Update docs

Author: thieu1995

docs/source/pages/quick_start.rst

@@ -95,7 +95,8 @@ If you want to use the Whale Optimization-based ELM (WO-ELM) model, you can chan
 
 	opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30}
 	model = MhaElmRegressor(layer_sizes=(10, ), act_name="elu", obj_name="MSE",
-			optim="OriginalWOA", optim_params=opt_paras, verbose=False, seed=42)
+			optim="OriginalWOA", optim_params=opt_paras, verbose=False, seed=42,
+			lb=None, ub=None, mode='single', n_workers=None, termination=None)
 
 In the example above, I had to import the MhaElmRegressor class. This is the class that contains all Metaheuristics-based ELM models for regression problems.
 Then, I defined parameters for the Whale Optimization algorithm. And I defined parameters for the Whale Optimization-based ELM model.
@@ -110,7 +111,8 @@ MhaElmClassifier class (these are hybrid models combining metaheuristics algorit
 
 	from intelelm import ElmClassifier
 
-	model = ElmClassifier(layer_sizes=(10, ), act_name="elu", seed=42)
+	model = ElmClassifier(layer_sizes=(10, ), act_name="elu", seed=42,
+	        lb=None, ub=None, mode='single', n_workers=None, termination=None)
 
 
 
@@ -120,7 +122,8 @@ MhaElmClassifier class (these are hybrid models combining metaheuristics algorit
 
 	opt_paras = {"name": "GA", "epoch": 100, "pop_size": 30}
 	model = MhaElmClassifier(layer_sizes=(10, ), act_name="elu", obj_name="BSL",
-			optim="BaseGA", optim_params=opt_paras, verbose=False, seed=42)
+			optim="BaseGA", optim_params=opt_paras, verbose=False, seed=42,
+			lb=None, ub=None, mode='single', n_workers=None, termination=None)
 
 
 -------------------
@@ -167,8 +170,9 @@ library to split and scale the data.
 	##### 5.2: Use Metaheuristic-based ELM model for classification problem
 	print(MhaElmClassifier.SUPPORTED_OPTIMIZERS)
 	print(MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES)
-	opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-	classifier = MhaElmClassifier(layer_sizes=(10, ), act_name="elu", obj_name="KLDL", optim="BaseGA", optim_params=opt_paras, seed=42)
+	classifier = MhaElmClassifier(layer_sizes=(10, ), act_name="elu", obj_name="KLDL",
+	        optim="BaseGA", optim_params={"name": "GA", "epoch": 10, "pop_size": 30}, seed=42,
+	        lb=None, ub=None, mode='single', n_workers=None, termination=None)
 
 	#### Step 6: Traint the model
 	classifer.fit(X_train, y_train)
@@ -217,7 +221,8 @@ In the example below, we use the Whale Optimization-based ELM model as the base
 	# create model and selector
 	opt_paras = {"name": "GA", "epoch": 100, "pop_size": 30}
 	model = MhaElmRegressor(layer_sizes=(10, ), act_name="relu", obj_name="MSE",
-			optim="BaseGA", optim_params=opt_paras, verbose=False, seed=42)
+			optim="BaseGA", optim_params=opt_paras, verbose=False, seed=42,
+			lb=None, ub=None, mode='single', n_workers=None, termination=None))
 
 	selector = RFE(estimator=model)
 	selector.fit(X_train, y_train)

docs/source/pages/support.rst

@@ -8,40 +8,38 @@ Citation Request
   The paper can be accessed at the following `this link <https://doi.org/10.1016/j.procs.2020.03.063>`_
 
 
-Please include these citations if you plan to use this library
-
-.. bibtex::
+Please include these citations if you plan to use this library::
 
     @article{van2025intelelm,
-      title={IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine},
-      author={Van Thieu, Nguyen and Houssein, Essam H and Oliva, Diego and Hung, Nguyen Duy},
-      journal={Neurocomputing},
-      volume={618},
-      pages={129062},
-      year={2025},
-      publisher={Elsevier},
-      doi={10.1016/j.neucom.2024.129062}
+        title={IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine},
+        author={Van Thieu, Nguyen and Houssein, Essam H and Oliva, Diego and Hung, Nguyen Duy},
+        journal={Neurocomputing},
+        volume={618},
+        pages={129062},
+        year={2025},
+        publisher={Elsevier},
+        doi={10.1016/j.neucom.2024.129062}
+    }
+
+    @article{nguyen2020new,
+        title={A new workload prediction model using extreme learning machine and enhanced tug of war optimization},
+        author={Nguyen, Thieu and Hoang, Bao and Nguyen, Giang and Nguyen, Binh Minh},
+        journal={Procedia Computer Science},
+        volume={170},
+        pages={362--369},
+        year={2020},
+        publisher={Elsevier},
+        doi={10.1016/j.procs.2020.03.063}
     }
 
-	@article{nguyen2020new,
-	  title={A new workload prediction model using extreme learning machine and enhanced tug of war optimization},
-	  author={Nguyen, Thieu and Hoang, Bao and Nguyen, Giang and Nguyen, Binh Minh},
-	  journal={Procedia Computer Science},
-	  volume={170},
-	  pages={362--369},
-	  year={2020},
-	  publisher={Elsevier},
-	  doi={10.1016/j.procs.2020.03.063}
-	}
-
-	@article{van2023mealpy,
-	  title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
-	  author={Van Thieu, Nguyen and Mirjalili, Seyedali},
-	  journal={Journal of Systems Architecture},
-	  year={2023},
-	  publisher={Elsevier},
-	  doi={10.1016/j.sysarc.2023.102871}
-	}
+    @article{van2023mealpy,
+        title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
+        author={Van Thieu, Nguyen and Mirjalili, Seyedali},
+        journal={Journal of Systems Architecture},
+        year={2023},
+        publisher={Elsevier},
+        doi={10.1016/j.sysarc.2023.102871}
+    }
 
 
 If you have an open-ended or a research question, you can contact me via [email protected]