SWATHI MOHAN KUMAR

Experienced Undergraduate Teaching Assistant with background in delivering comprehensive support to faculty and students. Skilled in preparation of course materials, facilitating class discussions, and grading assignments. Strengths include strong communication skills, ability to explain complex concepts simply, and capability to handle multiple priorities effectively. Made significant impact by improving student engagement and contributing to the creation of an inclusive learning environment.

• Received award for the hyper personalized solution of a business model under the CONNECTED CONSUMER category in PRODIGI(Academia) Innovation Contest of Products and Resources practice 2022 conducted by COGNIZANT-a leading corporate in India.
• Played a key role in organizing various events and volunteered for National level Symposium.
• Eco club- Secretary, RMK Engineering College; Participated in the Computer literacy program for rural people which was organized by our college.
• Took part in a Guinness World Record event- Most users to take an online computer programming lesson in 24hours on 04/24/21 6PM to 04/25/21 6PM.

THE DISEASE PREDICTION SYSTEM BASED ON SYMPTOM IDENTIFICATION AND IMAGE PROCESSING, HTML, CSS, DJANGO, DENSENET-121, CNN, MACHINE LEARNING ALGORITHMS, NLP, Identified the need for early diagnosis of the disease and developed a user-friendly system. Integration of machine learning algorithms like Random-forest, Decision tree, XGBoost, LSTM, Logistic regression and Naive bayes for symptom-based prediction. Allows to upload the pictures of skin diseases along with X-RAY’s and MRI for image prediction and processed by using DenseNet-121 model. 

BEYOU: HYPER-PERSONALISED SOLUTION FOR BUSINESS MODEL, REACT.JS, PYTHON, KNN, STREAMLIT, To digitize the buying cycle of a retail shop, a model is constructed by analysing the buyer’s preferences through a survey and plotted various pain points of the consumer for effective recommendation of the product to retain loyal customers. KNN algorithm is employed and the ML algorithm is fed to StreamLit to get appropriate suggestions according to the preferences of the buyer. Added features for customers to get notified about the updates by subscribing the newsletter. 

CAREER PATH NAVIGATOR, REACT.JS, FLASK, AXIOS, BAGGING, API, Discovered the need to guide students in making informed career decisions. Leveraged the unique machine learning techniques known as bagging to assimilate the dataset provide. The Flask backend will expose RESTful API’s to facilitate communication between frontend and backend allowing the users to view and explore potential career options. 

FITNESS MOBILE APP, ANDROID STUDIO, JAVA, FLASK, PYTORCH MOBILE, FIREBASE, CLOUD, The application utilizes java for user friendly interface and backend is powered by Flask. Firebase is employed for its real-time database capabilities, providing reliable storage solutions. Features are driven by PyTorch Mobile model where the model is trained and converted to TorchScript for mobile deployment. Azure services are integrated to enhance the app’s capabilities.

• The disease prediction system using symptom identification and image processing, International Conference on Communication, Computer Science and Engineering (IEEE)
• Innovation in SME’s with reference to manufacturing companies, First International Conference on Opportunities for Business, Technology Innovation and Social Science, MVN university