web , Google Assistant , Messenger , Telegram ,
Implemented AI to optimise the motion parameters( turning speeds in different curves/straight line ) to maximize the speed and accuracy of a line following robot
Name: Rahul Babu K
Type: User
Bio: Researcher | Data Scientist | Mechanical engineer
Twitter: iamrahulr2k
Location: Kochi
Implemented AI to optimise the motion parameters( turning speeds in different curves/straight line ) to maximize the speed and accuracy of a line following robot
Project descriptionBusiness Objective: An e-commerce company wants to build an algorithm to retrieve top 5 Question and answers based on the user given user input.
The dataset is having incidents raised by customers.Which contains an event log of an incident management process extracted from a service desk platform of an IT company.Using this log data,I created a model that will predict and classify the impact as High Medium and Low. Model was built using Uber's Ludwig framework
A.I is used to optimize the conventional full-time sensor monitored zigzag motion of the line follower robot by training it in the same route. Here the path of the line follower is convoluted to find the anomalies and then these instances of anomalies (timeframe points) is used for creating a reward function.Then by using reinforcement learning , sensor detection timings , motion control parameters ( frequency, magnitude of each motor current) etc are optimized for fast and accurate movement
A.I-Chatbot Mar 2020 – May 2020 Project descriptionBusiness Objective: An e-commerce company wants to build an algorithm to retrieve top 5 Question and answers based on the user given user input. Solution : The input given by the user is first cleaned and features extracted using 3 different algorithms. LSTM Deep Learning classifier will first identify the type of user question (Yes/No ,Open-ended , etc.) The second algorithm Gensim model will identify the 'Product' from the user user input , and the final K-means clustering algorithm will find the cluster in which the user input will fall . This three predictions are embedded to the user input and fed into Cosine similarity algorithm to find similar questions from the Tfidf vector space. See the deployed model in heroku http://aiquestionsearch.herokuapp.com/
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