Version 1.1.1

Update seed value in all 4 classes to ensure reproducibility of your results
Add mode, n_workers, and termination parameter in model.fit() of MhaElmRegressor and MhaElmClassifier classes
- These parameters are derived from Mealpy library
- With mode parameter, you can speed your training model
- With n_workers, you can set the number of threads or CPUs to speed up the training process
- With termination, you can set early stopping strategy for your model.
Update docs, examples, and tests.

Version 1.1.0

Update core modules to fit upgraded version of Mealpy>=3.0.1, PerMetrics>=2.0.0, Scikit-Learn>=1.2.1
IntelELM no longer support Python 3.7. Only support Python >= 3.8
Update docs and add examples

Add "evaluate" function to all Estimators (ElmRegressor, ElmClassifier, MhaElmRegressor, MhaElmClassifier)
Add new module "scaler"
Our scaler can be utilized with multiple methods.
Add "save_loss_train" and "save_metrics" functions to all Estimators
Add "save_model" and "load_model" functions to all Estimators
Add "save_y_predicted" function to all Estimators
Update all examples and documents

Add supported information for each classes.
Restructure intelelm module to based_elm module and model subpackage that includes mha_elm and standard_elm modules.
Add traditional/standard ELM models (ElmRegressor and ElmClassifier classes) to standard_elm module.
Add examples and tests for traditional models
Add score and scores functions to all classes.
Fix bug calculate metrics and objective in ELM-based models.
Add examples with real-world datasets and examples with GridsearchCV to tune hyper-parameters of ELM-based models.
Add documents

Add infors (CODE_OF_CONDUCT.md, MANIFEST.in, LICENSE, README.md, requirements.txt, CITATION.cff)
Add supported classification and regression datasets
Add util modules (data_loader, validator, evaluator, encoder, activation)
Add MhaElmRegressor and MhaElmClassifier classes
Add publish workflows
Add examples and tests folders