Csmas

PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning

“…LightGBM has proved great potential in predicting the market price movement in finance and economics [ 18 ]. Y. Tounsi, L. Hassouni, and H. Anoun have introduced a new model CSMAS to predict problems in data mining of credit scoring domain using state-of-the-art gradient boosting methods (XGBoost, CatBoost, and LightGBM) [ 25 ]. Sunghyeon Choi forecasted solar energy output by employing RF, XGBoost, and LightGBM models [ 26 ].…”

“… Reference Year Base model Time series prediction Data set [ 28 ] 2021 SVM, LR, Multi-layer perceptron, RF Covid-19 pandemic cumulative case forecasting Data of COVID-19 between January 20, 2020, and September 18, 2020, for the USA, Germany, and global was obtained from the World Health Organization website. [ 17 ] 2020 LR, SVM, ANN, RF, XGBoost, LightGBM Prediction of peak demand days of cardiovascular disease(CVD) admissions Health Information Center of Sichuan Province, China: the daily number of admissions of CVD patients in hospital Chengdu Meteorological Monitoring Database: Meteorological data China National Environmental Monitoring Cente: air pollutants data [ 25 ] 2020 XGBoost, CatBoost, LightGBM Prediction of problems in data mining of credit scoring domain Home Credit Default Risk from Kaggle Challenge [ 26 ] 2020 RF, XGBoost, LightGBM Photovoltaic Forecasting Data of a Photovoltaic plant in South Korea [ 18 ] 2020 LightGBM Cryptocurrency price trend Daily trading data from https://www.investing.com/ [ 29 ] 2020 XGBoost, ARIMA Hemorrhagic fever with renal syndrome Monthly hemorrhagic fever with renal syndrome incidence data from 2004 to 2018 from the official website of the National Health Commission of the People's Republic of China [ 27 ] …”

Agent mining approaches: an ontological view

“…For a better view, the article (Tounsi et al ., 2020) can be used as a complete example including instances of the ontology concepts, from the addressed problem to the elements used for the system design. The paper (Tounsi et al ., 2020) presents a software system to address a credit scoring problem, based on specialized agents that perform data pre-processing and mining. To that end, the authors describe the solution using the following elements represented in the ontology: one instance of the concept and more specifically of the concept ; instances for the concepts , and ; one instance of the concept : a CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology extension for Credit Scoring is described; one instance of the concept : several types of agent and the coordination between them are detailed; instances of the concept : informal diagrams are used to explain the CRISP-DM framework, in addition to a detailing the different layers of the system general architecture. …”

“…In ComputerScienceDomain,Calpur2018DomainSC presents an example of solution focused on human computer interaction, specially on natural language processing. SecurityDomain is well represented in Tangod & Kulkarni ( 2020 For a better view, the article (Tounsi et al, 2020) can be used as a complete example including instances of the ontology concepts, from the addressed problem to the elements used for the system design. The paper (Tounsi et al, 2020) presents a software system to address a credit scoring problem, based on specialized agents that perform data pre-processing and mining.…”

“…SecurityDomain is well represented in Tangod & Kulkarni ( 2020 For a better view, the article (Tounsi et al, 2020) can be used as a complete example including instances of the ontology concepts, from the addressed problem to the elements used for the system design. The paper (Tounsi et al, 2020) presents a software system to address a credit scoring problem, based on specialized agents that perform data pre-processing and mining. To that end, the authors describe the solution using the following elements represented in the ontology:…”