Intelelm

+ Rename `ELM` class to `MultiLayerELM` class. This new class can be used to define deep ELM network
by `layer_sizes` parameter.
+ Add `AutomatedMhaElmTuner` class that can be used to perform hyperparameter tuning for MhaElm models using
either GridSearchCV or RandomizedSearchCV. Provides an interface for fitting and predicting using the best found model.
+ Add `AutomatedMhaElmComparator` class that automatic compare different MhaElm models based on provided optimizer
configurations. It provides methods for cross-validation and train-test split evaluation.
+ Update docs, examples, and tests.

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+ 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.

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+ 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

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+ Fix bug lb and ub in BaseMhaElm class
+ Update docs and add example

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+ Fix bug in DataTransformer class
+ Fix bug in LabelEncoder class
+ Add more activation functions
+ Update documents, examples

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+ 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

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