Institution
Indian Institute of Technology Indore
Education•Indore, 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: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.
Topics: Fading, Support vector machine, Raman spectroscopy, Band gap, Thin film
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a simple, green, and efficient one-pot three component strategy for the synthesis of 2-aryl-3-nitro-4-hydroxy-2,3,4,9-tetrahydrothiopyrano[2, 3-b]indole derivatives has been achieved by the combination of N-protected-2-chloro,3-formylindoles, sodium hydrosulfide with β-nitrostyrenes at room temperature in ethanol using DABCO as a catalyst.
34 citations
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TL;DR: Significant differences in the oxidation state distribution at the {Ru-Q} interface for 1(n)-3(n) are revealed, revealing the diminished propensity of the NH-containing systems for reduction results in the preference for Ru(II)(Q(0)) relative to Ru(III)(Q˙(-)) (neutral compounds) and for Ru (II)(Z˙ (-)) over the Ru( III)(Q (2-)) alternative in the case of the monoanionic
Abstract: Bis(acetylacetonato)ruthenium complexes [Ru(acac)2(Q1–3)], 1–3, incorporating redox non-innocent 9,10-phenanthrenequinonoid ligands (Q1 = 9,10-phenanthrenequinone, 1; Q2 = 9,10-phenanthrenequinonediimine, 2; Q3 = 9,10-phenanthrenequinonemonoimine, 3) have been characterised electrochemically, spectroscopically and structurally. The four independent molecules in the unit cell of 2 are involved in intermolecular hydrogen bonding and π–π interactions, leading to a 2D network. The oxidation state-sensitive bond distances of the coordinated ligands Qn at 1.296(5)/1.289(5) A (C–O), 1.315(3)/1.322(4) A (C–N), and 1.285(3)/1.328(3) A (C–O/C–N) in 1, 2 and 3, respectively, and the well resolved 1H NMR resonances within the standard chemical shift range suggest DFT supported variable contributions from valence formulations [RuIII(acac)2(Q˙−)] (spin-coupled) and [RuII(acac)2(Q0)], respectively. Complexes 1–3 exhibit one oxidation and two reduction steps with comproportionation constants Kc ∼ 107–1022 for the intermediates. The electrochemically generated persistent redox states 1n (n = 0, 1−, 2−) and 2n/3n (n = 1+, 0, 1−, 2−) have been analysed by UV-vis-NIR spectroelectrochemistry and by EPR for the paramagnetic intermediates in combination with DFT and TD-DFT calculations, revealing significant differences in the oxidation state distribution at the {Ru–Q} interface for 1n–3n. In particular, the diminished propensity of the NH-containing systems for reduction results in the preference for RuII(Q0) relative to RuIII(Q˙−) (neutral compounds) and for RuII(Q˙−) over the RuIII(Q2−) alternative in the case of the monoanionic complexes.
34 citations
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TL;DR: The Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT) is proposed for automated alcoholism detection using electroencephalogram (EEG) signals and suggests that LS-SVM with radial basis function (RBF) kernel achieves a highest average accuracy, sensitivity, and specificity of 99.1% with top 20 significant features.
Abstract: In this paper, the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT) is proposed for automated alcoholism detection using electroencephalogram (EEG) signals. The FBSE-EWT is applied to decompose EEG signals into narrow sub-band signals using a boundary detection approach. The accumulated line length, log energy entropy, and norm entropy features are extracted from different frequency scales of narrow sub-band signals. A total of twenty features are extracted from each attribute and out of which ten features are from low to high frequency sub-band signals and other ten features are from high to low frequency sub-band signals. In order to reduce the classification model complexity, the most significant features are selected using feature selection techniques. Six feature ranking methods such as Relief-F, ${t}$ -test, Chi-test, relief attribute evaluation, correlation attribute evaluation, and gain ratio are used to select the most common features based on the majority voting technique. Experiments are performed by considering top ranked 5, 10, 15, and 20 features and classification methods such as least square support vector machine (LS-SVM), support vector machine (SVM), and ${k}$ nearest neighbor ( ${k}$ -NN) classifiers. The training and testing is done using leave-one out cross-validation (LOOCV) in order to avoid over-fitting. The performances of classifiers are evaluated using accuracy, sensitivity, and specificity measures. The results suggest that LS-SVM with radial basis function (RBF) kernel achieves a highest average accuracy of 98.8%, sensitivity of 98.3%, and specificity of 99.1% with top 20 significant features.
34 citations
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TL;DR: In this paper, the impact of India's R&D tax credit scheme and its 2010-11 reform on the innovation activity of the country's private firms was evaluated using firm-level data from 2001 to 2016.
34 citations
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TL;DR: In this article, the combined influence of Prandtl number and Darcy number variations on heat transfer from a two-dimensional porous square cylinder, placed in an unconfined computational domain, is investigated numerically for Pr = 0.71 − 100.
34 citations
Authors
Showing all 1738 results
Name | H-index | Papers | Citations |
---|---|---|---|
Raghunath Sahoo | 106 | 556 | 37588 |
Biswajeet Pradhan | 98 | 735 | 32900 |
A. Kumar | 96 | 505 | 33973 |
Franco Meddi | 84 | 476 | 24084 |
Manish Sharma | 82 | 1407 | 33361 |
Anindya Roy | 59 | 301 | 14306 |
Krishna R. Reddy | 58 | 400 | 11076 |
Sudipan De | 54 | 99 | 10774 |
Sudip Chakraborty | 51 | 343 | 9319 |
Shaikh M. Mobin | 51 | 515 | 11467 |
Ashok Kumar | 50 | 405 | 10001 |
Ankhi Roy | 49 | 259 | 8634 |
Aditya Nath Mishra | 49 | 139 | 7607 |
Ram Bilas Pachori | 48 | 182 | 8140 |
Pragati Sahoo | 47 | 133 | 6535 |