Pełny tekst źródłaMassoud, Rana, Riccardo Berta, Stefan Poslad, Alessandro De Gloria i Francesco Bellotti. "IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case". Sensors 21, nr 10 (20.05.2021): 3559. http://dx.doi.org/10.3390/s21103559.
Pełny tekst źródłaFu, Mingliang, Yuquan Leng, Haitao Luo i Weijia Zhou. "An Occlusion-Aware Framework for Real-Time 3D Pose Tracking". Sensors 18, nr 8 (20.08.2018): 2734. http://dx.doi.org/10.3390/s18082734.
Pełny tekst źródłaWang, Chao, Yunxiao Sun, Wenting Wang, Hongri Liu i Bailing Wang. "Hybrid Intrusion Detection System Based on Combination of Random Forest and Autoencoder". Symmetry 15, nr 3 (21.02.2023): 568. http://dx.doi.org/10.3390/sym15030568.
Pełny tekst źródłaMuruganantham, Kavitha, i Subbaiah Shanmugasundaram. "Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning". International Journal on Recent and Innovation Trends in Computing and Communication 11, nr 4 (4.05.2023): 261–68. http://dx.doi.org/10.17762/ijritcc.v11i4.6448.
Pełny tekst źródłaKulkarni, Prasad, Tushar Patil, Aditya Pandey, Vishwesh Vyawahare, Dhiraj Magare i Gajanan Birajdar. "Performance Assessment of Hetero-Junction Intrinsic Thin Film HIT Photovoltaic Module Using Machine Learning Methods". ITM Web of Conferences 44 (2022): 01009. http://dx.doi.org/10.1051/itmconf/20224401009.
Pełny tekst źródłaRen, Keyu, Heqing Peng, Junwei Wu, Shengtao Yao, Jinfeng Li i Pingyu Li. "PT module -A Traffic Signal Classification Model Based on Convolutional Neural Networks and Random Forests". Applied and Computational Engineering 2, nr 1 (22.03.2023): 374–81. http://dx.doi.org/10.54254/2755-2721/2/20220531.
Pełny tekst źródłaJi, Yin, Jiandong Fang i Yudong Zhao. "Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest". Applied Sciences 13, nr 21 (26.10.2023): 11742. http://dx.doi.org/10.3390/app132111742.
Pełny tekst źródłaEu, Song, Chang-Woo Lee, Junpyo Seo i Choongshik Woo. "Analyzing the Effect of Check Dam in Debris Flow Hazard Map Using Random Walk Model". Crisis and Emergency Management: Theory and Praxis 17, nr 9 (30.09.2021): 91–103. http://dx.doi.org/10.14251/crisisonomy.2021.17.9.91.
Pełny tekst źródłaChen, Zheng, i Weixiong Zhang. "Integrative Analysis Using Module-Guided Random Forests Reveals Correlated Genetic Factors Related to Mouse Weight". PLoS Computational Biology 9, nr 3 (7.03.2013): e1002956. http://dx.doi.org/10.1371/journal.pcbi.1002956.
Pełny tekst źródłaPaunović-Pantić, Jovana, Danijela Vučević, Igor Pantić, Svetlana Valjarević i Tatjana Radosavljević. "Development of random forest machine learning model for the detection of changes in liver tissue after exposure to iron oxide nanoparticles". Medicinska istrazivanja 57, nr 1 (2024): 21–26. http://dx.doi.org/10.5937/medi57-46969.
Pełny tekst źródłaElsayed, Khaled, Azrul A. Mutalib, Mohamed Elsayed i Mohd Reza Azmi. "Optimising Plate Thickness in Interlocking Inter-Module Connections for Modular Steel Buildings: A Finite Element and Random Forest Approach". Buildings 14, nr 5 (29.04.2024): 1254. http://dx.doi.org/10.3390/buildings14051254.
Pełny tekst źródłaAfiantara, Agus, Bagus Mahawan i Eka Budiarto. "Predicting of Banking Stability Using Machine Learning Technique of Random Forests". ACMIT Proceedings 6, nr 1 (5.07.2021): 1–8. http://dx.doi.org/10.33555/acmit.v6i1.89.
Pełny tekst źródłaChristian, Robby, Balza Achmad i Hyun Gook Kang. "Prognostic Methods on Accelerator’s Anode Voltage Regulator". E3S Web of Conferences 43 (2018): 01020. http://dx.doi.org/10.1051/e3sconf/20184301020.
Pełny tekst źródłaYan, Guobing, Qiang Sun, Jianying Huang i Yonghong Chen. "Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety". Journal of Advanced Computational Intelligence and Intelligent Informatics 25, nr 1 (20.01.2021): 40–49. http://dx.doi.org/10.20965/jaciii.2021.p0040.
Pełny tekst źródłaUmutoni, C., i I. Ngaruye. "Prediction of Tea Production in Rwanda Using Data Mining Techniques". Agricultural and Food Science Journal of Ghana 15, nr 1 (22.03.2023): 1631–40. http://dx.doi.org/10.4314/afsjg.v15i1.10.
Pełny tekst źródłaIlbeigipour, Sadegh, Amir Albadvi i Elham Akhondzadeh Noughabi. "Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming". Journal of Healthcare Engineering 2021 (22.04.2021): 1–13. http://dx.doi.org/10.1155/2021/6624829.
Pełny tekst źródłaWang, Shun-Yuan, Wen-Bin Lin i Yu-Chieh Shu. "Design of Machine Learning Prediction System Based on the Internet of Things Framework for Monitoring Fine PM Concentrations". Environments 8, nr 10 (24.09.2021): 99. http://dx.doi.org/10.3390/environments8100099.
Pełny tekst źródłaHeras, Diego, i Carlos Matovelle. "Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador". Ambiente e Agua - An Interdisciplinary Journal of Applied Science 16, nr 3 (27.05.2021): 1. http://dx.doi.org/10.4136/ambi-agua.2708.
Pełny tekst źródłaKim, Sunhae, Hye-Kyung Lee i Kounseok Lee. "Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches". International Journal of Environmental Research and Public Health 18, nr 7 (24.03.2021): 3339. http://dx.doi.org/10.3390/ijerph18073339.
Pełny tekst źródłaSRISANKAR, M., i Dr K. P. LOCHANAMBAL. "THE SENTIMENTAL ANALYSIS USING DEEP LEARNING MODELS". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, nr 11 (1.11.2023): 1–11. http://dx.doi.org/10.55041/ijsrem27151.
Pełny tekst źródłaLemenkova, Polina. "Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique". Coasts 4, nr 1 (26.02.2024): 127–49. http://dx.doi.org/10.3390/coasts4010008.
Pełny tekst źródłaKurade, Chinmay, Maninder Meenu, Sahil Kalra, Ankur Miglani, Bala Chakravarthy Neelapu, Yong Yu i Hosahalli S. Ramaswamy. "An Automated Image Processing Module for Quality Evaluation of Milled Rice". Foods 12, nr 6 (16.03.2023): 1273. http://dx.doi.org/10.3390/foods12061273.
Pełny tekst źródłaZhang, Huacong, Huaiqing Zhang, Keqin Xu, Yueqiao Li, Linlong Wang, Ren Liu, Hanqing Qiu i Longhua Yu. "A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm". Remote Sensing 15, nr 14 (10.07.2023): 3480. http://dx.doi.org/10.3390/rs15143480.
Pełny tekst źródłaFried, J. S., i J. K. Gilless. "Stochastic Representation of Fire Occurrence in a Wildland Fire Protection Planning Model for California". Forest Science 34, nr 4 (1.12.1988): 948–59. http://dx.doi.org/10.1093/forestscience/34.4.948.
Pełny tekst źródłaLiu, Jin, Shanshan Qiu i Zhenbo Wei. "Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies". Chemosensors 10, nr 10 (20.09.2022): 376. http://dx.doi.org/10.3390/chemosensors10100376.
Pełny tekst źródłaRodríguez-Azar, Paula Ivone, Jose Manuel Mejía-Muñoz, Oliverio Cruz-Mejía, Rafael Torres-Escobar i Lucero Verónica Ruelas López. "Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI". Sensors 24, nr 1 (27.12.2023): 149. http://dx.doi.org/10.3390/s24010149.
Pełny tekst źródłaMizuno, Osamu, Naoki Kawashima i Kimiaki Kawamoto. "Fault-Prone Module Prediction Approaches Using Identifiers in Source Code". International Journal of Software Innovation 3, nr 1 (styczeń 2015): 36–49. http://dx.doi.org/10.4018/ijsi.2015010103.
Pełny tekst źródłaR, Virupaksha Gouda, Anoop R, Joshi Sameerna, Arif Basha i Sahana Gali. "Forest Fire Prediction Using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 11, nr 5 (31.05.2023): 792–97. http://dx.doi.org/10.22214/ijraset.2023.51496.
Pełny tekst źródłaJeong, YiNa, SuRak Son i ByungKwan Lee. "The Lightweight Autonomous Vehicle Self-Diagnosis (LAVS) Using Machine Learning Based on Sensors and Multi-Protocol IoT Gateway". Sensors 19, nr 11 (3.06.2019): 2534. http://dx.doi.org/10.3390/s19112534.
Pełny tekst źródłaPei, Huiqing, Toshiaki Owari, Satoshi Tsuyuki i Yunfang Zhong. "Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs". Remote Sensing 15, nr 4 (11.02.2023): 1001. http://dx.doi.org/10.3390/rs15041001.
Pełny tekst źródłaGao, Meizhen, Li Li i Yetong Gao. "Statistics and Analysis of Targeted Poverty Alleviation Information Integrated with Big Data Mining Algorithm". Security and Communication Networks 2022 (23.04.2022): 1–10. http://dx.doi.org/10.1155/2022/1496170.
Pełny tekst źródłaLee, Sang J., Dahee Chung, Akiko Asano, Daisuke Sasaki, Masahiko Maeno, Yoshiki Ishida, Takuya Kobayashi, Yukinori Kuwajima, John D. Da Silva i Shigemi Nagai. "Diagnosis of Tooth Prognosis Using Artificial Intelligence". Diagnostics 12, nr 6 (9.06.2022): 1422. http://dx.doi.org/10.3390/diagnostics12061422.
Pełny tekst źródłaAlalayah, Khaled M., Khadija M. Alaidarous, Samah M. Alzanin, Mohammed A. Mahdi, Mohamed A. G. Hazber, Ibrahim M. Alwayle i Khaled M. G. Noaman. "Design an Internet of Things Standard Machine Learning Based Intrusion Detection for Wireless Sensing Networks". Journal of Nanoelectronics and Optoelectronics 18, nr 2 (1.02.2023): 217–26. http://dx.doi.org/10.1166/jno.2023.3383.
Pełny tekst źródłaXue, Hongxiang, Mingxia Shen, Yuwen Sun, Haonan Tian, Zihao Liu, Jinxin Chen i Peiquan Xu. "Instance Segmentation and Ensemble Learning for Automatic Temperature Detection in Multiparous Sows". Sensors 23, nr 22 (12.11.2023): 9128. http://dx.doi.org/10.3390/s23229128.
Pełny tekst źródłaYao, Jiaqi, Ying Zhang i Chen Xin. "Network-on-Chip hardware Trojan detection platform based on machine learning". Journal of Physics: Conference Series 2189, nr 1 (1.02.2022): 012004. http://dx.doi.org/10.1088/1742-6596/2189/1/012004.
Pełny tekst źródłaJiang, Tingyao, i Shuo Chen. "A Lightweight Forest Pest Image Recognition Model Based on Improved YOLOv8". Applied Sciences 14, nr 5 (27.02.2024): 1941. http://dx.doi.org/10.3390/app14051941.
Pełny tekst źródłaBelova, Ye P. "Using Formant Characteristics of Russian Vowels and Consonants for User Authentication". Herald of the Siberian State University of Telecommunications and Information Science 18, nr 1 (17.12.2023): 59–69. http://dx.doi.org/10.55648/1998-6920-2024-18-1-59-69.
Pełny tekst źródłaNastić, Filip. "Predlog modela za predviđanje koncentracije suspendovanih (PM2.5) čestica u vazduhu". Energija, ekonomija, ekologija XXV, nr 3 (2023): 39–44. http://dx.doi.org/10.46793/eee23-3.39n.
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