Academic literature on the topic 'Machine learning not elsewhere classified'
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Journal articles on the topic "Machine learning not elsewhere classified"
Yao, Hannah, Sina Rashidian, Xinyu Dong, Hongyi Duanmu, Richard N. Rosenthal, and Fusheng Wang. "Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach." Journal of Medical Internet Research 22, no. 11 (November 27, 2020): e15293. http://dx.doi.org/10.2196/15293.
Full textHassan, Md Shareful, Md Tariqul Islam, and Mohammad Amir Hossain Bhuiyan. "Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling." Journal of Hyperspectral Remote Sensing 11, no. 3 (December 20, 2021): 136. http://dx.doi.org/10.29150/2237-2202.2021.251959.
Full textZhang, Meiling, Miaojun Zhu, Qin Hu, Chun Li, Zhenhua Zhu, Yingyong Hou, Jing Xu, et al. "Genome-wide microRNA expression profiling in malignant pleural effusion to identify a ten-microRNA signature." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e23123-e23123. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e23123.
Full textChapman, Alec B., Kelly Peterson, Wathsala Widanagamaachchi, and Makoto M. Jones. "616. Predicting Misdiagnoses of Infectious Disease in Emergency Department Visits." Open Forum Infectious Diseases 8, Supplement_1 (November 1, 2021): S411. http://dx.doi.org/10.1093/ofid/ofab466.814.
Full textSpelda, Petr, and Vit Stritecky. "Human Induction in Machine Learning." ACM Computing Surveys 54, no. 3 (June 2021): 1–18. http://dx.doi.org/10.1145/3444691.
Full textCauvin, Bertrand, and Pierre Benning. "Machine Learning." International Journal of 3-D Information Modeling 6, no. 3 (July 2017): 1–16. http://dx.doi.org/10.4018/ij3dim.2017070101.
Full textRaji-Lawal, H. Y., A. O. Oloyede, O. Aiyeniko, P. E. Ishola, T. T. Ajagbe, and A. Abayomi-Alli. "ENSEMBLE OF MACHINE LEARNING CLASSIFIERS FOR SCHIZOPHRENIA DETECTION." Caleb International Journal of Development Studies 05, no. 02 (December 3, 2022): 386–405. http://dx.doi.org/10.26772/cijds-2022-05-02-20.
Full textYuyun, Yuyun. "KLASIFIKASI SURAT DIGITAL MENGGUNAKAN ALGORITMA MACHINE LEARNING." JURNAL IT 13, no. 2 (August 30, 2023): 66–71. http://dx.doi.org/10.37639/jti.v13i2.350.
Full textHiray, Prof S. R. "Book Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1981–83. http://dx.doi.org/10.22214/ijraset.2021.39658.
Full textKulkarni, Prasad, Suyash Karwande, Rhucha Keskar, Prashant Kale, and Sumitra Iyer. "Fake News Detection using Machine Learning." ITM Web of Conferences 40 (2021): 03003. http://dx.doi.org/10.1051/itmconf/20214003003.
Full textDissertations / Theses on the topic "Machine learning not elsewhere classified"
Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.
Full text(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.
Find full text(9189263), Shuo Han. "FLUORESCENCE MICROSCOPY IMAGES SEGMENTATION AND ANALYSIS USING MACHINE LEARNING." Thesis, 2020.
Find full textMicroscopy image analysis can provide substantial information for clinical study and understanding of the biological structure. Two-photon microscopy is a type of fluorescence microscopy that can visualize deep into tissue with near-infrared excitation light. Large 3D image volumes of complex subcellular are often produced, which calls for automatic image analysis techniques. Automatic methods that can obtain nuclei quantity in microscopy image volumes are needed for biomedical research and clinical diagnosis. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of two or morenuclei, and shape and size variances of the nuclei. In this thesis, a 3D nuclei counterusing two different generative adversarial networks (GAN) is proposed and evaluated.Synthetic data that resembles real microscopy image is generated with a GAN. The synthetic data is used to train another 3D GAN network that counts the number o fnuclei. Our approach is evaluated with respect to the number of ground truth nuclei and compared with common ways of counting used in the biological research.Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The evaluation of both networks shows that the proposed technique is successful for counting nuclei in 3D. Then, a 3D segmentation and classification method to segment and identify individual nuclei in fluorescence microscopy volumes without having ground truth volumes is introduced. Three dimensional synthetic data is generated using the Recycle-GAN with the Hausdorff distance loss introduced into preserve the shape of individual nuclei. Realistic microscopy image volumes with nuclei segmentation mask and nucleus boundary ground truth volumes are generated.A subsequent 3D CNN with a regularization term that discourage detection out of nucleus boundary is used to detect and segment nuclei. Nuclei boundary refinement is then performed to enhance nuclei segmentation. Experimental results on our rat kidney dataset show the proposed method is competitive with respect to several state-of-the-art methods. A Distributed and Networked Analysis of Volumetric Image Data(DINAVID) system is developed to enable remote analysis of microscopy images for biologists. There are two main functions integrated in the system, a 3D visualization tool and a remote computing tool for nuclei segmentation. The 3D visualization enables real-time rendering of large volumes of microscopy data. The segmentation tool provides fast inferencing of pre-trained deep learning models trained with 5 different types of microscopy data.
(5930189), Javier Ribera Prat. "Image-based Plant Phenotyping Using Machine Learning." Thesis, 2019.
Find full textThis thesis also examines the use of crowdsourcing information in video analytics. The large number of cameras deployed for public safety surveillance systems requires intelligent processing capable of automatically analyzing video in real time. We incorporate crowdsourcing in an online basis to improve a crowdflow estimation method. We present various approaches to characterize this uncertainty and to aggregate crowdsourcing results. Our techniques are evaluated using publicly available datasets.
(7534550), David Güera. "Media Forensics Using Machine Learning Approaches." Thesis, 2019.
Find full text(9136835), Sungbum Jun. "SCHEDULING AND CONTROL WITH MACHINE LEARNING IN MANUFACTURING SYSTEMS." Thesis, 2020.
Find full text(8812160), Alex Joseph Raynor. "DEVELOPMENT OF MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN THE STEEL INDUSTRY." Thesis, 2020.
Find full text(9811085), Anand Koirala. "Precision agriculture: Exploration of machine learning approaches for assessing mango crop quantity." Thesis, 2020. https://figshare.com/articles/thesis/Precision_agriculture_Exploration_of_machine_learning_approaches_for_assessing_mango_crop_quantity/13411625.
Full text(11173365), Youlin Liu. "MACHINE LEARNING METHODS FOR SPECTRAL ANALYSIS." Thesis, 2021.
Find full textmaking in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.
(10713342), Judy Yang. "Development of a warehouse model using machine learning technologies with application in receiving management." Thesis, 2021. https://figshare.com/articles/thesis/Development_of_a_warehouse_model_using_machine_learning_technologies_with_application_in_receiving_management/14499078.
Full textBooks on the topic "Machine learning not elsewhere classified"
Gries, Stefan Th. Data in Construction Grammar. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0006.
Full textBook chapters on the topic "Machine learning not elsewhere classified"
Emde, Werner. "Inductive learning of characteristic concept descriptions from small sets of classified examples." In Machine Learning: ECML-94, 103–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_53.
Full textCleophas, Ton J., and Aeilko H. Zwinderman. "Restructure Data Wizard for Data Classified the Wrong Way (20 Patients)." In Machine Learning in Medicine - a Complete Overview, 101–4. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15195-3_17.
Full textCleophas, Ton J., and Aeilko H. Zwinderman. "Restructure Data Wizard for Data Classified the Wrong Way (20 Patients)." In Machine Learning in Medicine – A Complete Overview, 117–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33970-8_17.
Full textDisabato, Simone. "Deep and Wide Tiny Machine Learning." In Special Topics in Information Technology, 79–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_7.
Full textKarimanzira, Divas, and Helge Renkewitz. "Detection and localization of an underwater docking station in acoustic images using machine learning and generalized fuzzy hough transform." In Machine Learning for Cyber Physical Systems, 23–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_3.
Full textPapenberg, Björn, Sebastian Hogreve, and Kirsten Tracht. "Machine Learning as an Enabler for Automated Assistance Systems for the Classification of Tool Wear on Milling Tools." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, 27–38. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-10071-0_3.
Full textVijayaragavan, P., R. Ponnusamy, and M. Arrmuthan. "Automated Socio-psycho-economic Knowledge Behavior Classified in E-Commerce Applying Various Machine Learning Techniques." In Information and Communication Technology for Sustainable Development, 405–13. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7166-0_40.
Full textMumu, Sabrina Mostafij, Hasibul Hoque, and Nazmus Sakib. "A Classified Mental Health Disorder (ADHD) Dataset Based on Ensemble Machine Learning from Social Media Platforms." In Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering, 395–405. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9483-8_33.
Full textSuda, Martin. "Improving ENIGMA-style Clause Selection while Learning From History." In Automated Deduction – CADE 28, 543–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_31.
Full textSingstad, Bjørn Jostein, Bendik Steinsvåg Dalen, Sandhya Sihra, Nickolas Forsch, and Samuel Wall. "Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers." In Computational Physiology, 91–109. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_8.
Full textConference papers on the topic "Machine learning not elsewhere classified"
Adnane, Marouane, Mohammed El, Sanaa El Fkihi, and Rachid Oulad Haj Thami. "Prediction Demand for Classified Ads Using Machine Learning." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3320326.3320371.
Full textRanawake, Dhanuja, Savandi Bandaranayake, Ravihari Jayasekara, Imashi Madhushani, Manori Gamage, and Suriyaa Kumari. "Tievs: Classified Advertising Enhanced Using Machine Learning Techniques." In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2021. http://dx.doi.org/10.1109/iemcon53756.2021.9623100.
Full textDing, Shi-fei, Zhong-zhi Shi, and Xi-jun Zhu. "Information Classified Recognition Method Based on Fuzzy Control." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259117.
Full textChang-Shun Yan and Yi-Jun Li. "Classified forgetting neural network and its effectiveness analysis." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527646.
Full textYang, Xu, De Xu, and Ying-Jian Qi. "Bag-of-words image representation based on classified vector quantization." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580564.
Full textItikawa, M. A., V. R. R. Ahón, T. A. Souza, A. M. V. Carrasco, J. C. Q. Neto, J. L. S. Gomes, R. R. H. Cavalcante, et al. "Automatic Cement Evaluation Using Machine Learning." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32961-ms.
Full textWu, Binlin, Yan Zhou, Liang Zhang, Shengjia Zhang, Xinguang Yu, Eric Wang, Ke Zhu, Cheng-hui Liu, and Robert R. Alfano. "Glioma Tumors Classified using Visible Resonance Raman Spectroscopy and Machine Learning." In Frontiers in Optics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/fio.2020.jw6a.17.
Full textAlexstan, Aarone Steve J., Krishna M. Monesh, M. Poonkodi, and Vineet Raj. "Used Car Price Prediction Using Machine Learning." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-9x4ue8.
Full textDomnik, Jan, and Alexander Holland. "On Data Leakage Prevention And Machine Learning." In Digital Restructuring and Human (Re)action. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.fov.4.2022.45.
Full textKAISER, ISAIAH, NATALIE RICHARDS, and K. T. TAN. "MACHINE LEARNING FOR STRENGTH AND DAMAGE PREDICTION OF ADHESIVE JOINTS." In Proceedings for the American Society for Composites-Thirty Seventh Technical Conference. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/asc37/36375.
Full textReports on the topic "Machine learning not elsewhere classified"
Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42425.
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