Added bai-3.1 Emotion

.gitattributes

@@ -37,3 +37,4 @@ Main[[:space:]]Models/bai-2.0/bai-2.0.keras filter=lfs diff=lfs merge=lfs -text
 Main[[:space:]]Models/bai-2.1/bai-2.1.keras filter=lfs diff=lfs merge=lfs -text
 Main[[:space:]]Models/bai-2.2/bai-2.2.keras filter=lfs diff=lfs merge=lfs -text
 Main[[:space:]]Models/bai-3.0[[:space:]]Emotion/bai-3.0[[:space:]]Emotion.keras filter=lfs diff=lfs merge=lfs -text
+Main[[:space:]]Models/bai-3.1[[:space:]]Emotion/bai-3.1[[:space:]]Emotion.keras filter=lfs diff=lfs merge=lfs -text

Main Models/bai-3.1 Emotion/README.md

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+# bai-3.1 Emotion (3549313 parametre)
+
+## "bai-3.1 Emotion" modeli, bir önceki sürümüne göre daha optimize ve kararlı hale getirilmiş versiyonudur. Tüm işlevleri aynıdır.
+
+#### 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-3.1 Emotion (3549313 parameters)
+
+## The "bai-3.1 Emotion" model is a more optimized and stable version compared to the previous version. All functions are the same.
+
+## 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: %95,1219512195122**
+
+[![bai-3.0](https://img.youtube.com/vi/qUkId3S9W94/0.jpg)](https://www.youtube.com/watch?v=qUkId3S9W94)
+
+# 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}.1)')
+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-3.1 Emotion/bai-3.1 Emotion.keras

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