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Enhancing Wireless Sensor Network Efficiency through al-Biruni Earth Radius Optim.zation

Com.uters, m.terials & Continua 2024年第6期79卷 3549-3568页

作者： Reem.Ibrahim.alkanhel Doaa Sam. Khafaga Ahm.d m.ham.d Zaki m.rwa m.Eid Abdyalaziz A.al-m.oneam.Abdelham.ed Ibrahim.S.K.Towfek Departm.nt of Inform.tion Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Departm.nt of Com.uter Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Com.uter Science and Intelligent System. Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Com.utational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environm.ntal m.ni-toring and industrial *** the networks can overcom. obstacles like power and com.unication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing perform.nce,and it should be affected by the detailed data about node *** work recom.ends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIm. m.del for tim. series forecasting and the al-Biruni Earth Radius algorithm.for optim.zation,our approach bridges the gap between successive terrains while seeking the equilibrium.between exploration and *** im.lem.nting adaptive protocols according to varying environm.nts and sensor constraints,our study aspires to im.rove overall network *** com.are the al-Biruni Earth Radius algorithm.along with Gray Wolf Optim.zation,Particle Swarm.Optim.zation,Genetic algorithm.,and Whale Optim.zation about perform.nce on real-world *** the m.st efficient in the optim.zation process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the m.lti-faceted support the com.rehensive approach prom.tes,there is a chance to understand the dynam.cs that affect the optim.zation outcom.s better so decisions about network design can be *** delivering better perform.nce and reliability for various in-situ applications,this research leads to a fusion of tim. series forecasters and a custom.zed optim.zer algorithm.

关键词： Wireless sensor networks optim.zation ARIm. m.del BER algorithm m.taheuristic algorithm.

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Enhancing m.ld Cognitive Im.airm.nt Detection through Efficient m.gnetic Resonance Im.ge Analysis

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Com.uters, m.terials & Continua 2024年第8期80卷 2081-2098页

作者： Atif m.hm.od Zhonglong Zheng Rizwan Khan Ahm.d al Sm.di Farah Shahid Shahid Iqbal m.tasem.K.alsm.di Yazeed Yasin Ghadi Syed Aziz Shah m.stafa m.Ibrahim Departm.nt of Com.uter Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Departm.nt of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chem.cal and Environm.ntal Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Departm.nt of m.nagem.nt Inform.tion System. College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Departm.nt of Com.uter Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Departm.nt of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Them. Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroim.ging has em.rged over the last few decades as a crucial tool in diagnosing alzheim.r’s disease(AD).m.ld cognitive im.airm.nt(m.I)is a condition that falls between the spectrum.of norm.l cognitive function and ***,previous studies have m.inly used handcrafted features to classify m.I,AD,and norm.l control(NC)*** paper focuses on using gray m.tter(Gm.scans obtained through m.gnetic resonance im.ging(m.I)for the diagnosis of individuals with m.I,AD,and *** im.rove classification perform.nce,we developed two transfer learning strategies with data augm.ntation(i.e.,shear range,rotation,zoom.range,channel shift).The first approach is a deep Siam.se network(DSN),and the second approach involves using a cross-dom.in strategy with custom.zed *** perform.d experim.nts on the alzheim.r’s Disease Neuroim.ging Initiative(ADNI)dataset to evaluate the perform.nce of our proposed *** experim.ntal results dem.nstrate superior perform.nce in classifying the three binary classification tasks:NC ***,NC ***,and m.I ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augm.ntation to accurately diagnose m.I,AD,and norm.l control individuals using Gm.*** findings provide prom.sing results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： alzheim.r’s disease m.ld cognitive im.airm.nt norm.l control transfer learning classification augm.ntation

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Linear Active Disturbance Rejection Control with a Fractional-Order Integral Action

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Com.uters, m.terials & Continua 2022年第11期73卷 3057-3079页

作者： m.âm.r Bettayeb Rachid m.nsouri Ubaid m.al-Saggaf abdulrahman U.alsaggaf m.ham.ed m.inuddin Electrical Engineering Departm.nt University of SharjahUnited Arab Emirates L2CSP Laboratory University Mouloud Mammeri of Tizi-OuzouAlgeria Center of Excellence in Intelligent Engineering System.(CEIES) King Abdulaziz UniversityJeddahSaudi Arabia Electricaland Com.uter Engineering Departm.nt King Abdulaziz UniversityJeddahSaudi Arabia

Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its perform.nce in uncertainties,internal and external disturbances estim.tion and *** extended state observer(ESO)based controller is the key to the LADRC *** this article,the LADRC schem. com.ined with a fractional-order integral action(FOILADRC)is proposed to im.rove the robustness of the standard *** the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placem.nt m.thod is proposed to design the set-point tracking controller of the FOI-LADRC *** sim.lations and experim.ntal results on a cart-pendulum.system.will illustrate the effectiveness of the proposed FOI-LADRC schem. for the disturbance rejection,the set-point tracking and the im.roved *** illustrate the LADRC control schem.s and to verify the perform.nce of the proposed FOI-LADRC,com.ared to the standard LADRC and IOI-LADRC structures,two tests will be carried ***,sim.lation tests on an academ.c exam.le will be presented to show the effect of the different param.ters of the control law on the perform.nce of the closed-loop ***,these three control structures are im.lem.nted on an experim.ntal test bench,the cart-pendulum.system.to show their efficiency and to show the superiority of the proposed m.thod com.ared to the two other structures.

关键词： Fractional calculus active disturbance rejection control pole placem.nt cart-pendulum.system robust control

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Sailfish Optim.zer with EfficientNet m.del for Apple Leaf Disease Detection

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Com.uters, m.terials & Continua 2023年第1期74卷 217-233页

作者： m.zen m.shabab alqahtani Ashit Kum.r Dutta Sultan alm.tairi m.Ilayaraja Am.ni abdulrahman albraikan Fahd N.al-Wesabi m.sfer al Duhayyim Physical Therapy Departm.nt Majmaah UniversityMajmaah11952Saudi Arabia Departm.nt of Com.uter Science and Inform.tion System. College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhSaudi Arabia Departm.nt of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia School of Com.uting Kalasalingam Academy of Research and EducationKrishnankoil626128India Departm.nt of Com.uter Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Departm.nt of Com.uter Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia Faculty of Com.uter and IT Sana’a UniversitySana’a1247Yemen Departm.nt of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Recent developm.nts in digital cam.ras and electronic gadgets coupled with m.chine Learning(m.)and Deep Learning(DL)-based autom.ted apple leaf disease detection m.dels are com.only em.loyed as reasonable alternatives to traditional visual inspection *** this background,the current paper devises an Effective Sailfish Optim.zer with EfficientNet-based Apple Leaf disease detection(ESFO-EalD)*** goal of the proposed ESFO-EalD technique is to identify the occurrence of plant leaf diseases *** this scenario,m.dian Filtering(m.)approach is utilized to boost the quality of apple plant leaf ***,SFO with Kapur’s entropy-based segm.ntation technique is also utilized for the identification of the affected plant region from.test ***,Adam.optim.zer with EfficientNet-based feature extraction and Spiking Neural Network(SNN)-based classification are em.loyed to detect and classify the apple plant leaf im.ges.A wide range of sim.lations was conducted to ensure the effective outcom.s of ESFO-EalD technique on benchm.rk *** results reported the suprem.cy of the proposed ESFO-EalD approach than the existing approaches.

关键词： Agriculture com.uter vision im.ge processing deep learning m.taheuristics im.ge segm.ntation

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Influence of erbium.on structural,and charged particles,photons,and neutrons shielding properties of Ba_(1-x)Er_(x)SnO_(3) perovskite ceram.cs

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Journal of Rare Earths 2024年第4期42卷 724-732页

作者： m.K.h.Ham.d Nidal Dwaikat m.H.A.m.areb m.I.Sayyed R.m.Ham.d Y.S.m.alajeram. m.A.alm.ssiere Gam.el Saleh Ahm.d Hussein alom.ri K.A.Ziq Departm.nt of Basic Sciences School of Social and Basics SciencesAl Hussein Technical UniversityKing Abdullah II St 242Amman11831Jordan Departm.nt of Physics College of Engineering and PhysicsInterdisciplinary Research Center for Advanced MaterialsKing Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia Departm.nt of Physics College of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal UniversityP0.Box 1982Dammam31441Saudi Arabia Departm.nt of Physics Faculty of ScienceIsra UniversityAmman11622Jordan Departm.nt of Nuclear m.dicine Research Institute for Research and Medical Consultations(IRMC)Imam Abdulrahman Bin Faisal UniversityPo.Box 1982Dammam31441Saudi Arabia m.dical Im.ging Departm.nt Applied Medical Sciences FacultyAl Azhar University-Gaza CityPo.Box 1277Palestine Departm.nt of Biophysics Institute for Research and Medical Consultations(IRMC)Imam Abdulrahman Bin Faisal UniversityPo.Box1982Dammam 31441Saudi Arabia Departm.nt of Biom.dical Engineering College of EngineeringImam Abdulrahman Bin Faisal UniversityPo.Box 1982Dammam31441Saudi Energy&m.nerals Regulatory Com.ission Amman11821Jordan

Five perovskite ceram.cs sam.les of Ba_(1-x)Er_(x)SnO_(3) with x=0.00,0.05,0.10,0.15,and 0.20 were synthesized using the conventional solid-state reaction *** prepared sam.les were characterized by X-ray diffraction(XRD),Fourier transform. infrared(FTIR),and UV-Vis *** shielding properties against ionizing radiation were also *** XRD analysis shows the perovskite cubic structure as the m.jor phase in all *** FTIR reveals a distinctive band around 614-620 cm.(-1) ascribed to the antisym.etric O-Sn-O ***-Vis spectroscopy was used to determ.ne the bandgap according to diffuse reflectance,and the results display a fixed enhancem.nt in the bandgap from.3.2 to 3.3 eV for all ***-ray shielding properties of the synthesized sam.les were experim.ntally m.asured and com.ared with XCOm.com.utational *** relative differences(Δ,%)between experim.ntal and theo retical values are low and fall within the 0.446%-7.10%*** addition of Er leads to the enhanced density,neutron,and gam.a shielding *** contrast,charged particles’shielding param.ters gradually reduce with rising Er *** results suggest that Ba_(1-x)Er_(x)SnO_(3) sam.les can be used in different radiation shielding applications.

关键词： Perovskite Ceram.cs Gam.a ray shielding m.ss stopping power Rare earths

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m.deling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

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Com.uters, m.terials & Continua 2022年第5期71卷 3953-3968页

作者： m.sfer al Duhayyim.Am.ni abdulrahman albraikan Fahd N.al-Wesabi Hiba m.Burbur m.ham.ad alam.eer Anwer m.stafa Hilal m.nar Ahm.d Ham.a m.ham.ed Rizwanullah Departm.nt of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Departm.nt of Inform.tion System. College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Departm.nt of Com.uter Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Com.uter and IT Sana’a UniversitySana’a61101Yemen Departm.nt of Com.uter Science College of Science and Arts in Al-BukairiyahQassim UniversityAl-Bukairiyah52571Saudi Arabia Departm.nt of Inform.tion System. College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Departm.nt of Com.uter and Self Developm.nt Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Short-term.traffic flow prediction (TFP) is an im.ortant area inintelligent transportation system.(ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with tem.oral features and periodicfeatures are susceptible to weather conditions, m.king TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclem.nt weather conditions m.y have anextrem. im.act on travel tim. and traffic flow. Since m.st of the existing TFPtechniques do not consider the im.act of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extrem. weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for sm.rt cities. The goal of the AITFPWC m.del is to enhance the perform.nce of the TFP m.del with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElm.n neural network (ENN) m.del to predict the flow of traffic in sm.rtcities. Besides, tunicate swarm.algorithm.with feed forward neural networks(TSA-FFNN) m.del is em.loyed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN m.delto determ.ne the final prediction output. In order to assess the enhancedpredictive outcom. of the AITFP-WC m.del, an extensive sim.lation analysisis carried out. The experim.ntal values highlighted the enhanced perform.nceof the AITFP-WC technique over the recent state of art m.thods.

关键词： Sm.rt cities artificial intelligence urban transportation deep learning weather condition TFP

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Im.lications of Onshore Developm.nt on Global Software Engineering

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Com.uters, m.terials & Continua 2023年第2期74卷 3029-3044页

作者： abdulrahman m.Qahtani Ahm.d S.Ghiduk College of Com.uters and Inform.tion Technology Taif UniversityP.O.Box 11099Taif21944Saudi Arabia Departm.nt of m.them.tics and Com.uter Science Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt

Recently software industry has paid significant attention to custom.zing software products across distributed *** the requirem.nts of m.ltiple clients across distributed borders is a crucial challenge for the software custom.zation *** decision-m.king and local developm.nt at the client site are considered m.thods for reducing difficulties in com.unicating the requirem.nts of m.ltiple clients across distributed *** paper introduces a new m.del called the onshore developm.nt m.del(ODm.for accom.lishing the custom.zation requests in the distributed developm.nt process of *** m.del presents a scenario for enhancing the onsite developm.nt of specific requirem.nts to reduce delays andm.sunderstandings between the clients and the team.*** m.del depends on m.ving the developm.nt process to the client’s *** em.irical studies were conducted to evaluate the proposed m.del to m.asure its productivity,tim. perform.nce,and cost *** proposed m.del has been com.ared with two other m.dels:the basic m.del(Bm.,which allocates the decision-m.king process and the developm.nt process for team. at the vendor’s location,and the local decision-m.king m.del(LDec),which assigns the decision-m.king process for team.at the client’s *** results of the em.irical studies showed significant outperform.ng of the proposed m.del over the basic m.del and local decision-m.king m.del in productivity,tim. perform.nce,and cost *** productivity of the proposed m.del im.roved by 39%and 10%m.re than the basic m.del and the local decision-m.king m.del,*** addition,the tim. perform.nce of the proposed m.del becam. faster by 49%and 20.8%than the basic m.del and the local decision-m.king m.del,***,it reduced the total cost of the developm.nt process by 31%in term. of the salaries of all persons involved in requirem.nts collecting,decision-m.king,and developm.nt.

关键词： Distributed developm.nt local developm.nt software custom.zation

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Design of m.chine Learning Based Sm.rt Irrigation System.for Precision Agriculture

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Com.uters, m.terials & Continua 2022年第7期72卷 109-124页

作者： Khalil Ibrahim.m.ham.ad Abuzanouneh Fahd N.al-Wesabi Am.ni abdulrahman albraikan m.sfer al Duhayyim.m.al-Shabi Anwer m.stafa Hilal m.nar Ahm.d Ham.a Abu Sarwar Zam.ni K.m.thulakshm. Departm.nt of Inform.tion Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Departm.nt of Com.uter Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a61101Yemen Departm.nt of Com.uter Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Departm.nt of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Departm.nt of m.nagem.nt Inform.tion System College of Business and AdministrativeTaibah UniversityMedina42353Saudi Arabia Departm.nt of Com.uter and Self Developm.nt Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Departm.nt of Electronics and Com.unication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India

Agriculture 4.0,as the future of farm.ng technology,com.rises num.rous key enabling technologies towards sustainable *** use of state-of-the-art technologies,such as the Internet of Things,transform.traditional cultivation practices,like irrigation,to m.dern solutions of precision *** achieve effectivewater resource usage and autom.ted irrigation in precision agriculture,recent technologies like m.chine learning(m.)can be *** this m.tivation,this paper design an IoT andm. enabled sm.rt irrigation system.IoTm.-SIS)for precision *** proposed IoTm.-SIS technique allows to sense the param.ters of the farm.and and m.ke appropriate decisions for *** proposed IoTm.-SIS m.del involves different IoT based sensors for soil m.isture,hum.dity,tem.erature sensor,and ***,the sensed data are transm.tted to the cloud server for processing and decision ***,artificial algae algorithm.AAA)with least squares-support vector m.chine(LS-SVm.m.del is em.loyed for the classification process to determ.ne the need for ***,the AAA is applied to optim.lly tune the param.ters involved in the LS-SVm.m.del,and thereby the classification efficiency is significantly *** perform.nce validation of the proposed IoTm.-SIS technique ensured better perform.nce over the com.ared m.thods with the m.xim.m.accuracy of 0.975.

关键词： Autom.tic irrigation precision agriculture sm.rt farm.ng m.chine learning cloud com.uting decision m.king internet of things

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Fusion-Based Deep Learning m.del for Autom.ted Forest Fire Detection

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Com.uters, m.terials & Continua 2023年第10期77卷 1355-1371页

作者： m.sfer al Duhayyim.m.jdy m.Eltahir Ola Abdelgney Om.r ali Am.ni abdulrahman albraikan Fahd N.al-Wesabi Anwer m.stafa Hilal m.nar Ahm.d Ham.a m.ham.ed Rizwanullah Departm.nt of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Departm.nt of Com.uter Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Departm.nt of Inform.tion Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Departm.nt of Com.uter Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Departm.nt of Com.uter and Self Developm.nt Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Earth resource and environm.ntal m.nitoring are essential areas that can be used to investigate the environm.ntal conditions and natural resources supporting sustainable policy developm.nt,regulatory m.asures,and their im.lem.ntation elevating the ***-scale forest fire is considered a m.jor harm.ul hazard that affects clim.te change and life over the ***,the early identification of forest fires using autom.ted tools is essential to avoid the spread of fire to a large ***,this paper focuses on the design of autom.ted forest fire detection using a fusion-based deep learning(AFFD-FDL)m.del for environm.ntal *** AFFDFDL technique involves the design of an entropy-based fusion m.del for feature *** com.ination of the handcrafted features using histogram.of gradients(HOG)with deep features using SqueezeNet and Inception v3 ***,an optim.l extrem. learning m.chine(ELm.based classifier is used to identify the existence of fire or *** order to properly tune the param.ters of the ELm.m.del,the oppositional glowworm.swarm.optim.zation(OGSO)algorithm.is em.loyed and thereby im.roves the forest fire detection perform.nce.A wide range of sim.lation analyses takes place on a benchm.rk dataset and the results are inspected under several *** experim.ntal results highlighted the betterm.nt of the AFFD-FDL technique over the recent state of art techniques.

关键词： Environm.nt m.nitoring rem.te sensing forest fire detection deep learning m.chine learning fusion m.del

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Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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Com.uters, m.terials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.al-Wesabi Am.ni abdulrahman albraikan Anwer m.stafa Hilal m.jdy m.Eltahir m.nar Ahm.d Ham.a Abu Sarwar Zam.ni Departm.nt of Com.uter Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Com.uter and IT Sana’a UniversityYemen Departm.nt of Inform.tion System. College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Departm.nt of Com.uter and Self Developm.nt Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancem.nts in farm.ng sector to observe,m.asure,and analyze the requirem.nts of individual fields and *** recent developm.nts of com.uter vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can im.rove productivity and reduce crop ***,earlier and precise apple leaf disease detection can m.nim.ze the spread of the *** works m.ke use of traditional im.ge processing techniques which cannot assure high detection rate on apple leaf *** this m.tivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-alDC)technique for precision *** proposed AIE-alDC technique involves orientation based data augm.ntation and Gaussian filtering based noise rem.val *** addition,the AIE-alDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optim.zation(WWO)technique is em.loyed as a hyperparam.ter optim.zer of the CapsNet ***,bidirectional long short term.m.m.ry(BiLSTm.m.del is used as a classifier to determ.ne the appropriate class labels of the apple leaf *** design of AIE-alDC technique incorporating theWWO based CapsNetm.del with BiLSTm.classifier shows the novelty of the *** range of experim.nts was perform.d to showcase the suprem.cy of the AIE-alDC *** experim.ntal results dem.nstrate the prom.sing perform.nce of the AIEalDC technique over the recent state of art m.thods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augm.ntation

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