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| 1 | Whole-genome,transcriptome,and methylome analyses provide insights into the evolution of platycoside biosynthesis in Platycodon grandiflorus,a medicinal plant显示文摘Triterpenoid saponins(TSs)are common plant defense phytochemicals with potential pharmaceutical properties.Platycodon grandiflorus(Campanulaceae)has been traditionally used to treat bronchitis and asthma in East Asia.The oleanane-type TSs,platycosides,are a major component of the P.grandiflorus root extract.Recent studies show that platycosides exhibit anti-inflammatory,antiobesity,anticancer,antiviral,and antiallergy properties.However,the evolutionary history of platycoside biosynthesis genes remains unknown.In this study,we sequenced the genome of P.grandiflorus and investigated the genes involved in platycoside biosynthesis.The draft genome of P.grandiflorus is 680.1 Mb long and contains 40,017 protein-coding genes.Genomic analysis revealed that the CYP716 family genes play a major role in platycoside oxidation.The CYP716 gene family of P.grandiflorus was much larger than that of other Asterid species.Orthologous gene annotation also revealed the expansion ofβ-amyrin synthases(bASs)in P.grandiflorus,which was confirmed by tissue-specific gene expression.In these expanded gene families,we identified key genes showing preferential expression in roots and association with platycoside biosynthesis.In addition,wholegenome bisulfite sequencing showed that CYP716 and bAS genes are hypomethylated in P.grandiflorus,suggesting that epigenetic modification of these two gene families affects platycoside biosynthesis.Thus whole-genome,transcriptome,and methylome data of P.grandiflorus provide novel insights into the regulation of platycoside biosynthesis by CYP716 and bAS gene families. | Jungeun Kim Sang-Ho Kang Sin-Gi Park Tae-Jin Yang Yi Lee Ok Tae Kim Oksung Chung Jungho Lee Jae-Pil Choi Soo-Jin Kwon Keunpyo Lee Byoung-Ohg Ahn Dong Jin Lee Seung-il Yoo In-Gang Shin Yurry Um Dae Young Lee Geum-Soog Kim Chang Pyo Hong Jong Bhak Chang-Kug Kim | 2020 | Horticulture Research2020，7，1： | 2 |
| 2 | A simple, flexible and high-throughput cloning system for plant genome editing via CRISPR-Cas system显示文摘CRISPR-Cas9 system is now widely used to edit a target genome in animals and plants. Cas9 protein derived from Streptococcus pyogenes(Sp Cas9) cleaves double-stranded DNA targeted by a chimeric single-guide RNA(sg RNA). For plant genome editing, Agrobacterium-mediated T-DNA transformation has been broadly used to express Cas9 proteins and sg RNAs under the control of Ca MV 35 S and U6/U3 promoter, respectively. We here developed a simple and high-throughput binary vector system to clone a 19 20 bp of sg RNA, which binds to the reverse complement of a target locus, in a large T-DNA binary vector containing an Sp Cas9 expressing cassette. Twostep cloning procedures:(1) annealing two target-specific oligonucleotides with overhangs specific to the Aar I restriction enzyme site of the binary vector; and(2) ligating the annealed oligonucleotides into the two Aar I sites of the vector, facilitate the high-throughput production of the positive clones. In addition, Cas9-coding sequence and U6/U3 promoter can be easily exchanged via the GatewayTMsystem and unique Eco RI/Xho I sites on the vector, respectively. We examined the mutation ratio and patterns when we transformed these constructs into Arabidopsis thaliana and a wild tobacco, Nicotiana attenuata. Our vector system will be useful to generate targeted large-scale knock-out lines of model as well as non-model plant. | Hyeran Kim Sang-Tae Kim Jahee Ryu Min Kyung Choi Jiyeon Kweon Beum-Chang Kang Hyo-Min Ahn Suji Bae Jungeun Kim Jin-Soo Kim Sang-Gyu Kim | 2016 | Journal of Integrative Plant Biology2016，58，8： | 2 |
| 3 | Enhancing Hydrophilicity of Thick Electrodes for High Energy Density Aqueous Batteries显示文摘Thick electrodes can substantially enhance the overall energy density of batteries.However,insufficient wettability of aqueous electrolytes toward electrodes with conventional hydrophobic binders severely limits utilization of active materials with increasing the thickness of electrodes for aqueous batteries,resulting in battery performance deterioration with a reduced capacity.Here,we demonstrate that controlling the hydrophilicity of the thicker electrodes is critical to enhancing the overall energy density of batteries.Hydrophilic binders are synthesized via a simple sulfonation process of conventional polyvinylidene fluoride binders,considering physicochemical properties such as mechanical properties and adhesion.The introduction of abundant sulfonate groups of binders(i)allows fast and sufficient electrolyte wetting,and(ii)improves ionic conduction in thick electrodes,enabling a significant increase in reversible capacities under various current densities.Further,the sulfonated binder effectively inhibits the dissolution of cathode materials in reactive aqueous electrolytes.Overall,our findings significantly enhance the energy density and contribute to the development of practical zinc-ion batteries. | Jungeun Lee Hyeonsoo Lee Cheol Bak Youngsun Hong Daeha Joung Jeong Beom Ko Yong Min Lee Chanhoon Kim | 2023 | Nano-Micro Letters2023，15，7： | 2 |
| 4 | Measurement of Atmospheric Formaldehyde and Monoaromatic Hydrocarbons using Differential Optical Absorption Spectroscopy during Winter and Summer Intensive Periods in Seoul, Korea显示文摘 | Chulkyu Lee Young Joon Kim Sang-Bum Hong Hanlim Lee Jinsang Jung Yeo-Jin Choi Jungeun Park Ki-Hyun Kim Jai-Hoon Lee Ki-Joon Chun Hyun-Ho Kim | 2005 | Water Air and Soil Pollution (-)2005，，1： | 1 |
| 5 | Time-resolved investigation of nanosecond crystal growth in rapid-phase-change materials : correlation with the recording speed of digital versatile disc media显示文摘 | Yoshimitsu Fukuyama Nobuhiro Yasuda Jungeun Kim | 2008 | Applied Physics Express2008，1，0405001： | 1 |
| 6 | Classification cost:an empirical comparison among traditional classifier,cost-sensitive classifier,and metacost显示文摘 | Kim Jungeun Choi Keunho Kim Gunwoo Suh Yongmoo | 2012 | Expert Systems with Applications2012，39，4： | 1 |
| 7 | Interaction of tetraspan(in) TM4SF5 with CD44 promotes self‐renewal and circulating capacities of hepatocarcinoma cells显示文摘 | Doohyung Lee Juri Na Jihye Ryu Hye‐Jin Kim Seo Hee Nam Minkyung Kang Jae Woo Jung Mi‐Sook Lee Haeng Eun Song Jungeun Choi Gyu‐Ho Lee Tai Young Kim June‐Key Chung Ki Hun Park Sung‐Hak Kim Hyunggee Kim Howon Seo Pilhan Kim Hyewon Youn Jung Weon Lee | 2015 | Hepatology2015，，6： | 1 |
| 8 | The tomato genome sequence provides insights into fleshy fruit evolution 显示文摘 | Sato S Tabata S Hirakawa H Asamizu E Shlrasawa K Isobe S Kaneko T Nakamura Y Shibata D Aoki K Egholm M Knight J Bogden R Li C Shuang Y Xu X Pan S Cheng S Liu X Ren Y Wang J Albiero A Dal Pero F Todesco S Van Eck J Buels R M Bombarely A Gosselin J R Huang M Leto J A Menda N Strickler S Mao L Gao S Tecle I Y York T Zheng Y Vrebalov JT Lee J Zhong S Mueller L A Stiekema W J Ribeca P Alioto T Yang W Huang S Du Y Zhang Z Gao J Guo Y Wang X Li Y He J Li C Cheng Z Zuo J Ren J Zhao J Yan L Jiang H Wang B Li H Li Z Fu F Chen B Feng Q Fan D Wang Y Ling H Xue Y Ware D McCombie W R Lippman Z B Chia J M Jiang K Pasternak S Gelley L Kramer M Anderson L K Chang S B Royer S M Shearer L A Stack S M Rose J K Xu Y Eannetta N Matas A J McQuinn R Tanksley S D Camara F Guiga R Rombauts S Fawcett J Van de Peer Y Zamir D Liang C Spannagl M Gundlach H Bruggmann R Mayer K Jia Z Zhang J Ye Z Bishop G J Butcher S Lopez-Cobollo R Buchan D Filippis I Abbott J Dixit R Singh M Singh A Pal J K Pandit A Singh P K Mahato A K Gaikwad V D Sharma R R Mohapatra T Singh N K Causse M Rothan C Schiex T Noirot C Bellec A Klopp C Delalande C Berges H Mariette J Frasse P Vautrin S Zouine M Latch6 A Rousseau C Regad F Pech J C Philippot M Bouzayen M Pericard P Osorio S Fernandez del Carmen A Monforte A Granell A Fernandez-Mufioz R Conte M Lichtenstein G Carrari F De Bellis G Fuligni F Peano C Grandillo S Termolino P Pietrella M Fantini E Falcone G Fiore A Giuliano G Lopez L Facella P Perotta G Daddiego L Bryan G Orozco M Pastor X Torrents D van Schriek M G Feron R M van Oeveren J de Heer P daPonte L Jacobs-Oomen S Cariaso M Prins M van Eijk M J Janssen A van Haaren M J Jungeun Kim S H Kwon S Y Kim S Koo D H Lee S Hur C G Clouser C Rico A Hallab A Gebhardt C Klee K Jocker A Warfsmann J Gobel U Kawamura S Yano K Sherman J D Fukuoka H Negoro S Bhutty S Chowdhury P Chattopadhyay D Datema E Smit S Schijlen E G van de Belt J van Haarst J C Peters S A van Staveren M A Henkens M H Mooyman P J Hesselink T van Ham R C Jiang G Droege M Choi D Kang B C Kim B D Park M Kim S Yeom SI Lee YH Choi Y D Li G Gao J Liu Y Huang S Fernandez-Pedrosa V Collado C Zufiiga S Wang G Cade R Dietrich R A Rogers J Knapp S Fei Z White R A Thannhauser T W Giovannoni J J Botella M A Gilbert L Gonzalez R Goieoechea J L Yu Y Kudrna D Collura K Wissotski M Wing R Meyers BC Gurazada AB Green P J Vyas S M Solanke A U Kumar R Gupta V Sharma A K Khurana P Khurana J P Tyagi A K Dalmay T Mohorianu 1 Waits B Chamala S Barbazuk W B Li J Guo H Lee T H Wang Y Zhang D Paterson A H Wang X Tang H Barone A Chiusano M L Ereolano M R D' Agostino N Di Filippo M Traini A Sanseverino W Frusciante L Seymour G B Elharam M Fu Y Hua A Kenton S Lewis J Lin S Najar F Lai H Qin B Qu C Shi R White D White J Xing Y Yang K Yi J Yao Z Zhou L Roe B A Vezzi A D' Angelo M Zimbello R Sehiavon R Caniato E Rigobello C Campagna D Vitulo N Valle G Nelson D R De Paoli E Szinay D de Jong H H Bai Y Visser R G Klein R Beasley H McLaren K Nicholson C Riddle C Gianese G | 2012 | Nature2012，485，7400： | 1 |
| 9 | Reduction of mint-1, mint-2, and APP overexpression in okadaic acid-treated neurons显示文摘 | SeungYong Yoon JungEun Choi JuHee Haam Han Choe DongHou Kim | 2007 | NeuroReport2007，，18： | 1 |
| 10 | Repression of porcine endogenous retrovirus infection by human APOBEC3 proteins显示文摘 | Jungeun Lee Jae Yoo Choi Hee-Jung Lee Kang-Chang Kim Byeong-Sun Choi Yu-Kyoung Oh Young Bong Kim | 2011 | Biochemical and Biophysical Research Communications2011，，1： | 1 |
| 11 | A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment显示文摘Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology. | Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim | 2024 | Computer Modeling in Engineering & Sciences2024，138，2： | 0 |
| 12 | Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs显示文摘Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its treatment.The researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their areas.One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)scans.At the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical imaging.Particularly,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest radiographs.While the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly CNN.This study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and classification.The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19.To do so,the presented IFFODLEF model applies image pre-processing at the initial stage.In addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature extraction.Moreover,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify COVID-19.The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance.The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches. | Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 13 | Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification显示文摘Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques. | Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 14 | Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning显示文摘Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967. | Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 15 | An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation显示文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy. | Junaid Rashid Sumera Kanwal Muhammad Wasif Nisar Jungeun Kim Amir Hussain | 2023 | Computer Systems Science & Engineering2023，44，2： | 0 |
| 16 | Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors显示文摘In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity. | P.Arunachalam N.Janakiraman Junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman A.Balasundaram | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 17 | 担子菌中牛磺酸的检测(英文) | Shim Mi ja 1 Park Taesun 2 Park Jungeun 2 Kim Byong kak 3 (1 Department of Life Science, The University of Seoul, Seoul 130 743 2 Department of Food and Nutrition, Yonsei University, Seoul 120 749 3 College of Pharmacy, Seoul Nationa | 1999 | 安徽农业大学学报1999，26，3： | 0 |
| 18 | Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques显示文摘Heart disease is one of the leading causes of death in the world today.Prediction of heart disease is a prominent topic in the clinical data processing.To increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical field.There are many studies on the prediction of heart disease,but limited work is done on the selection of features.The selection of features is one of the best techniques for the diagnosis of heart diseases.In this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease prediction.For performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation techniques.The results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior performance.The selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through cross-validation.Naive Bayes and Random Forest are found to outperform other classification approaches when accurately evaluated.The results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other state-of-the-artmethods.Therefore,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease. | Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem Amir Hussain | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 19 | CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification显示文摘Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II. | Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 20 | Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture显示文摘Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%. | Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
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