460ddb71-159d-4988-bce0-e3ecf2abe08f beie director@blueeyesintelligence.org 10.35940/ijitee.A9757.1213123 10.1007/978-3-030-66501-2_4Shahhosseini, Mohsen, and Guiping Hu. "Improved weighted random forest for classification problems." Progress in Intelligent Decision Science: Proceeding of IDS 2020. Springer International Publishing, 2021.10.3390/rs15030728Sudiana, D.; Lestari, A.I.; Riyanto, I.; Rizkinia, M.; Arief, R.; Prabuwono, A.S.; Sri Sumantyo, J.T. A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data. Remote Sens. 2023, 15, 728. https://doi.org/10.3390/rs1503072810.3389/frai.2023.1213436Togunwa Taofeeq Oluwatosin, Babatunde Abdulhammed Opeyemi, Abdullah Khalil-ur-Rahman "Deep hybrid model for maternal health risk classification in pregnancy: synergy of ANN and random forest" Frontiers in Artificial Intelligence, VOL-6 (2023 ) ,https://www.frontiersin.org/articles/10.3389/frai.2023.1213436 , 10.3389/frai.2023.1213436, ISSN:2624-821210.30534/ijatcse/2020/379942020Aruna Kumari G.L. , Dr Padmaja P. , Dr Jaya Suma G. , "Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification", International Journal of Advanced Trends in Computer Science and Engineering, Volume 9, No.4,(2020)http://www.warse.org/IJATCSE/static/pdf/file/ijatcse379942020.pdf https://doi.org/10.30534/ijatcse/2020/37994202010.1016/j.health.2023.100185E. Syed Mohamed, Tawseef Ahmad Naqishbandi, Syed Ahmad Chan Bukhari, Insha Rauf, Vilas Sawrikar, Arshad Hussain, "A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms", Healthcare Analytics,Volume 3,(2023), ISSN 2772-4425, https://doi.org/10.1016/j.health.2023.100185. (https://www.sciencedirect.com/science/article/pii/S2772442523000527)10.1016/j.eswa.2022.118227M Hemalatha, "A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection", Expert Systems with Applications, Volume 210, (2022), ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118227. (https://www.sciencedirect.com/science/article/pii/S0957417422013781)10.1109/CSITSS54238.2021.9682941P. Mazumder and S. Baruah, "A Community Based Study for Early Detection of Postpartum Depression using Improved Data Mining Techniques," 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2021, pp. 1-7, doi: 10.1109/CSITSS54238.2021.9682941.10.1023/A:1010933404324Breiman, Leo(2001), "Random Forests", Machine Learning,45(1),5-32 https://doi.org/10.1023/A:1010933404324.Machine LearningBreiman242123199610.1007/BF00058655Bagging predictorsBreiman, L. (1996). "Bagging predictors", Machine Learning, 24(2), 123-140.10.1007/BF00994018Cortes, Corinna, Vapnik, Vladimir (1995), "Support-vector networks",Machine Learning, 273- 297, 20(3), https://doi.org/10.1007/BF0099401810.1016/j.procs.2018.01.150Yassine Al Amrani, Mohamed Lazaar, Kamal Eddine El Kadiri, "Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis", Procedia Computer Science, Volume 127, (2018), 511-520,ISSN 1877-0509, https://doi.org/10.1016/j.procs.2018.01.150.10.1016/j.procs.2019.02.077L.A. Demidova, I.A. Klyueva, A.N. Pylkin, "Hybrid Approach to Improving the Results of the SVM Classification Using the Random Forest Algorithm", Procedia Computer Science,Volume 150, (2019), 455-461, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.02.077.10.11591/eei.v11i3.3787Admassu Tsehay, Subhashni Rajkumar, Napa, Komal Kumar, Prasath, Jijendira Duraisamy, Pradeep, Engidaye, Minychil (2022), "Random forest and support vector machine based hybrid liver disease detection", Bulletin of Electrical Engineering and Informatics ,VL - 11,10.11591/eei.v11i3.378710.4236/jilsa.2014.61005Hasan Mehedi A. M., Nasser M., Pal B., Ahmad S., "Support Vector Machine and Random Forest Modeling for Intrusion Detection System", Journal of Intelligent Learning Systems and Applications, (2014), Vol 6(1)10.1109/ICCASIT53235.2021.9633659H. Xu and M. Ma, "An Improved Hybrid Model base on SVM and Random Forest for the Prediction of Corporate Taxation," 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Changsha, China, 2021, pp. 1035-1038, doi: 10.1109/ICCASIT53235.2021.9633659.10.3390/s21248163Tun W, Wong JK, Ling SH. Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis. Sensors (Basel). 2021 Dec 7;21(24):8163. doi: 10.3390/s21248163. PMID: 34960257; PMCID: PMC8704049.10.1038/s41598-023-36605-3Lilhore, U.K., Manoharan, P., Sandhu, J.K. et al. "Hybrid model for precise hepatitis-C classification using improved random forest and SVM method". Sci Rep 13, 12473 (2023). https://doi.org/10.1038/s41598-023-36605-3.10.13053/cys-25-1-3431Jorge Alexander Ángeles Rojas, Hugo D. Calderón Vilcn,Ernesto N. Tumi Figueroa,Kent, Jhunior Cuadros Ramos, Steve S. Matos Manguinuri, Edwin F. Calderón Vilca, "Hybrid Model of Convolutional Neural Network and Support Vector Machine to Classify Basal Cell Carcinoma", Comp. y Sist. vol.25 no.1 Ciudad de México ene./mar. 2021 Epub 13-Sep-2021, https://doi.org/10.13053/cys-25-1-343110.1109/ACCESS.2023.3238570Fatih Bal, Fatih Kayaal, "A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products: Apple Case Study" ,IEEE Access, 25 January 2023, Digital Object Identifier 10.1109/ACCESS.2023.323857010.1007/s11227-020-03481-xJackins, V., Vimal, S., Kaliappan, M. et al. AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. J Supercomput 77, 5198-5219 (2021). https://doi.org/10.1007/s11227-020-03481-x10.1186/s41065-016-0012-2Huang, F., Shen, J., Guo, Q. et al. eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines. Hereditas 153, 6 (2016). https://doi.org/10.1186/s41065-016-0012-2.10.1186/s40537-020-00333-6Shen, J., Shafiq, M.O. Short-term stock market price trend prediction using a comprehensive deep learning system. J Big Data 7, 66 (2020). https://doi.org/10.1186/s40537-020-00333-610.1186/s40537-020-00327-4Chen, RC., Dewi, C., Huang, SW. et al. Selecting critical features for data classification based on machine learning methods. J Big Data 7, 52 (2020). https://doi.org/10.1186/s40537-020-00327-410.1063/5.0160228Kunihiro Kamataki, Hirohi Ohtomo, Naho Itagaki, Chawarambawa Fadzai Lesly, Daisuke Yamashita, Takamasa Okumura, Naoto Yamashita, Kazunori Koga, Masaharu Shiratani; Prediction by a hybrid machine learning model for high-mobility amorphous In2O3: Sn films fabricated by RF plasma sputtering deposition using a nitrogen-mediated amorphization method. J. Appl. Phys. 28 October 2023; 134 (16): 163301. https://doi.org/10.1063/5.0160228Sneha Bobde, Sharvari Role, Lokesh Khadke , Tejas Shirude , Ms. Shital Kakad, "Email Spam Detection Using Hybridization ofSVM and Random Forest", International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356, Volume 11 Issue 7, July 2023 ǁ PP. 188-19310.35940/ijitee.B6169.129219Wardhana, M. H., Basari, Prof. Dr. A. S. H., Mohd Jaya, Dr. A. S., Afandi, Prof. Dr. dr. D., & Dzakiyullah, N. R. (2019). A Hybrid Model using Artificial Neural Network and Genetic Algorithm for Degree of Injury Determination. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2, pp. 1357-1365). https://doi.org/10.35940/ijitee.b6169.12921910.35940/ijeat.A1187.109119Jebamalar, J. A., & Kumar, Dr. A. S. (2019). PM2.5 Prediction using Machine Learning Hybrid Model for Smart Health. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 6500-6505). https://doi.org/10.35940/ijeat.a1187.10911910.35940/ijrte.D4362.118419Behera*, D. K., Das, M., & Swetanisha, S. (2019). A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 10809-10814). https://doi.org/10.35940/ijrte.d4362.11841910.35940/ijbsac.G0486.039723Muthukrishnan, Dr. R., & Prakash, N. U. (2023). Validate Model Endorsed for Support Vector Machine Alignment with Kernel Function and Depth Concept to Get Superlative Accurateness. In International Journal of Basic Sciences and Applied Computing (Vol. 9, Issue 7, pp. 1-5). https://doi.org/10.35940/ijbsac.g0486.03972310.35940/ijsce.B3557.0512222Sistla, S. (2022). Predicting Diabetes u sing SVM Implemented by Machine Learning. In International Journal of Soft Computing and Engineering (Vol. 12, Issue 2, pp. 16-18). https://doi.org/10.35940/ijsce.b3557.0512222