Artykuły w czasopismach na temat „PREDICTION MODELS APPLICATIONS”
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Chung, Chang-Jo. "Spatial Prediction Models and Applications." GEOINFORMATICS 12, nr 2 (2001): 58–59. http://dx.doi.org/10.6010/geoinformatics.12.58.
Pełny tekst źródłaDammann, Maximilian Peter, Wolfgang Steger i Kristin Paetzold-Byhain. "OPTIMISED MODELS FOR AR/VR BY USING GEOMETRIC COMPLEXITY METRICS TO CONTROL TESSELLATION". Proceedings of the Design Society 3 (19.06.2023): 2855–64. http://dx.doi.org/10.1017/pds.2023.286.
Pełny tekst źródłaLei, Xiangdong, Changhui Peng, Haiyan Wang i Xiaolu Zhou. "Individual height–diameter models for young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations in New Brunswick, Canada". Forestry Chronicle 85, nr 1 (1.01.2009): 43–56. http://dx.doi.org/10.5558/tfc85043-1.
Pełny tekst źródłaPintelas, Emmanuel, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis i Panagiotis Pintelas. "Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction". Journal of Imaging 6, nr 6 (28.05.2020): 37. http://dx.doi.org/10.3390/jimaging6060037.
Pełny tekst źródłaMoskolaï, Waytehad Rose, Wahabou Abdou, Albert Dipanda i Kolyang. "Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review". Remote Sensing 13, nr 23 (27.11.2021): 4822. http://dx.doi.org/10.3390/rs13234822.
Pełny tekst źródłaKim, Donghyun, Heechan Han, Wonjoon Wang, Yujin Kang, Hoyong Lee i Hung Soo Kim. "Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction". Applied Sciences 12, nr 13 (1.07.2022): 6699. http://dx.doi.org/10.3390/app12136699.
Pełny tekst źródłaColditz, Graham A., i Esther K. Wei. "Risk Prediction Models: Applications in Cancer Prevention". Current Epidemiology Reports 2, nr 4 (30.09.2015): 245–50. http://dx.doi.org/10.1007/s40471-015-0057-1.
Pełny tekst źródłaHe, Jianqin, Yong Hu, Xiangzhou Zhang, Lijuan Wu, Lemuel R. Waitman i Mei Liu. "Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records". JAMIA Open 2, nr 1 (15.11.2018): 115–22. http://dx.doi.org/10.1093/jamiaopen/ooy043.
Pełny tekst źródłaHong, Feng, Lu Tian i Viswanath Devanarayan. "Improving the Robustness of Variable Selection and Predictive Performance of Regularized Generalized Linear Models and Cox Proportional Hazard Models". Mathematics 11, nr 3 (20.01.2023): 557. http://dx.doi.org/10.3390/math11030557.
Pełny tekst źródłaZhao, Zeyuan, Ping Li, Yongjie Dai, Zhaoe Min i Lei Chen. "Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction". Applied Sciences 13, nr 15 (3.08.2023): 8953. http://dx.doi.org/10.3390/app13158953.
Pełny tekst źródłaDaigger, Glen T., i Daniel Nolasco. "Evaluation and design of full-scale wastewater treatment plants using biological process models". Water Science and Technology 31, nr 2 (1.01.1995): 245–55. http://dx.doi.org/10.2166/wst.1995.0112.
Pełny tekst źródłaAbdullah, Radhwan M., Abedallah Zaid Abualkishik, Najla Matti Isaacc, Ali A. Alwan i Yonis Gulzar. "An investigation study for risk calculation of security vulnerabilities on android applications". Indonesian Journal of Electrical Engineering and Computer Science 25, nr 3 (1.03.2022): 1736. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1736-1748.
Pełny tekst źródłaMohammed, Mohammed Ali. "Investigation of financial applications with blockchain technology". Journal of Computer & Electrical and Electronics Engineering Sciences 1, nr 1 (28.04.2023): 10–14. http://dx.doi.org/10.51271/jceees-0003.
Pełny tekst źródłaMatsuzaka, Yasunari, i Yoshihiro Uesawa. "Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning". Processes 11, nr 4 (21.04.2023): 1296. http://dx.doi.org/10.3390/pr11041296.
Pełny tekst źródłaHasaballah, Mustafa M., Abdulhakim A. Al-Babtain, Md Moyazzem Hossain i Mahmoud E. Bakr. "Theoretical Aspects for Bayesian Predictions Based on Three-Parameter Burr-XII Distribution and Its Applications in Climatic Data". Symmetry 15, nr 8 (7.08.2023): 1552. http://dx.doi.org/10.3390/sym15081552.
Pełny tekst źródłaLan, Yu, i Daniel F. Heitjan. "Adaptive parametric prediction of event times in clinical trials". Clinical Trials 15, nr 2 (29.01.2018): 159–68. http://dx.doi.org/10.1177/1740774517750633.
Pełny tekst źródłaElish, Mahmoud. "Enhanced prediction of vulnerable Web components using Stochastic Gradient Boosting Trees". International Journal of Web Information Systems 15, nr 2 (17.06.2019): 201–14. http://dx.doi.org/10.1108/ijwis-05-2018-0041.
Pełny tekst źródłaMehdipour, Farhad, Wisanu Boonrat, April Naviza, Vimita Vidhya i Marianne Cherrington. "Reducing profiling bias in crime risk prediction models". Rere Āwhio - The Journal of Applied Research and Practice, nr 1 (2021): 86–93. http://dx.doi.org/10.34074/rere.00108.
Pełny tekst źródłaWang, Debby D., Haoran Xie i Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity". Bioinformatics 37, nr 17 (27.02.2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.
Pełny tekst źródłaLekea, Angella, i Wynand J. vdM Steyn. "Performance of Pavement Temperature Prediction Models". Applied Sciences 13, nr 7 (24.03.2023): 4164. http://dx.doi.org/10.3390/app13074164.
Pełny tekst źródłaKolaghassi, Rania, Gianluca Marcelli i Konstantinos Sirlantzis. "Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications". Sensors 23, nr 12 (18.06.2023): 5687. http://dx.doi.org/10.3390/s23125687.
Pełny tekst źródłaBrüdigam, Tim, Johannes Teutsch, Dirk Wollherr, Marion Leibold i Martin Buss. "Probabilistic model predictive control for extended prediction horizons". at - Automatisierungstechnik 69, nr 9 (1.09.2021): 759–70. http://dx.doi.org/10.1515/auto-2021-0025.
Pełny tekst źródłaJiang, Zhe. "Spatial Structured Prediction Models: Applications, Challenges, and Techniques". IEEE Access 8 (2020): 38714–27. http://dx.doi.org/10.1109/access.2020.2975584.
Pełny tekst źródłaLiski, Erkki P., i Tapio Nummi. "Prediction in Repeated-Measures Models With Engineering Applications". Technometrics 38, nr 1 (luty 1996): 25–36. http://dx.doi.org/10.1080/00401706.1996.10484413.
Pełny tekst źródłaHalim, Muhammad, Muslihah Wook, Nor Hasbullah, Noor Razali i Hasmeda Hamid. "Comparative Assessment of Data Mining Techniques for Flash Flood Prediction". International Journal of Advances in Soft Computing and its Applications 14, nr 1 (28.03.2022): 126–45. http://dx.doi.org/10.15849/ijasca.220328.09.
Pełny tekst źródłaChe, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong i Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 8 (18.05.2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.
Pełny tekst źródłaYu, Jiaqi, Wen-Shao Chang i Yu Dong. "Building Energy Prediction Models and Related Uncertainties: A Review". Buildings 12, nr 8 (21.08.2022): 1284. http://dx.doi.org/10.3390/buildings12081284.
Pełny tekst źródłaJekabsons, Gints, i Marina Uhanova. "Adaptive Regression and Classification Models with Applications in Insurance". Applied Computer Systems 15, nr 1 (1.07.2014): 28–31. http://dx.doi.org/10.2478/acss-2014-0004.
Pełny tekst źródłaStaffa, Steven J., i David Zurakowski. "Statistical Development and Validation of Clinical Prediction Models". Anesthesiology 135, nr 3 (30.07.2021): 396–405. http://dx.doi.org/10.1097/aln.0000000000003871.
Pełny tekst źródłaAlqahtani, Norah Dhafer, Bander Alzahrani i Muhammad Sher Ramzan. "Deep Learning Applications for Dyslexia Prediction". Applied Sciences 13, nr 5 (22.02.2023): 2804. http://dx.doi.org/10.3390/app13052804.
Pełny tekst źródłaLoukili, Manal. "Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization". International Journal of Advances in Soft Computing and its Applications 14, nr 3 (28.11.2022): 50–63. http://dx.doi.org/10.15849/ijasca.221128.04.
Pełny tekst źródłaNegi, Pankaj. "Application of Machine Learning in Predicting the Fatigue behaviour of Materials Using Deep Learning". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, nr 2 (30.12.2018): 541–53. http://dx.doi.org/10.17762/turcomat.v9i2.13858.
Pełny tekst źródłaBrunbauer, Julia, i Gerald Pinter. "Stiffness and Strength Based Models for the Fatigue-Life Prediction of Continuously Fiber Reinforced Composites". Materials Science Forum 825-826 (lipiec 2015): 960–67. http://dx.doi.org/10.4028/www.scientific.net/msf.825-826.960.
Pełny tekst źródłaBart, Evgeniy, Rui Zhang i Muzammil Hussain. "Where Would You Go this Weekend? Time-Dependent Prediction of User Activity Using Social Network Data". Proceedings of the International AAAI Conference on Web and Social Media 7, nr 1 (3.08.2021): 669–72. http://dx.doi.org/10.1609/icwsm.v7i1.14453.
Pełny tekst źródłade Zarzà, I., J. de Curtò, Enrique Hernández-Orallo i Carlos T. Calafate. "Cascading and Ensemble Techniques in Deep Learning". Electronics 12, nr 15 (5.08.2023): 3354. http://dx.doi.org/10.3390/electronics12153354.
Pełny tekst źródłaSCHOPF, JENNIFER M., i FRANCINE BERMAN. "USING STOCHASTIC INFORMATION TO PREDICT APPLICATION BEHAVIOR ON CONTENDED RESOURCES". International Journal of Foundations of Computer Science 12, nr 03 (czerwiec 2001): 341–63. http://dx.doi.org/10.1142/s0129054101000527.
Pełny tekst źródłaMyasnikova, Ekaterina, i Alexander Spirov. "Relative sensitivity analysis of the predictive properties of sloppy models". Journal of Bioinformatics and Computational Biology 16, nr 02 (kwiecień 2018): 1840008. http://dx.doi.org/10.1142/s0219720018400085.
Pełny tekst źródłaXue, Han, i Yanmin Niu. "Multi-Output Based Hybrid Integrated Models for Student Performance Prediction". Applied Sciences 13, nr 9 (26.04.2023): 5384. http://dx.doi.org/10.3390/app13095384.
Pełny tekst źródłaChen, Meng-Wei, Meng-Shiuh Chang, Yuehua Mao, Shuyin Hu i Chih-Chun Kung. "Machine learning in the evaluation and prediction models of biochar application: A review". Science Progress 106, nr 1 (styczeń 2023): 003685042211488. http://dx.doi.org/10.1177/00368504221148842.
Pełny tekst źródłaMuneer, Rizwan, Muhammad Rehan Hashmet, Peyman Pourafshary i Mariam Shakeel. "Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning". Nanomaterials 13, nr 7 (29.03.2023): 1209. http://dx.doi.org/10.3390/nano13071209.
Pełny tekst źródłaLevinson, Rich, Samantha Niemoeller, Sreeja Nag i Vinay Ravindra. "Planning Satellite Swarm Measurements for Earth Science Models: Comparing Constraint Processing and MILP Methods". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13.06.2022): 471–79. http://dx.doi.org/10.1609/icaps.v32i1.19833.
Pełny tekst źródłade-Miguel, Sergio, Lauri Mehtätalo i Ali Durkaya. "Developing generalized, calibratable, mixed-effects meta-models for large-scale biomass prediction". Canadian Journal of Forest Research 44, nr 6 (czerwiec 2014): 648–56. http://dx.doi.org/10.1139/cjfr-2013-0385.
Pełny tekst źródłaMa, Xin. "Research on a Novel Kernel Based Grey Prediction Model and Its Applications". Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/5471748.
Pełny tekst źródłaAnand, Mayank, Arun Velu i Pawan Whig. "Prediction of Loan Behaviour with Machine Learning Models for Secure Banking". Journal of Computer Science and Engineering (JCSE) 3, nr 1 (15.02.2022): 1–13. http://dx.doi.org/10.36596/jcse.v3i1.237.
Pełny tekst źródłaBecker, Steffen, i Vishy Karri. "Implementation of Neural Network Models for Parameter Estimation of a PEM-Electrolyzer". Journal of Advanced Computational Intelligence and Intelligent Informatics 14, nr 6 (20.09.2010): 735–45. http://dx.doi.org/10.20965/jaciii.2010.p0735.
Pełny tekst źródłaGao, Jian, i Tang-Wei Kuo. "Toward the accurate prediction of soot in engine applications". International Journal of Engine Research 20, nr 7 (14.05.2018): 706–17. http://dx.doi.org/10.1177/1468087418773937.
Pełny tekst źródłaHabib, M. A., J. J. O’Sullivan i M. Salauddin. "Prediction of Wave Overtopping Characteristics at Coastal Flood Defences Using Machine Learning Algorithms: A Systematic Rreview". IOP Conference Series: Earth and Environmental Science 1072, nr 1 (1.09.2022): 012003. http://dx.doi.org/10.1088/1755-1315/1072/1/012003.
Pełny tekst źródłaMoreira, Gabriel S., Heeseung Jo i Jinkyu Jeong. "NAP: Natural App Processing for Predictive User Contexts in Mobile Smartphones". Applied Sciences 10, nr 19 (23.09.2020): 6657. http://dx.doi.org/10.3390/app10196657.
Pełny tekst źródłaRashid, M., i Jafri Din. "Effects of reduction factor on rain attenuation predictions over millimeter-wave links for 5G applications". Bulletin of Electrical Engineering and Informatics 9, nr 5 (1.10.2020): 1907–15. http://dx.doi.org/10.11591/eei.v9i5.2188.
Pełny tekst źródłaTunthanathip, Thara, Sakchai Sae-heng, Thakul Oearsakul, Ittichai Sakarunchai, Anukoon Kaewborisutsakul i Chin Taweesomboonyat. "Machine learning applications for the prediction of surgical site infection in neurological operations". Neurosurgical Focus 47, nr 2 (sierpień 2019): E7. http://dx.doi.org/10.3171/2019.5.focus19241.
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