Added 'bai-3.0' Family

Main Models/bai-3.0 Emotion/README.md

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+# bai-3.0 Emotion (3549313 parametre)
+
+## "bai-3.0 Emotion" modeli, EEG üzerine eğitilmiş dünyanın en büyük üçüncü yapay zeka modelidir. Kişinin duygu durum analizini yapmaktadır.
+
+#### 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 Emotion (3549313 parameters)
+
+## The "bai-3.0 Emotion" model is the world's third largest artificial intelligence model trained on EEG. It analyzes the person's emotional state.
+
+## 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: %97,79549718574108**
+
+# Kullanım / Usage
+
+```python
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import load_model
+import matplotlib.pyplot as plt
+
+model_path = 'model-path'
+
+model = load_model(model_path)
+
+model_name = model_path.split('/')[-1].split('.')[0]
+
+plt.figure(figsize=(10, 6))
+plt.title(f'Duygu Tahmini ({model_name}.2)')
+plt.xlabel('Zaman')
+plt.ylabel('Sınıf')
+plt.legend(loc='upper right')
+plt.grid(True)
+plt.show()
+model.summary()
+```
+
+# Tahmin / Prediction
+
+```python
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import load_model
+
+model_path = 'model-path'
+
+model = load_model(model_path)
+
+scaler = StandardScaler()
+
+predictions = model.predict(X_new_reshaped)
+predicted_labels = np.argmax(predictions, axis=1)
+
+label_mapping = {'NEGATIVE': 0, 'NEUTRAL': 1, 'POSITIVE': 2}
+label_mapping_reverse = {v: k for k, v in label_mapping.items()}
+
+#new_input = np.array([[23, 465, 12, 9653] * 637])
+new_input = np.random.rand(1, 2548)  # 1 örnek ve 2548 özellik
+new_input_scaled = scaler.fit_transform(new_input)
+new_input_reshaped = new_input_scaled.reshape((new_input_scaled.shape[0], 1, new_input_scaled.shape[1]))
+
+new_prediction = model.predict(new_input_reshaped)
+predicted_label = np.argmax(new_prediction, axis=1)[0]
+predicted_emotion = label_mapping_reverse[predicted_label]
+
+if predicted_emotion == 'NEGATIVE':
+    predicted_emotion = 'Negatif'
+elif predicted_emotion == 'NEUTRAL':
+    predicted_emotion = 'Nötr'
+elif predicted_emotion == 'POSITIVE':
+    predicted_emotion = 'Pozitif'
+
+print(f'Giriş Verileri: {new_input}')
+print(f'Tahmin Edilen Duygu: {predicted_emotion}')
+```
+
+# 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
+```

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
+```