Added bai-3.0 Epilepsy

Main Models/bai-3.0 Epilepsy/README.md

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+# bai-3.0 Epilepsy (45851parametre)
+
+## "bai-3.0 Epilepsy" modeli, hastanın epilepsi nöbeti durumunu tespit eder.
+
+#### NOT: Gerçek zamanlı EEG veri takibi uygulamasına modeli entegre ederseniz, gerçek zamanlı olarak duygu tahmini yapabilmektedir. Uygulamaya erişebilmek için: https://github.com/neurazum/Realtime-EEG-Monitoring
+
+## -----------------------------------------------------------------------------------
+
+# bai-3.0 Epilepsy (45851 parameters)
+
+## The "bai-3.0 Epilepsy" model detects the patient's epileptic seizure status.
+
+#### NOTE: If you integrate the model into a real-time EEG data tracking application, it can predict emotions in real time. To access the application: https://github.com/neurazum/Realtime-EEG-Monitoring
+**Doğruluk/Accuracy: %68,90829694323143**
+
+# Kullanım / Usage
+
+```python
+import pandas as pd
+import numpy as np
+import ast
+from tensorflow.keras.models import load_model, Sequential
+from sklearn.metrics import accuracy_score
+
+model_path = 'model/path'
+
+model = load_model(model_path)
+
+test_data_path = 'epilepsy/dataset'
+test_data = pd.read_csv(test_data_path)
+
+test_data['sample'] = test_data['sample'].apply(ast.literal_eval)
+
+X_test = np.array(test_data['sample'].tolist())
+y_test = test_data['label'].values.astype(int)
+
+timesteps = 10
+
+X_test_reshaped = []
+
+for i in range(len(X_test) - timesteps):
+    X_test_reshaped.append(X_test[i:i + timesteps])
+
+X_test_reshaped = np.array(X_test_reshaped)
+
+y_pred = model.predict(X_test_reshaped)
+y_pred_classes = (y_pred > 0.77).astype(int) # En kararlı sonuçlar -> 0.78 ve 0.77. Eşik değeri: çıkan sonucun yuvarlama değerini artırıp azaltma.
+# Örn. Olasılık < 0.77 ise "0", olasılık >= 0.77 ise "1" tahminini yap.
+
+accuracy = accuracy_score(y_test[timesteps:], y_pred_classes)
+
+print("Gerçek Değerler (1: Nöbet, 0: Nöbet Değil) ve Tahminler:")
+for i in range(len(y_pred_classes)):
+    print(f"Gerçek: {y_test[i + timesteps]}, Tahmin: {y_pred_classes[i][0]}")
+print(f"Modelin doğruluk oranı: %{accuracy * 100}")
+model.summary()
+```
+
+# Python Sürümü / Python Version
+
+### 3.9 <=> 3.13
+
+# Modüller / Modules
+
+```bash
+matplotlib==3.8.0
+matplotlib-inline==0.1.6
+numpy==1.26.4
+pandas==2.2.2
+scikit-learn==1.3.1
+tensorflow==2.15.0
+```