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Institution

Indian Institute of Technology Indore

EducationIndore, Madhya Pradesh, India
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: Computer science & Chemistry. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection and evaluates the effectiveness of eight pre-trained Convolutional Neural Network models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, Res net-50 and Inception-V3 for classification of CO VID-19 from normal cases.

266 citations

Journal ArticleDOI
TL;DR: The accumulating evidence suggesting novel therapeutic approaches that explore both H1R and H4R as therapeutic targets for histamine-mediated allergic diseases is examined.
Abstract: Histamine and its receptors (H1R-H4R) play a crucial and significant role in the development of various allergic diseases. Mast cells are multifunctional bone marrow-derived tissue-dwelling cells that are the major producer of histamine in the body. H1R are expressed in many cells, including mast cells, and are involved in Type 1 hypersensitivity reactions. H2R are involved in Th1 lymphocyte cytokine production. H3R are mainly involved in blood-brain barrier function. H4R are highly expressed on mast cells where their stimulation exacerbates histamine and cytokine generation. Both H1R and H4R have important roles in the progression and modulation of histamine-mediated allergic diseases. Antihistamines that target H1R alone are not entirely effective in the treatment of acute pruritus, atopic dermatitis, allergic asthma, and other allergic diseases. However, antagonists that target H4R have shown promising effects in preclinical and clinical studies in the treatment of several allergic diseases. In the present review, we examine the accumulating evidence suggesting novel therapeutic approaches that explore both H1R and H4R as therapeutic targets for histamine-mediated allergic diseases.

263 citations

Journal ArticleDOI
TL;DR: It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance and will be used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier.

256 citations

Journal ArticleDOI
Betty Abelev1, Jaroslav Adam2, Dagmar Adamová3, Andrew Marshall Adare4  +999 moreInstitutions (81)
02 Jan 2013
TL;DR: Measurements of charge-dependent azimuthal correlations with the ALICE detector at the LHC show a clear signal compatible with a charge- dependent separation relative to the reaction plane, which shows little or no collision energy dependence when compared to measurements at RHIC energies.
Abstract: Measurements of charge-dependent azimuthal correlations with the ALICE detector at the LHC are reported for Pb-Pb collisions at root s(NN) = 2.76 TeV. Two- and three-particle charge-dependent azimuthal correlations in the pseudorapidity range vertical bar eta vertical bar < 0.8 are presented as a function of the collision centrality, particle separation in pseudorapidity, and transverse momentum. A clear signal compatible with a charge-dependent separation relative to the reaction plane is observed, which shows little or no collision energy dependence when compared to measurements at RHIC energies. This provides a new insight for understanding the nature of the charge-dependent azimuthal correlations observed at RHIC and LHC energies. DOI: 10.1103/PhysRevLett.110.012301

255 citations

Journal ArticleDOI
Betty Abelev1, Jaroslav Adam2, Dagmar Adamová3, Madan M. Aggarwal4  +1065 moreInstitutions (103)
TL;DR: In this paper, the authors proposed an ultra-light, high-resolution Inner Tracking System (ITS) based on monolithic CMOS pixel detectors for detection of heavy-flavour hadrons, and of thermal photons and low-mass di- electrons emitted by the Quark-Gluon Plasma (QGP) at the CERN LHC (Large Hadron Collider).
Abstract: ALICE (A Large Ion Collider Experiment) is studying the physics of strongly interacting matter, and in particular the properties of the Quark–Gluon Plasma (QGP), using proton–proton, proton–nucleus and nucleus–nucleus collisions at the CERN LHC (Large Hadron Collider). The ALICE Collaboration is preparing a major upgrade of the experimental apparatus, planned for installation in the second long LHC shutdown in the years 2018–2019. A key element of the ALICE upgrade is the construction of a new, ultra-light, high- resolution Inner Tracking System (ITS) based on monolithic CMOS pixel detectors. The primary focus of the ITS upgrade is on improving the performance for detection of heavy-flavour hadrons, and of thermal photons and low-mass di- electrons emitted by the QGP. With respect to the current detector, the new Inner Tracking System will significantly enhance the determination of the distance of closest approach to the primary vertex, the tracking efficiency at low transverse momenta, and the read-out rate capabilities. This will be obtained by seven concentric detector layers based on a 50 μm thick CMOS pixel sensor with a pixel pitch of about 30×30 μm2. This document, submitted to the LHCC (LHC experiments Committee) in September 2013, presents the design goals, a summary of the R&D activities, with focus on the technical implementation of the main detector components, and the projected detector and physics performance.

252 citations


Authors

Showing all 1738 results

NameH-indexPapersCitations
Raghunath Sahoo10655637588
Biswajeet Pradhan9873532900
A. Kumar9650533973
Franco Meddi8447624084
Manish Sharma82140733361
Anindya Roy5930114306
Krishna R. Reddy5840011076
Sudipan De549910774
Sudip Chakraborty513439319
Shaikh M. Mobin5151511467
Ashok Kumar5040510001
Ankhi Roy492598634
Aditya Nath Mishra491397607
Ram Bilas Pachori481828140
Pragati Sahoo471336535
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202365
2022253
2021918
2020801
2019677
2018614