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.

Ensemble deep learning: A review.

Abstract: Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions.

Measurement of pion, kaon and proton production in proton–proton collisions at √s = 7 TeV

Abstract: The measurement of primary [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] production at mid-rapidity ([Formula: see text] 0.5) in proton-proton collisions at [Formula: see text][Formula: see text] 7 TeV performed with a large ion collider experiment at the large hadron collider (LHC) is reported. Particle identification is performed using the specific ionisation energy-loss and time-of-flight information, the ring-imaging Cherenkov technique and the kink-topology identification of weak decays of charged kaons. Transverse momentum spectra are measured from 0.1 up to 3 GeV/[Formula: see text] for pions, from 0.2 up to 6 GeV/[Formula: see text] for kaons and from 0.3 up to 6 GeV/[Formula: see text] for protons. The measured spectra and particle ratios are compared with quantum chromodynamics-inspired models, tuned to reproduce also the earlier measurements performed at the LHC. Furthermore, the integrated particle yields and ratios as well as the average transverse momenta are compared with results at lower collision energies.

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Abstract: Coronary artery disease (CAD) is the narrowing of coronary arteries leading to inadequate supply of nutrients and oxygen to the heart muscles. Over time, the condition can weaken the heart muscles and may lead to heart failure, arrhythmias and even sudden cardiac death. Hence, the early diagnosis of CAD can save life and prevent the risk of stroke. Electrocardiogram (ECG) depicts the state of the heart and can be used to detect the CAD. Small changes in the ECG signal indicate a particular disease. It is very difficult to decipher these minute changes in the ECG signal, as it is prone to artifacts and noise. Hence, we detect the R peaks from the ECG and use heart rate signals for our analysis. The manual inspection of the heart rate signals is time consuming, taxing and prone to errors due to fatigue. Hence, a decision support system independent of human intervention can yield accurate repeatable results. In this paper, we present a new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals. The heart rate signals are decomposed into various sub-bands using TQWT for better diagnostic feature extraction. The nonlinear feature called centered correntropy ( CC ) is computed on decomposed detail sub-band. Then the principal component analysis (PCA) is performed on these CC to transform the number of features. These clinically significant features are subjected to least squares support vector machine (LS-SVM) with different kernel functions for automated diagnosis. The experimental results demonstrate better classification accuracy, sensitivity, specificity and Matthews correlation coefficient using Morlet wavelet kernel function with optimized kernel and regularization parameters. Also, we have developed a novel CAD Risk index using significant features to discriminate the two classes using a single number. Our proposed methodology is more suitable in classification of normal and CAD heart rate signals and can aid the clinicians while screening the CAD patients.