Artigos de revistas sobre o tema "Potentiel machine learning"
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Ben Zid, Afef, Asma Najjar e Imen Hamrouni. "Classification automatique d’emprises au sol de maisons dites « andalouses » à l’aide de modèle de Machine Learning". SHS Web of Conferences 203 (2024): 02001. http://dx.doi.org/10.1051/shsconf/202420302001.
Texto completo da fonteBOUKHELEF, Faiza. "Investigating Students’ Attitudes Towards Integrating Machine Translation in the EFL Classroom: The case of Google Translate". Langues & Cultures 5, n.º 01 (30 de junho de 2024): 264–77. http://dx.doi.org/10.62339/jlc.v5i01.243.
Texto completo da fonteNg, Wenfa. "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization". Biotechnology and Bioprocessing 2, n.º 9 (2 de novembro de 2021): 01–07. http://dx.doi.org/10.31579/2766-2314/060.
Texto completo da fonteDatta, Debaleena, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi e Jaeyoung Choi. "Hyperspectral Image Classification: Potentials, Challenges, and Future Directions". Computational Intelligence and Neuroscience 2022 (28 de abril de 2022): 1–36. http://dx.doi.org/10.1155/2022/3854635.
Texto completo da fonteSrinivasaiah, Bharath. "The Power of Personalized Healthcare: Harnessing the Potential of Machine Learning in Precision Medicine". International Journal of Science and Research (IJSR) 13, n.º 5 (5 de maio de 2024): 426–29. http://dx.doi.org/10.21275/sr24506012313.
Texto completo da fonteKamoun-Abid, Ferdaous, Hounaida Frikha, Amel Meddeb-Makhoulf e Faouzi Zarai. "Automating cloud virtual machines allocation via machine learning". Indonesian Journal of Electrical Engineering and Computer Science 35, n.º 1 (1 de julho de 2024): 191. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp191-202.
Texto completo da fonteShoureshi, R., D. Swedes e R. Evans. "Learning Control for Autonomous Machines". Robotica 9, n.º 2 (abril de 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.
Texto completo da fonteAschepkov, Valeriy. "METHODS OF MACHINE LEARNING IN MODERN METROLOGY". Measuring Equipment and Metrology 85 (2024): 57–60. http://dx.doi.org/10.23939/istcmtm2024.01.057.
Texto completo da fonteLevantesi, Susanna, Andrea Nigri e Gabriella Piscopo. "Longevity risk management through Machine Learning: state of the art". Insurance Markets and Companies 11, n.º 1 (25 de novembro de 2020): 11–20. http://dx.doi.org/10.21511/ins.11(1).2020.02.
Texto completo da fonteShak, Md Shujan, Aftab Uddin, Md Habibur Rahman, Nafis Anjum, Md Nad Vi Al Bony, Murshida Alam, Mohammad Helal, Afrina Khan, Pritom Das e Tamanna Pervin. "INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE". International Interdisciplinary Business Economics Advancement Journal 05, n.º 11 (6 de novembro de 2024): 6–20. http://dx.doi.org/10.55640/business/volume05issue11-02.
Texto completo da fonteHossain, Nur, Nafis Anjum, Murshida Alam, Md Habibur Rahman, Md Siam Taluckder, Md Nad Vi Al Bony, S. M. Shadul Islam Rishad e Afrin Hoque Jui. "PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR LUNG CANCER PREDICTION: A COMPARATIVE STUDY". International Journal of Medical Science and Public Health Research 05, n.º 11 (14 de novembro de 2024): 41–55. http://dx.doi.org/10.37547/ijmsphr/volume05issue11-05.
Texto completo da fonteLyu, Nian. "The prospect and metaphysical analysis of conscious artificial intelligence". Applied and Computational Engineering 77, n.º 1 (16 de julho de 2024): 32–36. http://dx.doi.org/10.54254/2755-2721/77/20240632.
Texto completo da fonteKeneskyzy, K., e S. B. Yeskermes. "Метод машинного обучения для обратных задач теплопроводности". INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, n.º 1(5) (26 de março de 2021): 59–64. http://dx.doi.org/10.54309/ijict.2021.05.1.008.
Texto completo da fonteYang, Yinuo, Shuhao Zhang, Kavindri D. Ranasinghe, Olexandr Isayev e Adrian E. Roitberg. "Machine Learning of Reactive Potentials". Annual Review of Physical Chemistry 75, n.º 1 (28 de junho de 2024): 371–95. http://dx.doi.org/10.1146/annurev-physchem-062123-024417.
Texto completo da fonteShi, Yang. "Research on the Stock Price Prediction Using Machine Learning". Advances in Economics, Management and Political Sciences 22, n.º 1 (13 de setembro de 2023): 174–79. http://dx.doi.org/10.54254/2754-1169/22/20230307.
Texto completo da fonteMueller, Tim, Alberto Hernandez e Chuhong Wang. "Machine learning for interatomic potential models". Journal of Chemical Physics 152, n.º 5 (7 de fevereiro de 2020): 050902. http://dx.doi.org/10.1063/1.5126336.
Texto completo da fonteShih, David, Matthew R. Buckley, Lina Necib e John Tamanas. "via machinae: Searching for stellar streams using unsupervised machine learning". Monthly Notices of the Royal Astronomical Society 509, n.º 4 (24 de novembro de 2021): 5992–6007. http://dx.doi.org/10.1093/mnras/stab3372.
Texto completo da fonteSamahitha Kaliyuru Ravi, Sameera Kaliyuru Ravi e A. Hema Prabha. "Advent of machine learning in autonomous vehicles". International Journal of Science and Research Archive 13, n.º 1 (30 de setembro de 2024): 1219–26. http://dx.doi.org/10.30574/ijsra.2024.13.1.1760.
Texto completo da fonteAbro, Safdar Ali, Lyu Guang Hua, Javed Ahmed Laghari, Muhammad Akram Bhayo e Abdul Aziz Memon. "Machine learning-based electricity theft detection using support vector machines". International Journal of Electrical and Computer Engineering (IJECE) 14, n.º 2 (1 de abril de 2024): 1240. http://dx.doi.org/10.11591/ijece.v14i2.pp1240-1250.
Texto completo da fonteRamesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat e Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning". E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.
Texto completo da fonteNagaraju, Dr R. "XSS Attack Detection using Machine Learning Algorithms". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, n.º 12 (1 de dezembro de 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27487.
Texto completo da fonteKayathri, K., e Dr K. Kavitha. "CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification". International Journal of Electrical and Electronics Research 12, n.º 2 (28 de junho de 2024): 669–81. http://dx.doi.org/10.37391/ijeer.120245.
Texto completo da fonteLi, Keqin, Peng Zhao, Shuying Dai, Armando Zhu, Bo Hong, Jiabei Liu, Changsong Wei, Wenqian Huang e Yang Zhang. "Exploring the Impact of Quantum Computing on Machine Learning Performance". Middle East Journal of Applied Science & Technology 07, n.º 02 (2024): 145–61. http://dx.doi.org/10.46431/mejast.2024.7215.
Texto completo da fonteChinnala Balakrishna e Rambabu Bommisetti. "Detecting psychological uncertainty using machine learning". International Journal of Science and Research Archive 12, n.º 2 (30 de julho de 2024): 1365–70. http://dx.doi.org/10.30574/ijsra.2024.12.2.1399.
Texto completo da fonteNivas, K., M. Rajesh Kumar, G. Suresh, T. Ramaswamy e Yerraboina Sreenivasulu. "Facial Emotion Detection Using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 1 (31 de janeiro de 2023): 427–33. http://dx.doi.org/10.22214/ijraset.2023.48585.
Texto completo da fonteWilliam, Carter, Choki Wangmo e Anjali Ranjan. "Unravelling the application of machine learning in cancer biomarker discovery". Cancer Insight 2, n.º 1 (14 de junho de 2023): 1–8. http://dx.doi.org/10.58567/ci02010001.
Texto completo da fontePatil, Rohit, Priyadarshani Alandikar, Vaibhav Chaudhari, Pradnya Patil e Prof Swarupa Deshpande. "Water Demand Prediction Using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 12 (31 de dezembro de 2022): 122–28. http://dx.doi.org/10.22214/ijraset.2022.47797.
Texto completo da fonteAkrom, Muhamad. "Quantum Support Vector Machine for Classification Task: A Review". Journal of Multiscale Materials Informatics 1, n.º 2 (5 de julho de 2024): 1–8. http://dx.doi.org/10.62411/jimat.v1i2.10965.
Texto completo da fontePatil, Bhagyashree A., Sri Adithya S e Dr Jayanthi M G. "Detection of Malware using Machine Learning Approach". International Journal for Research in Applied Science and Engineering Technology 11, n.º 8 (31 de agosto de 2023): 736–41. http://dx.doi.org/10.22214/ijraset.2023.55233.
Texto completo da fonteSahoo, Abhilipsa, e Kaushika Patel. "Machine Learning-based Inverse Design Model of a Transistor". Indian Journal Of Science And Technology 17, n.º 7 (15 de fevereiro de 2024): 617–24. http://dx.doi.org/10.17485/ijst/v17i7.3076.
Texto completo da fonteTiffin, Paul A., e Lewis W. Paton. "Rise of the machines? Machine learning approaches and mental health: opportunities and challenges". British Journal of Psychiatry 213, n.º 3 (16 de agosto de 2018): 509–10. http://dx.doi.org/10.1192/bjp.2018.105.
Texto completo da fonteChoudhary, Laxmi, e Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence". Journal of Scientific Research and Reports 30, n.º 11 (16 de novembro de 2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.
Texto completo da fontePei, Jun, Lin Frank Song e Kenneth M. Merz. "Pair Potentials as Machine Learning Features". Journal of Chemical Theory and Computation 16, n.º 8 (19 de junho de 2020): 5385–400. http://dx.doi.org/10.1021/acs.jctc.9b01246.
Texto completo da fonteKobayashi, Keita, Hiroki Nakamura, Akiko Yamaguchi, Mitsuhiro Itakura, Masahiko Machida e Masahiko Okumura. "Machine learning potentials for tobermorite minerals". Computational Materials Science 188 (fevereiro de 2021): 110173. http://dx.doi.org/10.1016/j.commatsci.2020.110173.
Texto completo da fonteBarbour, Dennis L., e Jan-Willem A. Wasmann. "Performance and Potential of Machine Learning Audiometry". Hearing Journal 74, n.º 3 (26 de fevereiro de 2021): 40,43,44. http://dx.doi.org/10.1097/01.hj.0000737592.24476.88.
Texto completo da fonteTherrien, Audrey C., Berthié Gouin-Ferland e Mohammad Mehdi Rahimifar. "Potential of edge machine learning for instrumentation". Applied Optics 61, n.º 8 (2 de março de 2022): 1930. http://dx.doi.org/10.1364/ao.445798.
Texto completo da fonteAwan, Kamran H., S. Satish Kumar e Indu Bharkavi SK. "Potential Role of Machine Learning in Oncology". Journal of Contemporary Dental Practice 20, n.º 5 (2019): 529–30. http://dx.doi.org/10.5005/jp-journals-10024-2551.
Texto completo da fonteDral, Pavlo O., Alec Owens, Alexey Dral e Gábor Csányi. "Hierarchical machine learning of potential energy surfaces". Journal of Chemical Physics 152, n.º 20 (29 de maio de 2020): 204110. http://dx.doi.org/10.1063/5.0006498.
Texto completo da fonteWu, Yuexiang. "Potential pulsars prediction based on machine learning". Theoretical and Natural Science 12, n.º 1 (17 de novembro de 2023): 193–201. http://dx.doi.org/10.54254/2753-8818/12/20230466.
Texto completo da fonteZhou, Ziyun, Jingwei Shang e Yimang Li. "Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation". Electronics 12, n.º 17 (1 de setembro de 2023): 3700. http://dx.doi.org/10.3390/electronics12173700.
Texto completo da fonteLi, Jiarui. "Evaluative Comparison of Machine Learning Algorithms for Precision Diagnosis in Breast Cancer". Highlights in Science, Engineering and Technology 85 (13 de março de 2024): 354–62. http://dx.doi.org/10.54097/40fmfw48.
Texto completo da fonteØsterlund, Carsten, Kevin Crowston, Corey B. Jackson, Yunan Wu, Alexander O. Smith e Aggelos K. Katsaggelos. "Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy". Citizen Science: Theory and Practice 9, n.º 1 (9 de dezembro de 2024): 42. https://doi.org/10.5334/cstp.738.
Texto completo da fonteM, Senthil Raja, Arun Raj L e Arun A. "Detection of Depression among Social Media Users with Machine Learning". Webology 19, n.º 1 (20 de janeiro de 2022): 250–57. http://dx.doi.org/10.14704/web/v19i1/web19019.
Texto completo da fonteD. Nageswara Rao. "Predictive Modeling of Breast Cancer Outcomes Using Supervised Machine Learning Algorithms". International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, n.º 4 (15 de agosto de 2024): 258–66. http://dx.doi.org/10.32628/cseit2410416.
Texto completo da fonteSilva, Nuno A., Vicente Rocha e Tiago D. Ferreira. "Optical Extreme Learning Machines with Atomic Vapors". Atoms 12, n.º 2 (6 de fevereiro de 2024): 10. http://dx.doi.org/10.3390/atoms12020010.
Texto completo da fonteSumathi, P., Arun Kumar S e Balaji A. "Healthcare - Autism Predicting Tool Using Data Science / AI / ML". International Journal for Research in Applied Science and Engineering Technology 12, n.º 5 (31 de maio de 2024): 440–43. http://dx.doi.org/10.22214/ijraset.2024.60421.
Texto completo da fonteHossain, Md Shakhaowat, S. M. Shadul Islam Rishad, Md Mohibur Rahman, Sanjida Akter Tisha, Farhan Shakil, Ashim Chandra Das, Radha Das e Sadia Sultana. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS". International journal of networks and security 04, n.º 01 (22 de novembro de 2024): 22–32. http://dx.doi.org/10.55640/ijns-04-01-06.
Texto completo da fonteGittler, Thomas, Stephan Scholze, Alisa Rupenyan e Konrad Wegener. "Machine Tool Component Health Identification with Unsupervised Learning". Journal of Manufacturing and Materials Processing 4, n.º 3 (2 de setembro de 2020): 86. http://dx.doi.org/10.3390/jmmp4030086.
Texto completo da fonteAdewusi, Michael Adelani, Adeshina Wasiu Adebanjo, Tokunbo Odekeye e Sophia Kazibwe. "Rise of the Machines: Exploring the Emergence of Machine Consciousness". European Journal of Theoretical and Applied Sciences 2, n.º 4 (1 de julho de 2024): 563–73. http://dx.doi.org/10.59324/ejtas.2024.2(4).48.
Texto completo da fonteHidayat, Taufik, Kalamullah Ramli, Nadia Thereza, Amarudin Daulay, Rushendra Rushendra e Rahutomo Mahardiko. "Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement". Informatics 11, n.º 3 (19 de julho de 2024): 50. http://dx.doi.org/10.3390/informatics11030050.
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