Pinned Repositories
Android-Notes-App
anshus7007
Borderless-Javafx-Settings-UI
CodeMode
CollectMoneyApp_MVVM
A MVVM architecture that displays and stores the name and amount of the person who had owed some money from the user. Room Persistence library is being used in the app along with Kotlin Coroutines, View Model and material design of android.
Connect4-Game
Covid19Web
Login-and-SignUp-using-Firebase-Auth
Login and SignUp Page
Run-Tracker_Android_App
An app that shows the routes of your track and displays all the data such as distance covered,time taken, calories burned. The app also displays the essential data in form of bar chart.
Weather
anshus7007's Repositories
anshus7007/Run-Tracker_Android_App
An app that shows the routes of your track and displays all the data such as distance covered,time taken, calories burned. The app also displays the essential data in form of bar chart.
anshus7007/CodeMode
anshus7007/Login-and-SignUp-using-Firebase-Auth
Login and SignUp Page
anshus7007/Weather
anshus7007/Android-Notes-App
anshus7007/anshus7007
anshus7007/Borderless-Javafx-Settings-UI
anshus7007/CollectMoneyApp_MVVM
A MVVM architecture that displays and stores the name and amount of the person who had owed some money from the user. Room Persistence library is being used in the app along with Kotlin Coroutines, View Model and material design of android.
anshus7007/Connect4-Game
anshus7007/Covid19Web
anshus7007/Covid_Android_App
anshus7007/devops-projects
anshus7007/Devops-Projects-Demo
anshus7007/Draw-gestures-on-ImageView-Android-App
anshus7007/DrawRoute
anshus7007/Food-Running-App
Its just an simple app with static data fetched from the internet that shows various restaurants that are used to order food.The app has various features.
anshus7007/HelloGit
anshus7007/Invoice
anshus7007/jenkins
anshus7007/Paytm-UI-UX-Clone
anshus7007/Plant-Disease-Detection-Web-application
Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.
anshus7007/Quiz-App
anshus7007/Simple-maven-project
anshus7007/TCP_Server-Client_Program
A simple stream socket TCP server-client implementation in C.
anshus7007/WhatsAppClone_Android_App