Mounika Anireddy

Motivated Mechanical Engineer with a passion for data analysis seeking a challenging position that combines technical expertise in mechanical engineering with advanced data science skills.

Plant leaf disease detection using deep learning and convolutional neural networks

• The goal of this project is to use deep convolution networks to build a new approach to developing plant disease recognition models based on leaf image categorization
• The new training approach and methodology make it possible to put the system into practice quickly and easily
• With the ability to separate plant leaves from their surroundings, the developed model can recognize 10 different forms of plant diseases in healthy leaves
• Throughout the project, all the necessary processes for implementing this disease recognition model are thoroughly documented, beginning with the collection of photos to construct a database that is evaluated by agricultural experts
• Used different CNN architectures like google net, resent, and VGG16, we got an accuracy of 98%using resent

Malware detection and analysis using machine learning

• In this model we've implemented a machine learning model to detect and analyze malware effectively
• machine learning algorithms such as SVM, neural networks, and random forest are used for malware detection and analysis.
• feature selection techniques such as PCA are also used which can help reduce the complexity of the dataset and improve the accuracy of the models.
• Validated and compared the algorithms using Accuracy and F1 score metrics.

• Artificial intelligence – Fuel an online learning initiative of (friends union for engineering lives)
• Cloud Computing - AWS(amazon web services)
• Matlab - an offline course organized by mvsr engineering college
• Machine Learning Pipelines with Azure ML Studio-Coursera
• Data Structures and Software design-udemy