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AI/ML + Data Science

AI is the branch of computer science that aims to answer Turing's question in the affirmative.

AI

ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is a computer science technology that focuses on creating intelligent systems that have the ability to replicate the human brain. Systems designed using AI are capable of constructive reasoning and problem-solving ability.

AI allows the companies to perform efficiently with fewer resources by unique automation and reduces manual labor as well as time consumption. It is used to catch the new vision, improvise decisions and provide better results.

EMPOWER YOUR BUSINESS WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Automation system and Digitalisation is getting recognition in this rapidly growing world where the technologies are continuously setting high barriers. Artificial intelligence and machine learning are enhancing our system by automation. Every organization aims to enhance its decision-making process and get better results.

Artificial intelligence and Machine learning not only provide you the solutions for current problems but also enables your system to predict futuristic problems and provide different strategies to fix those problems.

Artificial intelligence

Our Project Portfolio

Problem Statement

Any food ordering & delivery system needs to maintain a large number of inquiries. We observed that among all the inquiries, more than 75% of inquiries were via calls and therefore engagement of the customer service staff for the same was high, which was preventing them from investing their time in more productive areas for the business. By understanding the need for an hour, Hvantage developed an intelligent system called “AI Conversational Assistant” to make the food delivery business more independent and transparent by reducing human involvement.

Our Solution

In the ever-changing Food Ordering & Delivery Market, the AI Conversational Assistant provides the customers with a choice to order food in a more personal & interactive way. The assistant using Natural Language Processing (NLP), establishes a voice also as text conversations sort of a real human being and thus has proved to possess a high return on investment (ROI). The Assistant has the potential to deliver all the business requirements that the food delivery agency/business wants to supply to its customers.

Area - NLP, AI
Language - Python 3.x
Business Area - Food Delivery and Ordering
Framework - Flask
Libraries - flask, JSON, urllib
Third-Party NLP service - Google DialogFlow
Cloud Infrastructure & Services - Heroku
tools and technology stack

Problem Statement

The use of intelligent machines represents a challenge in financial sectors. Financial institutions are therefore reluctant to offer machines full independence because of their unpredictable behavior. It becomes difficult for the machine to decode every query sent by the end-user. Validation becomes a necessity for the machine to make decisions.

Our Solution

Understanding the Finance Terminologies and providing assistance to the Financial Organisations, the Assistant is capable of handling varied frequency voice & complex text conversations so as to fulfill user needs. Whether to find the maturity date, downloading the cashflow or even opening the loan documents, the assistant is trained to perform critical activities.

The Assistant has been developed to reduce the overhead delays and provide a smooth experience to the user so that the members of the organization can focus on their main task and draw out some extra benefits for the business.

Area - NLP, AI
Language - Python 3.x
Business Area - Commercial Real Estate, Finance
Framework - Flask
DBMS - My SQL
Libraries - pyodbc, flask, JSON, urllib
Third-Party NLP service - Google DialogFlow
Cloud Infrastructure & Services - Heroku
Front End - Angular 6
Financial AI Assistant

Problem Statement

The automatic conversion of scanned and digitized images of character is running text into its corresponding image forms. A complete solution of data conversion is necessary for a business to reduce costs and improve the efficiency of the system. Business demands documents that can be managed anytime, anywhere and therefore Document Image analysis is needed which aims to store contents of paper documents in computer storage memory to read and search the content as and when needed.

Our Solution

The system involves Using Computer Vision Techniques and is being developed to give users the ability to extract the text from the desired location of the input image. Using the Canny and HoughLines Algorithm, the orientation of the input image is adjusted automatically. The system also consists of integrations with Open Source Computer Vision Engine Tesseract and Google Vision.

Area - Computer Vision, AI
Language - Python 3.x
Business Area - Inhouse Implementation
Libraries - OpenCV, pytesseract, google. cloud, PIL, Pandas
Third-Party CV service - Google Vision
CV Engine - Tesseract

Problem Statement

This is the generation where chatbots are replacing humans in customer service. When it is about talking with a human, randomness can be always expected and for a program to understand that randomness, the system needs to be trained for these random possibilities. Listing down every random possibility is often a shortfall for humans. It is just similar to handle the case, you don’t know whether it exists or not.

Our Solution

Utilizing the Natural Language Processing, our artificial intelligence experts team has designed an AI conversational chatbot that addresses the specific user needs to be developed. This bot is under development so as to replace the previous version (HBot) that is built using Google DialogFlow. This bot utilizes the concepts of Spoken Language Understanding (SLU) for User Intent Identification and Entity Extraction (Slot Filling). Bidirectional RNNs (LSTMs) are used along with the Attention mechanism. Various other model architecture is also under consideration like ALBERT, BERT, Elmo, etc. FastText word embeddings are used as an embedding layer in the model. General entities like number, date, time, location, etc will be detected using spacy and duckling.

Area - Data Science, Machine Learning, Artificial Intelligence
Language - Python 3.x, Haskel (in future for general entity extraction)
Libraries - Gensim, Keras, Pandas, spacy

For Online Business, the first and foremost thing you need is an attractive website. Websites use various images and graphics tools to make their website interesting. Image Mapping forms a crucial part of this process. Unlike a normal image link where the entire area of the image is linked to a single destination, an image map is created to hyperlink sections in the image to different destinations.

Our Solution

Image maps provide a convenient way of linking different sections of an image without the need to create image files for the image. This system gives users the ability to find an image in some other image. It uses Template Mapping wherein the template image slides over the input image (as in 2D convolution) and compares the template and patch of an input image.

Area - Computer Vision, AI
Language - Python 3.x
Business Area - Inhouse Implementation
Libraries - OpenCV, PIL

Problem Statement

Current business demands new approaches for image enhancement, focusing on realistic textures instead of pixel-accuracy and thus providing a high image quality. Technology requires enhancement by developing and including new implementation which is able to generate unique and appealing abstract images.

Our Solution

The future scope and implementation of the GANs were realized. Their capabilities to generate fake real images and their application towards image to text and text to image were also explored. It uses a deep Artificial neural network Model as an intelligence system along with advanced AI algorithms for training purposes.

Area - Deep Learning, AI
Business Area - In-house Study and Research
generative Adversial Network

Problem Statement

The current need to drive the market is the data and We all have long-faced this drawback to separate the relevant and important features from the collection of information and removing the immaterial or slighter options which do not contribute a lot to our target variable so as to realize higher accuracy for our model.

Our Solution

Feature Selection in machine learning and statistics is also known as variable selection, attribute selection or variable subset selection is the process of selecting a subset of relevant features for use in model construction. Hvantage provides you with efficient feature selection techniques which reduces data redundancy, improves accuracy and reduces the Training time of your model.

Area - Machine Learning
Language - Python 3.x
Business Area - In-house Study and Research
Libraries - Matplotlib,NumPy,pandas,seaborn,sci-kit-learn
Feature selection techniques

Problem Statement

The problem deals with the typical traditional chatbot simply created only on 1 output rather than multiple outputs. The fundamental method flow is the same whenever any input is entered. The new search is done, regardless of any association with previous output. The analysis should focus on enabling the chatbot to become intelligent ensuring that the search is based on the previous learnings.

Our Solution

AI Chatbot platform is being developed with the vision to provide a common service provider for various AI conversational API including Dialog Flow, AWS Lex, and IBM Watson as well as self-developed AI Conversational Bot. The user will be given the option to choose which API to use whether Dilogflow, Custom Model, Watson or any other. The data will remain on the platform and so the user won't have to start from scratch if they want to change the API provider.

Area - Data Science, ML, AI
Language - Python, Haskel, React JS
Framework - Flask
Libraries - Gensim, Keras, Pandas, spacy
AI Chatbot

Problem Statement

Several tweets will occur on twitter based on various topics, among these, some tweets may not be related to the topics or are less important therefore the classification of tweets based on the interest of the individual is important in some cases so that personalization becomes possible for the next incoming tweets. Excessive incoming tweets will occur if the user follows many accounts when they are interested in the content’s subset. In order to overcome this problem, filtering the incoming tweets becomes important based on the interest of the user.

Our Solution

Using KMeans & DBSCAN, Hvanatge provides you a system for clustering of the tweets relating to health and relatively key topics of discussion are drawn out. In the K means clustering, the data is clustered into groups based on the similarity in the observations of each cluster that is able to extract the insights from a large amount of unstructured data which is collected from several sources. Whereas DBSCAN contributes by iteratively expanding the cluster, by going through each individual point within the cluster, and counting the number of other data points nearby, hence provides the optimum results.

Area - Machine Learning, NLP
Language - Python 3.x
Business Area - Healthcare
Libraries - Matplotlib,numpy,pandas,seaborn,sci-kit learn,nltk
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