Shapna Muralidharan
Being a R & Manager in Radionode, and previously as a PostDoc has brought over 5 years of experience leading and managing R and D in IoT projects including the field of Artificial Intelligence. My expertise spans across various domains of IoT like networking, Digital Twins for IoT applications and orchestrating the IoT metrics using Machine learning, AI, computer vision, deep learning, and eXplainable AI (XAI).
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Selected IoT and Standard projects in Data Science, Machine Learning and AI
Predicting Concrete Compressive Strength using Sensor Data and applying ML Algorithms
Compressive strength determines the quality of concrete. To predict the compressive strength of concrete currently destructive methods are used, which are time consuming and not precise. To enable reliable prediction IoT sensors are used to monitor the concrete and ML algorithms are used to predict the compressive strength of concrete.
Multi-Class Classification Algorithm for Customer Segmentation
Classification jobs with more than two class or types of labels are classified as multi-class classification. Multiclass classification in machine learning, is different from binary classification since involves many classes or labels. There are many ML Algorithms to perform this task. In this scenario we verify all the classification algorithms to find out the best solution for the customer segmentation problem.
Exploratory Data Analysis (EDA) for Time-Series Data
Preparing a a time-series dataset from a temperature and humidity IoT sensor for Exploratory Data Analysis (EDA). This can help in Analyzing the data and proceed in implementing any data preparation from scratch for Deep Neural Networks.
