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Journal articles on the topic 'Unsupervised machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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S Thakare Jayshri, Vishal. "An Effective Unsupervised Machine Learning Technique and Research Challenges." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 2141–43. http://dx.doi.org/10.21275/sr23523214829.

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Roohi, Adil, Kevin Faust, Ugljesa Djuric, and Phedias Diamandis. "Unsupervised Machine Learning in Pathology." Surgical Pathology Clinics 13, no. 2 (2020): 349–58. http://dx.doi.org/10.1016/j.path.2020.01.002.

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Sibyan, Hidayatus, Wildan Suharso, Edi Suharto, Melda Agnes Manuhutu, and Agus Perdana Windarto. "Optimization of Unsupervised Learning in Machine Learning." Journal of Physics: Conference Series 1783, no. 1 (2021): 012034. http://dx.doi.org/10.1088/1742-6596/1783/1/012034.

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Jha, Ritambhara. "Analyzing Credit Card Consumer Behavior using Unsupervised Machine Learning Techniques." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 460–63. http://dx.doi.org/10.21275/sr24106025150.

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S Nair, Aparna, and Sindhu Daniel. "Customer Segmentation Using K-Means Clustering in Unsupervised Machine Learning." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1376–79. https://doi.org/10.21275/sr25417125301.

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Mounika, D. Venkata, and Mr K. Padmanaban. "Unsupervised Machine Learning For Managing Safety Accidents In Railway Stations." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12240–50. https://doi.org/10.55248/gengpi.6.0525.18149.

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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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Wu, Wei, Jaime Alvarez, Chengcheng Liu, and Hung-Min Sun. "Bot detection using unsupervised machine learning." Microsystem Technologies 24, no. 1 (2016): 209–17. http://dx.doi.org/10.1007/s00542-016-3237-0.

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Meingast, Stefan, Marco Lombardi, and João Alves. "Estimating extinction using unsupervised machine learning." Astronomy & Astrophysics 601 (May 2017): A137. http://dx.doi.org/10.1051/0004-6361/201630032.

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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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Zaadnoordijk, Lorijn, Tarek R. Besold, and Rhodri Cusack. "Lessons from infant learning for unsupervised machine learning." Nature Machine Intelligence 4, no. 6 (2022): 510–20. http://dx.doi.org/10.1038/s42256-022-00488-2.

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Zhu, Tianyi. "Machine Learning Models in Quantitative Investment." Applied and Computational Engineering 115, no. 1 (2024): 165–70. https://doi.org/10.54254/2755-2721/2025.18521.

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This paper explores the application of machine learning models in the realm of quantitative investment, emphasizing their potential to enhance decision-making processes and predictive accuracy in financial markets. Beginning with an overview of machine learning typessupervised, unsupervised, and semi-supervisedthe paper delves into specific models commonly employed in investment strategies. These include supervised models such as Random Forests and Support Vector Machines, as well as unsupervised models like K-Means Clustering and Bayesian Networks. The practical applications and advantages of

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Gittler, Thomas, Stephan Scholze, Alisa Rupenyan, and Konrad Wegener. "Machine Tool Component Health Identification with Unsupervised Learning." Journal of Manufacturing and Materials Processing 4, no. 3 (2020): 86. http://dx.doi.org/10.3390/jmmp4030086.

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Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount

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Shih, David, Matthew R. Buckley, Lina Necib, and John Tamanas. "via machinae: Searching for stellar streams using unsupervised machine learning." Monthly Notices of the Royal Astronomical Society 509, no. 4 (2021): 5992–6007. http://dx.doi.org/10.1093/mnras/stab3372.

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ABSTRACT We develop a new machine learning algorithm, via machinae, to identify cold stellar streams in data from the Gaia telescope. via machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, via machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search

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Singh, Shatrunjai P., Swagata Karkare, Sudhir M. Baswan, and Vijendra P. Singh. "Unsupervised Machine Learning for Co/Multimorbidity Analysis." International Journal of Statistics and Probability 7, no. 6 (2018): 23. http://dx.doi.org/10.5539/ijsp.v7n6p23.

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Although co/multimorbidities are associated with a significant increase in mortality, lack of quantitative exploratory techniques often impedes an in-depth analysis of their association. In the current study, we explore the clustering of co/multimorbid patients in the Texas patient population. We employ unsupervised agglomerative hierarchical clustering to find clusters of co/multimorbid patients within this population. Our analysis revealed the presence of nine distinct, clinically relevant clusters of co/multimorbidities within the study population of interest. This technique provides a quan

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Kung, Benson, Maurice Chiang, Gayan Perera, Megan Pritchard, and Robert Stewart. "Unsupervised Machine Learning to Identify Depressive Subtypes." Healthcare Informatics Research 28, no. 3 (2022): 256–66. http://dx.doi.org/10.4258/hir.2022.28.3.256.

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Objectives: This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. Methods: Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-us

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Song, Tao, Jiarong Wang, Danya Xu, et al. "Unsupervised Machine Learning for Improved Delaunay Triangulation." Journal of Marine Science and Engineering 9, no. 12 (2021): 1398. http://dx.doi.org/10.3390/jmse9121398.

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Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were clo

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Rodriguez-Nieva, Joaquin F., and Mathias S. Scheurer. "Identifying topological order through unsupervised machine learning." Nature Physics 15, no. 8 (2019): 790–95. http://dx.doi.org/10.1038/s41567-019-0512-x.

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Huang, Weilin. "Seismic signal recognition by unsupervised machine learning." Geophysical Journal International 219, no. 2 (2019): 1163–80. http://dx.doi.org/10.1093/gji/ggz366.

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SUMMARY Seismic signal recognition can serve as a powerful auxiliary tool for analysing and processing ever-larger volumes of seismic data. It can facilitate many subsequent procedures such as first-break picking, statics correction, denoising, signal detection, events tracking, structural interpretation, inversion and imaging. In this study, I propose an automatic technique of seismic signal recognition taking advantage of unsupervised machine learning. In the proposed technique, seismic signal recognition is considered as a problem of clustering data points. All the seismic sampling points i

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Alabidi, Shahad A., and Ehsan Ali Al-Zubaidi. "Satellite Image Classification Using Unsupervised Machine Learning." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 276–98. http://dx.doi.org/10.46649/fjiece.v3.2.19a.28.5.2024.

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In recent years, humans rely heavily on spaceSatellites are especially for communications, military defence, intelligence, science and Trade. As a result of the development of artificial intelligence technology, especially machine learning and deep learning, Timecan be saved by using satellite images, which can offer a wealth of large-scale information on the Earth's surfaces quickly. Advanced image processing techniques have been used to improve the resolution of acquired objects with the emergence of sensors that give satellite images.Research on ways to apply it in various fields has been a

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Yousefi, Siamak, Ebrahim Yousefi, Hidenori Takahashi, et al. "Keratoconus severity identification using unsupervised machine learning." PLOS ONE 13, no. 11 (2018): e0205998. http://dx.doi.org/10.1371/journal.pone.0205998.

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Schutter, A., and L. Shamir. "Galaxy morphology — An unsupervised machine learning approach." Astronomy and Computing 12 (September 2015): 60–66. http://dx.doi.org/10.1016/j.ascom.2015.05.002.

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Ding, Shifei, Nan Zhang, Jian Zhang, Xinzheng Xu, and Zhongzhi Shi. "Unsupervised extreme learning machine with representational features." International Journal of Machine Learning and Cybernetics 8, no. 2 (2015): 587–95. http://dx.doi.org/10.1007/s13042-015-0351-8.

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Patané, Giuseppe, and Marco Russo. "Distributed unsupervised learning using the multisoft machine." Information Sciences 143, no. 1-4 (2002): 181–96. http://dx.doi.org/10.1016/s0020-0255(02)00198-6.

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Koball, Carson, Bhaskar P. Rimal, Yong Wang, Tyler Salmen, and Connor Ford. "IoT Device Identification Using Unsupervised Machine Learning." Information 14, no. 6 (2023): 320. http://dx.doi.org/10.3390/info14060320.

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Device identification is a fundamental issue in the Internet of Things (IoT). Many critical services, including access control and intrusion prevention, are built on correctly identifying each unique device in a network. However, device identification faces many challenges in the IoT. For example, a common technique to identify a device in a network is using the device’s MAC address. However, MAC addresses can be easily spoofed. On the other hand, IoT devices also include dynamic characteristics such as traffic patterns which could be used for device identification. Machine-learning-assisted a

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Naeem, Samreen, Aqib Ali, Sania Anam, and Muhammad Munawar Ahmed. "An Unsupervised Machine Learning Algorithms: Comprehensive Review." International Journal of Computing and Digital Systems 13, no. 1 (2023): 911–21. http://dx.doi.org/10.12785/ijcds/130172.

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Chen, Wanyi, and Mary Cummings. "Subjectivity in Unsupervised Machine Learning Model Selection." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 22–29. http://dx.doi.org/10.1609/aaaiss.v3i1.31174.

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Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and metrics, model selection remains subjective. A high degree of subjectivity may lead to questions about repeatability and reproducibility of various machine learning studies and doubts about the robustness of models deployed in the real world. Yet, the impact of modelers' preferences on model selection outcomes remains largely unexplored. This study uses the Hidden Markov Model as an example to investigate the subjectivity involved in model selection. We asked 33 participants and three Large Lang

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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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Akshada, Sunil Shitole, and Priyadarshini I. "Survey of Machine Learning Algorithms & its Applications." Journal of Advances in Computational Intelligence Theory 3, no. 2 (2021): 1–5. https://doi.org/10.5281/zenodo.5090570.

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Machine Learning is a subset of Artificial Intelligence. Machine learning is one of the latest technologies which has brings new innovations in various fields. Machine learning refers to the concept of train the machine in such a way it can learns from a past experiences or it can learn from a data provided to it. The concept machine learning can be implemented in various fields using its various algorithms. The machine learning contains various algorithms like KNN, K means, decision tree, random forest, support vector machine etc. Machine Learning can be further classified into Supervised Lea

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Husnaningtyas, Nadia, and Totok Dewayanto. "FINANCIAL FRAUD DETECTION AND MACHINE LEARNING ALGORITHM (UNSUPERVISED LEARNING): SYSTEMATIC LITERATURE REVIEW." Jurnal Riset Akuntansi Dan Bisnis Airlangga 8, no. 2 (2023): 1521–42. http://dx.doi.org/10.20473/jraba.v8i2.49927.

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This research aims to assess the usage of unsupervised learning in detecting financial fraud across various financial industries by identifying cognitive constructs, benefits, economic optimization, and challenges associated with fraud detection necessitating innovative approaches for effective detection. This study conducts Systematic Literature Review following PRISMA protocol for article selection of 27 journal articles published between 2010 and 2023, sourced from Scopus database. The analysis discloses that unsupervised learning has been implemented across diverse financial sectors, inclu

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Gurpreet Singh. "Key Features and Techniques of Unsupervised Learning." Tuijin Jishu/Journal of Propulsion Technology 45, no. 02 (2024): 479–82. http://dx.doi.org/10.52783/tjjpt.v45.i02.5825.

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Machine learning (ML) has emerged as a transformative technology with profound implications for industrial operations across diverse sectors. This paper provides a comprehensive analysis of the applications and challenges, of machine learning in industrial settings. The paper begins by outlining the foundational concepts of machine learning and its relevance to industrial processes. It explores various ML techniques, including supervised learning, unsupervised learning, and reinforcement learning, and discusses their applicability in optimizing production, enhancing quality control, and predic

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Zhuk, Andrei. "UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS." American Journal of Engineering and Technology 6, no. 10 (2024): 23–32. http://dx.doi.org/10.37547/tajet/volume06issue10-04.

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This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optim

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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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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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Eppa, Akhilesh Reddy. "Machine Learning Algorithms: A Comparative Study on Efficiency, Effectiveness, and Practical Applications Using the GRA Method." Journal of Artificial intelligence and Machine Learning 3, no. 1 (2025): 1–12. https://doi.org/10.55124/jaim.v3i1.260.

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Introduction: Machine learning has emerged as a powerful tool for data analysis, enabling systems to identify patterns and make predictions without explicit programming. Among its various approaches, unsupervised learning plays a crucial role in discovering hidden structures within data, especially in scenarios where labeled examples are scarce or costly to obtain. This study provides a comprehensive analysis of unsupervised learning techniques, with a particular focus on clustering and reinforcement learning. Research significance: This study provides an in-depth exploration of unsupervised l

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Hsu, Chia-Yi, Pin-Yu Chen, Songtao Lu, Sijia Liu, and Chia-Mu Yu. "Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6926–34. http://dx.doi.org/10.1609/aaai.v36i6.20650.

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Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised learning tasks, relying on the ground truth data label, a targeted objective, or supervision from a trained classifier. In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. Our framework exploits a mutual information neural estimator as an information theoretic similarity measure to generate adversarial examples without

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Lo, James Ting-Ho, and Bryce Mackey-Williams Carey. "A Cortical Learning Machine for Learning Real-Valued and Ranked Data." International Journal of Clinical Medicine and Bioengineering 1, no. 1 (2021): 12–24. http://dx.doi.org/10.35745/ijcmb2021v01.01.0003.

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The cortical learning machine (CLM) introduced in [1-3] is a low-order computational model of the neocortex. It has the real-time, photogragraphic, unsupervised, and hierarchical learning capabilities, which existing learning machines such as the multilayer perceptron and convolutional neural network do not have. The CLM is a network of processing units (PUs) each comprising novel computational models of dendrites (for encoding), synapses (for storing code covariance matrices), spiking/nonspiking somas (for evaluating empirical probabilities and generating spikes), and unsupervised/supervised

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Soares, Nielson, Eduardo Pestana de Aguiar, Amanda Campos Souza, and Leonardo Goliatt. "Unsupervised machine learning techniques to prevent faults in railroad switch machines." International Journal of Critical Infrastructure Protection 33 (June 2021): 100423. http://dx.doi.org/10.1016/j.ijcip.2021.100423.

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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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Luca, Daniele Vailati. "Methods and Applications of Unsupervised Learning Machines." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 11 (2024): 1608–32. https://doi.org/10.5281/zenodo.14273283.

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This article offers a comprehensive introduction to the key concepts of unsupervised machine learning, a type of machine learning where models are trained using data that has not been labeled or categorized. The primary goal of unsupervised learning is to identify hidden patterns or structures within the data without the need for predefined labels. It explores various unsupervised learning techniques, such as clustering, dimensionality reduction, and anomaly detection, highlighting their potential applications in fields like data mining, pattern recognition, and market segmentation. In additio

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Wöber, Wilfried, Papius Tibihika, Cristina Olaverri-Monreal, Lars Mehnen, Peter Sykacek, and Harald Meimberg. "Comparison of Unsupervised Learning Methods for Natural Image Processing." Biodiversity Information Science and Standards 3 (July 4, 2019): e37886. https://doi.org/10.3897/biss.3.37886.

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For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object detection (Ren et al. 2015) or pixel-wise weed classification (Milioto et al. 2017) machine learning is used for both feature extraction and processing (e.g. classification or regression). Historically, feature extraction (e.g. PCA; Ch. 12.1. in Bishop 2006) and processing were sequential and independent tasks (Wöber et al. 2013). Since the rise of convolutional neuronal networks (LeCun et al. 1989), a deep machine learning approach optimized for images, in 2012 (Krizhevsky et al. 2012), feature

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Suyal, Manish, and Sanjay Sharma. "A Review on Analysis of K-Means Clustering Machine Learning Algorithm based on Unsupervised Learning." Journal of Artificial Intelligence and Systems 6, no. 1 (2024): 85–95. http://dx.doi.org/10.33969/ais.2024060106.

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The process of machine learning is understood within Artificial Intelligence. Machine learning process gives the tools the ability to learn from their experiences and improve themselves without any coding. In machine learning, we program a computer or machine in such a way that the user wants the work done by the machine. It can give such work and in this process the computer does its work on the basis of the data already with it and gives its performance. The objective of writing the paper is how K- Means clustering algorithm is applied on the model dataset based on unsupervised learning. We

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Rathi, Krishna. "A Comprehensive Analysis Of The Techniques In Unsupervised Machine Learning For Removing Artefacts In Electrodermal Activity." International Journal of Research in Medical Sciences and Technology 14, no. 1 (2022): 198–208. http://dx.doi.org/10.37648/ijrmst.v14i01.022.

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In any data preprocessing pipeline for physiological time series data, artefact detection and removal is an essential step, particularly when the data are obtained outside of controlled experimental circumstances. Given that these artefacts are frequently easily recognised with the naked eye, unsupervised machine learning methods seem like a viable alternative to manually labelled training datasets. Current techniques are frequently heuristic-based, non-generalizable, or designed for less artifact-prone, controlled experimental environments. In this work, we evaluate three such unsupervised le

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Howard Miller, Alfred. "Using unsupervised machine learning to model tax practice learning theory." International Journal of Engineering & Technology 7, no. 2.4 (2018): 109. http://dx.doi.org/10.14419/ijet.v7i2.4.13019.

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The aim of this study was to utilize unsupervised machine learning framework to explore a dataset comprised of assessed output by Bachelors of Business, Taxation learners over four successive semesters. The researcher sought to motivate deployment of an evidence-supported, data-driven approach to understand the scope of student learning from a bachelor’s degree in business class taxation class, as a tool for accreditation reporting purposes. Outcomes from the data analysis identified four factors; two related to tax and two related to learning. These factors are, tax theory, and tax practice,

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KUBO, Norio, and Serkan ATAMER. ""Tasting" sounds by A.I. Sommelier - Unsupervised machine learning of wine evaluation applied for sound quality evaluation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 8 (2024): 3223–33. http://dx.doi.org/10.3397/in_2024_3295.

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Wine produced countries in European Union categorize quality of wine as three classes by subjective judgements (called as "Appellation" in French) like taste, land of producer, type of grape seed and so on. In order to evaluate those wine quality, unsupervised machine learning with measured optical and chemical data of wine enables to categorize those 3 subjective classes at high accuracies. With those "A.I. Sommelier" techniques, vacuum cleaner sounds are categorized with psychoacoustic parameters and then modeling by image recognition AI of sFFT images correlate with annoyance rating at high

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Praveen, Halingali, Kumar Santosh, Desai Sanket, and S. Alagoudar Punith. "Survey on Applications of Machine Learning." Journal of Research and Review: Machine Learning 1, no. 2 (2025): 29–35. https://doi.org/10.5281/zenodo.14922864.

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<em>Machine learning (ML) has transformed research methodologies across technical disciplines by enabling data-driven decision-making and predictive analytics. This paper explores ML’s core principles, issues and future developments, emphasizing its impact on optimizing research processes in fields such as engineering, materials science, and telecommunications. Key ML paradigms, including supervised, unsupervised, and reinforcement learning, are analyzed in the context of technical applications. Additionally, this study addresses challenges such as data quality and model interpretability

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Mamun, Abdullah Al, Md Shakhaowat Hossain, S. M. Shadul Islam Rishad, et al. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." American Journal of Engineering and Technology 06, no. 11 (2024): 63–76. https://doi.org/10.37547/tajet/volume06issue11-08.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key

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Hossain, Md Shakhaowat, S. M. Shadul Islam Rishad, Md Mohibur Rahman, et al. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." International journal of networks and security 04, no. 01 (2024): 22–32. http://dx.doi.org/10.55640/ijns-04-01-06.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key

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Li, Guang, Zhushi He, Juzhi Deng, et al. "Robust CSEM data processing by unsupervised machine learning." Journal of Applied Geophysics 186 (March 2021): 104262. http://dx.doi.org/10.1016/j.jappgeo.2021.104262.

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