Ashad Ahmad Email & Phone Number

India

Pune, Maharashtra, India

• To search different electrolytes based on their chemical and physical properties and tried to find the different QSPR relationships• Used different platforms like PubChem and the DrugBank database to obtain different properties to calculate the coordination energy• Performed exploratory data analysis on datasets and then developed different machine learning models Lasso, Es-LiR, GA-SVR, etc• Finally obtained result with an accuracy of 67.45% using GA-SVR model for regression used to calculate E-corr of different electrolytes

Kharagpur, West Bengal, India

Objective: To create Xprize rapid Covid testing to develop faster, cheaper and easier to use Covid-19 testing ML methods• Datasets from many nations with various metrics such as GDP, daily cases, age group ratios, sex ratios, and so on has been collected• Performed the exploratory data analysis on the datasets with various parameters using Numpy, Pandas, and the other Python packages• Created the distinct correlations between parameters, by using spectral and K-means clustering which used to form groups of nations• RNN-LSTM (time series) model used to train the datasets and acquire an f-score of 0.89.63, as well as to predict Covid cases using models

Kharagpur, West Bengal, India

Objective: To create different machine learning models and to understand the water quality and turbidity in Hong Cong city• Performed exploratory data analysis on datasets containing different parameters using different python libraries like Numpy, Pandas etc.• Developed different the machine learning models like SVM, ANN etc. to find the relation between different parameters and the turbidity • Achieved the accuracy of 92% on highly imbalanced dataset by implementing using recurrent neural network- long short term memory• Identified the most accurate model and has written research paper for water quality measurement under the guidance of Prof. Saud Afzal

Varanasi, Uttar Pradesh, India

Image Captioning | Computer visionObjective: To generate caption from images by using different MSCOCO dataset by using different deep learning tools• Developed a merged model architecture that combines both the CNN and the RNN model to output textual description from an image• Performed data cleaning steps on captions and generated a vocabulary by selecting words with a threshold of ten using the NLTK library• Utilized Transfer Learning in the model, like ResNet50 to create image vectors and Glove6B50D as embedding layer to create captions