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REFERENCES (113)

[1]
[1] [학술지(정기간행물)] Abbas, S./BCD-WERT : a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm/PeerJ Computer Science/2021/7/e390~// Google Scholar ›
[2]
[2] [학술지(정기간행물)] Abdi, Y./Exploring the impact of sustainability(ESG)disclosure on firm value and financial performance(FP)in airline industry : the moderating role of size and age/Environment, Development and Sustainability/2022/24(4)/5052~5079// Google Scholar ›
[3]
[3] [학술지(정기간행물)] Abdul Rahman, R./Determinants of corporate environment, social and governance(ESG)reporting among Asian firms/Journal of Risk and Financial Management/2021/14(4)/167~// Google Scholar ›
[4]
[4] [학술지(정기간행물)] Abedin, M. Z./Tax default prediction using feature transformation-based machine learning/IEEE Access/2020/9/19864~19881// Google Scholar ›
[5]
[5] [학술지(정기간행물)] Aboud, A./The impact of social, environmental and corporate governance disclosures on firm value : Evidence from Egypt/Journal of Accounting in Emerging Economies/2018/8(4)/442~458// Google Scholar ›
[6]
[6] [학술지(정기간행물)] Aggarwal, R./Diagnostic accuracy of deep learning in medical imaging : a systematic review and meta-analysis/NPJ Digital Medicine/2021/4(1)/65~// Google Scholar ›
[7]
[7] [학술지(정기간행물)] Alakus, T. B./Comparison of deep learning approaches to predict COVID-19 infection/Chaos, Solitons and Fractals/2020/140/110120~// Google Scholar ›
[8]
[8] [학술지(정기간행물)] Alareeni, B. A./ESG impact on performance of US SandP 500-listed firms/Corporate Governance : The International Journal of Business in Society/2020/20(7)/1409~1428// Google Scholar ›
[9]
[9] [학술지(정기간행물)] Alsariera, Y. A./Ai meta-learners and extra-trees algorithm for the detection of phishing websites/IEEE Access/2020/8/142532~142542// Google Scholar ›
[10]
[10] [학술지(정기간행물)] Aouadi, A./Do ESG controversies matter for firm value? Evidence from international data/Journal of Business Ethics/2018/151/1027~1047// Google Scholar ›
[11]
[11] [학술지(정기간행물)] Arvidsson, S./Corporate ESG reporting quantity, quality and performance : Where to now for environmental policy and practice?/Business Strategy and the Environment/2022/31(3)/1091~1110// Google Scholar ›
[12]
[12] [학술지(정기간행물)] Asim, A./Impact of leverage on earning management : Empirical evidence from the manufacturing sector of Pakistan/Journal of Finance and Accounting Research/2019/1(1)/70~91// Google Scholar ›
[13]
[13] [학술지(정기간행물)] Barua, A./CFO gender and accruals quality/Accounting Horizons/2010/24(1)/25~39// Google Scholar ›
[14]
[14] [학술지(정기간행물)] Behl, A./Exploring the relationship of ESG score and firm value using cross-lagged panel analyses : Case of the Indian energy sector/Annals of Operations Research/2022/313(1)/231~256// Google Scholar ›
[15]
[15] [학술지(정기간행물)] Bentéjac, C./A comparative analysis of gradient boosting algorithms/Artificial Intelligence Review/2021/54/1937~1967// Google Scholar ›
[16]
[16] [학술지(정기간행물)] Chelgani, S. C./Modeling industrial hydrocyclone operational variables by SHAPCatBoost-A"conscious lab"approach/Powder Technology/2023/420/118416~// Google Scholar ›
[17]
[17] [학술지(정기간행물)] Chen, H./Shield attitude prediction based on Bayesian-LGBM machine learning/Information Sciences/2023/632/105~129// Google Scholar ›
[18]
[18] [학술지(정기간행물)] Chen, L./Deep learning in asset pricing/Management Science/2023/70(2)/714~750// Google Scholar ›
[19]
[19] [학술지(정기간행물)] Chen, Y./China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach/Expert Systems with Applications/2022/202/117370~// Google Scholar ›
[20]
[20] [학술지(정기간행물)] Chen, Y./Large group activity security risk assessment and risk early warning based on random forest algorithm/Pattern Recognition Letters/2021/144/1~5// Google Scholar ›
[21]
[21] [학술지(정기간행물)] Choi, J. H./Deep learning based sales estimation research for technology valuation : focusing on marine fisheries/Journal of the Society of Technological Innovation/2021/24(5)/951~965// Google Scholar ›
[22]
[22] [학술지(정기간행물)] Chouaibi, S./Social and ethical practices and firm value : The moderating effect of green innovation : Evidence from international ESG data/International Journal of Ethics and Systems/2021/37(3)/442~465// Google Scholar ›
[23]
[23] [학술지(정기간행물)] Christopher, M./Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs/Scientific Reports/2018/8(1)/16685~// Google Scholar ›
[24]
[24] [학술지(정기간행물)] Clarkson, P./Causes and consequences of voluntary assurance of CSR reports : International evidence involving Dow Jones Sustainability Index Inclusion and Firm Valuation/Accounting, Auditing and Accountability Journa/2019/32(8)/2451~2474// Google Scholar ›
[25]
[25] [학술지(정기간행물)] Csizmadia, G./Human activity recognition of children with wearable devices using LightGBM machine learning/Scientific Reports/2022/12(1)/5472~// Google Scholar ›
[26]
[26] [학술지(정기간행물)] D'Amato, A./Corporate social responsibility and firm value : Do firm size and age matter? Empirical evidence from European listed companies/Corporate Social Responsibility and Environmental Management/2020/27(2)/909~924// Google Scholar ›
[27]
[27] [학술지(정기간행물)] D'Amato, A./Corporate social responsibility and firm value : Do firm size and age matter? Empirical evidence from European listed companies/Corporate Social Responsibility and Environmental Management/2020/27(2)/909~924// Google Scholar ›
[28]
[28] [학술지(정기간행물)] Douiba, M./An improved anomaly detection model for IoT security using decision tree and gradient boosting/The Journal of Supercomputing/2023/79(3)/3392~3411// Google Scholar ›
[29]
[29] [학술대회논문] Duan, T./Ngboost : Natural gradient boosting for probabilistic prediction/International Conference on Machine Learning/2020//2690~2700/PMLR Google Scholar ›
[30]
[30] [학술지(정기간행물)] El Bilali, A./An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation/Journal of Environmental Management/2023/327/116890~// Google Scholar ›
[31]
[31] [학술지(정기간행물)] Eslami, E./A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks/Neural Computing and Applications/2020/32/7563~7579// Google Scholar ›
[32]
[32] [학술지(정기간행물)] Fatemi, A./ESG performance and firm value : The moderating role of disclosure/Global Finance Journal/2018/38/45~64// Google Scholar ›
[33]
[33] [학술지(정기간행물)] Feng, Z./ESG disclosure, REIT debt financing and firm value/The Journal of Real Estate Finance and Economics/2021//1~35// Google Scholar ›
[34]
[34] [학술지(정기간행물)] Ghorbani, A./Deep learning interpretation of echocardiograms/NPJ Digital Medicine/2020/3(1)/10~// Google Scholar ›
[35]
[35] [학술대회논문] Gong, D./A Mechine Learning Approach for Botnet Detection Using Light GBM/2022 3rd International Conference on Computer Vision/2022//829~833/IEEE Google Scholar ›
[36]
[36] [학술지(정기간행물)] Gu, Z./Determinants of restaurant systematic risk : A reexamination/The Journal of Hospitality Financial Management/2002/10(1)/1~13// Google Scholar ›
[37]
[37] [학술대회논문] Gupta, S./Model accuracy and runtime tradeoff in distributed deep learning : A systematic study/2016IEEE 16th International Conference on Data Mining (ICDM)/2016//171~180/IEEE Google Scholar ›
[38]
[38] [학술지(정기간행물)] Hamid, S. A./Financial forecasting with neural networks/Academy of Accounting and Financial Studies Journal/2014/18(4)/37~// Google Scholar ›
[39]
[39] [학술지(정기간행물)] Hancock, J. T./CatBoost for big data : an interdisciplinary review/Journal of Big Data/2020/7(1)/1~45// Google Scholar ›
[40]
[40] [학술대회논문] Hijab, A./Breast cancer classification in ultrasound images using transfer learning/2019Fifth international conference on advances in biomedical engineering(ICABME)/2019//1~4/IEEE Google Scholar ›
[41]
[41] [학술대회논문] HU, W./Innovation Novelty and Firm Value:Deep Learning based Text Understanding/International Conference on Information Systems 2023 (ICIS23)/2023//~/ Google Scholar ›
[42]
[42] [학술지(정기간행물)] Huang, D. Z./Environmental, social and governance(ESG)activity and firm performance : A review and consolidation/Accounting and Finance/2021/61(1)/335~360// Google Scholar ›
[43]
[43] [학술지(정기간행물)] Husna, A./Effects of return on asset, debt to asset ratio, current ratio, firm size, and dividend payout ratio on firm value/International Journal of Economics and Financial Issues/2019/9(5)/50~54// Google Scholar ›
[44]
[44] [학술지(정기간행물)] Islam, S./Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques/Journal of Big Data/2020/7/1~22// Google Scholar ›
[45]
[45] [학술지(정기간행물)] Iwendi, C./COVID-19 patient health prediction using boosted random forest algorithm/Frontiers in Public Health/2020/8/357~// Google Scholar ›
[46]
[46] [학술지(정기간행물)] Jiang, M./The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm/Annals of Operations Research/2022//1~33// Google Scholar ›
[47]
[47] [학술지(정기간행물)] Jing, N./A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction/Expert Systems with Applications/2021/178/115019~// Google Scholar ›
[48]
[48] [학술지(정기간행물)] Joo, C./Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations/Engineering Applications of Artificial Intelligence/2023/126/106873~// Google Scholar ›
[49]
[49] [학술지(정기간행물)] Kalbuana, N./Earnings management is affected by firm size, leverage and roa : evidence from Indonesia/Academy of Strategic Management Journal/2021/20/1~12// Google Scholar ›
[50]
[50] [학술지(정기간행물)] Kang K. G./A comparative study of machine learning-based future corporate value prediction models : the impact of including ESG ratings/Journal of the Korean Society of Management/2023/36(9)/1515~1537// Google Scholar ›
[51]
[51] [학술지(정기간행물)] Kezelj, T./A Systematic Literature Review on Corporate Insolvency Prevention Using Artificial Intelligence Algorithms/Journal of Strategic Innovation and Sustainability/2021/16(4)/~// Google Scholar ›
[52]
[52] [학술지(정기간행물)] Kilimci, Z. H./An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain/Complexity/2019//~// Google Scholar ›
[53]
[53] [학술지(정기간행물)] Kim M. S./A study on deep learning-based intelligent technology valuation : a model for predicting qualitative evaluation indicators through deep neural network learning/Journal of Technological Innovation/2021/24(6)/1141~1162// Google Scholar ›
[54]
[54] [학술지(정기간행물)] Kim, H./Corporate bankruptcy prediction using machine learning methodologies with a focus on sequential data/Computational Economics/2022/59(3)/1231~1249// Google Scholar ›
[55]
[55] [학술지(정기간행물)] Kiran, R./DNNRec : A novel deep learning based hybrid recommender system/Expert Systems with Applications/2020/144/113054~// Google Scholar ›
[56]
[56] [학술지(정기간행물)] Krylov, S./Target financial forecasting as an instrument to improve company financial health/Cogent Business and Management/2018/5(1)/1540074~// Google Scholar ›
[57]
[57] [학술지(정기간행물)] Kureljusic, M./Forecasting in financial accounting with artificial intelligence –A systematic literature review and future research agenda/Journal of Applied Accounting Research/2023//~// Google Scholar ›
[58]
[58] [학술지(정기간행물)] Lee H. J./Machine Learning-based enterprise value prediction model : utilizing online enterprise reviews/Journal of Korean Society for Internet Information/2021/22(5)/~// Google Scholar ›
[59]
[59] [학술지(정기간행물)] Lee, J./Deep learningbased corporate performance prediction model considering technical capability/Sustainability/2017/9(6)/899~// Google Scholar ›
[60]
[60] [학술지(정기간행물)] Li, M./Share pledges and firm value/Pacific-Basin Finance Journal/2019/55/192~205// Google Scholar ›
[61]
[61] [학술지(정기간행물)] Li, Y./The impact of environmental, social, and governance disclosure on firm value : The role of CEO power/The British Accounting Review/2018/50(1)/60~75// Google Scholar ›
[62]
[62] [학술지(정기간행물)] Litjens, G./Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis/Scientific reports/2016/6(1)/26286~// Google Scholar ›
[63]
[63] [학술지(정기간행물)] Long, J./An integrated framework of deep learning and knowledge graph for prediction of stock price trend : An application in Chinese stock exchange market/Applied Soft Computing/2020/91/106205~// Google Scholar ›
[64]
[64] [학술지(정기간행물)] Louk, M. H. L./Dual-IDS : A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system/Expert Systems with Applications/2023/213/119030~// Google Scholar ›
[65]
[65] [학술지(정기간행물)] Luo, M./Combination of feature selection and catboost for prediction : The first application to the estimation of aboveground biomass/Forests/2021/12(2)/216~// Google Scholar ›
[66]
[66] [학술지(정기간행물)] Mai, F./Deep learning models for bankruptcy prediction using textual disclosures/European Journal of Operational Research/2019/274(2)/743~758// Google Scholar ›
[67]
[67] [학술지(정기간행물)] Manavalan, B./AtbPpred : a robust sequencebased prediction of anti-tubercular peptides using extremely randomized trees/Computational and Structural Biotechnology Journal/2019/17/972~981// Google Scholar ›
[68]
[68] [학술지(정기간행물)] Manikandan, G./Knowledge discovery in data of prostate cancer by applying ensemble learning/Indian Journal of Computer Science and Engineering (IJCSE)/2022//~// Google Scholar ›
[69]
[69] [학술지(정기간행물)] Massaoudi, M./A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting/Energy/2021/214/118874~// Google Scholar ›
[70]
[70] [학술지(정기간행물)] Mishra, A. K./Mitigating cyber threats through integration of feature selection and stacking ensemble learning : the LGBM and random forest intrusion detection perspective/Cluster Computing/2023/26(4)/2339~2350// Google Scholar ›
[71]
[71] [학술지(정기간행물)] Morgan, N. A./Market orientation, marketing capabilities, and firm performance/Strategic Management Journal/2009/30(8)/909~920// Google Scholar ›
[72]
[72] [학술지(정기간행물)] Nasiboglu, R./WABL method as a universal defuzzifier in the fuzzy gradient boosting regression model/Expert Systems with Applications/2023/212/118771~// Google Scholar ›
[73]
[73] [학술지(정기간행물)] Nhat-Duc, H./Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification/Automation in Construction/2023/148/104767~// Google Scholar ›
[74]
[74] [학술대회논문] Park W. J./An analysis of value investment by industry through deep learning models/Thesis book for the academic conference of the Korean Society of Communications/2022//914~915/ Google Scholar ›
[75]
[75] [학술지(정기간행물)] Patel, J./Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques/Expert Systems with Applications/2015/42(1)/259~268// Google Scholar ›
[76]
[76] [학술지(정기간행물)] Pavlyshenko, B. M./Machine-learning models for sales time series forecasting/Data/2019/4(1)/15~// Google Scholar ›
[77]
[77] [학술지(정기간행물)] Pechlivanidis, E./Can intangible assets predict future performance? A deep learning approach/International Journal of Accounting &Information Management/2022/30(1)/61~72// Google Scholar ›
[78]
[78] [학술지(정기간행물)] Polak, P./Intelligent finance and treasury management : what we can expect/Ai and Society/2020/35/715~726// Google Scholar ›
[79]
[79] [학술지(정기간행물)] Qiu, S. C./Can corporate social responsibility protect firm value during the COVID-19pandemic?/International Journal of Hospitality Management/2021/93/102759~// Google Scholar ›
[80]
[80] [학술지(정기간행물)] Rai, N./Applications of deep learning in precision weed management : A review/Computers and Electronics in Agriculture/2023/206/107698~// Google Scholar ›
[81]
[81] [학술대회논문] Ranjitha, P./Predictive analysis for big mart sales using machine learning algorithms/2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)/2021//1416~1421/ Google Scholar ›
[82]
[82] [학술지(정기간행물)] Rezaei, H./Stock price prediction using deep learning and frequency decomposition/Expert Systems with Applications/2021/169/114332~// Google Scholar ›
[83]
[83] [학술지(정기간행물)] Sadhukhan, B./Predicting the magnitude of an impending earthquake using deep learning techniques/Earth Science Informatics/2023/16(1)/803~823// Google Scholar ›
[84]
[84] [학술지(정기간행물)] Saeed, U./Fault diagnosis based on extremely randomized trees in wireless sensor networks/Reliability Engineering and System Safety/2021/205/107284~// Google Scholar ›
[85]
[85] [학술지(정기간행물)] Sanjeetha, R./Detection and mitigation of botnet based DDoS attacks using catboost machine learning algorithm in SDN environment/International Journal of Advanced Technology and Engineering Exploration/2021/8(76)/445~// Google Scholar ›
[86]
[86] [학술지(정기간행물)] Sharma, A./AE-LGBM : Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM/Computers in Biology and Medicine/2020/125/103964~// Google Scholar ›
[87]
[87] [학술지(정기간행물)] Sheykhmousa, M./Support vector machine versus random forest for remote sensing image classification : A meta-analysis and systematic review/IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing/2020/13/6308~6325// Google Scholar ›
[88]
[88] [학술지(정기간행물)] Sills, M. R./Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning/International Journal of Medical Informatics/2021/151/104468~// Google Scholar ›
[89]
[89] [학술지(정기간행물)] Sitaula, C./Monkeypox virus detection using pre-trained deep learningbased approaches/Journal of Medical Systems/2022/46(11)/78~// Google Scholar ›
[90]
[90] [학술지(정기간행물)] Speiser, J. L./A comparison of random forest variable selection methods for classification prediction modeling/Expert Systems with Applications/2019/134/93~101// Google Scholar ›
[91]
[91] [학술대회논문] Sung T. E./Technology valuation and estimation of evaluation variables based on deep learning/Paper of the Korean Society of Contents/2021/21(10)/48~58/ Google Scholar ›
[92]
[92] [학술지(정기간행물)] Syed, D./Household-level energy forecasting in smart buildings using a novel hybrid deep learning model/IEEE Access/2021/9/33498~33511// Google Scholar ›
[93]
[93] [학술지(정기간행물)] Taha, A. A./An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine/IEEE Access/2020/8/25579~25587// Google Scholar ›
[94]
[94] [학술지(정기간행물)] Traczynski, J./Firm default prediction : A Bayesian model-averaging approach/Journal of Financial and Quantitative Analysis/2017/52(3)/1211~1245// Google Scholar ›
[95]
[95] [학술지(정기간행물)] Tsoumakas, G/A survey of machine learning techniques for food sales prediction/Artificial Intelligence Review/2019/52(1)/441~447// Google Scholar ›
[96]
[96] [학술지(정기간행물)] Tyralis, H./A brief review of random forests for water scientists and practitioners and their recent history in water resources/Water/2019/11(5)/910~// Google Scholar ›
[97]
[97] [학술지(정기간행물)] Velthoen, J./Gradient boosting for extreme quantile regression/Extremes/2023//1~29// Google Scholar ›
[98]
[98] [학술대회논문] Wahid, N./Identification of Coronary Artery Disease using Extra Tree Classification/2023 International Conference on Inventive Computation Technologies(ICICT)/2023//787~792/IEEE Google Scholar ›
[99]
[99] [학술지(정기간행물)] Wang, J./Risk assessment of coronary heart disease based on cloud-random forest/Artificial Intelligence Review/2023/56(1)/203~232// Google Scholar ›
[100]
[100] [학술지(정기간행물)] Wang, S./A novel combined model for wind speed prediction –Combination of linear model, shallow neural networks, and deep learning approaches/Energy/2021/234/121275~// Google Scholar ›
[101]
[101] [학술지(정기간행물)] Wang, X./Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province/Remote Sensing/2023/15(5)/1386~// Google Scholar ›
[102]
[102] [학술대회논문] Wang, Y/Personality type prediction using decision tree, gbdt, and cat boost/2022International Conference on Big Data, Information and Computer Network (BDICN)/2022//552~558/IEEE Google Scholar ›
[103]
[103] [학술지(정기간행물)] wWang, Y./Low-Complexity Fast CU Classification Decision Method Based on LGBM Classifier/Electronics/2023/12(11)/2488~// Google Scholar ›
[104]
[104] [학술지(정기간행물)] Wei, X./Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model/Expert Systems with Applications/2023/219/119648~// Google Scholar ›
[105]
[105] [학술대회논문] Wisesa, O./Prediction analysis sales for corporate services telecommunications company using gradient boost algorithm/2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP)/2020//101~106/IEEE Google Scholar ›
[106]
[106] [학술지(정기간행물)] Wong, W. C./Does ESG certification add firm value?/Finance Research Letters/2021/39/101593~// Google Scholar ›
[107]
[107] [학술지(정기간행물)] Xi, B./LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis/Mechanics of Advanced Materials and Structures/2023//1~16// Google Scholar ›
[108]
[108] [학술지(정기간행물)] Yoon, J./Forecasting of real GDP growth using machine learning models : Gradient boosting and random forest approach/Computational Economics/2021/57(1)/247~265// Google Scholar ›
[109]
[109] [학술지(정기간행물)] Yu, B./Forecasting credit ratings of decarbonized firms : Comparative assessment of machine learning models/Technological Forecasting and Social Change/2022/174/121255~// Google Scholar ›
[110]
[110] [학술지(정기간행물)] Yu, P./Stock price prediction based on deep neural networks/Neural Computing and Applications/2020/32/1609~1628// Google Scholar ›
[111]
[111] [학술지(정기간행물)] Zhang, W./Landslide Susceptibility mapping using random forest and extreme gradient boosting : A case study of Fengjie/Chongqing. Geological Journal/2023/58(6)/2372~2387// Google Scholar ›
[112]
[112] [학술지(정기간행물)] Zhao, B./A survey on deep learning-based fine-grained object classification and semantic segmentation/International Journal of Automation and Computing/2017/14(2)/119~135// Google Scholar ›
[113]
[113] [학술지(정기간행물)] Zhu, H./Deep learningbased classification of weld surface defects/Applied Sciences/2019/9(16)/3312~// Google Scholar ›

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Published 2024-01-01

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