7 research outputs found
A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease
The diagnosis of the Erythemato-squamous disease (ESD) is accepted as a
difficult problem in dermatology. ESD is a form of skin disease. It generally
causes redness of the skin and also may cause loss of skin. They are generally
due to genetic or environmental factors. ESD comprises six classes of skin
conditions namely, pityriasis rubra pilaris, lichen planus, chronic dermatitis,
psoriasis, seboreic dermatitis and pityriasis rosea. The automated diagnosis of
ESD can help doctors and dermatologists in reducing the efforts from their end
and in taking faster decisions for treatment. The literature is replete with
works that used conventional machine learning methods for the diagnosis of ESD.
However, there isn't much instances of application of Deep learning for the
diagnosis of ESD. In this paper, we propose a novel hybrid deep learning
approach i.e. Derm2Vec for the diagnosis of the ESD. Derm2Vec is a hybrid deep
learning model that consists of both Autoencoders and Deep Neural Networks. We
also apply a conventional Deep Neural Network (DNN) for the classification of
ESD. We apply both Derm2Vec and DNN along with other traditional machine
learning methods on a real world dermatology dataset. The Derm2Vec method is
found to be the best performer (when taking the prediction accuracy into
account) followed by DNN and Extreme Gradient Boosting.The mean CV score of
Derm2Vec, DNN and Extreme Gradient Boosting are 96.92 percent, 96.65 percent
and 95.80 percent respectively.Comment: Pre-review version of the paper accepted at the 2020 IEEE
International Conference on Electronics, Computing and Communication
Technologies (CONECCT
A Modified Bayesian Optimization based Hyper-Parameter Tuning Approach for Extreme Gradient Boosting
It is already reported in the literature that the performance of a machine
learning algorithm is greatly impacted by performing proper Hyper-Parameter
optimization. One of the ways to perform Hyper-Parameter optimization is by
manual search but that is time consuming. Some of the common approaches for
performing Hyper-Parameter optimization are Grid search Random search and
Bayesian optimization using Hyperopt. In this paper, we propose a brand new
approach for hyperparameter improvement i.e. Randomized-Hyperopt and then tune
the hyperparameters of the XGBoost i.e. the Extreme Gradient Boosting algorithm
on ten datasets by applying Random search, Randomized-Hyperopt, Hyperopt and
Grid Search. The performances of each of these four techniques were compared by
taking both the prediction accuracy and the execution time into consideration.
We find that the Randomized-Hyperopt performs better than the other three
conventional methods for hyper-paramter optimization of XGBoost.Comment: Pre-review version of the paper submitted to IEEE 2019 Fifteenth
International Conference on Information Processing (ICINPRO). The paper is
accepted for publicatio
A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce
According to the literature, Product reviews are an important source of
information for customers to support their buying decision. Product reviews
improve customer trust and loyalty. Reviews help customers in understanding
what other customers think about a particular product and helps in driving
purchase decisions. Therefore, for an e-commerce platform it is important to
understand the sentiments in customer reviews to understand their products and
services, and it also allows them to potentially create positive consumer
interaction as well as long lasting relationships. Reviews also provide
innovative ways to market the products for an ecommerce company. One such
approach is Nudge Marketing. Nudge marketing is a subtle way for an ecommerce
company to help their customers make better decisions without hesitation.Comment: Submitted to a Journal for revie
Travel Time Prediction in Real time for GPS Taxi Data Streams and its Applications to Travel Safety
Abstract The analysis of data streams offers a great opportunity for development of new methodologies and applications in the area of Intelligent Transportation Systems. In this paper, we propose two new incremental learning approaches for the travel time prediction problem for taxi GPS data streams in different scenarios and compare the same with three other existing methods. An extensive performance evaluation using four real life datasets indicate that when the training data size is small the Support Vector Regression method is the best choice considering both prediction accuracy and total computation time. However when the training data size is large to moderate then the Randomized K-Nearest Neighbor Regression with Spherical Distance (RKNNRSD) and the Incremental Polynomial Regression become the methods of choice. When continuous prediction of remaining travel time along the trajectory of a trip is considered we find that the RKNNRSD is the method of choice. A Real-time Speeding Alert System (RSAS) and a Driver Suspected Speeding Scorecard (DSSS) using the RKNNRSD method are proposed which have great potential for improving travel safety