diff --git a/.gitignore b/.gitignore
index e43b0f9..87fd9ed 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
 .DS_Store
+*.pyc
+*.xml
diff --git a/Examples/FELM.py b/Examples/FELM.py
new file mode 100644
index 0000000..654d902
--- /dev/null
+++ b/Examples/FELM.py
@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import RepeatedKFold, cross_val_score
+from sklearn.preprocessing import LabelEncoder
+from sklearn import preprocessing
+
+from Layers.ELMLayer import ELMLayer
+from Models.ELMModel import ELMModel
+
+
+# Hyperparameters:
+num_neurons = 1000
+n_splits = 10
+n_repeats = 50
+
+# Loading sample dataset from Data folder
+path = "../Data/ionosphere.txt"
+df = pd.read_csv(path, delimiter='\t').fillna(0)
+X = df.values[:, 1:]
+y = df.values[:, 0]
+
+# Label encoding and features normalization
+label_encoder = LabelEncoder()
+y = label_encoder.fit_transform(y)  # Encode class labels to numerical values
+X = preprocessing.normalize(X)  # Normalize feature vectors
+
+
+# Fuzzify the imput
+def fuzzify_input(X):
+    # Define triangular membership functions for fuzzification
+    # Here, we use a simple approach where we assume that the features are normalized between 0 and 1
+    fuzzified_X = np.zeros_like(X)
+    for i in range(X.shape[1]):
+        fuzzified_X[:, i] = np.maximum(0, 1 - np.abs(X[:, i] - 0.5) / 0.5)
+    return fuzzified_X
+
+
+X = fuzzify_input(X)
+
+# Initialize an Extreme Learning Machine (ELM) layer
+elm = ELMLayer(number_neurons=num_neurons, activation='mish')
+
+# Create an ELM model using the trained ELM layer
+model = ELMModel(elm)
+
+# Define a cross-validation strategy
+cv = RepeatedKFold(n_splits=n_splits, n_repeats=n_repeats)
+
+# Perform cross-validation to evaluate the model performance
+scores = cross_val_score(model, X, y, cv=cv, scoring='accuracy', error_score='raise')
+
+# Print the mean accuracy score obtained from cross-validation
+print(np.mean(scores))
+
+# Fit the ELM model to the entire dataset
+model.fit(X, y)
+
+# Save the trained model to a file
+model.save("Saved Models/FELM_Model.h5")
+
+# Load the saved model from the file
+model = model.load("Saved Models/FELM_Model.h5")
+
+# Evaluate the accuracy of the model on the training data
+acc = accuracy_score(model.predict(X), y)
+print(acc)
diff --git a/Examples/Saved Models/FELM_Model.h5 b/Examples/Saved Models/FELM_Model.h5
new file mode 100644
index 0000000..f0ee7a2
Binary files /dev/null and b/Examples/Saved Models/FELM_Model.h5 differ
diff --git a/Models/USKELMModel.py b/Models/USKELMModel.py
index 8f3a4d7..f1ad682 100644
--- a/Models/USKELMModel.py
+++ b/Models/USKELMModel.py
@@ -148,7 +148,7 @@ def predict(self, X, clustering=False, k=None):
             >>> pred = model.predict(X)
         """
         pred = self.layer.predict(X, clustering, k)
-        return pred
+        return pred.numpy()
 
     def save(self, file_path):
         """