Tanuj Bohra

Sign Language is the most expressive form of communication for speech and hearing impaired people to communicate with normal person but a normal person cannot understand sign language. So in order to break this barrier of communication there needs to be a system that can enable conversion of sign language to voice or text and voice or text to sign language and do it in real time. The systems that currently exist are not real time, do not facilitate two-way communication, require static… Mehr zeigen

Sign Language is the most expressive form of communication for speech and hearing impaired people to communicate with normal person but a normal person cannot understand sign language. So in order to break this barrier of communication there needs to be a system that can enable conversion of sign language to voice or text and voice or text to sign language and do it in real time. The systems that currently exist are not real time, do not facilitate two-way communication, require static surrounding conditions or have low recognition accuracy. There exist systems that have good accuracy but require external hardware like gloves [3] which increases the cost. Our contribution to solving this problem consists of a Sign Language Communication System. It is a real-time communications system built using the advancements in Image Processing, Deep Learning and Computer Vision that provides real-time sign language to text and text to sign language conversion. The project is software-based which can be installed on any computer with good specifications. It is also a two-way communication system allowing not just speech and hearing impaired to communicate with normal people but also other way around. The primary goal of our system is to enable hearing and speech impaired people to communicate with people that are not disabled in real time by interpreting alphabets, numbers and words in the Indian sign language. The system is able to predict 17600 test images in 14 seconds with an average prediction time of 0.000805 seconds with an accuracy of 99%. Weniger anzeigen

The increase in competition in customer service sector has become paramount to invest time in customer behavior and to accurately predict the customer churn. Customer churn occurs when the customer decides to discontinue their relations with the company. Many traditional algorithms have been used to predict the churn, and thus devise various techniques for customer retention, but with the advent of deep learning paradigms, we have witnessed algorithms that give a new prospect to this very task.… Mehr zeigen

The increase in competition in customer service sector has become paramount to invest time in customer behavior and to accurately predict the customer churn. Customer churn occurs when the customer decides to discontinue their relations with the company. Many traditional algorithms have been used to predict the churn, and thus devise various techniques for customer retention, but with the advent of deep learning paradigms, we have witnessed algorithms that give a new prospect to this very task. Deep learning permits multilayered models to represent data in multiple abstraction levels. It also greatly reduces the work of feature engineering as it automatically comes up with good features. This chapter comprises an experimental comparison of various traditional classification algorithms, namely K-nearest neighbors, naive Bayes, random forest, decision tree, and logistic regression, with artificial neural network to predict the customer churn on IBM’s Telco Customer Churn dataset. We have compared these models based on their accuracy in predicting customer churn. Our ANN model achieves an accuracy score of 82.83% on validation data, better than our performance of 79.86% achieved for the traditional approach of using K-nearest neighbors. The results suggest that the multilayered ANN model with self-learning ability and tokenized data input outperforms traditional classification algorithms. Weniger anzeigen