Artículos de revistas sobre el tema "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, n.º 3 (8 de octubre de 2019): 1. http://dx.doi.org/10.20849/abr.v4i3.652.
Texto completoMakarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan y Bikalpa Neupane. "Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise". Computers in Biology and Medicine 177 (julio de 2024): 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.
Texto completoKim, 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, n.º 3 (30 de septiembre de 2021): 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.
Texto completoMalik, Sehrish y DoHyeun Kim. "Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory". Actuators 10, n.º 2 (31 de enero de 2021): 27. http://dx.doi.org/10.3390/act10020027.
Texto completoPREETHAM S, M C CHANDRASHEKHAR y M Z KURIAN. "METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING". international journal of engineering technology and management sciences 7, n.º 3 (2023): 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.
Texto completoKurniawan, Robi y Shunsuke Managi. "Forecasting annual energy consumption using machine learnings: Case of Indonesia". IOP Conference Series: Earth and Environmental Science 257 (10 de mayo de 2019): 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.
Texto completoSingh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal y 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.
Texto completoDas, Aditi. "Automatic Personality Identification using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (30 de junio de 2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.
Texto completoMalinda 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, n.º 11 (30 de noviembre de 2023): 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.
Texto completoSendak, 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, n.º 7 (15 de julio de 2020): e15182. http://dx.doi.org/10.2196/15182.
Texto completoUdomchaipitak, Tanatpong, Nathaphon Boonnam, Supattra Puttinaovarat y Paramate Horkaew. "Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data". International Journal of Design & Nature and Ecodynamics 17, n.º 3 (30 de junio de 2022): 423–31. http://dx.doi.org/10.18280/ijdne.170313.
Texto completoQian, Qingwen, Junfeng Wu y 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, n.º 01 (29 de marzo de 2019): 1941002. http://dx.doi.org/10.1142/s0219691319410029.
Texto completoKokozinski, Andre, Christian Kubik y 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, n.º 10 (2022): 696–700. http://dx.doi.org/10.37544/1436-4980-2022-10-66.
Texto completoAsari, Yusuke. "SM-3 Noise Reduction Method Based on Machine Learnings for Electron Holography". Microscopy 68, Supplement_1 (1 de noviembre de 2019): i7. http://dx.doi.org/10.1093/jmicro/dfz056.
Texto completoZhang, Evan. "Treating COVID-19 with machine learning". Applied and Computational Engineering 30, n.º 1 (22 de enero de 2024): 1–11. http://dx.doi.org/10.54254/2755-2721/30/20230202.
Texto completoTang, Muran, Lingyue Gao, Yutong Bian, Shang Xiang y Kaijun Zhang. "Brain tumor MRI images classification based on machine learning". Applied and Computational Engineering 29, n.º 1 (26 de diciembre de 2023): 19–29. http://dx.doi.org/10.54254/2755-2721/29/20230765.
Texto completoJin, Xiangyu, Luya Wei y Qihua Zhang. "The Stock Price Prediction Based on Time Series Model, Multifactorial Regression, Machine Learnings". BCP Business & Management 23 (4 de agosto de 2022): 903–9. http://dx.doi.org/10.54691/bcpbm.v23i.1471.
Texto completoZhai, Weiguang, Changchun Li, Qian Cheng, Bohan Mao, Zongpeng Li, Yafeng Li, Fan Ding, Siqing Qin, Shuaipeng Fei y 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, n.º 14 (21 de julio de 2023): 3653. http://dx.doi.org/10.3390/rs15143653.
Texto completoHwang, Gyuyeong, Taehun Kim, Juyong Shin, Naechul Shin y Sungwon Hwang. "Machine learnings for CVD graphene analysis: From measurement to simulation of SEM images". Journal of Industrial and Engineering Chemistry 101 (septiembre de 2021): 430–44. http://dx.doi.org/10.1016/j.jiec.2021.05.031.
Texto completoKim, Gyeung Min. "Analysis for Factors Determining the Price of Multi-family Housing through Machine Learnings". Residential Environment Institute Of Korea 14, n.º 3 (30 de junio de 2016): 29–40. http://dx.doi.org/10.22313/reik.2016.14.3.29.
Texto completoNikam, 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, n.º 2 (15 de junio de 2023): 319–39. http://dx.doi.org/10.15175/1984-2503-202315207.
Texto completoKang, In-Ae, Soualihou Ngnamsie Njimbouom, Kyung-Oh Lee y Jeong-Dong Kim. "DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine". Applied Sciences 12, n.º 6 (16 de marzo de 2022): 3043. http://dx.doi.org/10.3390/app12063043.
Texto completoChao, Paul C. P., Chih-Cheng Wu, Duc Huy Nguyen, Ba-Sy Nguyen, Pin-Chia Huang y Van-Hung Le. "The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage". IEEE Sensors Journal 21, n.º 11 (1 de junio de 2021): 12498–510. http://dx.doi.org/10.1109/jsen.2021.3073850.
Texto completoHasan, Md Mahadi, Saba Binte Murtaz, Muhammad Usama Islam, Muhammad Jafar Sadeq y Jasim Uddin. "Robust and efficient COVID-19 detection techniques: A machine learning approach". PLOS ONE 17, n.º 9 (15 de septiembre de 2022): e0274538. http://dx.doi.org/10.1371/journal.pone.0274538.
Texto completoGanie, Shahid Mohammad, Pijush Kanti Dutta Pramanik, Saurav Mallik y Zhongming Zhao. "Chronic kidney disease prediction using boosting techniques based on clinical parameters". PLOS ONE 18, n.º 12 (1 de diciembre de 2023): e0295234. http://dx.doi.org/10.1371/journal.pone.0295234.
Texto completoKumar, Yogesh. "The Fellow Traveller: A Machine Learning Approach to Travel Management". International Journal for Research in Applied Science and Engineering Technology 10, n.º 4 (30 de abril de 2022): 1798–802. http://dx.doi.org/10.22214/ijraset.2022.41613.
Texto completoM. Brandao, Iago y Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING". Eletrônica de Potência 27, n.º 03 (22 de septiembre de 2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.
Texto completoXue, Yang, Mariela Araujo, Jorge Lopez, Kanglin Wang y Gautam Kumar. "Machine learning to reduce cycle time for time-lapse seismic data assimilation into reservoir management". Interpretation 7, n.º 3 (1 de agosto de 2019): SE123—SE130. http://dx.doi.org/10.1190/int-2018-0206.1.
Texto completoBile, Alessandro, Hamed Tari y Eugenio Fazio. "Episodic Memory and Information Recognition Using Solitonic Neural Networks Based on Photorefractive Plasticity". Applied Sciences 12, n.º 11 (31 de mayo de 2022): 5585. http://dx.doi.org/10.3390/app12115585.
Texto completoZhou, Wangbao, Lijun Xiong, Lizhong Jiang, Lingxu Wu, Ping Xiang y Liqiang Jiang. "Optimal combinations of parameters for seismic response prediction of high-speed railway bridges using machine learnings". Structures 57 (noviembre de 2023): 105089. http://dx.doi.org/10.1016/j.istruc.2023.105089.
Texto completoLatif, Sarmad Dashti, Vivien Lai, Farah Hazwani Hahzaman, Ali Najah Ahmed, Yuk Feng Huang, Ahmed H. Birima y Ahmed El-Shafie. "Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia". Results in Engineering 21 (marzo de 2024): 101872. http://dx.doi.org/10.1016/j.rineng.2024.101872.
Texto completoAnam, Khairul, Harun Ismail, Faruq Sandi Hanggara, Cries Avian, Safri Nahela y 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, n.º 5 (30 de diciembre de 2023): 587–99. http://dx.doi.org/10.5614/j.eng.technol.sci.2023.55.5.8.
Texto completoSabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang y Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning". International Journal of Trade, Economics and Finance 11, n.º 6 (diciembre de 2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.
Texto completoChen, JueYu. "Identification and analysis of real and fake news by XGBoost algorithm of machine learning". Applied and Computational Engineering 40, n.º 1 (21 de febrero de 2024): 255–62. http://dx.doi.org/10.54254/2755-2721/40/20230661.
Texto completoAqil, 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 de abril de 2022): 1–15. http://dx.doi.org/10.1155/2022/6588949.
Texto completoAqil, 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 de abril de 2022): 1–15. http://dx.doi.org/10.1155/2022/6588949.
Texto completoJin, Yu, Zhe Ren, Wenjie Wang, Yulei Zhang, Liang Zhou, Xufeng Yao y Tao Wu. "Classification of Alzheimer's disease using robust TabNet neural networks on genetic data". Mathematical Biosciences and Engineering 20, n.º 5 (2023): 8358–74. http://dx.doi.org/10.3934/mbe.2023366.
Texto completoSong, Yiyan, Shaowei Gao, Wulin Tan, Zeting Qiu, Huaqiang Zhou y 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, n.º 21 (2018): 3971–78. http://dx.doi.org/10.7150/jca.26649.
Texto completoPuttinaovarat, Supattra y 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, n.º 4 (25 de junio de 2019): 429–46. http://dx.doi.org/10.1007/s12145-019-00387-y.
Texto completoAhmed Taialla, Omer, Umar Mustapha, Abdul Hakam Shafiu Abdullahi, Esraa Kotob, Mohammed Mosaad Awad, Aliyu Musa Alhassan, Ijaz Hussain, Khalid Omer, Saheed A. Ganiyu y 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 (abril de 2024): 215669. http://dx.doi.org/10.1016/j.ccr.2024.215669.
Texto completoBahrawi, Nfn. "Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based". Journal of Information Technology and Its Utilization 2, n.º 2 (19 de diciembre de 2019): 29. http://dx.doi.org/10.30818/jitu.2.2.2695.
Texto completoJain, Vanita, Monu Gupta, Neeraj Joshi, Anubhav Mishra y Vishakha Bansal. "E-College : an aid for E-Learning systems". Fusion: Practice and Applications 3, n.º 2 (2021): 66–72. http://dx.doi.org/10.54216/fpa.030202.
Texto completoXu, Pufan, Fei Li y Haipeng Wang. "A novel concatenate feature fusion RCNN architecture for sEMG-based hand gesture recognition". PLOS ONE 17, n.º 1 (20 de enero de 2022): e0262810. http://dx.doi.org/10.1371/journal.pone.0262810.
Texto completoNaeini, Ehsan Zabihi y Kenton Prindle. "Machine learning and learning from machines". Leading Edge 37, n.º 12 (diciembre de 2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.
Texto completoZhang, Shenghan, Yufeng Gu, Yinshan Gao, Xinxing Wang, Daoyong Zhang y 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 (5 de octubre de 2022): 1–31. http://dx.doi.org/10.1155/2022/9443955.
Texto completoTurner, A., J. Fyfe, P. Rickwood y S. Mohr. "Evaluation of implemented Australian efficiency programs: results, techniques and insights". Water Supply 14, n.º 6 (10 de julio de 2014): 1112–23. http://dx.doi.org/10.2166/ws.2014.065.
Texto completoS.Sureshkumar, Et al. "Neural Network-Based Multiplicatively Gait Feature Eradication and Detection". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 4 (30 de abril de 2023): 375–79. http://dx.doi.org/10.17762/ijritcc.v11i4.9843.
Texto completoTrott, David. "Deceiving Machines: Sabotaging Machine Learning". CHANCE 33, n.º 2 (2 de abril de 2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.
Texto completoBonnevie, Erika, Jennifer Sittig y Joe Smyser. "The case for tracking misinformation the way we track disease". Big Data & Society 8, n.º 1 (enero de 2021): 205395172110138. http://dx.doi.org/10.1177/20539517211013867.
Texto completoSilva Pereira, Fernando. "A prova resultante de “software de aprendizagem automática”". Revista Electrónica de Direito 23, n.º 3 (octubre de 2020): 79–98. http://dx.doi.org/10.24840/2182-9845_2020-0003_0006.
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