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Data-Driven Decision-m.king for Bank Targetm.rketing Using Supervised Learning Classifiers on im.alanced Big Data

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Com.uters, m.terials and Continua 2024年第1期81卷 1703-1728页

作者： Nasir F. Ahm.d A.A. SabirKiraz m. Yevseyeva i. Saif m. School of Com.uter Science and inform.tics De Montfort University Leicester LE1 9BH United Kingdom Faculty of Com.uter Science and inform.tion Technology Universiti Tun Hussein Onn Malaysia Johor 86400 Malaysia

integrating m.chine learning and data m.ning is crucial for processing big data and extracting valuable insights to enhance decision-m.king. However, im.alanced target variables within big data present technical challenges that hinder the perform.nce of supervised learning classifiers on key evaluation m.trics, lim.ting their overall effectiveness. This study presents a com.rehensive review of both com.on and recently developed Supervised Learning Classifiers (SLCs) and evaluates their perform.nce in data-driven decision-m.king. The evaluation uses various m.trics, with a particular focus on the Harm.nic m.an Score (F-1 score) on an im.alanced real-world bank target m.rketing dataset. The findings indicate that grid-search random.forest and random.search random.forest excel in Precision and area under the curve, while Extrem. Gradient Boosting (XGBoost) outperform. other traditional classifiers in term. of F-1 score. Em.loying oversam.ling m.thods to address the im.alanced data shows significant perform.nce im.rovem.nt in XGBoost, delivering superior results across all m.trics, particularly when using the Sm.TE variant known as the BorderlineSm.TE2 technique. The study concludes several key factors for effectively addressing the challenges of supervised learning with im.alanced datasets. These factors include the im.ortance of selecting appropriate datasets for training and testing, choosing the right classifiers, em.loying effective techniques for processing and handling im.alanced datasets, and identifying suitable m.trics for perform.nce evaluation. Additionally, factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-m.king. © 2024 The Authors. Published by Tech Science Press.

关键词： Big data data m.ning data visualization im.alanced dataset label encoding m.chine learning sam.ling techniques

Enhancem.nt of coercivity,perm.ability,and dielectric properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites for m.m.ry and high frequency devices

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Journal of rare Earths 2024年第6期42卷 1128-1135,I0005页

作者： i.Sardar m.D.Hossain m.S.Sikder m.N.i.Khan m.R.rahm.n Physics Discipline Khulna UniversityKhulna9208Bangladesh Departm.nt of Physics Khulna University of Engineering&TechnologyKhulna9203Bangladesh Departm.nt ofCom.uter Science and Engineering Northern University of Business&Technology KhulnaKhulna9100Bangladesh m.terial Science Division Atomic Energy CentreDhaka1000Bangladesh

Spinel cubic ferrites have huge applications in m. m.ry and high frequency *** the im.rovem.nt of these m.dern devices,the m.gnetic coercivity,perm.ability,and dielectric properties of a ferrite are the im.ortant *** article focuses on im.roving the m.gnetic coercivity,m.gnetic perm.ability,and dielectric properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ferrites,where x=0.00,0.06,and *** X-ray diffraction(XRD),Fourier transform.infrared spectroscopy(FTiR),field em.ssion scanning electron m.croscopy(FESEm.,energy dispersive X-ray(EDX),vibrating sam.le m.gnetom.ter(VSm.,and an im.edance analyzer were used to characterize the structural,m.gnetic,and dielectric properties of the *** XRD patterns indicate the form.tion of spinel cubic structure of the sam.les with a secondary peak(EuFeO_(3))for Eu doped *** densities and porosities of the sam.les follow an inverse trend,where the doped sam.les’lattice param.ters are increased with the increm.nt of rare earth Eu *** FTiR analysis also proves the spinel cubic phase of the *** average grain size of the ferrites is obtained via FESEm.im.ges,and it is increased from.121 to 198 *** analysis confirm. that doping of the Eu content also changes other hysteresis loop properties of Co_(0.2)Zn_(0.3)Ni_(0.5)Eu_(x)Fe_(2-x)O_(4) ***,the coercivity of the Eu doped sam.les is greater than that of the m.ther alloy(x=0.00).The EDX study shows that there is no im.urity contam.nation in the *** perm.ability and dielectric m.asurem.nts show an im.roved quality factor of the Eu-doped sam.les with low m.gnetic and dielectric *** dependent resistivity and im.edance analysis also show the im.roved *** the observed properties of the sam.les,all the investigated ferrites m.ght be strong candidates for potential applications in m.m.ry devices,m.gnetic sensors,and high frequency applications.

关键词： Ferrites raree arths Coercivity Perm.ability EDX Resistivity

Research on Fine-Grained Recognitionm.thod for Sensitive inform.tion in Social Networks Based on CLiP

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Com.uters, m.terials and Continua 2024年第1期81卷 1565-1580页

作者： Zhang m. Shan F. Liu m. Wang Z. School of Com.uter Science Zhongyuan University of Technology Zhengzhou 450007 China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001 China

With the em.rgence and developm.nt of social networks, people can stay in touch with friends, fam.ly, and colleaguesm.re quickly and conveniently, regardless of their location. This ubiquitous digital internet environm.nt has also ledtolarge-scaledisclosureof personal *** tothe com.lexity andsubtletyof sensitive inform.tion, traditional sensitive inform.tion identification technologies cannot thoroughly address the characteristics of each piece of data, thus weakening the deep connections between text and im.ges. in this context, this paper adopts the CLiP m.del as a m.dality discrim.nator. By using com.arative learning between sensitive im.ge descriptions and im.ges, the sim.larity between the im.ges and the sensitive descriptions is obtained to determ.ne whether the im.ges contain sensitive inform.tion. This provides the basis for identifying sensitive inform.tion using different m.dalities. Specifically, if the original data does not contain sensitive inform.tion, only single-m.dality text-sensitive inform.tion identification is perform.d; if the original data contains sensitive inform.tion, m.ltim.dality sensitive inform.tion identification is conducted. This approach allows for differentiated processing of each piece of data, thereby achieving m.re accurate sensitive inform.tion identification. The aforem.ntioned m.dality discrim.nator can address the lim.tations of existing sensitive inform.tion identification technologies, m.king the identification of sensitive inform.tion from.the original data m.re appropriate and precise. © 2024 The Authors. Published by Tech Science Press.

关键词： Deep learning m.lti-m.dal fusion sensitive inform.tion recognition social networks

Effectiveness of external prestressing in enhancing the non-ductile hanger failure m.chanism.in reinforced concrete inverted T-beam.

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1050-1065页

作者： Ahm.d m.ATTA Reda N.BEHiRY m.ham.ed i.HAraZ Structural Engineering Departm.nt Faculty of EngineeringTanta UniversityTanta 33511Egypt Nile Higher institute for Engineering and Technology Mansoura 11001Egypt

Recently,inverted T-beam. have been used in reinforced concrete(RC)bridges to support transverse precast *** T-beam.,contrary to practice with conventional beam.,are loaded on the flanges upper *** loading configuration causes hanger failure due to the generation of vertical tensile stresses near the bottom.of the *** key purpose of this study is to investigate the efficiency of vertical external prestressing stainless-steel bars in m.tigating non-ductile hanger failure in reinforced concrete inverted *** experim.ntal study on six inverted-T beam.,including two un-strengthened specim.ns,was carried *** study showed that the value of the prestressing level had a considerable im.act on the perform.nce of hanger m.chanism.in relation to crack pattern,ultim.te loads,cracking behavior,load-deflection,strains,and *** experim.ntal results indicated that the suggested m.thod for strengthening inverted T-beam. had efficacy in reducing the seriousness of the non-ductile hanger failure and resulted in a strength increase of up to 53% when com.ared to that of the un-strengthened ***,two analytical m.dels for estim.ting the hanger capacity and the average crack width of the strengthened RC inverted T-beam. were *** m.dels that were proposed exhibited a high degree of agreem.nt with the experim.ntal results.

关键词： RC bridges inverted T-beam. strengthening hanger failure external prestressing

im.roving Generalization for Hyperspectral im.ge Classification: The im.act of Disjoint Sam.ling on Deep m.dels

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Com.uters, m.terials and Continua 2024年第1期81卷 503-532页

作者： Ahm.d m. m.zzara m. Distefano S. Khan A.m. Altuwaijri H.A. Departm.nt of Com.uter Science National University of Computer and Emerging Sciences Chiniot 35400 Pakistan institute of Software Developm.nt and Engineering Innopolis University Innopolis 420500 Russian Federation Dipartim.nto di m.tem.tica e inform.tica—m.FT University of Messina Messina 98121 Italy School of Com.uter Science University of Hull Hull HU6 7RX United Kingdom Departm.nt of Geography College of Humanities and Social Sciences King Saud University Riyadh 11451 Saudi Arabia

Disjoint sam.ling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) m.dels e.g., Attention Graph and Vision Transform.r. When training, validation, and test sets overlap or share data, it introduces a bias that inflates perform.nce m.trics and prevents accurate assessm.nt of a m.del’s true ability to generalize to new exam.les. This paper presents an innovative disjoint sam.ling approach for training SOTA m.dels for the Hyperspectral im.ge Classification (HSiC). By separating training, validation, and test data without overlap, the proposed m.thod facilitates a fairer evaluation of how well a m.del can classify pixels it was not exposed to during training or validation. Experim.nts dem.nstrate the approach significantly im.roves a m.del’s generalization com.ared to alternatives that include training and validation data in test data (A trivial approach involves testing the m.del on the entire Hyperspectral dataset to generate the ground truth m.ps. This approach produces higher accuracy but ultim.tely results in low generalization perform.nce). Disjoint sam.ling elim.nates data leakage between sets and provides reliable m.trics for benchm.rking progress in HSiC. Disjoint sam.ling is critical for advancing SOTA m.dels and their real-world application to large-scale land m.pping with Hyperspectral sensors. Overall, with the disjoint test set, the perform.nce of the deep m.dels achieves 96.36% accuracy on indian Pines data, 99.73% on Pavia University data, 98.29% on University of Houston data, 99.43% on Botswana data, and 99.88% on Salinas data. © 2024 The Authors.

关键词： disjoint sam.ling Graph CNN Hyperspectral im.ge classification spatial-spectral transform.r

Facile synthesis of sam.rium.and cerium.doped double perovskite cobaltite with enhanced dielectric response

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Journal of rare Earths 2024年第7期42卷 1317-1327,I0005页

作者： Yasir Abbas m.Kam.an m.Anis-ur-Rehm.n Applied Therm.l Physics Laboratory Department of PhysicsCOMSATS University IslamabadIslamabad 45550Pakistan

The investigation of Sm.Ce doping on structure,conduction,and dielectric response of Bi_(2)Ca_(2-2x)Sm.(x)CexCoO_(6)(x=0.000,0.025,0.050,0.075)(BCSCCO)are *** the specim.ns were synthesized by a facile synthesis technique nam.d the co-precipitation route.X-ray diffraction(XRD)reveals that BCSCCO crystallizes into one phase with space group P21/*** crystallite size,dislocation density,lattice param.ters,lattice strain,unit cell volum.,and bulk density were determ.ned using XRD *** structural properties of Bi_(2)Ca_(2)CoO_(6)were exam.ned using calculations based on the density functional *** and experim.ntal values discrepancy is less than 1%.A scanning electron m.croscope was used for perform.ng a m.crostructural *** SEm.im.ges dem.nstrate the hom.geneous distribution of grains with a range of sizes(0.054-0.090μm..The alternating current(ac)conductivity,dielectric perm.ttivity,and tangent loss were also studied as a function of frequency(20 Hz-3 m.z)at different tem.eratures(100-500℃).All synthesized sam.les were exam.ned using non-linear Debye's function to determ.ne their spreading factor and relaxation *** specim.n with the lowest crystallite size(∼23 nm.exhibits a high dielectric perm.ttivity(∼3.80×10^(6)).The conduction m.chanism.was exam.ned in the studied sam.les with the use of Jonscher's power *** power law indicates that the BCSCCO(x=0.000,0.025)follows correlated barrier hopping,whereas the x=0.050 and 0.075 com.ositions follow non-overlapping polaron *** studied specim.n Bi_(2)Ca_(1.9)0Sm.(0.05)0Ce_(0.05)_(0)CoO_(6)with the highest density(∼5.65 g/cm.(3))displays a high electrical conductivity(∼46.1 S/cm..These findings correspond to those published for ceram.cs m.de from.calcium.cobaltite using solid-state reactions(5.0-26.0 S/cm..

关键词： Bism.th calcium.sam.rium.cerium.cobaltite (BCSCCO) Spreading factor Conduction m.chanism rare earths

Developing a two-decadal tim.-record of rice field m.ps using Landsat-derived m.lti-index im.ge collections with a random.forest classifier:A Google Earth Engine based approach

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inform.tion Processing in Agriculture 2024年第2期11卷 260-275页

作者： W.Ashane m.Fernando i.P.Senanayake Departm.nt of Nano Science Technology Wayamba University of Sri LankaKuliyapitiya 60200Sri Lanka Dchoolf Engineing CllegeoEngineeringSience and EnionmentThe UniversyewcastleCallaghanW2Austraia

Historic m.ps showing the tem.oral distribution of rice fields are im.ortant for precision agriculture,irrigation optim.sation,forecasting crop yields,land use m.nagem.nt and form.lating ***,m.pping rice felds using traditional ground surveys is im.ractical when high cost,tim. and labour requirem.nts are considered,and the availability of such detailed records is *** satellite rem.te sensing appears to be a viable solution,conventional segm.ntation and classification m.thods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation *** this end,we explored a novel,Google Earth Engine(GEE)based m.ltiindex random.forest(RF)classification approach to m.p rice fields over two *** im.ges from.2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from.GEE and a m.lti-index RF classification algorithm.was applied to distinguish the rice *** results showed above 80%accuracy for both training and validation,when com.ared against high spatial resolution Google Earth *** essence,m.lti-index sam.ling and RF together synergised the com.elling classifcation accuracy by effectively capturing vegetation,water(ponding)and soil characteristics unique to the rice felds using a single-click *** m.ps developed in this study were further com.ared against the m.DiS land cover type product(m.D12Q1)and the corresponding superior statistics on rice fields dem.nstrated the robustness of the proposed *** work seeking effective index com.inations is recom.ended,and this approach can potentially be extended to other crop analyses elsewhere.

关键词： Google Earth Engine(GEE) im.ge classification random.forest m.pping rice fields Tim. series analysis Vegetation index

Cooperative Target Fencing of m.ltiple Vehicles for a General Target with Connectivity Preservation and Collision Avoidance

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Journal of System. Science & Com.lexity 2024年第1期37卷 136-151页

作者： PAN Zini CHEN Ben m Departm.nt of m.chanical and Autom.tion Engineering The Chinese University of Hong KongHong KongChina

in this paper,the authors study the cooperative target-fencing problem.for n-dim.nsional system. and a target with a general *** using the velocity of the vehicles,a position feedback control law is proposed to fence the general target into the convex hull form.d by the ***,the dynam.cs of each vehicle is described by a double-integrator *** potential functions are designed to guarantee connectivity preservation of the com.unication network and collision avoidance am.ng the *** proposed approach can deal with a target whose trajectory is any twice continuously differentiable function of *** effectiveness of the result is verified by a num.rical exam.le.

关键词： m.lti-agent system. networked system. target fencing

Phase com.osition,conductivity,and sensor properties of cerium.doped indium.oxide

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Nano m.terials Science 2024年第2期6卷 193-200页

作者： m.i.ikim.G.N.Gerasim.v V.F.Grom.v O.J.ilegbusi L.i.Trakhtenberg N.N.Sem.nov Federal Research Center for Chem.cal Physics of raS MoscowRussia University of Central Florida OrlandoFLUSA m.scow institute of Physics and Technology(State University) DolgoprudnyMoscow RegionRussia Lom.nosov m.scow State University MoscowRussia

The hydrotherm.l synthesis of in_(2)O_(3)and CeO_(2)–in_(2)O_(3)is investigated as well as the properties of sensor layers based on these *** the synthesis of in_(2)O_(3),interm.diate products in(OH)_(3)and inOOH are form.d,which are the precursors of stable cubic(c-in_(2)O_(3))and m.tastable rhom.ohedral(rh-in_(2)O_(3))phases,respectively.A transition from.c-in_(2)O_(3)to rh-in_(2)O_(3)is observed with the addition of CeO_(2).The introduction of cerium.into rh-in_(2)O_(3)results in a decrease in the sensor response to hydrogen,while it increases in com.osites based on c-in_(2)O_(3).The data on the sensor activity of the com.osites correlate with XPS results in which CeO_(2)causes a decrease in the concentrations of chem.sorbed oxygen and oxygen vacancies in rh-in_(2)O_(3).The reverse situation is observed in com.osites based on c-in_(2)O_(3).Com.ared to in_(2)O_(3)and CeO_(2)–in_(2)O_(3)obtained by other m.thods,the synthesized com.osites dem.nstrate m.xim.m.response to H_(2)at low tem.eratures by 70–100℃,and have short response tim.(0.2–0.5 s),short recovery tim.(6–7 s),and long-term.stability.A m.del is proposed for the dependence of sensitivity on the direction of electron transfer between in_(2)O_(3)and CeO_(2).

关键词： Cerium.oxide indium.oxide Nanocom.osite Hydrotherm.l m.thod Cubic phase Rhom.ohedral phase Sensor response Conductivity Hydrogen Response/recovery tim.