publications/joss/paper.md

publications/joss/paper.md

@@ -61,7 +61,9 @@ extract crucial information, such as atmospheric temperature, metallicity, carbo
 datasets generated by simulating the physics and chemistry of these atmospheres for a wide range of thermal structures 
 and compositions. In addition, different machine learning and deep learning methods have been developed in recent years 
 to perform various tasks, such as confirming the detection of exoplanets [e.g., @Valizadegan2021 by implementing CNN 
-techniques] and brown dwarfs [e.g., @Lueber2023RandomForesr_BDs using Random Forest ensembles].
+techniques] and brown dwarfs [e.g., @Lueber2023RandomForesr_BDs using Random Forest ensembles]. However, to the best of
+our knowledge, this is the first time, deep learning and convolutional neural networks is implemented on brown dwarf 
+atmospheric datasets to predict parameters such as temperature and gravity simultaneously.  
 
 With the continuous observation of these objects and the increasing amount of data coming to Earth each day, there is a 
 critical need for a systematic pipeline to explore the datasets and extract important information from them. This 
@@ -81,10 +83,10 @@ statistical capabilities for conducting Convolutional Neural Networks (CNN) or M
 captured from the atmospheres of extrasolar planets and brown dwarfs. The tasks executed by the `TelescopeML` modules are 
 outlined below and visualized in Figure \autoref{fig:TelescopeML_modules}:
 
-    * __StatVisAnalyzer module__: Explore and process the synthetic datasets (or the training examples) and provide functions for statistical analysis.
-    * __DeepBuilder module__: Specify training and target features, normalize/scale datasets, and pass them to the ML training phase.
-    * __DeepTrainer module__: Create an ML model, train the model with the training examples, and utilize hyperparameters.
-    * __Predictor module__: Process the observational datasets and deploy the trained ML model to predict atmospheric parameters such as gravity and temperature.
+    -  __StatVisAnalyzer module__: Explore and process the synthetic datasets (or the training examples) and provide functions for statistical analysis.
+    - __DeepBuilder module__: Specify training and target features, normalize/scale datasets, and pass them to the ML training phase.
+    - __DeepTrainer module__: Create an ML model, train the model with the training examples, and utilize hyperparameters.
+    - __Predictor module__: Process the observational datasets and deploy the trained ML model to predict atmospheric parameters such as gravity and temperature.
 
 ![TelescopeML main modules to manipulate the training example, build the ML model, train and tune it, and ultimately 
 extract the target features from the observational data.\label{fig:TelescopeML_modules}](TelescopeML_modules.png)
@@ -98,11 +100,11 @@ extract the target features from the observational data.\label{fig:TelescopeML_m
 documentation is hosted with _Sphinx_ using _ReadtheDocs_ tools and includes several instructions and tutorials 
 as follows: 
 
-    * __Main page__: https://ehsangharibnezhad.github.io/TelescopeML/
-    * __Installation__: https://ehsangharibnezhad.github.io/TelescopeML/installation.html
-    * __Tutorials and examples__: https://ehsangharibnezhad.github.io/TelescopeML/tutorials.html
-    * __The code__: https://ehsangharibnezhad.github.io/TelescopeML/code.html
-    * __ML Concepts__: https://ehsangharibnezhad.github.io/TelescopeML/knowledgebase.html
+    - __Main page__: https://ehsangharibnezhad.github.io/TelescopeML/
+    - __Installation__: https://ehsangharibnezhad.github.io/TelescopeML/installation.html
+    - __Tutorials and examples__: https://ehsangharibnezhad.github.io/TelescopeML/tutorials.html
+    - __The code__: https://ehsangharibnezhad.github.io/TelescopeML/code.html
+    - __ML Concepts__: https://ehsangharibnezhad.github.io/TelescopeML/knowledgebase.html
 
 
 #  Users  and Future Developments