Microstructure and wear behavior of austempered high carbon high silicon steel

In the present investigation, the influence of austempering temperature and time on the microstructure and dry sliding wear behavior of high silicon steel was studied. The test specimens were initially austenitised at 900°C for 30 minutes, thereafter austempered at various temperatures 280°C, 360°C and 400°C, for varying duration from 30 to 120 minutes. These samples after austempering heat treatment were subsequently air cooled to room temperature, to generate typical ausferritic microstructures and then correlated with the wear property. The test outcomes demonstrate the slight increase in specific wear rate with increase in both austempering temperature and time. Specific wear rate was found to be minimum at an austempering temperature of 280°C, that exhibits lower bainite microstructure with high hardness, on the other hand specific wear rate was found to be slightly high at increased austempering temperatures at 360°C and 400°C, due to the upper bainite structure that offered lower hardness to the matrix. The sample austempered at 280°C for 30 minutes offered superior wear resistance when compared to other austempering conditions, mainly due to the presence of fine acicular bainitic ferrite along with stabilized retained austenite and also some martensite in the microstructure

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

COMPARATIVE EFFICACY OF CLOSED AND OPEN KINETIC CHAIN EXERCISES ON DEVELOPMENT OF EXPLOSIVE STRENGTH IN LOWER EXTREMITY

The purpose of the study is to compare the efficacy of closed and open kinetic chain exercises on development of explosive strength in lower extremity and to determine which mode of training resulted in the greatest performance enhancement. The case study incorporated randomly a total of seventy five college level players of different games, each aged between 19 to 24 years. The total seventy five players were randomly divided into equal three groups. Among these groups, two different groups were gone through closed chain kinetic and open chain kinetic exercises respectively and one group kept as control group. Progressive weight training thrice a week for 7 weeks scheduled. Necessary data was collected by administering standing broad jump performance prior to training and at the completion of the training period. Statistical technique Analysis of covariance test was applied to compare the efficacy between closed chain and open chain kinetic exercise at 0.05 level of significance, while significant changes were seen in the open kinetic chain group and close chain kinetic group. The closed kinetic chain group improved in .16 meters which was significantly more than the .08 meters seen in the open kinetic chain group. The result reveals close chain kinetic exercise mode of training resulted in the greatest strength development in lower extremity.  Article visualizations

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