Merge joss review branch to main

paper/paper.md

@@ -38,7 +38,7 @@ In deep learning projects based on Keras [@Chollet:2015], the visualkeras projec
 
 # Introduction
 
-Recent advancements in artificial intelligence have sparked widespread interest among researchers, particularly in exploring innovative algorithmic approaches such as neural networks or deep learning architectures. These architectures have demonstrated remarkable utility across various AI applications, including computer vision, natural language processing, and robotics. To implement neural network architectures, many researchers and practitioners often utilize established deep learning frameworks, such as PyTorch [@Paszke:2019], TensorFlow [@Abadi:2016], and Keras [@Chollet:2015]
+Recent advancements in artificial intelligence have sparked widespread interest among researchers, particularly in exploring innovative algorithmic approaches such as neural networks or deep learning architectures. These architectures have demonstrated remarkable utility across various AI applications, including computer vision, natural language processing, and robotics. To implement neural network architectures, many researchers and practitioners often utilize established deep learning frameworks, such as PyTorch [@Paszke:2019], TensorFlow [@Abadi:2016], and Keras [@Chollet:2015].
 
 To effectively communicate their ideas, practitioners often employ architecture diagrams as aids for comprehension. While detailed mathematical descriptions help in understanding the intricacies of algorithms, visual representations of architectures offer an additional means of conveying concepts, enabling individuals to grasp the overall visual representation. VisualTorch is designed to facilitate the visualization of PyTorch-based neural network architectures. Instead of manually constructing diagrams from scratch, practitioners can simply leverage our library to generate visualizations. With a variety of customization options, users can tailor visualizations to suit their preferences.