use updated test result

paper/paper.md

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 # Summary
-Deep Waste[^1] is a mobile application that leverages state-of-the art computer vision and deep learning technologies to accurately classify waste materials into ten categories: battery, biological, trash, plastic, paper, shoes, clothes, metal, glass, and cardboard. This innovative solution was developed to address the challenges of costly, imprecise, and unclear waste classification methods. Deep Waste has undergone rigorous testing on a variety of neural network architectures and real-world images, achieving an impressive average precision of 97% on the test set. By enabling efficient waste processing, reducing greenhouse gas emissions, and promoting sustainable practices, this app has the potential to transform the field of waste management.
+Deep Waste[^1] is a mobile application that leverages state-of-the art computer vision and deep learning technologies to accurately classify waste materials into ten categories: battery, biological, trash, plastic, paper, shoes, clothes, metal, glass, and cardboard. This innovative solution was developed to address the challenges of costly, imprecise, and unclear waste classification methods. Deep Waste has undergone rigorous testing on a variety of neural network architectures and real-world images, achieving an impressive average precision of 96.41% on the test set. By enabling efficient waste processing, reducing greenhouse gas emissions, and promoting sustainable practices, this app has the potential to transform the field of waste management.
 
 [^1]: Previously known as MWaste.
 
@@ -32,7 +32,7 @@ The escalating global waste crisis, projected to surge by 70% by 2050 without in
 
 Recent advancements leverage deep learning models to streamline waste sorting and management [@liu_image_2022]. These models, like RWNet and ConvoWaste, exhibit high accuracy, emphasizing the role of accurate waste disposal in mitigating climate change and reducing greenhouse gas emissions. Some studies incorporate IoT and waste grid segmentation to classify and segregate waste items in real time [@m_technical_2023].
 
-Integration of machine learning models with mobile devices presents a promising avenue for precise waste management [@narayan_deepwaste:_2021]. The use of optimized deep learning techniques in an app demonstrates potential, achieving an accuracy of 0.881 in waste classification. However, limitations persist, prompting the introduction of Deep Waste, a mobile app employing computer vision to classify waste into ten types. Using transfer learning [@5288526], Deep Waste attains a remarkable 97% precision on the test set, functioning both online and offline.
+Integration of machine learning models with mobile devices presents a promising avenue for precise waste management [@narayan_deepwaste:_2021]. The use of optimized deep learning techniques in an app demonstrates potential, achieving an accuracy of 0.881 in waste classification. However, limitations persist, prompting the introduction of Deep Waste, a mobile app employing computer vision to classify waste into ten types. Using transfer learning [@5288526], Deep Waste attains a remarkable 96.41% precision on the test set, functioning both online and offline.