\u2022Examples<\/strong><\/p>\n\n\n\n \u2022Results section: Page 51: STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH \u2022https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1692&context=etd_projects<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n \u2022Check Results and Discussion sections<\/strong><\/p>\n\n\n\n \u2022https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2203\/2203.06848.pdf<\/a><\/p>\n\n\n\n \u2022A Comparative Study on Forecasting of Retail Sales<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n \u2022https:\/\/arxiv.org\/pdf\/2303.11633.pdf<\/a><\/p>\n\n\n\n \u2022Learning Context-Aware Classifier for Semantic Segmentation<\/p>\n\n\n\n \u2022Check results section; also Discussion Section: SPEECH INTELLIGIBILITY CLASSIFIERS FROM 550K DISORDERED SPEECH SAMPLES<\/p>\n\n\n\n \u2022https:\/\/arxiv.org\/pdf\/2303.07533.pdf<\/a><\/p>\n\n\n\n \u2022You can notice: results reported under different criteria, use of tables and figures.<\/p>\n\n\n\n \u2022Notice\/read the descriptions<\/p>\n\n\n\n <\/p>\n\n\n\n Data Analytics, Machine Learning, Data Science<\/p>\n\n\n\n <\/p>\n\n\n\n ROC\/AUC: Classification: ROC and AUC<\/strong><\/p>\n\n\n\n https:\/\/developers.google.com\/machine-learning\/crash-course\/classification\/roc-and-auc<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n F1 Score in Machine Learning<\/strong>: https:\/\/www.geeksforgeeks.org\/f1-score-in-machine-learning\/<\/a><\/p>\n\n\n\n Ref: https:\/\/www.geeksforgeeks.org\/f1-score-in-machine-learning\/<\/a><\/p>\n\n\n\n “This formula ensures that both precision and recall must be high for the F1 score to be high. If either one drops significantly, the F1 score will also drop.”<\/p>\n\n\n\n LEAF and CNN:<\/p>\n\n\n\n LEAF: \u201cLeaf: A learnable frontend for audio classification,\u201d ICLR, 2021<\/p>\n\n\n\n <\/p>\n\n\n\n ARIMA: Introducing ARIMA models<\/p>\n\n\n\n https:\/\/www.ibm.com\/think\/topics\/arima-model<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n Autoregressive Integrated Moving Average (ARIMA) Prediction Model<\/strong><\/p>\n\n\n\n https:\/\/www.investopedia.com\/terms\/a\/autoregressive-integrated-moving-average-arima.asp<\/a><\/p>\n\n\n\n “What Is an Autoregressive Integrated Moving Average (ARIMA)?<\/p>\n\n\n\n An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data<\/a> to either better understand the data set or to predict future trends. <\/p>\n\n\n\n A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock’s future prices based on its past performance or forecast a company’s earnings based on past periods.” : Ref: Investopedia<\/p>\n\n\n\n GCN: Graph Convolutional Networks (GCNs): Architectural Insights and Applications<\/strong><\/p>\n\n\n\n “GCNs are tailored\u00a0to work with\u00a0non-Euclidean data, making them\u00a0suitable for\u00a0a wide range\u00a0of applications\u00a0including social\u00a0networks, molecular\u00a0structures, and\u00a0recommendation\u00a0systems.<\/strong><\/em>“<\/p>\n\n\n\n https:\/\/www.geeksforgeeks.org\/deep-learning\/graph-convolutional-networks-gcns-architectural-insights-and-applications<\/a><\/p>\n\n\n\n Facebook Prophet:<\/strong><\/p>\n\n\n\n Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<\/p>\n\n\n\n https:\/\/facebook.github.io\/prophet<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n Single community based linear models:<\/strong><\/p>\n\n\n\n Google AI Overview:<\/p>\n\n\n\n “Single community-based linear models refer to statistical models where a single linear equation is used to predict a response variable based on the characteristics of a single community or group.\u00a0These models assume a linear relationship between the predictor variables and the outcome within that specific community”<\/p>\n\n\n\n <\/p>\n\n\n\n “The term \u201cMultiple Community-Based Linear Models\u201d<\/strong> likely refers to a modeling framework where separate linear models are fitted for different communities<\/strong> (e.g., neighborhoods, schools, cities, regions), rather than combining all data into a single model.” Reference: ChatGPT Also, this may be reference: https:\/\/www.stats.ox.ac.uk\/~snijders\/mlbook.htm<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Reporting and Analysis \u2022Examples \u2022Results section: Page 51: STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPHTHEORY, MACHINE LEARNING AND DEEP LEARNING MODELS \u2022https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1692&context=etd_projects \u2022Check Results and Discussion sections \u2022https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2203\/2203.06848.pdf \u2022A Comparative Study on Forecasting of Retail Sales May be complicated: Learning Context-Aware Classifier for Semantic Segmentation \u2022https:\/\/arxiv.org\/pdf\/2303.11633.pdf \u2022Learning Context-Aware Classifier for Semantic Segmentation \u2022Check results section; … <\/p>\n
THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS<\/p>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-12.png?resize=705%2C305\")
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<\/figure>\n\n\n\nMay be complicated: Learning Context-Aware Classifier for Semantic Segmentation<\/h3>\n\n\n\n
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<\/figure>\n\n\n\nMultiple Community-Based Linear Models<\/strong> <\/h3>\n\n\n\n