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

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