From b5e40293240c17da7b7a2a0e2b21a4358ed4cbe4 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:47:42 +0530 Subject: [PATCH 01/11] Create README.md --- Persian Alphabets Classification/README.md | 62 ++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 Persian Alphabets Classification/README.md diff --git a/Persian Alphabets Classification/README.md b/Persian Alphabets Classification/README.md new file mode 100644 index 000000000..1d668e8e7 --- /dev/null +++ b/Persian Alphabets Classification/README.md @@ -0,0 +1,62 @@ +# Persian Alphabet Classification with Hybrid CNN-LSTM Model + +This repository contains the code and models for classifying Persian alphabets using a hybrid CNN-LSTM deep learning model. + +## Overview + +In this repo, I developed a novel deep learning approach for classifying Persian alphabet characters. We utilized a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively capture spatial and sequential patterns in the input data, respectively. + +## Dataset + +The dataset used in this project consists of 51,744 images of Persian alphabets and numbers. The images are organized into folders based on their corresponding labels, making it suitable for training and evaluating machine learning models for Persian alphabet classificatiaon. + +You can download the dataset from [Kaggle](https://www.kaggle.com/datasets/mostafamohammadi1/persian-alphabets-and-numbers). + +## Files + +- `model/notebook.ipynb`: Jupyter notebook containing the Python code for training the hybrid CNN-LSTM model and evaluating its performance on the test dataset. +- `model/cnn_model.h5 | lstm_model.h5 | hybrid_model.h5` : Model files containing the weights and architecture of the trained standalone and hybrid model of CNN and LSTM architectures. + +## Usage + +To use the pre-trained model for inference, follow these steps: + +1. Clone or download this repository to your local machine. +2. Open the Jupyter notebook `notebook.ipynb` using Jupyter Notebook or JupyterLab. +3. Follow the instructions in the notebook to load the pre-trained model and perform inference on your own data. + +## Requirements + +1. To run the Jupyter notebook, you need the following dependencies: + +- Python 3 +- Jupyter Notebook or JupyterLab +- TensorFlow 2.x +- NumPy +- Matplotlib +- Seaborn +- scikit-learn + +2. You can install the required packages using pip: + +```bash +pip install jupyter tensorflow numpy matplotlib seaborn scikit-learn +``` + +3. Download the dataset +4. Navigate to the `model/notebook.ipynb` +5. Run all the cells + +## Contributor: +

+ +

+

+ Kamalakar Satapathi +

+ + +Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar) + +Explore my magical codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar) + From cd4597cb2c5f2266646482347cc6054ab66024f2 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:48:28 +0530 Subject: [PATCH 02/11] Create readme.md --- .../models/readme.md | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 Persian Alphabets Classification/models/readme.md diff --git a/Persian Alphabets Classification/models/readme.md b/Persian Alphabets Classification/models/readme.md new file mode 100644 index 000000000..b9f120157 --- /dev/null +++ b/Persian Alphabets Classification/models/readme.md @@ -0,0 +1,31 @@ +## Work flow + +1. Data Collection: + +- Download the dataset from [Kaggle](https://www.kaggle.com/datasets/mostafamohammadi1/persian-alphabets-and-numbers) +- Curate and organize the dataset into appropriate folders. + +2. Data Preprocessing: + +- Load the dataset and preprocess the images. +- Resize the images to a uniform size. +- Split the dataset into training and testing sets. +- Convert images to numerical arrays for model input. + +3. Model Architecture Design: + +- Design a hybrid architecture combining CNN and LSTM layers. +- Define the number of layers, filter sizes, and activation functions. +- Compile the model with appropriate loss function and optimizer. + +4. Model Training: + +- Feed the preprocessed data into the model for training. +- Iterate over multiple epochs to allow the model to learn the patterns in the data. +- Adjust the model parameters based on training performance. + +5. Model Evaluation: + +- Evaluate the trained model on the test dataset. +- Calculate performance metrics such as accuracy, loss, and classification reports. +- Visualize the training and validation metrics to assess model performance. 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zcb{(D=$nJ0*hr|Ykr;&L5xjdPSIXsd8c9Hc_m_Bk^=Ru{9vhdkh?r>nZ00u*j|#%(q6r=Tk$=IdKK`cS{XL< z9>^&}R^am-H_8rq#j?+n`FgC3IBMZ_S~YKmTZA6RHcsZIe{qO_7p_G{;JD&hcHL|M z>-~>WvUUKj(mf6xVY5i?b~j@Q>8X+73=iap%emNLF4=R~;?v{`I0P->n5)yp zg`rDX*Sn0?$&)$W>jVOtJMr%79Kh}^j(jgfwbg$(bby>QbEfcMh%1|WoWX-ldwEuM zx)9P!VxE5-huqKo7*ZqU5;Dy=I(-T|yKQFiuh)2NVZ^mTHZ(0gOx5az4E{a|f1MXo zWAsE6WY+P;jCAIjD2lcT$GQB8HF`VdO5IH%Z18@L#bMvkY#W9N2Q8>jUB{Pt19)lj NIjYSFVh5Sy_#Z(jPow|< literal 0 HcmV?d00001 diff --git a/Persian Alphabets Classification/models/notebook.ipynb b/Persian Alphabets Classification/models/notebook.ipynb new file mode 100644 index 000000000..24a023456 --- /dev/null +++ b/Persian Alphabets Classification/models/notebook.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":7307871,"sourceType":"datasetVersion","datasetId":4232268}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**Objective**\n\nThe objective of this notebook is to develop a deep learning model capable of classifying Persian alphabet characters using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. By leveraging the strengths of CNNs for spatial feature extraction and LSTMs for sequential pattern recognition, the aim is to build a robust classifier that accurately identifies Persian alphabet characters from input images. The model will be trained on a dataset containing images of Persian alphabet characters and evaluated based on its classification performance.","metadata":{}},{"cell_type":"markdown","source":"## Import all necessary libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport cv2\nimport os\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.layers import Conv2D, LSTM, Dense, Flatten, MaxPool2D, Dropout, MaxPooling2D, TimeDistributed\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.utils import to_categorical\nfrom tqdm import tqdm","metadata":{"execution":{"iopub.status.busy":"2024-02-23T09:41:58.825694Z","iopub.execute_input":"2024-02-23T09:41:58.826579Z","iopub.status.idle":"2024-02-23T09:41:58.832155Z","shell.execute_reply.started":"2024-02-23T09:41:58.826545Z","shell.execute_reply":"2024-02-23T09:41:58.831262Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"## Loading Data and Preprocessing","metadata":{}},{"cell_type":"code","source":"inp_dir='/kaggle/input/persian-alphabets-and-numbers/data/data/'\ndf=pd.read_csv('/kaggle/input/persian-alphabets-and-numbers/labels.csv')\n\nlabel=df['Label']\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-02-23T09:29:01.505802Z","iopub.execute_input":"2024-02-23T09:29:01.506282Z","iopub.status.idle":"2024-02-23T09:29:01.584948Z","shell.execute_reply.started":"2024-02-23T09:29:01.506243Z","shell.execute_reply":"2024-02-23T09:29:01.583888Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" File_Name Label\n0 0 0\n1 1 0\n2 2 0\n3 3 0\n4 4 0","text/html":"

"},"metadata":{}}]},{"cell_type":"code","source":"image_data=[]\nlabels=[]\n\nfor index, row in tqdm(df.iterrows(), total=len(df), desc=\"Preprocessing Images\"):\n img_name=str(row['File_Name'])+'.png'\n img=tf.keras.preprocessing.image.load_img(os.path.join(inp_dir, img_name), target_size=(30, 30), color_mode=\"grayscale\")\n img=tf.keras.preprocessing.image.img_to_array(img)\n img=img/255.0\n image_data.append(img)\n labels.append(row['Label'])","metadata":{"execution":{"iopub.status.busy":"2024-02-23T09:29:03.345574Z","iopub.execute_input":"2024-02-23T09:29:03.345949Z","iopub.status.idle":"2024-02-23T09:34:25.239361Z","shell.execute_reply.started":"2024-02-23T09:29:03.345909Z","shell.execute_reply":"2024-02-23T09:34:25.238393Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"Preprocessing Images: 100%|██████████| 51744/51744 [05:21<00:00, 160.75it/s]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Splitting the dataset","metadata":{}},{"cell_type":"code","source":"image_data = np.array(image_data)\nlabels = np.array(labels)\n\n# Split the data\nX_train, X_test, y_train, y_test = train_test_split(image_data, labels, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-02-23T09:34:45.630618Z","iopub.execute_input":"2024-02-23T09:34:45.631445Z","iopub.status.idle":"2024-02-23T09:34:45.817620Z","shell.execute_reply.started":"2024-02-23T09:34:45.631413Z","shell.execute_reply":"2024-02-23T09:34:45.816646Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"X_train[0].shape","metadata":{"execution":{"iopub.status.busy":"2024-02-23T09:34:46.530788Z","iopub.execute_input":"2024-02-23T09:34:46.531159Z","iopub.status.idle":"2024-02-23T09:34:46.538703Z","shell.execute_reply.started":"2024-02-23T09:34:46.531128Z","shell.execute_reply":"2024-02-23T09:34:46.537910Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"(30, 30, 1)"},"metadata":{}}]},{"cell_type":"code","source":"y_train","metadata":{"execution":{"iopub.status.busy":"2024-02-23T09:34:47.915463Z","iopub.execute_input":"2024-02-23T09:34:47.915828Z","iopub.status.idle":"2024-02-23T09:34:47.921821Z","shell.execute_reply.started":"2024-02-23T09:34:47.915799Z","shell.execute_reply":"2024-02-23T09:34:47.920904Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array(['5', 'ﻏ', 'ﻎ', ..., 'ﺻ', '7', 'ﯼ'], 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\n\n\ny_pred_categorical = np.argmax(y_pred_categorical, axis=1)\nconf_matrix = confusion_matrix(y_test_encoded, y_pred_categorical)\n\n# Plot the confusion matrix using Seaborn's heatmap\nplt.figure(figsize=(10, 8))\nsns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix')\nplt.xlabel('Predicted Labels')\nplt.ylabel('True Labels')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:01:55.095719Z","iopub.execute_input":"2024-02-23T10:01:55.096451Z","iopub.status.idle":"2024-02-23T10:02:05.391709Z","shell.execute_reply.started":"2024-02-23T10:01:55.096412Z","shell.execute_reply":"2024-02-23T10:02:05.390831Z"},"trusted":true},"execution_count":68,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## LSTM Model","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense\n\n# Define the LSTM model\nlstm_model = Sequential([\n LSTM(units=64, return_sequences=True, input_shape=(30,30)), # First LSTM layer with return_sequences=True\n LSTM(units=64), # Second LSTM layer (default: return_sequences=False)\n Dense(units=len(set(y_train_encoded)), activation='softmax') # Output layer\n])\n\nlstm_model.summary()\n","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:04:06.110576Z","iopub.execute_input":"2024-02-23T10:04:06.110936Z","iopub.status.idle":"2024-02-23T10:04:06.622257Z","shell.execute_reply.started":"2024-02-23T10:04:06.110908Z","shell.execute_reply":"2024-02-23T10:04:06.621386Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"Model: \"sequential_10\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n lstm_10 (LSTM) (None, 30, 64) 24320 \n \n lstm_11 (LSTM) (None, 64) 33024 \n \n dense_10 (Dense) (None, 77) 5005 \n \n=================================================================\nTotal params: 62349 (243.55 KB)\nTrainable params: 62349 (243.55 KB)\nNon-trainable params: 0 (0.00 Byte)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"# Compile the model\nlstm_model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'])","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:04:07.171908Z","iopub.execute_input":"2024-02-23T10:04:07.172782Z","iopub.status.idle":"2024-02-23T10:04:07.184675Z","shell.execute_reply.started":"2024-02-23T10:04:07.172743Z","shell.execute_reply":"2024-02-23T10:04:07.183920Z"},"trusted":true},"execution_count":73,"outputs":[]},{"cell_type":"code","source":"# Train the model\nlstm_history = lstm_model.fit(X_train, y_train_encoded, epochs=20, batch_size=32, validation_data=(X_test, y_test_encoded))","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:04:07.450267Z","iopub.execute_input":"2024-02-23T10:04:07.450603Z","iopub.status.idle":"2024-02-23T10:07:00.380083Z","shell.execute_reply.started":"2024-02-23T10:04:07.450575Z","shell.execute_reply":"2024-02-23T10:07:00.379276Z"},"trusted":true},"execution_count":74,"outputs":[{"name":"stdout","text":"Epoch 1/20\n1294/1294 [==============================] - 13s 7ms/step - loss: 2.2848 - acc: 0.3669 - val_loss: 1.4143 - val_acc: 0.5675\nEpoch 2/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 1.1085 - acc: 0.6630 - val_loss: 0.9178 - val_acc: 0.7165\nEpoch 3/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.7511 - acc: 0.7657 - val_loss: 0.6223 - val_acc: 0.8011\nEpoch 4/20\n1294/1294 [==============================] - 8s 7ms/step - loss: 0.5621 - acc: 0.8253 - val_loss: 0.5031 - val_acc: 0.8368\nEpoch 5/20\n1294/1294 [==============================] - 9s 7ms/step - loss: 0.4461 - acc: 0.8587 - val_loss: 0.4297 - val_acc: 0.8586\nEpoch 6/20\n1294/1294 [==============================] - 8s 7ms/step - loss: 0.3633 - acc: 0.8831 - val_loss: 0.3549 - val_acc: 0.8814\nEpoch 7/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.3151 - acc: 0.8964 - val_loss: 0.3030 - val_acc: 0.8988\nEpoch 8/20\n1294/1294 [==============================] - 9s 7ms/step - loss: 0.2640 - acc: 0.9131 - val_loss: 0.2944 - val_acc: 0.9016\nEpoch 9/20\n1294/1294 [==============================] - 8s 7ms/step - loss: 0.2399 - acc: 0.9199 - val_loss: 0.2869 - val_acc: 0.9016\nEpoch 10/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.2090 - acc: 0.9306 - val_loss: 0.2557 - val_acc: 0.9152\nEpoch 11/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1910 - acc: 0.9358 - val_loss: 0.2047 - val_acc: 0.9295\nEpoch 12/20\n1294/1294 [==============================] - 9s 7ms/step - loss: 0.1727 - acc: 0.9434 - val_loss: 0.2041 - val_acc: 0.9302\nEpoch 13/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1520 - acc: 0.9488 - val_loss: 0.2427 - val_acc: 0.9177\nEpoch 14/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1662 - acc: 0.9420 - val_loss: 0.2106 - val_acc: 0.9261\nEpoch 15/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1294 - acc: 0.9553 - val_loss: 0.1772 - val_acc: 0.9384\nEpoch 16/20\n1294/1294 [==============================] - 8s 7ms/step - loss: 0.1361 - acc: 0.9527 - val_loss: 0.1601 - val_acc: 0.9432\nEpoch 17/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1185 - acc: 0.9586 - val_loss: 0.1652 - val_acc: 0.9442\nEpoch 18/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1137 - acc: 0.9600 - val_loss: 0.1589 - val_acc: 0.9450\nEpoch 19/20\n1294/1294 [==============================] - 8s 6ms/step - loss: 0.1043 - acc: 0.9631 - val_loss: 0.1789 - val_acc: 0.9389\nEpoch 20/20\n1294/1294 [==============================] - 9s 7ms/step - loss: 0.1020 - acc: 0.9639 - val_loss: 0.1392 - val_acc: 0.9497\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the model\ntest_loss, test_acc = lstm_model.evaluate(X_test, y_test_encoded)\nprint(\"Test Accuracy:\", test_acc)","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:07:00.381862Z","iopub.execute_input":"2024-02-23T10:07:00.382145Z","iopub.status.idle":"2024-02-23T10:07:01.644733Z","shell.execute_reply.started":"2024-02-23T10:07:00.382120Z","shell.execute_reply":"2024-02-23T10:07:01.643678Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"324/324 [==============================] - 1s 3ms/step - loss: 0.1392 - acc: 0.9497\nTest Accuracy: 0.9496569633483887\n","output_type":"stream"}]},{"cell_type":"code","source":"lstm_model.save('lstm_model.h5')","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:07:01.721266Z","iopub.execute_input":"2024-02-23T10:07:01.722022Z","iopub.status.idle":"2024-02-23T10:07:01.755979Z","shell.execute_reply.started":"2024-02-23T10:07:01.721985Z","shell.execute_reply":"2024-02-23T10:07:01.755077Z"},"trusted":true},"execution_count":76,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n saving_api.save_model(\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# Plot training & validation accuracy values\nplt.plot(lstm_history.history['acc'])\nplt.plot(lstm_history.history['val_acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='best')\nplt.show()\n\n# Plot training & validation loss values\nplt.plot(lstm_history.history['loss'])\nplt.plot(lstm_history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='best')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:07:06.930167Z","iopub.execute_input":"2024-02-23T10:07:06.930517Z","iopub.status.idle":"2024-02-23T10:07:07.443734Z","shell.execute_reply.started":"2024-02-23T10:07:06.930491Z","shell.execute_reply":"2024-02-23T10:07:07.442822Z"},"trusted":true},"execution_count":77,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Hybrid Model (CNN+LSTM)","metadata":{}},{"cell_type":"code","source":"X_train_reshaped = X_train.reshape((-1, 1, 30, 30, 1))\nX_test_reshaped = X_test.reshape((-1, 1, 30, 30, 1))\nX_train_reshaped.shape","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:26:04.340555Z","iopub.execute_input":"2024-02-23T10:26:04.340891Z","iopub.status.idle":"2024-02-23T10:26:04.348104Z","shell.execute_reply.started":"2024-02-23T10:26:04.340864Z","shell.execute_reply":"2024-02-23T10:26:04.346998Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"(41395, 1, 30, 30, 1)"},"metadata":{}}]},{"cell_type":"code","source":"hybrid_model = Sequential()\n\n#CNN part\nhybrid_model.add(TimeDistributed(Conv2D(64,(3,3),activation='relu'),input_shape=(1,30,30,1)))\nhybrid_model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))\nhybrid_model.add(TimeDistributed(Conv2D(64,(3,3),activation='relu')))\nhybrid_model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))\nhybrid_model.add(TimeDistributed(Conv2D(32,(3,3),activation='relu')))\nhybrid_model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))\nhybrid_model.add(TimeDistributed(Flatten()))\n\n#LSTM Part\nhybrid_model.add(LSTM(100,return_sequences=False))\n\nhybrid_model.add(Dense(len(set(y_train_encoded)),activation='softmax'))\n\nhybrid_model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:26:10.315392Z","iopub.execute_input":"2024-02-23T10:26:10.316221Z","iopub.status.idle":"2024-02-23T10:26:10.689862Z","shell.execute_reply.started":"2024-02-23T10:26:10.316181Z","shell.execute_reply":"2024-02-23T10:26:10.689019Z"},"trusted":true},"execution_count":96,"outputs":[{"name":"stdout","text":"Model: \"sequential_15\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n time_distributed_44 (TimeD (None, 1, 28, 28, 64) 640 \n istributed) \n \n time_distributed_45 (TimeD (None, 1, 14, 14, 64) 0 \n istributed) \n \n time_distributed_46 (TimeD (None, 1, 12, 12, 64) 36928 \n istributed) \n \n time_distributed_47 (TimeD (None, 1, 6, 6, 64) 0 \n istributed) \n \n time_distributed_48 (TimeD (None, 1, 4, 4, 32) 18464 \n istributed) \n \n time_distributed_49 (TimeD (None, 1, 2, 2, 32) 0 \n istributed) \n \n time_distributed_50 (TimeD (None, 1, 128) 0 \n istributed) \n \n lstm_14 (LSTM) (None, 100) 91600 \n \n dense_13 (Dense) (None, 77) 7777 \n \n=================================================================\nTotal params: 155409 (607.07 KB)\nTrainable params: 155409 (607.07 KB)\nNon-trainable params: 0 (0.00 Byte)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"hybrid_model.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['acc'])","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:26:11.603135Z","iopub.execute_input":"2024-02-23T10:26:11.603971Z","iopub.status.idle":"2024-02-23T10:26:11.616636Z","shell.execute_reply.started":"2024-02-23T10:26:11.603935Z","shell.execute_reply":"2024-02-23T10:26:11.615664Z"},"trusted":true},"execution_count":97,"outputs":[]},{"cell_type":"code","source":"hybrid_history = hybrid_model.fit(X_train_reshaped,y_train_encoded,epochs = 20,batch_size=32, validation_data = (X_test_reshaped, y_test_encoded))","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:26:13.215232Z","iopub.execute_input":"2024-02-23T10:26:13.215601Z","iopub.status.idle":"2024-02-23T10:28:37.535411Z","shell.execute_reply.started":"2024-02-23T10:26:13.215570Z","shell.execute_reply":"2024-02-23T10:28:37.534546Z"},"trusted":true},"execution_count":98,"outputs":[{"name":"stdout","text":"Epoch 1/20\n1294/1294 [==============================] - 10s 6ms/step - loss: 2.1926 - acc: 0.4299 - val_loss: 1.2219 - val_acc: 0.6625\nEpoch 2/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.9232 - acc: 0.7292 - val_loss: 0.7294 - val_acc: 0.7812\nEpoch 3/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.5910 - acc: 0.8207 - val_loss: 0.5036 - val_acc: 0.8427\nEpoch 4/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.4302 - acc: 0.8657 - val_loss: 0.4304 - val_acc: 0.8549\nEpoch 5/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.3355 - acc: 0.8925 - val_loss: 0.3487 - val_acc: 0.8841\nEpoch 6/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.2757 - acc: 0.9106 - val_loss: 0.3059 - val_acc: 0.8965\nEpoch 7/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.2310 - acc: 0.9230 - val_loss: 0.2743 - val_acc: 0.9085\nEpoch 8/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.2030 - acc: 0.9326 - val_loss: 0.2607 - val_acc: 0.9114\nEpoch 9/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1788 - acc: 0.9404 - val_loss: 0.2385 - val_acc: 0.9171\nEpoch 10/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1660 - acc: 0.9417 - val_loss: 0.2178 - val_acc: 0.9237\nEpoch 11/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1476 - acc: 0.9488 - val_loss: 0.2193 - val_acc: 0.9213\nEpoch 12/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1368 - acc: 0.9521 - val_loss: 0.1975 - val_acc: 0.9284\nEpoch 13/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1226 - acc: 0.9570 - val_loss: 0.1992 - val_acc: 0.9299\nEpoch 14/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1214 - acc: 0.9577 - val_loss: 0.1899 - val_acc: 0.9325\nEpoch 15/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1102 - acc: 0.9616 - val_loss: 0.1974 - val_acc: 0.9335\nEpoch 16/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.1081 - acc: 0.9611 - val_loss: 0.1901 - val_acc: 0.9313\nEpoch 17/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.0978 - acc: 0.9648 - val_loss: 0.1736 - val_acc: 0.9402\nEpoch 18/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.0984 - acc: 0.9645 - val_loss: 0.1761 - val_acc: 0.9381\nEpoch 19/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.0915 - acc: 0.9670 - val_loss: 0.2000 - val_acc: 0.9293\nEpoch 20/20\n1294/1294 [==============================] - 7s 5ms/step - loss: 0.0883 - acc: 0.9671 - val_loss: 0.1659 - val_acc: 0.9422\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the model\ntest_loss, test_acc = hybrid_model.evaluate(X_test_reshaped, y_test_encoded)\nprint(\"Test Accuracy:\", test_acc)","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:28:37.537359Z","iopub.execute_input":"2024-02-23T10:28:37.537664Z","iopub.status.idle":"2024-02-23T10:28:38.583618Z","shell.execute_reply.started":"2024-02-23T10:28:37.537638Z","shell.execute_reply":"2024-02-23T10:28:38.582166Z"},"trusted":true},"execution_count":99,"outputs":[{"name":"stdout","text":"324/324 [==============================] - 1s 3ms/step - loss: 0.1659 - acc: 0.9422\nTest Accuracy: 0.9422166347503662\n","output_type":"stream"}]},{"cell_type":"code","source":"hybrid_model.save('hybrid_model.h5')","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:28:38.585027Z","iopub.execute_input":"2024-02-23T10:28:38.585379Z","iopub.status.idle":"2024-02-23T10:28:38.640451Z","shell.execute_reply.started":"2024-02-23T10:28:38.585351Z","shell.execute_reply":"2024-02-23T10:28:38.639391Z"},"trusted":true},"execution_count":100,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n saving_api.save_model(\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# Plot training & validation accuracy values\nplt.plot(hybrid_history.history['acc'])\nplt.plot(hybrid_history.history['val_acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper left')\nplt.show()\n\n# Plot training & validation loss values\nplt.plot(hybrid_history.history['loss'])\nplt.plot(hybrid_history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper right')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:28:38.642737Z","iopub.execute_input":"2024-02-23T10:28:38.647225Z","iopub.status.idle":"2024-02-23T10:28:39.219328Z","shell.execute_reply.started":"2024-02-23T10:28:38.647169Z","shell.execute_reply":"2024-02-23T10:28:39.218260Z"},"trusted":true},"execution_count":101,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Models Performance","metadata":{}},{"cell_type":"code","source":"plt.plot(cnn_history.history['val_acc'],label='CNN')\nplt.plot(lstm_history.history['val_acc'],label='LSTM')\nplt.plot(hybrid_history.history['val_acc'],label='CNN+LSTM')\nplt.title(\"Accuracy\")\nplt.legend()\nplt.show()\n\nplt.plot(cnn_history.history['val_loss'],label='CNN')\nplt.plot(lstm_history.history['val_loss'],label='LSTM')\nplt.plot(hybrid_history.history['val_loss'],label='CNN+LSTM')\nplt.title(\"Loss\")\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-02-23T10:10:33.170555Z","iopub.execute_input":"2024-02-23T10:10:33.170941Z","iopub.status.idle":"2024-02-23T10:10:33.739163Z","shell.execute_reply.started":"2024-02-23T10:10:33.170912Z","shell.execute_reply":"2024-02-23T10:10:33.738201Z"},"trusted":true},"execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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YZIbRwOg9zCUrIKil3jR7Lyndeey3+0swqKyT5tX0aes25t/K2WyglJez86h/rTub0fHYJ98PJAz0RdGIZBTkEpqWUJTXkS40pocgtJKytrqJ6K+vK2mAj29SbI15uQ05KO8i3ozDI/52OIr7Xaf4HXhWE4kyZXUlP+41zkvGxSfkmlusstBSWlGIbzEp1X2WU6i8mEl8V5qc7LbDptnxmLGefjGXUs1W1ldWq7lGXzqt85N6Tyz6/8c6mc9NhPS3oqPr/Tk6CCklJX3YKS049zXuY6W29Sc0pOGlOp3UFKTiGHMysnMuW9M8ezC1ydsGYcxHlnk/SHOxr0c3Dn99utbozi4mI2btzIjBkzXGVms5mRI0eybt26ao8pKirCx8enUpmvry/ffvttpbLdu3cTHR2Nj48Pw4YNY/bs2XTq1KnGNouKilyvc3Jy3DkNacPsDmfPwcn8YrLyi09LNkrIzi8uSzZOu0xQVlY+KK4+wgJsdG7vR+fQsoSkvR+dypKS9v7WFvk/QZPJ5Pxh9/OmR2RgjfUcDoOT+cWnJTHOhOZEbhEGBl5mM15mE16W8kdT5ddmExaLGe+yH1xvi7ns0flj7ap/xnOrl5mgsl4RX29Lk3/GJpMJH2/npYh2/tYmfe/W4PTPL7QRPj+Hw6iUyDifO5OZjiF+rTI5qY6XxVzWc+IHtK+yv7jUwYnDu3Fseof2u9+nyBKAiTuaPtAybiUs6enp2O12IiMjK5VHRkbWON4kISGBOXPmcNlll9GtWzeSkpJYvnw5drvdVSc+Pp5FixbRs2dPjh8/znPPPcell17K1q1bCQys+j/D2bNnVxnzIm2LYTgHnJ0sSzpO5hdzMr/ElYRUV34yr5iccxys6m+1EOJndf1rPcTPuQX7Wp3PfStet/P3pmM7PwJsbffyptlson2AjfYBNvqg3k9pHsxmE/42L/zb8N9mrUqLYOenWDf9g5h9awDnv9b8fIsh5ygEd/RIWI3+bf3tb39jypQp9OrVC5PJRLdu3Zg8eTILFy501Rk9erTr+YABA4iPj6dz5868//773HvvvVXanDFjBomJia7XOTk5xMbGNu6JSBXZBSWcyC0qG9TnqDLQz142MLC0bByC3ag6ELDyAEJHlQGEp4rszmTj9MQj3zmIsi5zG9Qk0OZFiL837fyszu5/P+tpyUbl1+UJSLCvN1av5nmJRkTknKVuh5/egZ+XQkFmRXnXEXD+XdDrOvCq28DgxuBWwhIWFobFYiE1NbVSeWpqKlFRUdUeEx4ezscff0xhYSEZGRlER0fz5JNP0rVr1xrfJyQkhB49erBnz55q99tsNmw2z31obUluYQkH0vPZn5HHgfSyLSOPAxn5ZOYVezo8rBYzIX7OxKP8sZ1/RcLhKve30s7PWR7s6+2Ru1ZERJqdolzYutyZqBz5oaI8MBoG3wmD74B2cR4L73RuJSxWq5UhQ4aQlJTEuHHjAOeg26SkJKZPn17rsT4+PsTExFBSUsJHH33ErbfeWmPdU6dOsXfvXu666y53wmsURaV2bF6tc66JcqeKSisSkfQ89qfncyAjj4MZeaSfqj0pCfLxwuplrmYwYPl2+qBA8DKbqx0MWOlYU0W5n9WrLNFwJhtnJiCeGJ8gIi2UwwGlBVBSACX51T+WFjk3exGUFlc8lhZWLbMXnaV+ceV9GBDZFzoOhY4XQOxQCI513krXlAzDmZxs+oczWSkpu0PI7AU9R8P5d0O3X4G5ef32uX1JKDExkbvvvpsLLriAoUOHMnfuXPLy8pg8eTIAEydOJCYmhtmzZwOwfv16jh49yqBBgzh69CjPPvssDoeDxx9/3NXmY489xpgxY+jcuTPHjh1j1qxZWCwWJkyY0ECnWT+GYdBn5kpsXmbXv9zb+TlnFGxX9uMZ6u/88Qz1c64F4VwTwrvZTaiVV1TKwQxnIrL/tJ6S/en5pJ8qqvXYsAAbXcKct9N2CfMnrr0/cWF+xLX31zVgEWl89hLI2AsndsCptGqSjcJqyqpJRuy1/7+uSRzd6NzWl70OiKpIXjoOhehB4O3bOO+dlwG/LHUmKidOG3fa/jznJZ+BEyCg6vQkzYXbvzbjx4/nxIkTzJw5k5SUFAYNGsSKFStcA3EPHTqE2VzR3V5YWMjTTz/Nvn37CAgI4JprruGdd94hJCTEVefIkSNMmDCBjIwMwsPDGT58ON9//z3h4eHnfobnIKewtGz2x7PPJnim8imTQ8vWf2jn513ldXmSYzZDSalBsd1OcalzLokSu3PugWK7gxK7QXGpo6K8bF/JafuK7Q5KTisrKnueX+xMVNJya/9Dbe9vJS7MeedKl/b+xIU5k5PO7f0I9PE+149SROTsHA7IOghpOyBte9njDkjfBY4GnofFy8eZGHj7lT36gpevc4yGl4/z0WKt5rGGfV4+1dS3le0rK7OXwLGf4MgGZw9HyhY4lQI7/+vcwNnLEdW/rBfmQoi9EEI6178XxuGAfaudScrOTys+Ry9f6HsDnD8ROl1UY/uGYXCq5BQ5xTnkleTRo12P+sXRANyeh6U5aqx5WAzDIKewlKz8iumQT+Y57zw5/XVm2R0o5QND7R6ed6Im7fy8nYlIe386l/WSOJMSf4J9lZSISBMxDMhNqZyUpG2HE8kVlyfOZA2AiN4QFANW/4okw5Vw+FUkHTXtc9XxAXMzGMdWnA/HNzuTl8NlScyp1Kr1/COcyUt5T0z0YOdnUJvsI/DTEvjpXcg+hB3INZvJ7tCP7J6jyI4dQrZRSnZRNjlFOWQXZ5NdVLYVl5UVZZNTnIPdcN7VG+AdwLrbq5/CpL7c+f1WwtLAHA7nlMkn85xTJjuTnZIzXlckOln5zjEi3hYz3hbn/BHeFjNWi8n1vHyzeZnxtpgq1ysrs1oseHuZsJ7Rjo+38z77Lu39CfZTUiIiTSw/s2qPSdp2KMyqvr7FBuE9IKKPM0Epf/TEWI+mZhiQfbgsefnR2RNz/JeqvUsmi3MsTOxQSqKHsDc4nJ32PA7nHCA7dQs56TvIzksj22Im22wm2+JF7jmuF+Rj8SHIFsTnN32OpQHHtihhERGRppdzHPZ8UTlBOZVSfV2TGdp3r5yURPSBdl3AorFxLiWFcPxnOLKBU4e/Z2fKJpLtOeywWkm2Wtlj9abUjUTO39ufYGswwbZggmxBrufBtuAay4OsQfh4+Zy98XpotJluRUREqkjZAmtfg60fgqOayRmDO5UlJL2dPQMRvZ0DPb0b50ewpTMMg7T8NJJPJrMjY4fr8UjhEQjxAkIr1Q90OOhVVEzXkhLaWfwIjh5CUJfLCQ49r1ICEmQLwtvccnvalbCIiIj7DMPZm7L2Vdj/VUV5zAXO8RblPSbhPcFHPd81sTvsHMw9SHJmMjsyd7AzYyfJJ5PJLMystn6UfxS9QntV2qJt7TGlbHHest3p4lbbQ9U6z0pERBpHSSFseR/Wzau4NdZkgb7jYNh0iDnfo+E1Z4Wlhew+uZudJ3eyM2MnO0/uZPfJ3RSUVr0D1Wwy0zW4Kz1De9I7tDc9Q3vSq10vQnxCqm889sLGDb4ZUMIiIiJnl5cBPy6ADW9C3glnmTUQhtwN8b+GkOoXqxXnJZ6FWxcyb/M8Sqq5PdvXy5fz2p3nSkx6h/ame0j3Rhs30lIpYRERkZql74Hv58HmfzovOQAEdYSLHnDO4eET7Nn4mjmH4eAvP/6Fd7a/A0CoTyi9QntV6jnpHNi5Qe+8aa2UsIiISGWGAQfXwrrXIPl/lK/WS4dBcPFD0GcsWFru4M2mUuooZdbaWXyy9xMAHr/wce7sfaeWE6knJSwiIqdzOCrWiHGtBVO2Nkz588AO0K6zpyOtE8MwOHrqKFvTt7I1fSt7s/cSYguhg38HOgR0INo/mg7+HYjyj8LPbIXtHzsTlWM/VTTSY5QzUel8SeufC6WBFNmL+N1Xv2P14dVYTBb+cMkfuL7b9Z4Oq0VTwiIirYdhwN4vYddK5/oxVRalq26hujOSk7pOAd9jdNmP+MXN6kf8ZOFJV3KyJX0LW9O3crLoZJ2ODXEYdCgpoUNpKdFhYXSIHECHnuOIjr6AKP8oQoGmOlOH4eBUySlOFZ8itziX3OJcTpWcoktwFzoHNe9k8VTxKR5e/TAbUjZgNVv5y+V/YUSnEZ4Oq8XTxHEi0jocXAtJz8OhtQ3brsV2xvow3nDyIK7LJDFD4OLfQO8xTb66bUFpATsydrAlfQvb0rexJX0LR04dqVLPy+xFz3Y96RfWjx7tepBbnMvxvOMcz9rHsYydHC/OIa8OM6HaLDZnz0xZ70z58+iAaKL8o4jyi8Lb4o1hGOSX5juTjOJTrrVoyp+fnoCc+bz8Ma+GKfotJgsPDnqQe/vd2yzHfWQWZjL1i6lsz9iOv7c/r/7qVS6Mav138NSXZroVkbbj6Cb48gXYm+R8bbHBoNshJLaaZKN88TrrGfuqKyt7rK73JH2387LJ5n9WrADcrgsMmwaD7gCrX4OfZqmjlL1Zeyv1nOzJ2uNa5+V0cUFx9A/rT7+wfvQL60fP0J7YLLaKCsd+ck70tu1fUHZ8Tth5HB88nuPRAzlelMHxU8c5nnecY3nHOH7qOCcKTpw1RhMmArwDyC/Nrzau+rCarQRaAwm0BuJl9mJP1h4AhkYNZfals4nwaz6rC6fkpTDl8ykcyDlAO1s73rjqDfq27+vpsJo1JSwi0vql7XAmKqevcjv4LrjsdxAc0zQxnEqDDW/BD29BQdllF99QGDoFht4P/mH1avb0cSflycmOzB3VztcR5htG/7D+rgSlb1hfgqzV/H8wNwV2rYBfPoCD31aUx13qvLTV/apaFwQstheTmpdakcTkHXclNeXPix3FlY7xMnkRYA0gwDvAlXQEeAcQYA0gyBpU7b4zn1st1kqfyyd7P+HF9S9SUFpAiC2EFy55gctjL3f/Q25g+7P3c/+q+0nJSyHKP4o3r3qTLsFdPB1Ws6eERURar4y9sOaPsOUDwHCuSTNgPFz+BIR66AeiOM+5Mu661yDroLPMy8fZ0zNsOrTvdtYm0gvSWXN4DV8d+Yqf036udtyJv7c//do7k5LyBCXSL7L6u04Mw7meT/Jnzjt9jm6s2Gf2gr43wsXTocPAep50ZQ7DQWZhJjnFOc6kxDsAXy/fRrkjZn/2fp74+gl2ZO4A4M7ed/LokEcrJTdNaVvGNqaumsrJopN0Ce7Cm1e9SZR/lEdiaWmUsIhI65N9BL76M/z0rusyBr2vhxFPQUQvz8ZWzl4KO/8D370CxzaVFZqg93XOcS6xQ11VDcNgf/Z+vjz8JasPr2bLiS0YVPzv+PRxJ+U9KHHBcZhNNfeCYC9xjuVJ/p8zUSlPnsrFDHEOFh50e9P1QjWSYnsxf934V97d8S4AvUN78+fL/kxccFyTxvFDyg889OVD5JXk0ad9H94Y+QahPqFnP1AAJSyeDkdEGtKpNPhmDvy4sGK8SPer4FdPQ/Qgj4ZWI8OAg985E5fdK13F9th4fh4wltUUsPrIGg7mVE4o+rXvx4hOI4jvEE+v0F6Vx53UpDAbdq9yJim7V0FRdsU+iw26XgG9rnHemhzY+v7V/9Xhr3j6u6fJKsrC18uXp+Kf4vpu1zfJXCerD63msa8eo9hRzNCoofxtxN8IsAY0+vu2JkpYRKTlKzjp/MFfP995izJA5+Fw5TPQ6SLPxuaGguM/s+67l1id+iNf+9rItFTc2eJt9mZoh6H8KvZXXN7xciL9I+vW6MmDzvEoyZ/BgW8rr5DsF+ZMTnqOhm4jwOrfwGfU/KTmpTLj2xn8kPIDANd2vZZnLnoGf+/GO/dP9n7CzO9mYjfsjIgdwcuXv1y3BFMqUcIiIi1XUS58P9+5CnB5b0HMEGePStcRzWrOk5pkFmby1eGvWH14NeuOraPQXujaF+gwuDQ/nxH5BQw3+RNw4a/hwnvBr5bLCA4HHP+p7FLP/yB1a+X9YT2cCUrPa6HjBU1+e3VzYHfYWbB1Aa9vfh27YSc2MJaXL3uZvmENf5fOO9vf4c8//BmAsd3G8uzFz+Jl1rRm9aGERURanpIC+GEBfDsH8jOcZRF9nYlKz9F1TlRKHaUczzuOv7c/QdagJvshOZhzkNWHVrP68Go2n9iMw3C49nXw78CI2BGM6DSCISE98d78Hqx7HXLK5kzx9nPe4TTsQWgX5ywrKYT9Xzt7UXatgNzjFW9mMkOnYc7PpcdoCOveJOfYEvyU9hNPfP0Ex/OO42X24pHzH+GuPnfVPvanjgzD4LXNr/HmL28CcFefu3jsgscapO22SgmLiLQcpcXw0zvw9csVP8qh3WDE7513stRyq+3pCkoL+HjPxyzetpijp466ygO9Awm2BRNiCyHYx/kYYgsh2BrsKj9zn5+X31nHQDgMB1vSt7iSlH3Z+yrt7x3a25Wk9GzXs2p79hLY9jGs/RukbHGWmczOgcSGHfZ8CadPnubtD92vhJ7XQI+E2ntk2rjsomyeW/ccqw6uAuCSmEt44ZIXCPOt323m4Py+X1r/EsuSlwHwm8G/4b7+92ldoHOkhEVEmj+HHX55H9bMrribJTjWeXvywAlgqVvPSHZRNkt3LuW9ne+RWZgJOO+wKT19XIebvMxeBFvLkpzypMan4vmhnEN8deQr0gvSK44xeXFB1AXOJCV2BB0COtTtzQwD9q2Bta84lxU4XWB02aWeayBuOHj71Puc2hrDMPhg1wf8+Yc/U2Qvor1Pe1669CUujr7Y7bZK7CU89d1T/G///zBh4qn4pxjfa3wjRN32KGERkXNiGAYGRuN0dTscsOMTWP0SpCc7y/wjnBO+DbnbOcNsHaTkpfDO9nf4cNeH5Jc6B+XGBMRwd9+7Gdd9HN5mb3KKc8gqyiK7KJuswiyyi7Odz4uyKsrPeCwqvxOpDgK8AxgeM5wRsSMY3nF49RO2uSNlK2xeArZAZ6LSYVCLGLPTnO0+uZvHv36cPVl7MGFicr/JTB88HW9z3VabLigtIHFNIt8e/RYvkxcvXfoSo7uMbuSo2w4lLCJSL8dOHeOd7e/w0e6P8LH4MLLzSBLiErgg8oJzX7fFYYft/3Ze+knb7izzbQeXPOKcFbaO09nvy9rH29ve5r/7/uvqRenRrgf39LuHhLiEcx6zUlBaUG0ic3qSE+AdwOUdL+fCqAvxttTth088p6C0gJd/eJkPdn0AwICwAfzpsj/RMbBjrcflFOcwPWk6P6X9hI/Fh7+O+CvDY4Y3RchthhIWEXHL9oztLNq6iM8Pfl7tGjChPqFc1fkqEuISOD/ifPeSF3spbFsOX/+lokfFFgQXPegcZOoTXKdmfj7xMwu3LOTLwxWXTS6IvIB7+t3D8JjhGksgZ7Xq4CpmrZ1FbnEuAd4BzBw2s8bekvSCdH696tfsOrmLQGsg866cx+CIwU0cceunhEVEzsowDL479h2Lti5ifcp6V/lFHS5iUt9JmEwmPj/wOV8c+oLs0yYjC/cNdyUvgyIG1XzZyF4KW953JiqZe51lPsFw0TSI/zX4htQ5xgVbFvBj6o+u8l/F/op7+t/DwPCGmVZe2o5jp47xxNdPsPnEZgBu6H4DTw59Ej/vih6+I7lHuH/V/RzOPUx7n/b8/aq/0zO0p4cibt2UsIhIjUrsJfzvwP9YtG0Ru0/uBsBispAQl8CkvpPo3b535fqOEjYc38CKAytIOpREbnGua1+EXwRXd76ahLgEBoQPcCYvpcXwy1L45v/ByQPOir7tnGvqDL0ffM7+N1rqKOXzA5+zcOtCkk86e2W8zF5c1/U6JvedTNeQrg3zYUibVOoo5Y2f3+CtX97CwKBLcBdevuxleob2ZPfJ3fx61a85UXCCmIAY3rrqLWKDYj0dcqulhEVEqsgtzuXDXR/y7o53SctPA8DPy4+betzEXb3vqtNdLSX2EtYdX8fKAytZfWg1uSUVyUuUXyRX+8aQsG8T/TMPYQLnrKsXP+ScGM0WeNb2C0sL+XjPxyzatsh1a7Kvly+39LiFu/rcpQXlpEFtOL6BGd/MIK0gDavZysS+E3k/+X1yinPoHtKdv1/1dyL8IjwdZqumhEVEXFLyUliyYwkf7PqAvLJ5PcJ8w7ij9x3c0uMWgm11G0NypmJ7MWuPrWXlvs9YfegL8hwlrn3RdoOEsEEkXPgIfaKGnHV8SXZRNsuSl7FkxxLXrcntbO24o/cd3NbrtnrHKHI2JwtP8sx3z/DVka9cZQPCB/D6la/rv7smoIRFREjOTGbxtsX8b///KDWcd9N0C+7G3X3v5tqu12K1WM/tDYrzYeMi+O5vFOWl8K2vLyuD27PG10aBUZG8dAzoSEJcAglxCfQK7VUpeUnNS+Wd7e/wwa4Pqr012dfL99xiFKkDwzBYsmMJf9v0N+I7xPPny/5caUyLNB4lLCJtlGEYrE9Zz6Kti/ju2Heu8gsiL2Byv8kMjxl+7nOrFJ2CHxc41/rJO+EsC46F4Y/C4DspxODbo9+y4sAKvj7yNQWlBa5DOwd15urOV3Nh1IX8b///+M++/zTKrcki9VHiKKnz/CzSMJSwiLQx5YNUF21bxI7MHQCYTWau6nwVk/pOol9Yv3N/k8Ic+OEtWPsaFDgv2xDSGS79rXNmWq+qPTb5Jfl8c/QbVh5YyTdHvqm0CGA53Zos0nYpYRFpI/JK8li+eznvbH+H43nOdXh8vXwZ130cd/W5i9jABri7oSALNrwJ6+ZBYZazLLQrXPoYDLgV6jhxWn5JPl8d+YqVB1byU9pPDAwfyL3979WtySJtWKMnLPPmzePll18mJSWFgQMH8uqrrzJ06NBq65aUlDB79mwWL17M0aNH6dmzJ3/6058YNWpUvds8kxIWaUtyi3M5euooKw+sZFnyMtdtxqE+odze63bG9xxPiE/Iub9RfiZ8/wasnw9FOc6ysB7OKfT73ljntX5ERGrizu+32//HWbZsGYmJicyfP5/4+Hjmzp1LQkICycnJRERUvf3r6aef5t133+Wtt96iV69erFy5khtuuIG1a9cyePDgerUp0prll+Rz9NRR13Ys5zBHs/dxLPcoRwtOkGMvqFQ/zuzHRN/OjLF1wOdwMhz+w7kHUVronEa/+JTzdXhvuPx30GccnOsU/SIi9eB2D0t8fDwXXnghr732GgAOh4PY2FgeeughnnzyySr1o6Ojeeqpp5g2bZqr7KabbsLX15d33323Xm2eST0s0pIUlBZw/NRxjuQc5FjmLo5l7eVI7hGO5adytDiLLEfxWdtoZ7fTo7iECTm5jMgvoBGWKHSK7O9MVHqNAXOjvYuItFGN1sNSXFzMxo0bmTFjhqvMbDYzcuRI1q1bV+0xRUVF+PhUXhLd19eXb7/99pzaLCqqWFE1JyfHndMQaXT20hJ+SP6IQ5k7OZpziGP5aRwrOskRex6ZVF2r50xBdjsxpXZiSkuJLi0lxg4x1iBifMKIDojGL6gj+IQ07kq+0YPhvKu1WrCINAtuJSzp6enY7XYiIyMrlUdGRrJz585qj0lISGDOnDlcdtlldOvWjaSkJJYvX47dbq93m7Nnz+a5555zJ3SRJlN87CceWnkfa80195QEOBzElJQSbbcTY7IR4x1ItE8YMQHRRAd3JjC4MwRGQWAH5+bbTomDiLRpjT5q7m9/+xtTpkyhVy/nhFHdunVj8uTJLFy4sN5tzpgxg8TERNfrnJwcYmO11oN4WEkBpWtm88Tu91jr74uvw2Co4U2MVyDRPqHE+EcTE9SZ6NDzCArpgikoGvzDNCZERKQO3EpYwsLCsFgspKamVipPTU0lKqr6NT7Cw8P5+OOPKSwsJCMjg+joaJ588km6du1a7zZtNhs2m82d0EUa197VOP77CM9ZcvgiMABvTLxy6R+5qPu1no5MRKRVcGsUndVqZciQISQlJbnKHA4HSUlJDBs2rNZjfXx8iImJobS0lI8++oixY8eec5siHpeXDst/jfHOOP5iyuLjwADMmHj5ir8qWRERaUBuXxJKTEzk7rvv5oILLmDo0KHMnTuXvLw8Jk+eDMDEiROJiYlh9uzZAKxfv56jR48yaNAgjh49yrPPPovD4eDxxx+vc5sizY5hwM//hJVPQUEmfw8J5p1g5wj3P1zyPFd2vtLDAYqItC5uJyzjx4/nxIkTzJw5k5SUFAYNGsSKFStcg2YPHTqE+bTbHwsLC3n66afZt28fAQEBXHPNNbzzzjuEhITUuU2RZiVjL/z3Edj/NQBLYs5jntV519oTFz7B2O5jPRiciEjrpKn5RerKXgJrX4Gv/uycWM3Lh0+G3MRTad8A8ODAB5k6aKqHgxQRaTkadaZbkTbp8A/wn99A2nbn664j+PKC8cz88U8A3Nn7Th4Y+IAHAxQRad2UsIjUpjAHkv4AP/wfYIBfe0iYzfrwzjyW9CB2w87YbmP53YW/00rDIiKNSAmLSE12/Ac++x3kOldBZuDtcPULbMk/xkOf30uJo4QrO13Jsxc/i9mkaetFRBqTEhaRM2Ufhf89Djv/63wd2hWu+yt0vYLdJ3czNWkqBaUFxHeI50+X/Qkvs/6MREQam/5PK1LOYYcfFjgvARXngtkLLnkELnsMvH05nHuYX6/6NdlF2QwIG8ArI17BZtEEhiIiTUEJiwhAylb4z8Nw9Efn644XwphXILIPAGn5aUz5fAonCk7QPaQ7r498HT9vPw8GLCLStihhkbatpAC++hOsfRUcpWANhJGz4IJ7oWw+oazCLH696tccPXWU2MBY3rzqTYJtwR4OXESkbVHCIm3X3tXw30fh5H7n695jYPSfISjaVSWvJI8Hkx5kT9YeInwjePOqNwn3C/dQwCIibZcSFml7HA5Y/QJ88/+crwOj4dq/QK/Ka/8U2Yt4+MuH2ZK+hWBbMH+/6u90DOzogYBFREQJi7QtRadg+f2Q/Knz9YX3wZWzwKfyDIuljlIe/+px1qesx8/Lj/kj59O9XXcPBCwiIqCERdqSrEPwzwmQuhUsVrj+VRh4W5VqDsPBrLWz+PLwl1jNVl791av0C+vngYBFRKScEhZpGw6ug2V3Qn46+EfAbe9B7IVVqhmGwZ82/IlP9n6CxWThL5f/haEdhnogYBEROZ0SFmn9Nr3jHFzrKIGoATDhnxBc/ViU139+nfd2vgfA85c8z4hOI5oyUhERqYESFmm9HHZYNRPWveZ83ft6uGE+WP2rrf7O9neY//N8AH4f/3vGdBvTVJGKiMhZKGGR1qkwGz68B/Z84Xx9+ZNw+ROuuVXO9K/d/+LPP/wZgOmDpjOh14SmilREROpACYu0Phl74Z+3Qfou8PKFca9DvxtrrP7FwS94dt2zAEzsM5H7B9zfRIGKiEhdKWGR1mXfV/D+RCjMcs6vMuE9iB5cY/V1x9bx+NeP4zAc3ND9Bh674DFMJlPTxSsiInWihEVajx/+Dz57HAw7xAxx3gkUGFVj9c1pm3l49cOUOEq4qvNVzBo2S8mKiEgzpYRFWj57Cax40pmwAPS/Fa5/Bbx9q62elp/GJ3s/YeHWhRSUFnBx9MX88dI/YjFbmjBoERFxhxIWadnyM+GDu2H/14AJrpwJwx+FM3pKSh2lfHPkG5bvXs43R7/BbtgBGBQ+iL9e8VesFqsHghcRkbpSwiIt14lk5+DazH3g7Q83vVVlPaBDOYdYvns5n+z9hBMFJ1zlgyMGc+N5NzK6y2hsFltTRy4iIm5SwiIt0+4v4MPJUJQDwZ2ck8FFOafPLywt5ItDX7B893J+SPnBdUioTyjXd7ueG867ga7BXT0VuYiI1IMSFmlZDAO+fwM+fwoMB3QaBuPfBf8wdmbu5KNdH/Hp/k/JLc4FwISJS2Iu4cbzbuSKjlfgbfH28AmIiEh9KGGRlqO0GD5NhJ/ecb4efCe5V/2Bzw59wfI9y9mesd1VNdo/mnHnjWNct3F0COjgoYBFRKShKGGRliEvHZbdBYfWYpjMbBo+jeVWB58vH0WhvRAAL7MXV3a6khvPu5GLOlyE2VT9rLYiItLyKGGR5i91G7x3G+m5R/ikfTj/iujEgSP/du3uFtyNG8+7kTHdxtDOp50HAxURkcaihEWatdId/2HtZw+x3NfCV+06UmoCCk/g6+XL6C6jufG8GxkQNkATvomItHJKWKRZKHWUciL/BCn5KaTkpZCal8LR3Z/xZeZW0sICXfUGhA/gpvNuIiEuAX/v6lddFhGR1kcJizQ6u8NOekG6KxlJyUshNT+1LDFxPqYXpuMwHFUP9vIixOTNmJ63cmOPm+nernvTn4CIiHicEhY5Jw7DQWZhpisROT0ZSclLISU/hRP5J1wzy9bGy+xFpC2UyFPpRBbkEmU36Nv3VkZc8bxmohURaeOUsEi9/b8f/x9LdiyhxFFy1roWk4Vwv3Ci/KKI8ndukX6RlZ63P/QD5n89AEXZEBAFt/4DOsU3wZmIiEhzp4RF6mVbxjYWbVsEgNlkJsw3rHISUpaYRPpHEuUXRZhvWM2LCzoc8PWfYc1s5+vYi+DWxbWutCwiIm1LvSaqmDdvHnFxcfj4+BAfH8+GDRtqrT937lx69uyJr68vsbGxPProoxQWFrr2P/vss5hMpkpbr1696hOaNAHDMPjrxr8CMLrLaDbeuZGkW5JYcs0S5lwxh8cvfJyJfSdyddzVDAwfSKR/ZM3JSmE2LL29Ilm5cArc/R8lKyIiUonbPSzLli0jMTGR+fPnEx8fz9y5c0lISCA5OZmIiIgq9d977z2efPJJFi5cyMUXX8yuXbuYNGkSJpOJOXPmuOr17duXL774oiIwL3X+NFdrj61l/fH1eJu9efj8h/Ey1/O7StvpTFYy94LFBtf9FQbf0bDBiohIq+D2L82cOXOYMmUKkydPBmD+/Pl8+umnLFy4kCeffLJK/bVr13LJJZdw++23AxAXF8eECRNYv3595UC8vIiK0r+qmzuH4XD1rtzW6zZiAmLq19C2j+HjB6EkD4I6wvh3IOb8hgtURERaFbcuCRUXF7Nx40ZGjhxZ0YDZzMiRI1m3bl21x1x88cVs3LjRddlo3759fPbZZ1xzzTWV6u3evZvo6Gi6du3KHXfcwaFDh2qMo6ioiJycnEqbNI1P931K8slkAr0Dub///e434LDDqlnwwd3OZKXLZfDrr5SsiIhIrdzqYUlPT8dutxMZGVmpPDIykp07d1Z7zO233056ejrDhw/HMAxKS0t54IEH+P3vf++qEx8fz6JFi+jZsyfHjx/nueee49JLL2Xr1q0EBgZWaXP27Nk899xz7oQuDaDIXsSrP70KwD397yHEJ8S9BvIz4cN7YN9q5+th02Hkc2DR5T8REaldo68Ot2bNGl566SVef/11Nm3axPLly/n00095/vnnXXVGjx7NLbfcwoABA0hISOCzzz4jKyuL999/v9o2Z8yYQXZ2tms7fPhwY5+GAEt3LuV43nEi/CK4s/ed7h18/Bd483JnsuLtBzctgIQXlayIiEiduPVrERYWhsViITU1tVJ5ampqjeNPnnnmGe666y7uu+8+APr3709eXh73338/Tz31FGZz1ZwpJCSEHj16sGfPnmrbtNls2Gw2d0KXc5RdlM2bv7wJwPRB0/Hx8qn7wb+8D5/8BkoLoF0cjF8CUf0aJ1AREWmV3OphsVqtDBkyhKSkJFeZw+EgKSmJYcOGVXtMfn5+laTEYnHe4moYRrXHnDp1ir1799KhQwd3wpNGtGDrAnKKc+ge0p3ru11ft4PsJfC/J2H5FGey0v0quH+NkhUREXGb2/3xiYmJ3H333VxwwQUMHTqUuXPnkpeX57praOLEicTExDB7tnNejTFjxjBnzhwGDx5MfHw8e/bs4ZlnnmHMmDGuxOWxxx5jzJgxdO7cmWPHjjFr1iwsFgsTJkxowFOV+krJS2HJ9iUAPHL+IzXPqXK6U2nwwSQ4+J3z9aWPwYjfQ12OFREROYPbCcv48eM5ceIEM2fOJCUlhUGDBrFixQrXQNxDhw5V6lF5+umnMZlMPP300xw9epTw8HDGjBnDiy++6Kpz5MgRJkyYQEZGBuHh4QwfPpzvv/+e8PDwBjhFOVev/fQaxY5ihkQO4bKOl539gCMbYdmdkHsMrIFww3zofV3jByoiIq2WyajpukwLkpOTQ3BwMNnZ2QQFBXk6nFZl18ld3PzJzRgYLLlmCQPCB9R+wMbF8NljYC+G9ufBbe9BeI+mCVZERFoUd36/dYuG1Opvm/6GgcFVna+qPVkpLYL/PQ4bFzlf97oOxr0BPkogRUTk3ClhkRr9kPIDXx/5GovJwm8G/6bmijnH4P2JcOQHwAS/egqG/xaquQNMRESkPpSwSLVOX+Dw5h43ExccV33Fg2vh/bshLw18gp3zq5x3VdMFKiIibYISFqnW5wc/Z0v6Fny9fHlg4APVV9r0Dvz3EXCUQkRfuO1dCO3apHGKiEjboIRFqihxlPDKplcAmNR3EmG+YVUrHfgO/vMwGHbodxNc/ypY/Zs4UhERaSuUsEgVH+76kEO5hwj1CeXuvndXrZCbAh9OdiYr/W+FG98Ek6npAxURkTZDoyKlkrySPOb/PB+AqQOn4u99Rq+JvdS5gOGpVIjoA2PmKlkREZFGp4RFKlm0bRGZhZl0DurMTT1uqloh6Tnn7LXWQLj1HV0GEhGRJqGERVzSC9JZvG0xAL8Z/Bu8zd6VK2z/BNY6x7Ywbh6EdW/iCEVEpK1SwiIub2x+g4LSAgaEDeCqzmfcmpy+Bz5+0Pl82HToM7bpAxQRkTZLCYsAsD97Px/t/giAR4c8iun0cSnF+c6J4YpzodMwGPmsZ4IUEZE2SwmLAPDKplewG3Yu73g5F0RdULHDMOC/j0LaNvCPgJvfBot3zQ2JiIg0AiUswua0zXxx6AvMJjOPnP9I5Z0b34ZfloLJAre8DUEdPBKjiIi0bUpY2rjTp+Af220s3dudNpD26Cb43xPO51fOhLjhHohQRERECUub99WRr9iUtgmbxcaDgx6s2JGf6VwjyF7sXHn5koc9F6SIiLR5SljasFJHKXM3zgXgzt53EuUf5dzhcMDyKZB9yLk20LjXNTmciIh4lBKWNuyTvZ+wN3svwbZg7ul/T8WOr1+GPV+Alw/c+g/nKswiIiIepISljSooLWDeT/MAmNJ/CkHWIOeOPUmwZrbz+XV/haj+HopQRESkghKWNmrJjiWkFaQR7R/NhF4TnIVZh+Gj+wADhkyCQbd7MkQREREXJSxt0MnCkyzYsgCA6YOnY7VYobQIPrgbCjKhwyAY9SfPBikiInIaJSxt0Ju/vMmpklP0Cu3FtV2vdRau/D0c3Qg+Ic5xK94+Ho1RRETkdEpY2pgjuUdYmrwUgEfPfxSzyQy/vA8//J+zwo1vQbvOHoxQRESkKiUsbcyrP71KqaOUizpcxMUxF0PqdvhP2Rwrlz0OPa72bIAiIiLVUMLShmzP2M5n+z8DnAscUpgD798FJfnQdQRc8aSHIxQREamel6cDkKZTPgX/NV2uoU9ob+cg24w9EBQDN/0fmC0ejlBERKR66mFpI9YeXcv3x7/H2+zNQ4Mfgu9fh+3/BrM33LIY/MM8HaKIiEiNlLC0AQ7DwV83OXtXxvccT8fMw/D5M86dCS9B7IUejE5EROTsdEmoDfh036fszNxJgHcA93e7Ed6+Dgw79LsZhk7xdHgiIiJnpR6WVq7IXsRrP70GwL19J9HuP4/CqRQI7wVj/qZFDUVEpEVQwtLKLdu5jGN5x4jwi+COlENw4BuwBsCt74AtwNPhiYiI1IkSllYspziHN7e8CcC0yEvxXefsaeH6VyG8hwcjExERcY8Sllbsne3vkF2UTdeAjlz/3UJn4UUPQr8bPRuYiIiIm+qVsMybN4+4uDh8fHyIj49nw4YNtdafO3cuPXv2xNfXl9jYWB599FEKCwvPqU2pXZG9iPeT3wdgakYGXkXZEBsPV/3Bw5GJiIi4z+2EZdmyZSQmJjJr1iw2bdrEwIEDSUhIIC0trdr67733Hk8++SSzZs1ix44dLFiwgGXLlvH73/++3m3K2a08sJLMwkwiTVZGHksGvzC4ZRFYvD0dmoiIiNvcTljmzJnDlClTmDx5Mn369GH+/Pn4+fmxcOHCauuvXbuWSy65hNtvv524uDiuvvpqJkyYUKkHxd02pXaGYfDejvcAGJ+RhpfJDDcvhKBoD0cmIiJSP24lLMXFxWzcuJGRI0dWNGA2M3LkSNatW1ftMRdffDEbN250JSj79u3js88+45prrql3m0VFReTk5FTapMIv6b+wLWMbVgNuyj0FwxOh6+WeDktERKTe3Jo4Lj09HbvdTmRkZKXyyMhIdu7cWe0xt99+O+np6QwfPhzDMCgtLeWBBx5wXRKqT5uzZ8/mueeecyf0NqW8d2XUqTxCHQ4YMsmzAYmIiJyjRr9LaM2aNbz00ku8/vrrbNq0ieXLl/Ppp5/y/PPP17vNGTNmkJ2d7doOHz7cgBG3bOkF6Xx+8HMAbs/JgejzISTWw1GJiIicG7d6WMLCwrBYLKSmplYqT01NJSoqqtpjnnnmGe666y7uu+8+APr3709eXh73338/Tz31VL3atNls2Gw2d0JvMz5I/oBSRykDsdG3uAT6XO/pkERERM6ZWz0sVquVIUOGkJSU5CpzOBwkJSUxbNiwao/Jz8/HbK78NhaLBXAODq1Pm1K9EnsJ7+9y3sp8+4njzsLeSlhERKTlc3vxw8TERO6++24uuOAChg4dyty5c8nLy2Py5MkATJw4kZiYGGbPng3AmDFjmDNnDoMHDyY+Pp49e/bwzDPPMGbMGFficrY2pW5WHVxFekE6YV7+XHXqEET2h/bdPB2WiIjIOXM7YRk/fjwnTpxg5syZpKSkMGjQIFasWOEaNHvo0KFKPSpPP/00JpOJp59+mqNHjxIeHs6YMWN48cUX69ym1M17O52DbW91+OINuhwkIiKthskwDMPTQZyrnJwcgoODyc7OJigoyNPheMS2jG3c9t/b8DJ7sergUcJKi+DB9RDRy9OhiYiIVMud32+tJdRKlN/KfHVwL2eyEtZDyYqIiLQaSlhagczCTFbsXwHA7acKnIV9xnowIhERkYalhKUV+GjXRxQ7iukb2psB+8pmB9bdQSIi0oooYWnhSh2lLEteBsDtQb0xlRZCuziI6u/ZwERERBqQEpYW7stDX5Kan0qoTyijUg84C/uMBZPJo3GJiIg0JCUsLVz5rcw3dRuLdfcqZ2FvjV8REZHWRQlLC5acmczG1I1YTBZu9Y6CkjwI6ggx53s6NBERkQalhKUF++fOfwJwZacridr7lbOw9xhdDhIRkVZHCUsLlV2Uzaf7PgXg9h63QvL/nDt0O7OIiLRCSlhaqOW7l1NoL6Rnu56cfyobirIhIBJi4z0dmoiISINTwtIC2R32iluZe9+Oaccnzh29rgOzvlIREWl99OvWAn115CuOnjpKsC2YazpdDTudl4a02KGIiLRWSlhaoPJbmW8870Z8jm6EgkzwDYXOwz0cmYiISONQwtLC7M3ay/rj6zGbzNzW8zbY/m/njl7XgsXLs8GJiIg0EiUsLUz5rcxXdLyCaL8o2Plf5w7dHSQiIq2YEpYWJLc4l0/2OgfY3t77dji8Hk6lgi0Yulzu4ehEREQajxKWFuTjPR9TUFpA95DuDI0aWnE5qOdo8LJ6NjgREZFGpISlhXAYDpbuXArAhF4TMAHs+I9zp+4OEhGRVk4JSwvx7dFvOZR7iEBrINd1vQ6OboKcI+DtD91+5enwREREGpUSlhai/FbmG7rfgJ+3H+wouxzUIwG8fT0YmYiISONTwtICHMg+wHdHv8OEidt63QaGUTF+RZeDRESkDVDC0gIsTXaOXbms42XEBsZCyhY4eQC8fKD7VZ4NTkREpAkoYWnm8kry+HjPxwDc3ut2Z2H52kHdR4ItwDOBiYiINCElLM3cJ3s/Ia8kj7igOC6KvshZuL0sYdFkcSIi0kYoYWnGDMNwzWw7odcEzCYzpO2E9GQwezsH3IqIiLQBSliasXXH17E/ez/+3v6M7V7Wm1J+OajbCPAJ9lxwIiIiTUgJSzP2zx3O3pWx3cbi7+3vLCy/HNRbdweJiEjboYSlmTqce5ivjnwFOC8HAZCxF1K3gMniXJ1ZRESkjVDC0kwt27kMA4NLoi8hLjjOWVh+OajLpeAX6rHYREREmpoSlmYovySf5XuWA2WrMpfT5SAREWmjlLA0Q5/u/5Tc4lxiA2MZHjPcWZh1CI5tAkzQe4xH4xMREWlqSliaGcMweG+Hc92g23re5ryVGSpWZu58MQREeCg6ERERz6hXwjJv3jzi4uLw8fEhPj6eDRs21Fj3iiuuwGQyVdmuvbZi0OikSZOq7B81alR9Qmvxfkz9kT1Ze/D18mXceeMqduhykIiItGFe7h6wbNkyEhMTmT9/PvHx8cydO5eEhASSk5OJiKj6L//ly5dTXFzsep2RkcHAgQO55ZZbKtUbNWoUb7/9tuu1zWZzN7RWobx3ZUzXMQRZg5yFuSlweL3zuS4HiYhIG+R2D8ucOXOYMmUKkydPpk+fPsyfPx8/Pz8WLlxYbf3Q0FCioqJc26pVq/Dz86uSsNhstkr12rVrV78zasGOnTrGl4e/BE67lRnKLgcZ0PFCCI7xTHAiIiIe5FbCUlxczMaNGxk5cmRFA2YzI0eOZN26dXVqY8GCBdx22234+/tXKl+zZg0RERH07NmTqVOnkpGRUWMbRUVF5OTkVNpag2XJy3AYDuKj4unernvFju3/dj7qcpCIiLRRbiUs6enp2O12IiMjK5VHRkaSkpJy1uM3bNjA1q1bue+++yqVjxo1in/84x8kJSXxpz/9ia+++orRo0djt9urbWf27NkEBwe7ttjYWHdOo1kqLC3ko90fATCh92m9K3npcPA75/M+SlhERKRtcnsMy7lYsGAB/fv3Z+jQoZXKb7vtNtfz/v37M2DAALp168aaNWu48sorq7QzY8YMEhMTXa9zcnJafNLyv/3/I7som2j/aK7oeEXFjp2fguGAqAHQLs5T4YmIiHiUWz0sYWFhWCwWUlNTK5WnpqYSFRVV67F5eXksXbqUe++996zv07VrV8LCwtizZ0+1+202G0FBQZW2lswwDN7b6RxsO77XeCxmS8XO8stBfcZ6IDIREZHmwa2ExWq1MmTIEJKSklxlDoeDpKQkhg0bVuuxH3zwAUVFRdx5551nfZ8jR46QkZFBhw4d3Amvxfop7Sd2Zu7EZrFxY/cbK3YUnIT9zvWElLCIiEhb5vZdQomJibz11lssXryYHTt2MHXqVPLy8pg8eTIAEydOZMaMGVWOW7BgAePGjaN9+/aVyk+dOsXvfvc7vv/+ew4cOEBSUhJjx46le/fuJCQk1PO0Wpby3pVru15LiE9IxY7kFeAohfDeEHaeZ4ITERFpBtwewzJ+/HhOnDjBzJkzSUlJYdCgQaxYscI1EPfQoUOYzZXzoOTkZL799ls+//zzKu1ZLBZ++eUXFi9eTFZWFtHR0Vx99dU8//zzbWIultS8VL44+AUAt/e6vfLO8sUO1bsiIiJtnMkwDMPTQZyrnJwcgoODyc7ObnHjWV796VXe/OVNzo84n8WjF1fsKMqFP3cDexFMXQuRfT0XpIiISCNw5/dbawl5ULG9mA93fQicsSozwK6VzmQltBtE9PFAdCIiIs2HEhYP+u7od2QWZhLhF8GvOv2q8k7X5aDrwWRq+uBERESaESUsHvTTiZ8AuDTmUrzN3hU7ivNh9yrnc41fERERUcLiST+n/QzAoIhBlXfs+QJK8iGkE3QYVOU4ERGRtkYJi4eU2EvYmr4VgEHhgyrvLL8c1FuXg0REREAJi8fsyNxBsaOYEFsInYM6V+woLXLOvwJa7FBERKSMEhYP2Zy2GYCB4QMxnd6Lsnc1FOdCYAfoeKFnghMREWlmlLB4yOYTm4Fqxq+4LgeNAbO+HhEREVDC4hGGYbgG3A4MH1ixw17iXJ0ZdDlIRETkNEpYPCAlL4W0gjQsJgv9wvpV7DjwDRRmgV8YdL7YY/GJiIg0N0pYPKD8clCv0F74evlW7Nj+b+dj7+vAbGn6wERERJopJSweUD7gttL4FYddl4NERERqoITFA1wDbk+ff+XQOsg7AT4h0OUyT4QlIiLSbClhaWL5JfkkZyYDZ/SwbC+7O6jXtWDxrnqgiIhIG6aEpYlty9iG3bAT6RdJlH+Us9DhqDy7rYiIiFSihKWJVTt+5eiPkHscrIHQbYRH4hIREWnOlLA0sWrHr5TfHdQjAbxsTR6TiIhIc6eEpQk5DAc/nzhjhWbDqBi/0mesZwITERFp5pSwNKEDOQfILsrGx+JDz9CezsLjmyH7EHj7QfeRHo1PRESkuVLC0oTKp+PvG9YXb3PZnUDlvSvdR4LVz0ORiYiING9KWJpQlfErhlExfkWXg0RERGqkhKUJld8h5FrwMG0HZO4Fi8054FZERESqpYSliWQXZbMvex8AAyPKEpbD652PnYeBLdBDkYmIiDR/SliayC8nfgGgc1BnQn1CnYWpW52PUQM8FJWIiEjLoISliZSPX3FdDgJIKU9Y+jd9QCIiIi2IEpYmUn6HkGv+FYcDUrc5nythERERqZUSliZQ6ijll3TnJSHXHUJZB6A41zngtv15HotNRESkJVDC0gR2n9xNQWkBAd4BdAvp5iwsvxwU0QssXp4LTkREpAVQwtIETh+/YjaVfeSpGr8iIiJSV0pYmoBr/pWI0wfcbnE+RiphERERORslLE3AteDh6Ss0u+4Q6tf0AYmIiLQwSlgaWVp+GkdPHcVsMtM/rKw3pSDLueAhQKQSFhERkbOpV8Iyb9484uLi8PHxIT4+ng0bNtRY94orrsBkMlXZrr32WlcdwzCYOXMmHTp0wNfXl5EjR7J79+76hNbslPeunBdyHgHWAGdh+fiV4E7gG+KZwERERFoQtxOWZcuWkZiYyKxZs9i0aRMDBw4kISGBtLS0ausvX76c48ePu7atW7disVi45ZZbXHX+/Oc/88orrzB//nzWr1+Pv78/CQkJFBYW1v/Mmony8Suu+VdAl4NERETc5HbCMmfOHKZMmcLkyZPp06cP8+fPx8/Pj4ULF1ZbPzQ0lKioKNe2atUq/Pz8XAmLYRjMnTuXp59+mrFjxzJgwAD+8Y9/cOzYMT7++ONzOrnmoNoZblPLB9wqYREREakLtxKW4uJiNm7cyMiRIysaMJsZOXIk69atq1MbCxYs4LbbbsPf3x+A/fv3k5KSUqnN4OBg4uPja2yzqKiInJycSltzVGQvYnvGdqCmHhbdISQiIlIXbiUs6enp2O12IiMjK5VHRkaSkpJy1uM3bNjA1q1bue+++1xl5ce50+bs2bMJDg52bbGxse6cRpPZnrGdUkcp7X3a0zGgo7PQXgppO5zPdUlIRESkTpr0LqEFCxbQv39/hg4dek7tzJgxg+zsbNd2+PDhBoqwYZWvHzQwfCAmk8lZmLEb7EVgDYCQOM8FJyIi0oK4lbCEhYVhsVhITU2tVJ6amkpUVFStx+bl5bF06VLuvffeSuXlx7nTps1mIygoqNLWHJWPX6n2clBkPzDrrnIREZG6cOsX02q1MmTIEJKSklxlDoeDpKQkhg0bVuuxH3zwAUVFRdx5552Vyrt06UJUVFSlNnNycli/fv1Z22zODMOo4Q4h5yKIuhwkIiJSd26vupeYmMjdd9/NBRdcwNChQ5k7dy55eXlMnjwZgIkTJxITE8Ps2bMrHbdgwQLGjRtH+/btK5WbTCYeeeQRXnjhBc477zy6dOnCM888Q3R0NOPGjav/mXnYkVNHyCjMwMvsRZ/2fSp2pJ7WwyIiIiJ14nbCMn78eE6cOMHMmTNJSUlh0KBBrFixwjVo9tChQ5jPuNSRnJzMt99+y+eff15tm48//jh5eXncf//9ZGVlMXz4cFasWIGPj089Tql5KO9d6dO+DzaLrWKH6w6hAU0flIiISAtlMgzD8HQQ5yonJ4fg4GCys7ObzXiWF75/gWXJy5jYZyK/u/B3zsLcVPh/PcBkhhlHwern2SBFREQ8yJ3fb436bCTVjl8pnzAutJuSFRERETcoYWkEp4pPsTvLuRZSpRluNSW/iIhIvShhaQRb0rfgMBzEBMQQ4RdRsSOlrIdFM9yKiIi4RQlLI6h2/SA47Q4hJSwiIiLuUMLSCMpnuK00fqWkENKdl4l0SUhERMQ9SlgamMNw8POJsoQlfFDFjhM7wLCDX3sI7OCZ4ERERFooJSwNbG/WXk6VnMLXy5fz2p1XsaN8/EpkPyhfV0hERETqRAlLAysfvzIgbABe5tPm5XPdIaTxKyIiIu5SwtLAyudfGRhRw4BbJSwiIiJuU8LSwH454VzcsNIdQoZReZVmERERcYsSlgZ0svAkB3IOAGckLFmHoCgbzN4Q1sMzwYmIiLRgSlgaUPndQV2DuxJsC67YUX45KKIXeFk9EJmIiEjLpoSlAVW7fhCcdoeQxq+IiIjUhxKWBlR+h1Cl+VfgtCn5NX5FRESkPpSwNJASRwlb052Xfmq8Q0gDbkVEROpFCUsDSc5MpsheRLAtmLiguIodhTlw8oDzuW5pFhERqRclLA3ENf9K+EDMptM+1tRtzsegGPALbfrAREREWgElLA2kxvEruhwkIiJyzpSwNJCz3iGky0EiIiL1poSlAaTkpZCan4rFZKFv+75n7NQdQiIiIudKCUsDKO9d6RnaEz9vv4odDjuk7XA+1xwsIiIi9aaEpQHUOH4lYy+UFoC3P4R2afK4REREWgslLA2g5vErzoUQiewDZkuTxiQiItKaKGE5RwWlBSRnJgO6Q0hERKSxKGE5R9vSt1FqlBLhF0GUf1TlnSllCYsG3IqIiJwTJSznqHz8ysDwgZhMpso7y3tYogY0bVAiIiKtjBKWc/Rz2s9ANZeD8tIh9zhggog+TR6XiIhIa6KE5RwYhlFxh1BNE8aFdgFbQJPGJSIi0tooYTkHB3MOklWUhdVspXdo78o7XZeDNP+KiIjIuVLCcg7Ke1f6hfXD2+JdeWd5D4smjBMRETlnSljOgWuF5oiBVXfqDiEREZEGo4TlHPx8ooYBt6VFkO6cm0WXhERERM6dEpZ6yinOYU/WHsB5S3MlJ3aCoxR8QiAopumDExERaWXqlbDMmzePuLg4fHx8iI+PZ8OGDbXWz8rKYtq0aXTo0AGbzUaPHj347LPPXPufffZZTCZTpa1Xr171Ca3J/HLCOe1+p8BOtPdtX3lnymkDbs+cm0VERETc5uXuAcuWLSMxMZH58+cTHx/P3LlzSUhIIDk5mYiIiCr1i4uLueqqq4iIiODDDz8kJiaGgwcPEhISUqle3759+eKLLyoC83I7tCZV4/pBoDuEREREGpjbWcGcOXOYMmUKkydPBmD+/Pl8+umnLFy4kCeffLJK/YULF5KZmcnatWvx9nbeSRMXF1c1EC8voqKiqpRXp6ioiKKiItfrnJwcd0/jnJ0+w20VrjuENOBWRESkIbh1Sai4uJiNGzcycuTIigbMZkaOHMm6deuqPeaTTz5h2LBhTJs2jcjISPr168dLL72E3W6vVG/37t1ER0fTtWtX7rjjDg4dOlRjHLNnzyY4ONi1xcbGunMa56zUUcqWE86kpEoPi2FUJCy6Q0hERKRBuJWwpKenY7fbiYyMrFQeGRlJSkpKtcfs27ePDz/8ELvdzmeffcYzzzzD//t//48XXnjBVSc+Pp5FixaxYsUK3njjDfbv38+ll15Kbm5utW3OmDGD7Oxs13b48GF3TuOc7cnaQ35pPgHeAXQL7lZ5Z85RKMwCsxeEN+9xOCIiIi1Fow8UcTgcRERE8Oabb2KxWBgyZAhHjx7l5ZdfZtasWQCMHj3aVX/AgAHEx8fTuXNn3n//fe69994qbdpsNmw2W2OHXqPy8SsDwgdgMVsq7ywfcBvWE7w8F6OIiEhr4lbCEhYWhsViITU1tVJ5ampqjeNPOnTogLe3NxZLxQ977969SUlJobi4GKvVWuWYkJAQevTowZ49e9wJr8nUOP8K6HKQiIhII3DrkpDVamXIkCEkJSW5yhwOB0lJSQwbNqzaYy655BL27NmDw+Fwle3atYsOHTpUm6wAnDp1ir1799KhQwd3wmsyrhluqxtwm6oBtyIiIg3N7XlYEhMTeeutt1i8eDE7duxg6tSp5OXlue4amjhxIjNmzHDVnzp1KpmZmTz88MPs2rWLTz/9lJdeeolp06a56jz22GN89dVXHDhwgLVr13LDDTdgsViYMGFCA5xiw0ovSOfIqSOYMNE/vJrbllN0S7OIiEhDc3sMy/jx4zlx4gQzZ84kJSWFQYMGsWLFCtdA3EOHDmE2V+RBsbGxrFy5kkcffZQBAwYQExPDww8/zBNPPOGqc+TIESZMmEBGRgbh4eEMHz6c77//nvDw8AY4xYb1c5rzclD3dt0JtAZW3ll0CjL3OZ8rYREREWkw9Rp0O336dKZPn17tvjVr1lQpGzZsGN9//32N7S1durQ+YXhE+fwr1Y5fSdsOGBAQBf5hTRmWiIhIq6a1hNxU6wy3rgG36l0RERFpSEpY3FBsL2ZbxjZAdwiJiIg0JSUsbtiesZ0SRwmhPqHEBlYzu275GkK6Q0hERKRBKWFxQ/n8KwPDB2I6cxVmhwNStzuf65KQiIhIg1LC4oZax6+c3A8leeDlC+27N2lcIiIirV2jT83fWhiGUfsdQim/OB8jesOZ0/WLiAh2u52SkhJPhyFN7MzZ7utLCUsdHT11lPSCdLzMXvRp36dqBdeEcRq/IiJyOsMwSElJISsry9OhiIeEhIQQFRVVdTiFG5Sw1FF570qf0D74ePlUrVA+4DZqQNMFJSLSApQnKxEREfj5+Z3Tj5a0LIZhkJ+fT1paGsA5LbmjhKWOXOsHRVSzfhBU3NKsO4RERFzsdrsrWWnfvr2nwxEP8PX1BSAtLY2IiIh6Xx7SoNs6+uWEc4xKteNX8jMh56jzeWTfpgtKRKSZKx+z4ufn5+FIxJPKv/9zGcOkhKUO8kvyST6ZDNRwh1D55aB2ceAT1GRxiYi0FLoM1LY1xPevhKUOtqRvwWE4iPaPJsIvomoFXQ4SERFpVEpY6sA1fiW8pvEr5QNuNWGciIhIY1DCUgfldwjVOOA2VYseioiINCYlLGfhMByuKfmrHb9SWgxpO53PdUlIRKTVSUlJ4aGHHqJr167YbDZiY2MZM2YMSUlJAMTFxWEymfj+++8rHffII49wxRVXuF4/++yzmEwmHnjggUr1Nm/ejMlk4sCBA419Ki2aEpaz2J+9n9ziXHy9fOnRrkfVCum7wFECtmAI6dT0AYqISKM5cOAAQ4YM4csvv+Tll19my5YtrFixghEjRjBt2jRXPR8fH5544omztufj48OCBQvYvXt3Y4bdKmkelrMoH7/SL6wf3mbvqhVcKzT3BY2CFxE5K8MwKCixe+S9fb0tbt2x8uCDD2IymdiwYQP+/v6u8r59+3LPPfe4Xt9///3Mnz+fzz77jGuuuabG9nr27ElERARPPfUU77//fv1Ooo1SwnIWta4fBBV3CGn8iohInRSU2Okzc6VH3nv7HxLws9btpy8zM5MVK1bw4osvVkpWyoWEhLied+nShQceeIAZM2YwatQozOaaL2D88Y9/5MILL+THH3/kggsucPsc2ipdEjqLWldohtMSFo1fERFpTfbs2YNhGPTq1atO9Z9++mn279/PkiVLaq13/vnnc+utt9bpEpJUUA9LLU4WnuRAzgEABoRVs0aQYZx2SUgJi4hIXfh6W9j+hwSPvXddGYbhVtvh4eE89thjzJw5k/Hjx9da94UXXqB37958/vnnRERUM7+XVKGEpRZWi5UXh7/IwZyDhPiEVK2QmwL5GWCyQETvJo9PRKQlMplMdb4s40nnnXceJpOJnTt31vmYxMREXn/9dV5//fVa63Xr1o0pU6bw5JNPsmDBgnMNtU3QJaFa+Hv7c32363lo8EPVVyi/HBR2Hnj7Nl1gIiLS6EJDQ0lISGDevHnk5eVV2Z+VlVWlLCAggGeeeYYXX3yR3NzcWtufOXMmu3btYunSpQ0VcqumhOVcpGpKfhGR1mzevHnY7XaGDh3KRx99xO7du9mxYwevvPIKw4YNq/aY+++/n+DgYN57771a246MjCQxMZFXXnmlMUJvdZSwnAtNyS8i0qp17dqVTZs2MWLECH7729/Sr18/rrrqKpKSknjjjTeqPcbb25vnn3+ewsLCs7b/2GOPERAQ0NBht0omw91RRc1QTk4OwcHBZGdnExTUhKslv3oBZOyGOz+C7iOb7n1FRFqIwsJC9u/fT5cuXfDx8fF0OOIhNf134M7vt3pY6qs4HzL3Op9HqodFRESkMSlhqa+0HWA4wD8CAiM9HY2IiEirpoSlvlI1YZyIiEhTUcJSXym6Q0hERKSpKGGpL90hJCIi0mSUsNSHwwGp25zPlbCIiIg0OiUs9ZF1AIpzwWKD9ud5OhoREZFWr14Jy7x584iLi8PHx4f4+Hg2bNhQa/2srCymTZtGhw4dsNls9OjRg88+++yc2vSo8stBEb3A0vzXwxAREWnp3E5Yli1bRmJiIrNmzWLTpk0MHDiQhIQE0tLSqq1fXFzMVVddxYEDB/jwww9JTk7mrbfeIiYmpt5telyqxq+IiIg0JbcTljlz5jBlyhQmT55Mnz59mD9/Pn5+fixcuLDa+gsXLiQzM5OPP/6YSy65hLi4OC6//HIGDhxY7zaLiorIycmptDUp1x1CSlhERFqrSZMmMW7cuGr3/fzzz1x//fVERETg4+NDXFwc48ePJy0tjWeffRaTyVTrVt6+yWTigQceqNL+tGnTMJlMTJo0qRHPsGVxK2EpLi5m48aNjBxZMQ292Wxm5MiRrFu3rtpjPvnkE4YNG8a0adOIjIykX79+vPTSS9jt9nq3OXv2bIKDg11bbGysO6dx7lx3COmWZhGRtubEiRNceeWVhIaGsnLlSnbs2MHbb79NdHQ0eXl5PPbYYxw/fty1dezYkT/84Q+VysrFxsaydOlSCgoKXGWFhYW89957dOrUyROn12y5NQAjPT0du91OZGTlmV0jIyPZuXNntcfs27ePL7/8kjvuuIPPPvuMPXv28OCDD1JSUsKsWbPq1eaMGTNITEx0vc7JyWm6pKUgC7IPOZ9rDhYRkTbnu+++Izs7m//7v//Dy8v5M9qlSxdGjBjhqnP6goYWi4XAwECioqKqtHX++eezd+9eli9fzh133AHA8uXL6dSpE126dGnkM2lZGn3EqMPhICIigjfffBOLxcKQIUM4evQoL7/8MrNmzapXmzabDZvN1sCR1lH5+JXgTuAb4pkYRERaMsOAknzPvLe3H5RdkqmvqKgoSktL+de//sXNN9/susRTX/fccw9vv/22K2FZuHAhkydPZs2aNefUbmvjVsISFhaGxWIhNTW1Unlqamq1mSNAhw4d8Pb2xmKxuMp69+5NSkoKxcXF9WrTo3Q5SETk3JTkw0vRnnnv3x8Dq/85NXHRRRfx+9//nttvv50HHniAoUOH8qtf/YqJEydWuVpQF3feeSczZszg4MGDgLMHZ+nSpUpYzuDWGBar1cqQIUNISkpylTkcDpKSkhg2bFi1x1xyySXs2bMHh8PhKtu1axcdOnTAarXWq02PStWU/CIibd2LL75ISkoK8+fPp2/fvsyfP59evXqxZcsWt9sKDw/n2muvZdGiRbz99ttce+21hIWFNULULZvbl4QSExO5++67ueCCCxg6dChz584lLy+PyZMnAzBx4kRiYmKYPXs2AFOnTuW1117j4Ycf5qGHHmL37t289NJL/OY3v6lzm82KpuQXETk33n7Ong5PvXcDad++Pbfccgu33HILL730EoMHD+Yvf/kLixcvdrute+65h+nTpwPOecmkKrcTlvHjx3PixAlmzpxJSkoKgwYNYsWKFa5usEOHDmE2V3TcxMbGsnLlSh599FEGDBhATEwMDz/8ME888USd22w27KWQtsP5XJeERETqx2Q658syzY3VaqVbt27k5eXV6/hRo0ZRXFyMyWQiISGhgaNrHeo16Hb69OmuTPBM1V1zGzZsGN9//32922w2MnaDvQisARAS5+loRESkkWVnZ7N58+ZKZVu2bGHlypXcdttt9OjRA8Mw+M9//sNnn33G22+/Xa/3sVgs7Nixw/VcqtK88u4ovxwU2Q/MWoZJRKS1W7NmDYMHD65UNmLECLp3785vf/tbDh8+jM1m47zzzuP//u//uOuuu+r9XkFBQecabqumhMUdKb84H3U5SESk1Vu0aBGLFi0653YOHDhQY/u1+fjjj8/5vVsTdRO4I/W0HhYRERFpMkpY3OG6Q2iAZ+MQERFpY5Sw1FVuKuSlgckMEb09HY2IiEibooSlrsonjAvtBtaGu49fREREzk4JS11pwjgRERGPUcJSV6laQ0hERMRTlLDUVUr5GkLqYREREWlqSljqoqQQ0nc7n6uHRUREpMkpYamLEzvAsINfewjs4OloRERE2hwlLHXhuhzUz7lol4iIiDQpJSx1oTuERETarJSUFB566CG6du2KzWYjNjaWMWPGkJSUBEBcXBwmk6nKIr+PPPIIV1xxhev1s88+i8lk4oEHHqhUb/PmzZhMphqn8D+bZ599lkGDBtW4f//+/dx+++1ER0fj4+NDx44dGTt2LDt37mTRokWYTKZatwMHDrhiHzVqVJX2X375ZUwmU6VzbQxKWOoiVQmLiEhbdODAAYYMGcKXX37Jyy+/zJYtW1ixYgUjRoxg2rRprno+Pj488cQTZ23Px8eHBQsWsHv37jrHsGbNGuLi4uoTPiUlJVx11VVkZ2ezfPlykpOTWbZsGf379ycrK4vx48dz/Phx1zZs2DCmTJlSqSw2NhaADh06sHr1ao4cOVLpPRYuXEinTp3qFZ87tPjh2RhG5VWaRUSkzXjwwQcxmUxs2LABf39/V3nfvn255557XK/vv/9+5s+fz2effcY111xTY3s9e/YkIiKCp556ivfff79RYwfYtm0be/fuJSkpic6dOwPQuXNnLrnkElcdX19f13Or1Yqfnx9RUVFV2oqIiGDIkCEsXryYp556CoC1a9eSnp7OLbfcwvbt2xv1XNTDcjZZh6AoG8zeENbD09GIiLR4hmGQX5Lvkc0wjDrHmZmZyYoVK5g2bVqlZKVcSEiI63mXLl144IEHmDFjBg6Ho9Z2//jHP/LRRx/x448/1jmW+goPD8dsNvPhhx9it9vPub177rmn0irTCxcu5I477sBqtZ5z22ejHpazKb8cFNELvBr/CxERae0KSguIfy/eI++9/vb1+HnXbXmVPXv2YBgGvXr1qlP9p59+mrfffpslS5Zw11131Vjv/PPP59Zbb+WJJ55wjYNpLDExMbzyyis8/vjjPPfcc1xwwQWMGDGCO+64g65du7rd3nXXXccDDzzA119/zZAhQ3j//ff59ttvWbhwYSNEX5l6WM5GE8aJiLRJ7vTGgLM347HHHmPmzJkUFxfXWveFF17gm2++4fPPP692f0BAgGsbPXo0hw4dqlR25sDd2kybNo2UlBSWLFnCsGHD+OCDD+jbty+rVq1y6/wAvL29ufPOO3n77bf54IMP6NGjBwMGDHC7nfpQD8vZlCcsmjBORKRB+Hr5sv729R5777o677zzMJlM7Ny5s87HJCYm8vrrr/P666/XWq9bt25MmTKFJ598kgULFlTZv3nzZtfz9evX88QTT7BmzRpXWVBQUJ1jAggMDGTMmDGMGTOGF154gYSEBF544QWuuuoqt9oB52Wh+Ph4tm7dWmkcT2NTwnI2qRpwKyLSkEwmU50vy3hSaGgoCQkJzJs3j9/85jdVxrFkZWVVGscCzp6RZ555hmeffZbrr7++1vZnzpxJt27dWLp0aZV93bt3dz0/cuQIXl5elcrOhclkolevXqxdu7Zex/ft25e+ffvyyy+/cPvttzdITHWhS0K1KcyBkwecz3VLs4hImzNv3jzsdjtDhw7lo48+Yvfu3ezYsYNXXnmFYcOGVXvM/fffT3BwMO+9916tbUdGRpKYmMgrr7xyznEWFBSwefPmStvevXvZvHkzY8eO5cMPP2T79u3s2bOHBQsWsHDhQsaOHVvv9/vyyy85fvx4lYStMamHpVYGJLzkvFPIL9TTwYiISBPr2rUrmzZt4sUXX+S3v/0tx48fJzw8nCFDhvDGG29Ue4y3tzfPP/98nXofHnvsMd544w0KCwvPKc5du3YxePDgSmVXXnklS5cuJS4ujueee44DBw5gMplcrx999NF6v191d001NpPh7qiiZignJ4fg4GCys7Pdvq4nIiKNp7CwkP3799OlSxd8fHw8HY54SE3/Hbjz+61LQiIiItLsKWERERGRZk8Ji4iIiDR7SlhERESk2VPCIiIiIs2eEhYREWl0Z1sQUFq3hvj+NQ+LiIg0GqvVitls5tixY4SHh2O1WjGZTJ4OS5qIYRgUFxdz4sQJzGbzOa3qrIRFREQajdlspkuXLhw/fpxjx455OhzxED8/Pzp16oTZXP8LO0pYRESkUVmtVjp16kRpaSl2u93T4UgTs1gseHl5nXPPWr0Slnnz5vHyyy+TkpLCwIEDefXVVxk6dGi1dRctWsTkyZMrldlstkrTEE+aNInFixdXqpOQkMCKFSvqE56IiDQzJpMJb29vvL29PR2KtFBuJyzLli0jMTGR+fPnEx8fz9y5c0lISCA5OZmIiIhqjwkKCiI5Odn1urosa9SoUbz99tuu1zabzd3QREREpJVy+2LSnDlzmDJlCpMnT6ZPnz7Mnz8fPz8/Fi5cWOMxJpOJqKgo1xYZGVmljs1mq1SnXbt27oYmIiIirZRbCUtxcTEbN25k5MiRFQ2YzYwcOZJ169bVeNypU6fo3LkzsbGxjB07lm3btlWps2bNGiIiIujZsydTp04lIyOjxvaKiorIycmptImIiEjr5dYlofT0dOx2e5UeksjISHbu3FntMT179mThwoUMGDCA7Oxs/vKXv3DxxRezbds2OnbsCDgvB91444106dKFvXv38vvf/57Ro0ezbt06LBZLlTZnz57Nc889V6VciYuIiEjLUf67bRjG2Ssbbjh69KgBGGvXrq1U/rvf/c4YOnRondooLi42unXrZjz99NM11tm7d68BGF988UW1+wsLC43s7GzXtn37dgPQpk2bNm3atLXA7fDhw2fNH9zqYQkLC8NisZCamlqpPDU1laioqDq14e3tzeDBg9mzZ0+Ndbp27UpYWBh79uzhyiuvrLLfZrNVGpQbEBDA4cOHCQwMbPAJiXJycoiNjeXw4cMEBQU1aNvNTVs6V2hb56tzbb3a0vnqXFsfwzDIzc0lOjr6rHXdSlisVitDhgwhKSmJcePGAc7pdpOSkpg+fXqd2rDb7WzZsoVrrrmmxjpHjhwhIyODDh061KlNs9nsurzUWIKCglr1fzSna0vnCm3rfHWurVdbOl+da+sSHBxcp3pu3yWUmJjIW2+9xeLFi9mxYwdTp04lLy/PNdfKxIkTmTFjhqv+H/7wBz7//HP27dvHpk2buPPOOzl48CD33Xcf4ByQ+7vf/Y7vv/+eAwcOkJSUxNixY+nevTsJCQnuhiciIiKtkNvzsIwfP54TJ04wc+ZMUlJSGDRoECtWrHANxD106FClqXdPnjzJlClTSElJoV27dgwZMoS1a9fSp08fwDkD3i+//MLixYvJysoiOjqaq6++mueff15zsYiIiAhQz5lup0+fXuMloDVr1lR6/de//pW//vWvNbbl6+vLypUr6xNGk7DZbMyaNatNJE9t6VyhbZ2vzrX1akvnq3Nt20yGUZd7iUREREQ8p/7LJoqIiIg0ESUsIiIi0uwpYREREZFmTwmLiIiINHtKWERERKTZU8ICzJs3j7i4OHx8fIiPj2fDhg211v/ggw/o1asXPj4+9O/fn88++6yJIq2/2bNnc+GFFxIYGEhERATjxo0jOTm51mMWLVqEyWSqtPn4+DRRxOfm2WefrRJ7r169aj2mJX6vAHFxcVXO1WQyMW3atGrrt7Tv9euvv2bMmDFER0djMpn4+OOPK+03DIOZM2fSoUMHfH19GTlyJLt37z5ru+7+3TeF2s61pKSEJ554gv79++Pv7090dDQTJ07k2LFjtbZZn7+FpnC273XSpElV4h41atRZ222O3yuc/Xyr+xs2mUy8/PLLNbbZXL/bxtLmE5Zly5aRmJjIrFmz2LRpEwMHDiQhIYG0tLRq669du5YJEyZw77338tNPPzFu3DjGjRvH1q1bmzhy93z11VdMmzaN77//nlWrVlFSUsLVV19NXl5erccFBQVx/Phx13bw4MEmivjc9e3bt1Ls3377bY11W+r3CvDDDz9UOs9Vq1YBcMstt9R4TEv6XvPy8hg4cCDz5s2rdv+f//xnXnnlFebPn8/69evx9/cnISGBwsLCGtt09+++qdR2rvn5+WzatIlnnnmGTZs2sXz5cpKTk7n++uvP2q47fwtN5WzfK8CoUaMqxf3Pf/6z1jab6/cKZz/f08/z+PHjLFy4EJPJxE033VRru83xu200dVpiuRUbOnSoMW3aNNdru91uREdHG7Nnz662/q233mpce+21lcri4+ONX//6140aZ0NLS0szAOOrr76qsc7bb79tBAcHN11QDWjWrFnGwIED61y/tXyvhmEYDz/8sNGtWzfD4XBUu78lf6+A8a9//cv12uFwGFFRUcbLL7/sKsvKyjJsNpvxz3/+s8Z23P2794Qzz7U6GzZsMADj4MGDNdZx92/BE6o717vvvtsYO3asW+20hO/VMOr23Y4dO9b41a9+VWudlvDdNqQ23cNSXFzMxo0bGTlypKvMbDYzcuRI1q1bV+0x69atq1QfICEhocb6zVV2djYAoaGhtdY7deoUnTt3JjY2lrFjx7Jt27amCK9B7N69m+joaLp27codd9zBoUOHaqzbWr7X4uJi3n33Xe65555aVy5vyd/r6fbv309KSkql7y44OJj4+Pgav7v6/N03V9nZ2ZhMJkJCQmqt587fQnOyZs0aIiIi6NmzJ1OnTiUjI6PGuq3pe01NTeXTTz/l3nvvPWvdlvrd1kebTljS09Ox2+2udZDKRUZGkpKSUu0xKSkpbtVvjhwOB4888giXXHIJ/fr1q7Fez549WbhwIf/+97959913cTgcXHzxxRw5cqQJo62f+Ph4Fi1axIoVK3jjjTfYv38/l156Kbm5udXWbw3fK8DHH39MVlYWkyZNqrFOS/5ez1T+/bjz3dXn7745Kiws5IknnmDChAm1rubr7t9CczFq1Cj+8Y9/kJSUxJ/+9Ce++uorRo8ejd1ur7Z+a/leARYvXkxgYCA33nhjrfVa6ndbX/VaS0hatmnTprF169azXuscNmwYw4YNc72++OKL6d27N3//+995/vnnGzvMczJ69GjX8wEDBhAfH0/nzp15//336/SvlpZqwYIFjB49mujo6BrrtOTvVZxKSkq49dZbMQyDN954o9a6LfVv4bbbbnM979+/PwMGDKBbt26sWbOGK6+80oORNb6FCxdyxx13nHUwfEv9buurTfewhIWFYbFYSE1NrVSemppKVFRUtcdERUW5Vb+5mT59Ov/9739ZvXo1HTt2dOtYb29vBg8ezJ49exopusYTEhJCjx49aoy9pX+vAAcPHuSLL77gvvvuc+u4lvy9ln8/7nx39fm7b07Kk5WDBw+yatWqWntXqnO2v4XmqmvXroSFhdUYd0v/Xst98803JCcnu/13DC33u62rNp2wWK1WhgwZQlJSkqvM4XCQlJRU6V+gpxs2bFil+gCrVq2qsX5zYRgG06dP51//+hdffvklXbp0cbsNu93Oli1b6NChQyNE2LhOnTrF3r17a4y9pX6vp3v77beJiIjg2muvdeu4lvy9dunShaioqErfXU5ODuvXr6/xu6vP331zUZ6s7N69my+++IL27du73cbZ/haaqyNHjpCRkVFj3C35ez3dggULGDJkCAMHDnT72Jb63daZp0f9etrSpUsNm81mLFq0yNi+fbtx//33GyEhIUZKSophGIZx1113GU8++aSr/nfffWd4eXkZf/nLX4wdO3YYs2bNMry9vY0tW7Z46hTqZOrUqUZwcLCxZs0a4/jx464tPz/fVefMc33uueeMlStXGnv37jU2btxo3HbbbYaPj4+xbds2T5yCW377298aa9asMfbv32989913xsiRI42wsDAjLS3NMIzW872Ws9vtRqdOnYwnnniiyr6W/r3m5uYaP/30k/HTTz8ZgDFnzhzjp59+ct0Z88c//tEICQkx/v3vfxu//PKLMXbsWKNLly5GQUGBq41f/epXxquvvup6fba/e0+p7VyLi4uN66+/3ujYsaOxefPmSn/HRUVFrjbOPNez/S14Sm3nmpubazz22GPGunXrjP379xtffPGFcf755xvnnXeeUVhY6GqjpXyvhnH2/44NwzCys7MNPz8/44033qi2jZby3TaWNp+wGIZhvPrqq0anTp0Mq9VqDB061Pj+++9d+y6//HLj7rvvrlT//fffN3r06GFYrVajb9++xqefftrEEbsPqHZ7++23XXXOPNdHHnnE9blERkYa11xzjbFp06amD74exo8fb3To0MGwWq1GTEyMMX78eGPPnj2u/a3ley23cuVKAzCSk5Or7Gvp3+vq1aur/W+3/JwcDofxzDPPGJGRkYbNZjOuvPLKKp9D586djVmzZlUqq+3v3lNqO9f9+/fX+He8evVqVxtnnuvZ/hY8pbZzzc/PN66++mojPDzc8Pb2Njp37mxMmTKlSuLRUr5Xwzj7f8eGYRh///vfDV9fXyMrK6vaNlrKd9tYTIZhGI3ahSMiIiJyjtr0GBYRERFpGZSwiIiISLOnhEVERESaPSUsIiIi0uwpYREREZFmTwmLiIiINHtKWERERKTZU8IiIiIizZ4SFhEREWn2lLCIiIhIs6eERURERJq9/w8PVC1tfOSPkgAAAABJRU5ErkJggg=="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Findings**\n\n1. Standalone CNN Model: The standalone CNN model achieved a classification accuracy of 95.1% on the test dataset. This indicates that the CNN model was effective in extracting relevant features from the input images of Persian alphabet characters and making accurate predictions.\n\n2. Standalone LSTM Model: The standalone LSTM model reported a classification accuracy of 94.9% on the test dataset. This suggests that the LSTM model was successful in capturing sequential patterns in the input data and performing well in classifying Persian alphabet characters.\n\n3. Hybrid Model: Surprisingly, the hybrid CNN-LSTM model yielded a slightly lower classification accuracy of 94.2% compared to the standalone models. This suggests that the combination of CNN and LSTM layers did not provide significant improvement over using either model alone for Persian alphabet classification.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file From 1cf9035584aa2bf4e1ab6d0df1a8256b58bd4eff Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:49:46 +0530 Subject: [PATCH 04/11] Create readme.md --- .../dataset/readme.md | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 Persian Alphabets Classification/dataset/readme.md diff --git a/Persian Alphabets Classification/dataset/readme.md b/Persian Alphabets Classification/dataset/readme.md new file mode 100644 index 000000000..c08122133 --- /dev/null +++ b/Persian Alphabets Classification/dataset/readme.md @@ -0,0 +1,34 @@ +# Persian Alphabets Dataset + +## Overview + +This dataset contains a collection of 51.7K images of Persian alphabets and numbers. The image files are named from 0 to 51.7K and **labels.csv** contains `File_Name` mapped to their corresponding `Label`, making it suitable for use in classification tasks. + +## Dataset Contents + +The dataset is organized into the following structure: + +- **data** + - **/data**: contains images of 77 different persian characters + - **labels.csv**: CSV file containing the mapping of image filenames to their corresponding labels. + +## Dataset Source + +The dataset was sourced from [Kaggle](https://www.kaggle.com/datasets/mostafamohammadi1/persian-alphabets-and-numbers). + +## Usage + +You can use this dataset for various purposes, including but not limited to: + +- Training and evaluating DL models for Persian alphabet and number classification. +- Research in computer vision, pattern recognition, and deep learning. +- Educational purposes for learning image classification techniques. + +## Citation + +If you use this dataset in your work, please consider citing the dataset source on Kaggle. + +## License + +The dataset is provided under the terms specified by the dataset creator on Kaggle. Please refer to the Kaggle dataset page for details about the license. + From d94185fc27ea5a916b5be87eae6ec9064dee1753 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:50:05 +0530 Subject: [PATCH 05/11] Create readme.md --- Persian Alphabets Classification/images/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Persian Alphabets Classification/images/readme.md diff --git a/Persian Alphabets Classification/images/readme.md b/Persian Alphabets Classification/images/readme.md new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Persian Alphabets Classification/images/readme.md @@ -0,0 +1 @@ + From 05dbaf919f78435c51c9cacc06f4a4d729f6dbe3 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:50:49 +0530 Subject: [PATCH 06/11] Add files via upload --- .../images/accuracy.png | Bin 0 -> 32954 bytes .../images/cnn_accuracy.png | Bin 0 -> 23909 bytes .../images/cnn_loss.png | Bin 0 -> 24135 bytes .../images/hybrid_model_acc.png | Bin 0 -> 25657 bytes .../images/hybrid_model_loss.png | Bin 0 -> 22647 bytes .../images/loss.png | Bin 0 -> 28838 bytes .../images/lstm_acc.png | Bin 0 -> 26585 bytes .../images/lstm_loss.png | Bin 0 -> 23522 bytes 8 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Persian Alphabets Classification/images/accuracy.png create mode 100644 Persian Alphabets Classification/images/cnn_accuracy.png create mode 100644 Persian Alphabets Classification/images/cnn_loss.png create mode 100644 Persian Alphabets Classification/images/hybrid_model_acc.png create mode 100644 Persian Alphabets Classification/images/hybrid_model_loss.png create mode 100644 Persian Alphabets Classification/images/loss.png create mode 100644 Persian Alphabets Classification/images/lstm_acc.png create mode 100644 Persian Alphabets Classification/images/lstm_loss.png diff --git 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z+)L4yMq1Ce*G1K}wYpT9zTE8S<5L1tp}owG9a@urwT`3k%-x+{8D*vj;h6u42>)LthaVx#<3+q9aE%?;Wn z;=P4KN%ot4sDL$iW)mq@4;B)z!VOBE!!|5odO3>|`(4%KgKn$wz1KW;XfiZ3?0(a? zW1Fbkbc%A=J#Gt84cg-3;<0szPVEg(^GKMl{L;T~SKC?=?A zW16Bi%B&`3+Ej?2-^N1!=+XT?`|uz@kc1KhCEXTPRibySoe5pHnG-b+`&@Nib{kt; zQoU^*QO^I_Gl%FYUkn1N_`P$pv+W+Y>Q6z;Doq_68ggKn@v`F)=ohD@aL6HU~e|Dh#K)7{HrhzLzSfo-4xzAHkrvH84gIceh4JXjg%DU zN3UyY*ifx0LGW|+K>b+-+iEyS!u148g_-Is>4qP*dhd_H7qT~$Qzu>HyPi6%f3kR<@nnLW#98a{+l8%?ufq|ril*IOfL1%x7!?7Y!?5m gi5johFP7PC8NcYr1RPm}Z<}&VTVE?r<6Q8c0b3g!&Hw-a literal 0 HcmV?d00001 From 31af16cf0a50339306930c476cc779a0bff995b7 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:52:59 +0530 Subject: [PATCH 07/11] Update README.md --- Persian Alphabets Classification/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/Persian Alphabets Classification/README.md b/Persian Alphabets Classification/README.md index 1d668e8e7..f15a67f69 100644 --- a/Persian Alphabets Classification/README.md +++ b/Persian Alphabets Classification/README.md @@ -47,6 +47,10 @@ pip install jupyter tensorflow numpy matplotlib seaborn scikit-learn 4. Navigate to the `model/notebook.ipynb` 5. Run all the cells +## Additional Resources + +Check out the Kaggle notebook for Persian Alphabet Classification using CNN and LSTM: [Kaggle Notebook](https://www.kaggle.com/code/gowrivinaykamalakars/persian-alphabet-classification-using-cnn-and-lstm) + ## Contributor:

From c813dbfdbe86f446426cd746453a1ac8a8e3c5d1 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 16:53:12 +0530 Subject: [PATCH 08/11] Update README.md --- Persian Alphabets Classification/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Persian Alphabets Classification/README.md b/Persian Alphabets Classification/README.md index f15a67f69..cd2f83d97 100644 --- a/Persian Alphabets Classification/README.md +++ b/Persian Alphabets Classification/README.md @@ -8,7 +8,7 @@ In this repo, I developed a novel deep learning approach for classifying Persian ## Dataset -The dataset used in this project consists of 51,744 images of Persian alphabets and numbers. The images are organized into folders based on their corresponding labels, making it suitable for training and evaluating machine learning models for Persian alphabet classificatiaon. +The dataset used in this project consists of 51,744 images of Persian alphabets and numbers. The images are organized into folders based on their corresponding labels, making it suitable for training and evaluating machine learning models for Persian alphabet classification. You can download the dataset from [Kaggle](https://www.kaggle.com/datasets/mostafamohammadi1/persian-alphabets-and-numbers). From 6882acc0ca58cd009eec868d219badc0b35b227f Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 17:05:53 +0530 Subject: [PATCH 09/11] Update readme.md --- Persian Alphabets Classification/models/readme.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/Persian Alphabets Classification/models/readme.md b/Persian Alphabets Classification/models/readme.md index b9f120157..1f73e55c5 100644 --- a/Persian Alphabets Classification/models/readme.md +++ b/Persian Alphabets Classification/models/readme.md @@ -29,3 +29,18 @@ - Evaluate the trained model on the test dataset. - Calculate performance metrics such as accuracy, loss, and classification reports. - Visualize the training and validation metrics to assess model performance. + +## Evaluation Metrics + +

+ + +

+ +## Findings + +1. Standalone CNN Model: The standalone CNN model achieved a classification accuracy of 95.1% on the test dataset. This indicates that the CNN model was effective in extracting relevant features from the input images of Persian alphabet characters and making accurate predictions. + +2. Standalone LSTM Model: The standalone LSTM model reported a classification accuracy of 94.9% on the test dataset. This suggests that the LSTM model was successful in capturing sequential patterns in the input data and performing well in classifying Persian alphabet characters. + +3. Hybrid Model: Surprisingly, the hybrid CNN-LSTM model yielded a slightly lower classification accuracy of 94.2% compared to the standalone models. This suggests that the combination of CNN and LSTM layers did not provide significant improvement over using either model alone for Persian alphabet classification. From 05f03b7930d337dd448339a0b1750b389468634e Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Fri, 23 Feb 2024 17:08:14 +0530 Subject: [PATCH 10/11] Create requirements.txt --- Persian Alphabets Classification/requirements.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 Persian Alphabets Classification/requirements.txt diff --git a/Persian Alphabets Classification/requirements.txt b/Persian Alphabets Classification/requirements.txt new file mode 100644 index 000000000..8c9487357 --- /dev/null +++ b/Persian Alphabets Classification/requirements.txt @@ -0,0 +1,5 @@ +tensorflow +numpy +matplotlib +seaborn +scikit-learn From 2c6d70f51a64246ea67969f950c3c486b81e4ee6 Mon Sep 17 00:00:00 2001 From: Kamalakar Satapathi <103712713+Sgvkamalakar@users.noreply.github.com> Date: Sat, 24 Feb 2024 07:29:40 +0530 Subject: [PATCH 11/11] Delete Persian Alphabets Classification/images/readme.md --- Persian Alphabets Classification/images/readme.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Persian Alphabets Classification/images/readme.md diff --git a/Persian Alphabets Classification/images/readme.md b/Persian Alphabets Classification/images/readme.md deleted file mode 100644 index 8b1378917..000000000 --- a/Persian Alphabets Classification/images/readme.md +++ /dev/null @@ -1 +0,0 @@ -

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