Relevant bibliographies by topics / Supervised and unsupervised machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Supervised and unsupervised machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Supervised and unsupervised machine learning"

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d
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Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.

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Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure
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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val
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Amrita, Sadarangani *. Dr. Anjali Jivani. "A SURVEY OF SEMI-SUPERVISED LEARNING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 138–43. https://doi.org/10.5281/zenodo.159333.

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Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classification, and unsupervised clustering and semi-supervised clustering are also discussed.
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Silva, Hugo, and Jorge Bernardino. "Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems." Algorithms 15, no. 4 (2022): 130. http://dx.doi.org/10.3390/a15040130.

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Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Pyt
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Ezadeen Mehyadin, Aska, and Adnan Mohsin Abdulazeez. "CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW." Iraqi Journal for Computers and Informatics 47, no. 1 (2021): 1–11. http://dx.doi.org/10.25195/ijci.v47i1.277.

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Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serv
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Yin, Xinxin, Feng Liu, Run Cai, et al. "Research on Seismic Signal Analysis Based on Machine Learning." Applied Sciences 12, no. 16 (2022): 8389. http://dx.doi.org/10.3390/app12168389.

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In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRo
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Retnoningsih, Endang, and Rully Pramudita. "Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python." BINA INSANI ICT JOURNAL 7, no. 2 (2020): 156. http://dx.doi.org/10.51211/biict.v7i2.1422.

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Abstrak: Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Permasalahan yang akan diselesaikan bunga iris (iris tectorum) yang memiliki bun
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Nurhalizah, Ria Suci, Rian Ardianto, and Purwono Purwono. "Analisis Supervised dan Unsupervised Learning pada Machine Learning: Systematic Literature Review." Jurnal Ilmu Komputer dan Informatika 4, no. 1 (2024): 61–72. http://dx.doi.org/10.54082/jiki.168.

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Artikel ini menyajikan tinjauan sistematis mengenai dua paradigma utama dalam Machine Learning yaitu Supervised Learning dan Unsupervised Learning, dengan tujuan memberikan pemahaman mendalam tentang perbedaan, serta kelebihan dan kekurangan masing-masing metode. Penelitian ini menerapkan metode Literature Review (SLR) berdasarkan pedoman PRISMA untuk menganalisis studi-studi relevan yang dipublikasikan dalam lima tahun terakhir. Dari total 540 artikel yang diperoleh, 10 artikel dipilih untuk ditelaah lebih lanjut, terdiri dari lima mengenai Supervised Learning dan lima mengenai Unsupervised L
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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no s
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Dissertations / Theses on the topic "Supervised and unsupervised machine learning"

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Tsang, Wai-Hung. "Kernel methods in supervised and unsupervised learning /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.
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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.

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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance da
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Campbell, Benjamin W. "Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.

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Kégl, Balazs. "Contributions to machine learning: the unsupervised, the supervised, and the Bayesian." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00674004.

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Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.

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An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition
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Hussein, Abdul Aziz. "Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.

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Amershi, Saleema Amin. "Combining unsupervised and supervised machine learning to build user models for intelligent learning environments." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31622.

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Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the expert-elicited knowledge used in the user model is application and domain specific, the entire model development process must be repeated for each new application. In this thesis, we outline a data-based user modeling framework that uses both unsupervised and supervised machine learning in order to reduce the development
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Varshney, Varun. "Supervised and unsupervised learning for plant and crop row detection in precision agriculture." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35463.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop rows. Aerial images, taken by an Unmanned Aerial Vehicle (UAV), of a corn field at various stages of growth were acquired in RGB format through the Agronomy Department at the Kansas State University. Several segmentation and clustering approaches were applied to these images, namely K-Means clustering, Excessive Green (ExG) Index algorit
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Alirezaie, Marjan. "Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

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The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them. In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the s
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Zhang, Pin. "Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548.

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Books on the topic "Supervised and unsupervised machine learning"

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Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unsupervised Machine Learning. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557693.

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Berry, Michael W., Azlinah Mohamed, and Bee Wah Yap, eds. Supervised and Unsupervised Learning for Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22475-2.

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Cerulli, Giovanni. Fundamentals of Supervised Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41337-7.

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Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Springer, 2010.

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Vendan, S. Arungalai, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, and Akhil Garg. Welding and Cutting Case Studies with Supervised Machine Learning. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9382-2.

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H, Fisher Douglas, Pazzani Michael John 1958-, and Langley Pat, eds. Concept formation: Knowledge and experience in unsupervised learning. Morgan Kaufmann Publishers, 1991.

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Aldrich, Chris, and Lidia Auret. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5185-2.

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Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer London, 2013.

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Jo, Taeho. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer International Publishing AG, 2022.

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Jo, Taeho. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer International Publishing AG, 2021.

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Book chapters on the topic "Supervised and unsupervised machine learning"

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Martin, Joel D. "DP1: Supervised and unsupervised clustering." In Machine Learning: ECML-94. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_82.

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Caparrós, Marc Garnica. "Supervised and Unsupervised Learning." In Artificial Intelligence and Machine Learning in Sports Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-70155-3_2.

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Alloghani, Mohamed, Dhiya Al-Jumeily, Jamila Mustafina, Abir Hussain, and Ahmed J. Aljaaf. "A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22475-2_1.

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Polat, Ediz, and Murat Simsek. "Effectiveness Analysis of Example-Based Machine Learning and Deep Learning Methods for Super-resolution Hyperspectral Images." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68106-6_5.

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Jain, Ruby, Bhuvan Jain, and Manimala Puri. "Learning Theory (Supervised/Unsupervised) for Signal Processing." In Machine Learning in Signal Processing. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003107026-2.

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Prastyo, Dedy Dwi, Halwa Annisa Khoiri, Santi Wulan Purnami, Suhartono, Soo-Fen Fam, and Novri Suhermi. "Survival Support Vector Machines: A Simulation Study and Its Health-Related Application." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22475-2_5.

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El-Yaniv, Ran, and Oren Souroujon. "Iterative Double Clustering for Unsupervised and Semi-supervised Learning." In Machine Learning: ECML 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44795-4_11.

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Sowmya, K. B. "Supervised and Unsupervised Learning Theory for Signal Processing." In Machine Learning in Signal Processing. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003107026-3.

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Pitelis, Nikolaos, Chris Russell, and Lourdes Agapito. "Semi-supervised Learning Using an Unsupervised Atlas." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44851-9_36.

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Faouzi, Johann, and Olivier Colliot. "Classic Machine Learning Methods." In Machine Learning for Brain Disorders. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3195-9_2.

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AbstractIn this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest neighbor methods, linear and logistic regressions, support vector machines, and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely, for clustering and dimensionality reduction. The chapter does not cover neural networks and deep learning as these will be presented
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Conference papers on the topic "Supervised and unsupervised machine learning"

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Hossain, Mazharul, Aaron L. Robinson, Lan Wang, and Chrysanthe Preza. "Investigation of unsupervised and supervised hyperspectral anomaly detection." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3029916.

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Pucoe, Gloria, and Ibidun Christiana Obagbuwa. "Wine Quality Prediction Using Supervised and Unsupervised Machine Learning Techniques." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629999.

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Manzoor, Bisma, and Akram Al-Hourani. "Joint Supervised and Unsupervised Machine Learning for Spaceborne Spectrum Sensing." In 2024 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE). IEEE, 2024. https://doi.org/10.1109/wisee61249.2024.10850453.

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Agrawal, Shashwat, Gopal Kumar Gupta, Pandi Kirupa Gopalakrishna, Vanitha Sivasankaran Balasubramaniam, Lagan Goel, and Siddhey Mahadik. "Hybrid Machine Learning Models: Combining Strengths of Supervised and Unsupervised Learning Approaches." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10829140.

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Sehrawat, Deepthi, Yudhvir Singh, and Harkesh Sehrawat. "Comparative Analysis of Fraud Detection of Credit Card using Supervised and Unsupervised Learning." In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI). IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10781911.

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Romedenne, Marie, Praneeth Bachu, James Smialek, Govindarajan Muralidharan, and Rishi Pillai. "Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00554.

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Abstract High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can enable a cost-effective acceleration of the development of new materials. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of mate
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Wade, Daniel, Ramon Lugos, Lance Antolick, et al. "Machine Learning Algorithms for HUMS Improvement on Rotorcraft Components." In Vertical Flight Society 71st Annual Forum & Technology Display. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10196.

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The US Army Condition Based Maintenance program collects data from Health and Usage Monitoring Systems, Flight Data Recorders, Maintenance Records, and Reliability Databases. These data sources are not integrated, but decisions regarding the health of aircraft components are dependent upon the information stored within them. The Army has begun an effort to bring these data sources together using Machine Learning algorithms. Two prototypes will be built using decision-making machines: one for an engine output gearbox and another for a turbo-shaft engine. This paper will discuss the development
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Singh, Chandra Bhan, Anish Gupta, and Dr Rajeev Kumar. "Expression of Concern for: Diabetes Care Survey Using Supervised and Unsupervised Machine Learning." In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2022. http://dx.doi.org/10.1109/iciem54221.2022.10703468.

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Koball, Carson, Yong Wang, Varghese Vaidyan, and John Hastings. "Assessing Evasion Attacks on Tree-Based Machine Learning Models: Supervised vs. Unsupervised Approaches." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930040.

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Barbastathis, George, Sandip Mondal, Thiara Ahmed, et al. "On the use of Machine Learning for Quantifying Complex Processes, With Application to Retina Vasculature and Glaucoma Diagnostics." In Novel Optical Materials and Applications. Optica Publishing Group, 2024. https://doi.org/10.1364/noma.2024.nom3h.3.

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We investigate the mapping between flows on complex graphs, such as retina vasculature, and spatial-temporal flucturations in the far field. Unsupervised and supervised learning algorithms can be used to invert these maps and obtain quantitative information about connectivity and blood flow. Full-text article not available; see video presentation
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Reports on the topic "Supervised and unsupervised machine learning"

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Moral, Rafael. Introduction to Machine Learning. Instats Inc., 2024. http://dx.doi.org/10.61700/qfxukp14jlpfd1478.

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This comprehensive workshop provides a thorough introduction to machine learning, focusing on both theoretical concepts and practical applications using R. Designed for PhD students, professors, and researchers, it covers essential techniques such as supervised and unsupervised learning, dimension reduction, and tree-based methods, enhancing participants' data analysis skills and research capabilities.
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Ahmmed, Bulbul. Supervised and Unsupervised Machine Learning to Understanding Reactive-transport Data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1630844.

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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1994110.

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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collec
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Estrella, Tony, Carla Alfonso, Lluis Capdevila, and Josep-Maria Losilla. Machine learning for the analysis of healthy lifestyle data: a scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.3.0065.

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Review question / Objective: The objective of this scoping review is to identify and characterize machine learning algorithms used in data analysis of healthy lifestyle. The specific objectives are the study of a) terminology, b) healthy lifestyle variables analysed either input or output, c) programs and libraries used to analyse data, and d) sources, types, and quality of data analysed. Eligibility criteria: In this scoping review the inclusion criteria from studies that provide empirical information are as follows: a) studies must use machine learning models either supervised or unsupervise
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in
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Bhattarai, Manish. UNSUPERVISED AND SUPERVISED LEARNING FRAMEWORKS FOR KNOWLEDGE EXTRACTION. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2342020.

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Vesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493534.

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Vesselinov, Velimir, Bulbul Ahmmed, Maruti Mudunuru, et al. Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1781347.

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