Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at sNN =5.02 TeV

Abstract: The pseudorapidity density of charged particles, dNch/dη, at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of √sNN=5.02 TeV. For the 5% most central collisions, we measure a value of 1943 ± 54. The rise in dNch/dη as a function of √sNN p is steeper than that observed in proton-proton collisions and follows the trend established by measurements at lower energy. The increase of dNch/dη as a function of the average number of participant nucleons, ⟨Npart⟩, calculated in a Glauber model, is compared with the previous measurement at √sNN=2.76 TeV. A constant factor of about 1.2 describes the increase in dNch/dη from √sNN=2.76 to 5.02 TeV for all centrality classes, within the measured range of 0%–80% centrality. The results are also compared to models based on different mechanisms for particle production in nuclear collisions.

Breast cancer diagnosis using Genetically Optimized Neural Network model

Abstract: One in every eight women is susceptible to breast cancer, at some point of time in her life Early detection and effective treatment is the only rescue to reduce breast cancer mortality Accurate classification of a breast cancer tumor is an important task in medical diagnosis Machine learning techniques are gaining importance in medical diagnosis because of their classification capability In this paper, we propose a new, Genetically Optimized Neural Network (GONN) algorithm, for solving classification problems We evolve a neural network genetically to optimize its architecture (structure and weight) for classification We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators We use the GONN algorithm to classify breast cancer tumors as benign or malignant To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model Our algorithm gives classification accuracy of 9824%, 9963% and 100% for 50–50, 60–40, 70–30 training–testing partition respectively and 100% for 10 fold cross validation The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods

EEG signal classification using universum support vector machine

Abstract: Support vector machine (SVM) has been used widely for classification of electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as epilepsy and sleep disorders. SVM shows good generalization performance for high dimensional data due to its convex optimization problem. The incorporation of prior knowledge about the data leads to a better optimized classifier. Different types of EEG signals provide information about the distribution of EEG data. To include prior information in the classification of EEG signals, we propose a novel machine learning approach based on universum support vector machine (USVM) for classification. In our approach, the universum data points are generated by selecting universum from the EEG dataset itself which are the interictal EEG signals. This removes the effect of outliers on the generation of universum data. Further, to reduce the computation time, we use our approach of universum selection with universum twin support vector machine (UTSVM) which has less computational cost in comparison to traditional SVM. For checking the validity of our proposed methods, we use various feature extraction techniques for different datasets consisting of healthy and seizure signals. Several numerical experiments are performed on the generated datasets and the results of our proposed approach are compared with other baseline methods. Our proposed USVM and proposed UTSVM show better generalization performance compared to SVM, USVM, Twin SVM (TWSVM) and UTSVM. The proposed UTSVM has achieved highest classification accuracy of 99% for the healthy and seizure EEG signals.

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches.

Abstract: This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen's Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (-8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901-1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions' also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.