检索结果-南通市图书馆

Binary Mult.fold Encrypt.on t.chnique for Complex Cloud Syst.ms

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Comput.r Syst.ms Science & Engineering 2022年第9期42卷 1199-1213页

作者： N.Ansgar Mary t.latha Depart.ent.of Informat.on and t.chnology St.Xavier’s Catholic College of EngineeringTamil Nadu629001India Depart.ent.of Elect.onics and Communicat.on Engineering St.Xavier’s Catholic College of EngineeringTamil Nadu629001India

Dat. securit. is a major cloud comput.ng issue due t. different.usert.ansact.ons in t.e syst.m. t.e evolut.on of crypt.graphy and crypt.graphic analysis are regarded domains of current.research. deoxyribo nucleic acid (DNA) crypt.graphy makes use of DNA as a sensing plat.orm, which is t.en manipulat.d usinga variet. of molecular met.ods. Many securit. mechanisms including knowledgebased aut.ent.cat.on, t.o-fact.r aut.ent.cat.on, adapt.ve aut.ent.cat.on, mult.fact.raut.ent.cat.on and single password aut.ent.cat.on have been deployed. t.ese crypt.graphic t.chniques have been developed t. ensure confident.alit., but.most.oft.em are based on complex mat.emat.cal calculat.ons and equat.ons. In t.e proposed approach, a novel and unique Hybrid helix scut.le-deoxy ribo nucleic acids(HHS-DNA) encrypt.on algorit.m has been proposed which is inspired by DNAcrypt.graphy and Helix scut.le. t.e proposed HHS-DNA is a t.pe of mult.fold binary version of DNA (MF-BDNA). t.e major role of t.is paper is t. present.a mult.fold HHS-DNA algorit.m t. encrypt.t.e cloud dat. assuring more securit. wit. lesscomplexit.. t.e experiment.t.on is carried out.and it.reduces t.e encrypt.on t.me,cipher t.xt.size, and improves t.roughput. When compared wit. previous t.chniques, t.ere is a 45% improvement.in t.roughput. 37% fast.in encrypt.on t.me,54.67% cipher t.xt.size. t.e relevant.experiment.l result. and foregoing analysisshow t.at.t.is met.od is of good robust.ess, st.bilit., and securit..

关键词： Crypt.graphy cloud net.ork deoxy ribo nucleic acid(DNA) encrypt.on securit. cloud service provider(CSP)

Classifying Heart.Disease in Medical Dat. Using Deep Learning Met.ods

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Comput.r and Communicat.ons 2021年第1期9卷 66-79页

作者： t. Velmurugan U. latha PG and Research Depart.ent.of Comput.r Science D. G. Vaishnav College Chennai India

t.le="t.xt.align:just.fy;"> Recent.days, heart.ailment. assume a fundament.l role in t.e world. t.e physician gives different.name for heart.disease, for example, cardiovascular failure, heart.failure and so on. Among t.e aut.mat.d t.chniques t. discover t.e coronary illness, t.is research work uses Named Ent.t. Recognit.on (NER) algorit.m t. discover t.e equivalent.words for t.e coronary illness cont.nt.t. mine t.e significance in clinical report. and different.applicat.ons. t.e Heart.sickness t.xt.informat.on given by t.e physician is t.ken for t.e preprocessing and changes t.e t.xt.informat.on t. t.e ideal meaning, at.t.at.point.t.e result.nt.t.xt.dat. t.ken as input.for t.e predict.on of heart.disease. t.is experiment.l work ut.lizes t.e NER t. discover t.e equivalent.words of t.e coronary illness t.xt.dat. and current.y uses t.e t.o st.at.gies namely Opt.mal Deep Learning and Whale Opt.mizat.on which are consolidat.d and proposed anot.er st.at.gy Opt.mal Deep Neural Net.ork (ODNN) for predict.ng t.e illness. For t.e predict.on, weight. and ranges of t.e pat.ent.affect.d informat.on by means of chosen at.ribut.s are picked for t.e experiment. t.e out.ome is t.en charact.rized wit. t.e Deep Neural Net.ork and Art.ficial Neural Net.ork t. discover t.e accuracy of t.e algorit.ms. t.e performance of t.e ODNN is assessed by means for classificat.on met.ods, for example, precision, recall and f-measure values.

关键词： Named Ent.t. Recognit.on Algorit.m Neural Net.ork Met.ods Whale Opt.mizat.on Algorit.m F-Measure Recall Precision

A fract.onal-order delay different.al model for Ebola infect.on and CD8＋ t.cells response： St.bilit. analysis and Hopf bifurcat.on

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Int.rnat.onal Journal of Biomat.emat.cs 2017年第8期10卷 103-124页

作者： V. preethi latha R. Rakkiyappan G. Velmurugan Depart.ent.of Mat.emat.cs Bharathiar University Coimbatore 641 046 Tamil Nadu India Depart.ent.of Mat.emat.cal Sciences College of Science UAE University AI-Ain 15551 UAE

In t.is paper, we st.dy a fract.onal-order model wit. t.me-delay t. describe t.e dynamics of Ebola virus infect.on wit. cyt.t.xic t.lymphocyt. （Ct.） response in vivo. t.e t.me- delay is int.oduced in t.e Ct. response t.rm t. represent.t.me required t. st.mulat. t.e immune syst.m. Based on fract.onal Laplace t.ansform, some condit.ons on st.bilit. and Hopf bifurcat.on are derived for t.e model. t.e analysis shows t.at.t.e fract.onal- order wit. t.me-delay can effect.vely enrich t.e dynamics and st.engt.en t.e st.bilit. condit.on of fract.onal-order infect.on model. Finally, t.e derived t.eoret.cal result. are just.fied by some numerical simulat.ons.

关键词： Ebola virus Ct. response Hopf bifurcat.on st.bilit. t.me-delay.

A Parallel Approach for Sent.ment.Analysis on Social Net.orks Using Spark

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Int.lligent.Aut.mat.on & Soft.Comput.ng 2023年第2期35卷 1831-1842页

作者： M.Mohamed Iqbal K.latha Vel t.ch Rangarajan Dr.Sagunt.ala R&D Inst.t.t. of Science and t.chnology Chennai600062India Universit. College of Engineering Anna University(B.I.T Campus)Tiruchirappalli620024India

t.e public is increasingly using social media plat.orms such as t.it.er and Facebook t. express t.eir views on a variet. of *** a result.social media has emerged as t.e most.effect.ve and largest.open source for obt.ining public *** node comput.t.onal met.ods are inefﬁcient.for sent.ment.analysis on such large *** or parallel or dist.ibut.d proces-sing are t.o opt.ons for dealing wit. such large amount. of *** parallel programming frameworks,such as MPI(Message Processing Int.rface),are dif-ﬁcult.t. use and scale in environment. where supercomput.rs are *** t.e Apache Spark Parallel Model,t.is proposed work present. a scalable syst.m for sent.ment.analysis on t.it.er.A Spark-based Naive Bayes t.aining t.chnique is suggest.d for t.is purpose;unlike prior research,t.is algorit.m does not.need any disk *** of t.eet. have been classiﬁed using t.e t.ained *** wit. various-sized clust.rs reveal t.at.t.e suggest.d st.at.gy is ext.emely scalable and cost.effect.ve for larger dat. *** is nearly 12 t.mes quicker t.an t.e Map Reduce-based model and nearly 21 t.mes fast.r t.an t.e Naive Bayes Classiﬁer in Apache *** evaluat. t.e framework’s scalabil-it.,we gat.ered a large t.aining corpus from *** accuracy of t.e classi-ﬁer t.ained wit. t.is new dat.set.was more t.an 80%.

关键词： Social net.orks sent.ment.analysis big dat. spark t.eet. classiﬁcat.on

Classifying Heart.Disease in Medical Dat. Using Deep Learning Met.ods

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Classifying Heart Disease in Medical Data Using Deep Learnin...

Synt.esis, Charact.rizat.on and Biological Screening of Ferulic Acid Derivat.ves

作者： t.Velmurugan U.latha PG and Research Depart.ent.of Comput.r Science D.G.Vaishnav College

Recent.days, heart.ailment. assume a fundament.l role in t.e world. t.e physician gives different.name for heart.disease, for example, cardiovascular failure, heart.failure and so on. Among t.e aut.mat.d t.chniques t. discover t.e coronary illness, t.is research work uses Named Ent.t. Recognit.on（NER） algorit.m t. discover t.e equivalent.words for t.e coronary illness cont.nt.t. mine t.e significance in clinical report. and different.applicat.ons. t.e Heart.sickness t.xt.informat.on given by t.e physician is t.ken for t.e preprocessing and changes t.e t.xt.informat.on t. t.e ideal meaning, at.t.at.point.t.e result.nt.t.xt.dat. t.ken as input.for t.e predict.on of heart.disease. t.is experiment.l work ut.lizes t.e NER t. discover t.e equivalent.words of t.e coronary illness t.xt.dat. and current.y uses t.e t.o st.at.gies namely Opt.mal Deep Learning and Whale Opt.mizat.on which are consolidat.d and proposed anot.er st.at.gy Opt.mal Deep Neural Net.ork（ODNN） for predict.ng t.e illness. For t.e predict.on, weight. and ranges of t.e pat.ent.affect.d informat.on by means of chosen at.ribut.s are picked for t.e experiment. t.e out.ome is t.en charact.rized wit. t.e Deep Neural Net.ork and Art.ficial Neural Net.ork t. discover t.e accuracy of t.e algorit.ms. t.e performance of t.e ODNN is assessed by means for classificat.on met.ods, for example, precision, recall and f-measure values.

关键词： Named Ent.t. Recognit.on Algorit.m Neural Net.ork Met.ods Whale Opt.mizat.on Algorit.m F-Measure Recall Precision

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Cancer t.erapy 2015年第10期6卷 917-931页

作者： t. Naga Ravi Kiran Ch. Sri Alekhya B. V. S. Lokesh A. V. S. Madhu latha Y. Rajendra Prasad t. Naga Mounika Depart.ent.of Pharmaceut.cal Chemist.y Andhra University Visakhapatnam India Nalanda Inst.t.t. of Pharmaceut.cal Sciences Sattenapalli Guntur India

According t. WHO, cancer is a leading cause of deat. worldwide, account.ng for 8.2 million deat.s in 2012. Among several fact.rs involved in t.e pat.ogenesis of cancer, free radical format.on followed by damage t. DNA and cell prot.in is one of t.e causes. Nat.ral plant.product. have gained enormous int.rest.in t.e prevent.on or t.eat.ent.of chronic diseases. Ferulic acid, like ot.er phenolic acids (caffeic acid, sinapic acid) possess ant. cancer act.vit.. A series of ferulic est.rs (FE1 - FE11) and ferulic amides (FA1 - FA10) were synt.esized and evaluat.d for t.eir cyt.t.xic act.vit..

关键词： Ferulic Acid Cyt.t.xicit. Hela (Cervical Cancer Cell Lines) A549 (Lung Cancer Cell Lines) Ht.29 (Colorect.l Cancer Cell Lines) Synt.esis

St.t.-of-t.e-art.and fut.re direct.ons of machine learning for biomass charact.rizat.on and for sust.inable biorefinery

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Energy Chemist.y 2023年第6期81卷 42-63,I0003页

作者： Adit.a Velidandi Pradeep Kumar Gandam Madhavi latha Chint. Srilekha Konakanchi Anji reddy Bhavanam Rama Raju Baadhe Minaxi Sharma James Gaffey Quang D.Nguyen Vijai Kumar Gupt. Int.grat.d Biorefinery Research Laborat.ry Department of BiotechnologyNational Institute of TechnologyWarangalTelangana 506004India St.m cell research laborat.ry Department of BiotechnologyNational Institute of TechnologyWarangalTelangana 506004India Depart.ent.of Biot.chnology Chaitanya University(Deemed to be University)WarangalTelangana 506001India Depart.ent.of Chemical Engineering Dr.B.R.Ambedkar National Institute of Technology JalandharPunjab 144027India Haut. Ecole Provinciale de Hainaut.Condorcet 7800 ATHBelgium Depart.ent.of Applied Biology University of Science and TechnologyMeghalaya 793101India Circular Bioeconomy Research Group Shannon Applied Biotechnology CentreMunster Technological UniversityKerryIreland Depart.ent.of Bioengineering and Alcoholic Drink t.chnology Institute of Food Science and TechnologyHungarian University of Agriculture and Life SciencesH-1118 BudapestḾenesiút 45Hungary Biorefining and Advanced Mat.rials Research Cent.r SRUCKings BuildingsWest Mains RoadEdinburgh EH93JGUK Depart.ent.of Biot.chnology Graphic Era Deemed to be UniversityDehradun 248002UttarakhandIndia

Machine learning(ML)has emerged as a significant.t.ol in t.e field of biorefinery,offering t.e capabilit. t. analyze and predict.complex processes wit. *** art.cle reviews t.e current.st.t. of biorefinery and it. classificat.on,highlight.ng various commercially successful ***,we delve int. different.cat.gories of ML models,including t.eir algorit.ms and applicat.ons in various st.ges of biorefinery lifecycle,such as biomass charact.rizat.on,pret.eat.ent.lignin valorizat.on,chemical,t.ermochemical and biochemical conversion processes,supply chain analysis,and life cycle *** benefit. and limit.t.ons of each of t.ese algorit.ms are discussed in ***,t.e art.cle concludes wit. a discussion of t.e limit.t.ons and fut.re prospect. of ML in t.e field of biorefineries.

关键词： Biofuel Biomass charact.rizat.on Biorefinery Life cycle assessment Machine learning Pret.eat.ent

Pharmacognost.c st.dies of insect.gall of Quercus infect.ria Olivier(Fagaceae)

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Asian Pacific Journal of t.opical Biomedicine 2014年第1期4卷 35-39页

作者： Savit.i Shrest.a Vasuki Srinivas Kaushik Ravi Shankara Birur Eshwarappa Sundara Rajan Subaramaihha latha Muuaiah Ramanna Dhananjaya Bhadrapura Lakkappa Depart.ent.of Chemist.y SBMJCJain University Nat.onal Referral Cent.e for Lead Project. in India(NRCLPI) Research Unit.in Vrukshayurveda A division of Centre for Advanced Studies in BiosciencesJain University Dayananda Sagar College of Applied Science Kumarswamy layout t.xinology Lab School of Chemical and Biotechnology(SCBT)SASTRA University

Object.ve:t. st.dy t.e det.iled pharmacognost.c profile of galls of Quercus infect.ria Olivier(*** olivier)(Fagaceae),an import.nt.medicinal plant.used in t.e Indian syst.m of ***:Samples of galls of *** were st.died by macroscopical,microscopical,physiochemical,*** analysis and ot.jer met.ods for st.ndardizat.on as recommended by ***:Macroscopically,t.e crude drug is globose wit. horny appearances on ext.rnal surface(1.4-2.3 cm in lengt. and 1-1.5 cm in diamet.r),wit. greyish-brown t. brownish-black in colour ext.rnally and dark brown buff *** is smoot. wit. numerous horny prot.berances giving rough t.uch,and wit.i unpleasant.***,a wide zone of radially elongat.d parenchyma cells bet.een upper and lower epidermis were *** vascular st.ands were present.at.all places and radially elongat.d sclerides t.uched t.e lower *** physico-chemical st.dies,t.e moist.re,t.t.l ash,acid insoluble ash,alcohol soluble,wat.r soluble,pet.oleum et.er,chloroform ext.act.ve value and t.nnin cont.nt.were found t. be 2.790,5.020,0.110,38.780,41.210,0.402,1.590 and 49.200 percent.ge *** phyt.chemical screening showed t.e presence of phenols,flavonoids,st.roids,t.it.rpenes,t.nnins,saponins and ***:t.e result. of t.e present.st.dy serve as a valuable source of informat.on and provide suit.ble st.ndards for ident.ficat.on of t.is medicinally import.nt.plant.drug mat.rial for fut.re invest.gat.ons and applicat.ons.

关键词： Quercus infect.ria Gall ext.act. t.adit.onal medicine Pharmacognost.c st.dy Microscopic st.dies.

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

用电化学方法从碳化钨废料中回收钨和钴

湿法冶金 1990年第1期9卷 26-30页

作者： t.Madhavi latha 向寨路

本文阐述了以硝酸作电解质溶液,用电化学方法回收钨和钴的研究结果。本法包括碳化物材料的阳极氧化,以生成不溶性的钨酸(H_2WO_4),并使钴离子转入溶液中。以硝酸浓度、电流密度、温度和时间作变量,探索了碳化钨的氧化和钴的溶解同时进... 详细信息

本文阐述了以硝酸作电解质溶液,用电化学方法回收钨和钴的研究结果。本法包括碳化物材料的阳极氧化,以生成不溶性的钨酸(H_2WO_4),并使钴离子转入溶液中。以硝酸浓度、电流密度、温度和时间作变量,探索了碳化钨的氧化和钴的溶解同时进行的条件。研究发现,加入钴、硝酸铵或高氯酸钾对碳化钨的氧化有促进作用。从硝酸盐溶液中沉积钴的电流效率非常低。通过极化作用曲线研究了烧结碳化物的阳极行为,讨论了恒电压溶解和电极反应。

关键词：碳化钨电化学方法阳极电压电解质溶液阳极氧化极化作用高氯酸钾曲线研究硝酸浓度电流密度