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Institution

Shiv Nadar University

EducationDadri, Uttar Pradesh, India
About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.


Papers
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Book ChapterDOI
18 Dec 2014
TL;DR: The hybrid filter-wrapper algorithm, GSO-Infogain, employs Glowworm-Swarm Optimization algorithm with Support Vector Machine as its internal learning algorithm and utilizes feature ranking based on information gain as a heuristic for simultaneous feature selection for improved classification accuracy.
Abstract: In this paper, we propose a hybrid filter-wrapper algorithm, GSO-Infogain, for simultaneous feature selection for improved classification accuracy. GSO-Infogain employs Glowworm-Swarm Optimization(GSO) algorithm with Support Vector Machine(SVM) as its internal learning algorithm and utilizes feature ranking based on information gain as a heuristic. The GSO algorithm randomly generates a population of worms, each of which is a candidate subset of features. The fitness of each candidate solution, which is evaluated using Support Vector Machine, is encoded within its luciferin value. Each worm probabilistically moves towards the worm with the highest luciferin value in its neighbourhood. In the process, they explore the feature space and eventually converge to the global optimum. We have evaluated the performance of the hybrid algorithm for feature selection on a set of cancer datasets. We obtain a classification accuracy in the range 94-98 % for these datasets, which is comparable to the best results from other classification algorithms. We further tested the robustness of GSO-Infogain by evaluating its performance on the CoEPrA training and test datasets. GSO-Infogain performs well in this experiment too by giving similar prediction accuracies on the training and test datasets thus indicating its robustness.

7 citations

Journal ArticleDOI
TL;DR: In this article, a new 6-aminoazouracil ligand, 1,3-dimethyl-5-(o-phenolate-azo)-6-aminoracil (H2L, 1) with a NuNaO (Nu, uracil-N and Na, azo-N) chromophore was synthesized and used to generate a noble discrete doubly opened Cu4O4 cubane-like cluster ([CuL]4·2H2O, 2.2H 2O] for studying magnetism
Abstract: Tetranuclear copper(II) complexes are of paramount importance in structural biology, and they are potential materials for magnetism and catalysis. To develop such a system, a new 6-aminoazouracil ligand, 1,3-dimethyl-5-(o-phenolate-azo)-6-aminouracil (H2L, 1) with a NuNaO (Nu, uracil-N and Na, azo-N) chromophore was synthesized and used to generate a noble discrete doubly opened Cu4O4 cubane-like cluster ([CuL]4·2H2O, 2.2H2O) for studying magnetism. The coordination environment of Cu(II) is distorted square planar linked through phenolate-μ2-O bridges. The ligand crystallizes in the monoclinic space group P121/c1 and the complex is in the tetragonal I41/a space group with an S4 symmetry. The ligand has two dissociable hydrogen atoms in the solution with pK1 4.91 (hydrazone proton) and pK2 9.68 (phenolic proton). In the solid state, the ligand exhibits displaced stacking (energy, −69.5 and −77.3 kJ mol−1 for the molecules A and B, respectively) and tetrel bonding interactions (energy, −43.8 kJ mol−1). In 2.2H2O, the symmetrical units are stacked to show weak noncovalent interactions. The magnetic property of 2.2H2O was investigated based on the cubane [Cu4O4] core and discussed in detail, resulting in the exchange coupling parameter [(J1 (short Cu⋯Cu distances) = −110.1(1) cm−1, J2 (long Cu⋯Cu distances) = −27.1(2) cm−1)] that indicate a strong antiferromagnetic interaction between tetranuclear copper(II) ions through μ-phenolate linkers, which is a result of the interaction of dx2−y2 orbitals in the square plane. The EPR study is concomitant with the results of magnetism. Thus, it could be a potential material in the field of antiferromagnetic spintronics as well.

7 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigate the common qualitative idea that a solid is particularly stable if the electron states are filled up to some energy band gap, and discuss the question of why only the compounds with 2.4
Abstract: The authors investigate the common qualitative idea that a solid is particularly stable if the electron states are filled up to some energy band gap. Such ideas have previously been advanced to explain the occurrence of a certain family of M5X4 compounds (M=transition metal, X=chalcogen) only for particular ranges of valence electron concentration (VEC). In these materials octahedral metal clusters condense to form infinite chains. The authors study the stability of such chains against break-up on two models, (i) a regular octahedral chain and (ii) the chain of the compound Nb5Te4, and discuss the question of why only the compounds with 2.4

7 citations

Journal ArticleDOI
TL;DR: In this article, metal chalcogenide ZnS nanoparticles have been synthesized using simple capped precipitation method using Potassium pyrosulfate as capping agent.
Abstract: Metal chalcogenide ZnS nanoparticles have been synthesized using simple capped precipitation method using Potassium pyrosulfate as capping agent. The synthesized nano powder was characterized by X-ray diffraction (XRD), UV–Vis absorption, transmission electron microscopy, infrared spectroscopy, thermogravimetric analysis and fluorescence emission spectroscopy. The XRD pattern exhibits three major peaks at d-values 3.12, 1.904, 1.626 A belonging to 2θ = 28.60°, 47.76° and 56.60° respectively showing Wurtzite crystal phase of ZnS. UV–Vis spectrum exhibits quantum confinement effect by a very sharp peak around 290 nm remarkably blue shifted compared to its bulk counterpart. The room temperature photoluminescence spectrum of the nanoparticles showed two strong emission bands at 421 and 447 nm. This first blue emission peak at 421 nm is due to sulphur vacancy and other peak at 447 nm belongs to native defect states.

7 citations


Authors

Showing all 1055 results

NameH-indexPapersCitations
Dinesh Mohan7928335775
Vijay Kumar Thakur7437517719
Robert A. Taylor6257215877
Himanshu Pathak5625911203
Gurmit Singh542708565
Vijay Kumar5177310852
Dimitris G. Kaskaoutis431355248
Ken Haenen392886296
Vikas Dudeja391434733
P. K. Giri381584528
Swadesh M Mahajan382555389
Rohini Garg37884388
Rajendra Bhatia361549275
Rakesh Ganguly352404415
Sonal Singhal341804174
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20239
202256
2021356
2020322
2019227
2018176