Zeitschriftenartikel zum Thema „Machine learnings“
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Li, Tianshu. „Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks“. Asian Business Research 4, Nr. 3 (08.10.2019): 1. http://dx.doi.org/10.20849/abr.v4i3.652.
Der volle Inhalt der QuelleMakarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan und Bikalpa Neupane. „Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise“. Computers in Biology and Medicine 177 (Juli 2024): 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.
Der volle Inhalt der QuelleKim, Jin Kook. „A Study on the Estimation Model for the Visitors to Let’s Run Park Using Machine Learning“. Korean Journal of Sport Science 32, Nr. 3 (30.09.2021): 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.
Der volle Inhalt der QuelleMalik, Sehrish, und DoHyeun Kim. „Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory“. Actuators 10, Nr. 2 (31.01.2021): 27. http://dx.doi.org/10.3390/act10020027.
Der volle Inhalt der QuellePREETHAM S, M C CHANDRASHEKHAR und M Z KURIAN. „METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING“. international journal of engineering technology and management sciences 7, Nr. 3 (2023): 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.
Der volle Inhalt der QuelleKurniawan, Robi, und Shunsuke Managi. „Forecasting annual energy consumption using machine learnings: Case of Indonesia“. IOP Conference Series: Earth and Environmental Science 257 (10.05.2019): 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.
Der volle Inhalt der QuelleSingh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal und Rajesh Kumar Rai. „Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning“. E3S Web of Conferences 220 (2020): 01097. http://dx.doi.org/10.1051/e3sconf/202022001097.
Der volle Inhalt der QuelleDas, Aditi. „Automatic Personality Identification using Machine Learning“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. VI (30.06.2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.
Der volle Inhalt der QuelleMalinda Sari Sembiring, Windi Astuti, Iskandar Muda,. „The Influence of Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption on the Design of Accounting and Finance Functions Mediated by Data Processing“. International Journal on Recent and Innovation Trends in Computing and Communication 11, Nr. 11 (30.11.2023): 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.
Der volle Inhalt der QuelleSendak, Mark P., William Ratliff, Dina Sarro, Elizabeth Alderton, Joseph Futoma, Michael Gao, Marshall Nichols et al. „Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study“. JMIR Medical Informatics 8, Nr. 7 (15.07.2020): e15182. http://dx.doi.org/10.2196/15182.
Der volle Inhalt der QuelleUdomchaipitak, Tanatpong, Nathaphon Boonnam, Supattra Puttinaovarat und Paramate Horkaew. „Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data“. International Journal of Design & Nature and Ecodynamics 17, Nr. 3 (30.06.2022): 423–31. http://dx.doi.org/10.18280/ijdne.170313.
Der volle Inhalt der QuelleQian, Qingwen, Junfeng Wu und Zhe Wang. „Dynamic balance control of two-wheeled self-balancing pendulum robot based on adaptive machine learning“. International Journal of Wavelets, Multiresolution and Information Processing 18, Nr. 01 (29.03.2019): 1941002. http://dx.doi.org/10.1142/s0219691319410029.
Der volle Inhalt der QuelleKokozinski, Andre, Christian Kubik und Peter Groche. „Komplexität mehrstufiger Umformprozesse beherrschen/Mastering the complexity of multi-stage forming processes – The contribution of domain knowledge to a data-driven monitoring of progressive tools“. wt Werkstattstechnik online 112, Nr. 10 (2022): 696–700. http://dx.doi.org/10.37544/1436-4980-2022-10-66.
Der volle Inhalt der QuelleAsari, Yusuke. „SM-3 Noise Reduction Method Based on Machine Learnings for Electron Holography“. Microscopy 68, Supplement_1 (01.11.2019): i7. http://dx.doi.org/10.1093/jmicro/dfz056.
Der volle Inhalt der QuelleZhang, Evan. „Treating COVID-19 with machine learning“. Applied and Computational Engineering 30, Nr. 1 (22.01.2024): 1–11. http://dx.doi.org/10.54254/2755-2721/30/20230202.
Der volle Inhalt der QuelleTang, Muran, Lingyue Gao, Yutong Bian, Shang Xiang und Kaijun Zhang. „Brain tumor MRI images classification based on machine learning“. Applied and Computational Engineering 29, Nr. 1 (26.12.2023): 19–29. http://dx.doi.org/10.54254/2755-2721/29/20230765.
Der volle Inhalt der QuelleJin, Xiangyu, Luya Wei und Qihua Zhang. „The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings“. BCP Business & Management 23 (04.08.2022): 903–9. http://dx.doi.org/10.54691/bcpbm.v23i.1471.
Der volle Inhalt der QuelleZhai, Weiguang, Changchun Li, Qian Cheng, Bohan Mao, Zongpeng Li, Yafeng Li, Fan Ding, Siqing Qin, Shuaipeng Fei und Zhen Chen. „Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications“. Remote Sensing 15, Nr. 14 (21.07.2023): 3653. http://dx.doi.org/10.3390/rs15143653.
Der volle Inhalt der QuelleHwang, Gyuyeong, Taehun Kim, Juyong Shin, Naechul Shin und Sungwon Hwang. „Machine learnings for CVD graphene analysis: From measurement to simulation of SEM images“. Journal of Industrial and Engineering Chemistry 101 (September 2021): 430–44. http://dx.doi.org/10.1016/j.jiec.2021.05.031.
Der volle Inhalt der QuelleKim, Gyeung Min. „Analysis for Factors Determining the Price of Multi-family Housing through Machine Learnings“. Residential Environment Institute Of Korea 14, Nr. 3 (30.06.2016): 29–40. http://dx.doi.org/10.22313/reik.2016.14.3.29.
Der volle Inhalt der QuelleNikam, Rahul J. „Legality of usage of Artificial Intelligence and Machine Learnings by Share Market Intermediary“. Passagens: Revista Internacional de História Política e Cultura Jurídica 15, Nr. 2 (15.06.2023): 319–39. http://dx.doi.org/10.15175/1984-2503-202315207.
Der volle Inhalt der QuelleKang, In-Ae, Soualihou Ngnamsie Njimbouom, Kyung-Oh Lee und Jeong-Dong Kim. „DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine“. Applied Sciences 12, Nr. 6 (16.03.2022): 3043. http://dx.doi.org/10.3390/app12063043.
Der volle Inhalt der QuelleChao, Paul C. P., Chih-Cheng Wu, Duc Huy Nguyen, Ba-Sy Nguyen, Pin-Chia Huang und Van-Hung Le. „The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage“. IEEE Sensors Journal 21, Nr. 11 (01.06.2021): 12498–510. http://dx.doi.org/10.1109/jsen.2021.3073850.
Der volle Inhalt der QuelleHasan, Md Mahadi, Saba Binte Murtaz, Muhammad Usama Islam, Muhammad Jafar Sadeq und Jasim Uddin. „Robust and efficient COVID-19 detection techniques: A machine learning approach“. PLOS ONE 17, Nr. 9 (15.09.2022): e0274538. http://dx.doi.org/10.1371/journal.pone.0274538.
Der volle Inhalt der QuelleGanie, Shahid Mohammad, Pijush Kanti Dutta Pramanik, Saurav Mallik und Zhongming Zhao. „Chronic kidney disease prediction using boosting techniques based on clinical parameters“. PLOS ONE 18, Nr. 12 (01.12.2023): e0295234. http://dx.doi.org/10.1371/journal.pone.0295234.
Der volle Inhalt der QuelleKumar, Yogesh. „The Fellow Traveller: A Machine Learning Approach to Travel Management“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 4 (30.04.2022): 1798–802. http://dx.doi.org/10.22214/ijraset.2022.41613.
Der volle Inhalt der QuelleM. Brandao, Iago, und Cesar da Costa. „FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING“. Eletrônica de Potência 27, Nr. 03 (22.09.2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.
Der volle Inhalt der QuelleXue, Yang, Mariela Araujo, Jorge Lopez, Kanglin Wang und Gautam Kumar. „Machine learning to reduce cycle time for time-lapse seismic data assimilation into reservoir management“. Interpretation 7, Nr. 3 (01.08.2019): SE123—SE130. http://dx.doi.org/10.1190/int-2018-0206.1.
Der volle Inhalt der QuelleBile, Alessandro, Hamed Tari und Eugenio Fazio. „Episodic Memory and Information Recognition Using Solitonic Neural Networks Based on Photorefractive Plasticity“. Applied Sciences 12, Nr. 11 (31.05.2022): 5585. http://dx.doi.org/10.3390/app12115585.
Der volle Inhalt der QuelleZhou, Wangbao, Lijun Xiong, Lizhong Jiang, Lingxu Wu, Ping Xiang und Liqiang Jiang. „Optimal combinations of parameters for seismic response prediction of high-speed railway bridges using machine learnings“. Structures 57 (November 2023): 105089. http://dx.doi.org/10.1016/j.istruc.2023.105089.
Der volle Inhalt der QuelleLatif, Sarmad Dashti, Vivien Lai, Farah Hazwani Hahzaman, Ali Najah Ahmed, Yuk Feng Huang, Ahmed H. Birima und Ahmed El-Shafie. „Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia“. Results in Engineering 21 (März 2024): 101872. http://dx.doi.org/10.1016/j.rineng.2024.101872.
Der volle Inhalt der QuelleAnam, Khairul, Harun Ismail, Faruq Sandi Hanggara, Cries Avian, Safri Nahela und Muchamad Arif Hana Sasono. „Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband“. Journal of Engineering and Technological Sciences 55, Nr. 5 (30.12.2023): 587–99. http://dx.doi.org/10.5614/j.eng.technol.sci.2023.55.5.8.
Der volle Inhalt der QuelleSabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang und Mark D. Plumbley. „Credit Risk Rating Using State Machines and Machine Learning“. International Journal of Trade, Economics and Finance 11, Nr. 6 (Dezember 2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.
Der volle Inhalt der QuelleChen, JueYu. „Identification and analysis of real and fake news by XGBoost algorithm of machine learning“. Applied and Computational Engineering 40, Nr. 1 (21.02.2024): 255–62. http://dx.doi.org/10.54254/2755-2721/40/20230661.
Der volle Inhalt der QuelleAqil, M., M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati et al. „Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning“. Applied Computational Intelligence and Soft Computing 2022 (26.04.2022): 1–15. http://dx.doi.org/10.1155/2022/6588949.
Der volle Inhalt der QuelleAqil, M., M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati et al. „Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning“. Applied Computational Intelligence and Soft Computing 2022 (26.04.2022): 1–15. http://dx.doi.org/10.1155/2022/6588949.
Der volle Inhalt der QuelleJin, Yu, Zhe Ren, Wenjie Wang, Yulei Zhang, Liang Zhou, Xufeng Yao und Tao Wu. „Classification of Alzheimer's disease using robust TabNet neural networks on genetic data“. Mathematical Biosciences and Engineering 20, Nr. 5 (2023): 8358–74. http://dx.doi.org/10.3934/mbe.2023366.
Der volle Inhalt der QuelleSong, Yiyan, Shaowei Gao, Wulin Tan, Zeting Qiu, Huaqiang Zhou und Yue Zhao. „Multiple Machine Learnings Revealed Similar Predictive Accuracy for Prognosis of PNETs from the Surveillance, Epidemiology, and End Result Database“. Journal of Cancer 9, Nr. 21 (2018): 3971–78. http://dx.doi.org/10.7150/jca.26649.
Der volle Inhalt der QuellePuttinaovarat, Supattra, und Paramate Horkaew. „Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation“. Earth Science Informatics 12, Nr. 4 (25.06.2019): 429–46. http://dx.doi.org/10.1007/s12145-019-00387-y.
Der volle Inhalt der QuelleAhmed Taialla, Omer, Umar Mustapha, Abdul Hakam Shafiu Abdullahi, Esraa Kotob, Mohammed Mosaad Awad, Aliyu Musa Alhassan, Ijaz Hussain, Khalid Omer, Saheed A. Ganiyu und Khalid Alhooshani. „Unlocking the potential of ZIF-based electrocatalysts for electrochemical reduction of CO2: Recent advances, current trends, and machine learnings“. Coordination Chemistry Reviews 504 (April 2024): 215669. http://dx.doi.org/10.1016/j.ccr.2024.215669.
Der volle Inhalt der QuelleBahrawi, Nfn. „Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based“. Journal of Information Technology and Its Utilization 2, Nr. 2 (19.12.2019): 29. http://dx.doi.org/10.30818/jitu.2.2.2695.
Der volle Inhalt der QuelleJain, Vanita, Monu Gupta, Neeraj Joshi, Anubhav Mishra und Vishakha Bansal. „E-College : an aid for E-Learning systems“. Fusion: Practice and Applications 3, Nr. 2 (2021): 66–72. http://dx.doi.org/10.54216/fpa.030202.
Der volle Inhalt der QuelleXu, Pufan, Fei Li und Haipeng Wang. „A novel concatenate feature fusion RCNN architecture for sEMG-based hand gesture recognition“. PLOS ONE 17, Nr. 1 (20.01.2022): e0262810. http://dx.doi.org/10.1371/journal.pone.0262810.
Der volle Inhalt der QuelleNaeini, Ehsan Zabihi, und Kenton Prindle. „Machine learning and learning from machines“. Leading Edge 37, Nr. 12 (Dezember 2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.
Der volle Inhalt der QuelleZhang, Shenghan, Yufeng Gu, Yinshan Gao, Xinxing Wang, Daoyong Zhang und Liming Zhou. „Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs“. Geofluids 2022 (05.10.2022): 1–31. http://dx.doi.org/10.1155/2022/9443955.
Der volle Inhalt der QuelleTurner, A., J. Fyfe, P. Rickwood und S. Mohr. „Evaluation of implemented Australian efficiency programs: results, techniques and insights“. Water Supply 14, Nr. 6 (10.07.2014): 1112–23. http://dx.doi.org/10.2166/ws.2014.065.
Der volle Inhalt der QuelleS.Sureshkumar, Et al. „Neural Network-Based Multiplicatively Gait Feature Eradication and Detection“. International Journal on Recent and Innovation Trends in Computing and Communication 11, Nr. 4 (30.04.2023): 375–79. http://dx.doi.org/10.17762/ijritcc.v11i4.9843.
Der volle Inhalt der QuelleTrott, David. „Deceiving Machines: Sabotaging Machine Learning“. CHANCE 33, Nr. 2 (02.04.2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.
Der volle Inhalt der QuelleBonnevie, Erika, Jennifer Sittig und Joe Smyser. „The case for tracking misinformation the way we track disease“. Big Data & Society 8, Nr. 1 (Januar 2021): 205395172110138. http://dx.doi.org/10.1177/20539517211013867.
Der volle Inhalt der QuelleSilva Pereira, Fernando. „A prova resultante de “software de aprendizagem automática”“. Revista Electrónica de Direito 23, Nr. 3 (Oktober 2020): 79–98. http://dx.doi.org/10.24840/2182-9845_2020-0003_0006.
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