01272nas a2200217   4500008004100000022001400041245011200055210006900167260010600236300001000342490000700352520044400359653001900803653002400822653000800846653001800854100001400872700002600886700002100912856012100933       2005                        eng d  a0031-320300aSVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples0 aSVM classifier incorporating simultaneous noise reduction and fe  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcJAN  a41-490 v383 a<p>A hybrid technique involving symbolization of data to remove noise and use of conditional entropy minima to extract relevant and non-redundant features is proposed in conjunction with support vector machines to obtain more robust classification algorithm. The technique tested on three data sets shows improvements in classification efficiencies. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.</p>10aclassification10aconditional entropy10aSVM10asymbolization1 aKumar, R.1 aJayaraman, Valadi, K.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/svm-classifier-incorporating-simultaneous-noise-reduction-and-feature-selection-001794nas a2200229   4500008004100000022001400041245007000055210006900125260011100194300001200305490000700317520093200324653001901256653001801275653002101293653005601314653001501370100002601385700002601411700002601437856010101463       2009                        eng d  a1386-207300aReview on lazy learning regressors and their applications in QSAR0 aReview on lazy learning regressors and their applications in QSA  aEXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcMAY  a440-4500 v123 a<p>Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e. g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real life case studies to briefly outline the typical characteristics of the modeling formalism.</p>10aclassification10alazy learning10amachine learning10aQuantitative structure activity relationship (QSAR)10aregression1 aKulkarni, Abhijit, J.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/review-lazy-learning-regressors-and-their-applications-qsar-002744nas a2200433   4500008004100000022001400041245012800055210006900183260000800252300001200260490000900272520135800281653001901639653000601658653000601664653001901670653002501689653001401714653001901728653002801747653002301775653002301798653002401821653002801845653002501873653003101898653001301929653002801942653002001970653002901990653002102019653002202040100002302062700003002085700002402115700002002139700002002159856013102179       2017                        eng d  a0022-286000aSynthesis, characterization and electrochemical investigations of mixed-ligand copper(II)-organic supramolecular frameworks0 aSynthesis characterization and electrochemical investigations of  cNOV  a549-5570 v11473 a<p><span style="color: rgb(51, 51, 51); font-family: arial, helvetica, sans-serif; font-size: 13px; background-color: rgb(248, 248, 248);">Two mixed-ligand copper(II)-organic coordination compounds with 5,5'-dimethyl-2,2'-bipyridine (5,5'-Me(2)bpy) as a primary ligand while aliphatic malonate (Hmal) and aromatic 2-hydroxynicotinate (2-OHNA) as secondary ligands, were synthesized. These complexes are formulated as: [Cu(Hmal)(5,5'-Me(2)bPY)(H2O)](ClO4) 1 and [Cu-2(2-OHNA)(2)(5,5'-Me(2)bpy)(2)(NO3)KNO3) 2. These two complexes were structurally characterized by single crystal X-ray diffraction analysis. Characterization was further supported by powder X-ray diffraction analysis, elemental analyses, FT-IR, FAB-MASS and TGA, DSC studies. Cyclic voltammetric and UV-visible spectral studies of these two complexes have also been done. The electrochemical studies of complex 1 in DMSO and DMF have shown that this complex undergoes quasi reversible diffusion-controlled one-electron transfer reaction without any chemical complication while complex 2 in DMSO undergoes quasi-reversible diffusion-controlled one electron transfer reaction, following EC mechanism. The electrochemical behaviour of complex 2 in DMF is complicated probably due to presence of more than one species in solution phase. (C) 2017 Elsevier B.V. All rights reserved.</span></p>10a2 '-bipyridine10a310a510a5 '-dimethyl-210a5-Tricarboxylic acid10aBenzene-110aclassification10aCo-ordination-Compounds10aCrystal-structures10acyclic voltammetry10amagnetic properties10ametal organic framework10aMixed-ligand complex10api-pi stacking interaction10aPolymers10aSecondery Building Unit10aStructural data10aSupramolecular framework10aTernare Komplexe10aX-ray diffraction1 aSingh, Sandeep, K.1 aSrivastava, Ashish, Kumar1 aSrivastava, Krishna1 aBanerjee, Rahul1 aPrasad, Jagdish  uhttps://library.ncl.res.in/content/synthesis-characterization-and-electrochemical-investigations-mixed-ligand-copperii-organic02694nas a2200229   4500008004100000245008200041210006900123260000800192300000900200490000700209520197100216653001302187653002402200653001902224653001202243653001102255653001702266100002102283700002902304700002002333856011102353       2021                        eng d00aDiversity in chemical structures and biological properties of plant alkaloids0 aDiversity in chemical structures and biological properties of pl  cJUN  a33740 v263 aPhytochemicals belonging to the group of alkaloids are signature specialized metabolites endowed with countless biological activities. Plants are armored with these naturally produced nitrogenous compounds to combat numerous challenging environmental stress conditions. Traditional and modern healthcare systems have harnessed the potential of these organic compounds for the treatment of many ailments. Various chemical entities (functional groups) attached to the central moiety are responsible for their diverse range of biological properties. The development of the characterization of these plant metabolites and the enzymes involved in their biosynthesis is of an utmost priority to deliver enhanced advantages in terms of biological properties and productivity. Further, the incorporation of whole/partial metabolic pathways in the heterologous system and/or the overexpression of biosynthetic steps in homologous systems have both become alternative and lucrative methods over chemical synthesis in recent times. Moreover, in-depth research on alkaloid biosynthetic pathways has revealed numerous chemical modifications that occur during alkaloidal conversions. These chemical reactions involve glycosylation, acylation, reduction, oxidation, and methylation steps, and they are usually responsible for conferring the biological activities possessed by alkaloids. In this review, we aim to discuss the alkaloidal group of plant specialized metabolites and their brief classification covering major categories. We also emphasize the diversity in the basic structures of plant alkaloids arising through enzymatically catalyzed structural modifications in certain plant species, as well as their emerging diverse biological activities. The role of alkaloids in plant defense and their mechanisms of action are also briefly discussed. Moreover, the commercial utilization of plant alkaloids in the marketplace displaying various applications has been enumerated.10aalkaloid10aBiological activity10aclassification10adefense10aEnzyme10amodification1 aBhambhani, Sweta1 aKondhare, Kirtikumar, R.1 aGiri, Ashok, P.  uhttps://library.ncl.res.in/content/diversity-chemical-structures-and-biological-properties-plant-alkaloids