Towards the implementation of automated scoring in international large-scale assessments: Scalability and quality control by Ji Yoon Jung, Lillian Tyack, Matthias von Davier

“…This study addresses this challenge by investigating two machine learning approaches — supervised and unsupervised learning — for scoring multilingual responses. …”
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Self-Administered Information Sharing Framework Using Bioinspired Mechanisms by Huibo Bi, Yanyan Chen, Wen-Long Shang, Chengcheng Song, Wenbo Huang

“…However, data packet behaviours and significant parameters involved are mostly preconfigured in a supervised-learning fashion rather than using an unsupervised learning paradigm and therefore may not adapt to uncertain or fast-changing environments. …”
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Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data by Fan Zhang, Longgang Zhao, Tingting Guo, Ziyang Wang, Peng Lou, Juan Li

“…Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. …”
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An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals by Lorenzo Manoni, Claudio Turchetti, Laura Falaschetti

“…The source code of the algorithm for unsupervised learning of data is available at <uri>https://codeocean.com/capsule/6692152/tree/v3</uri>.…”
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Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“…It navigates through various ML methods utilized in healthcare, including Supervised Learning, Unsupervised Learning, Self-Supervised Learning, Deep Neural Networks, Reinforcement Learning, and Ensemble Methods. …”
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Rootlets Hierarchical Principal Component Analysis for Revealing Nested Dependencies in Hierarchical Data by Korey P. Wylie, Jason R. Tregellas

“…Hierarchical clustering analysis (HCA) is a widely used unsupervised learning method. Limitations of HCA, however, include imposing an artificial hierarchy onto non-hierarchical data and fixed two-way mergers at every level. …”
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Unsupervised Identification for 2-Additive Capacity by Principal Component Analysis and Kendall’s Correlation Coefficient in Multi-Criteria Decision-Making by Xueting Guan, Kaihong Guo, Ran Zhang, Xiao Han

“…Secondly, Kendall’s correlation coefficient stemmed from the decision data created to help identify the Shapley interaction index for each pair of criteria by unsupervised learning. The optimization model equipped with a new form of monotonicity conditions is then established to further determine the optimal Shapley interaction index. …”
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A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies by Devotha G. Nyambo, Edith T. Luhanga, Zaipuna Q. Yonah

“…In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models’ robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables.…”
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Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset by Asadi Srinivasulu, Clement Varaprasad Karu, G Sreenivasulu, Gayathri R

“…The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.…”
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Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning by Minjuan Zhong, Zhenjin Li, Shengzong Liu, Bo Yang, Rui Tan, Xilong Qu

“…The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. …”
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Study on a New Method of Link-Based Link Prediction in the Context of Big Data by Chen Jicheng, Chen Hongchang, Li Hanchao

“…A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset’s nature, it does not provide accurate information for sparse networks. …”
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A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) by Marziyeh Nourahmadi, Hojjatollah Sadeqi

“…Clustering is one of the unsupervised learning methods and has a descriptive nature. …”
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Uniform Quantization for Multi-Antenna Amplify&#x2013;Quantize&#x2013;Forward Relay by Gangsan Jeong, Xianglan Jin

“…Specifically, we present supervised learning (SL) and unsupervised learning (USL) methodologies, with the latter employing a novel loss function designed to avoid the need for extensive training data collection. …”
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A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing by Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim, Jongpil Jeong

“…In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. …”
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Novel Statistical Analysis Schemes for Frequency-Modulated Thermal Wave Imaging for Inspection of Ship Hull Materials by Ishant Singh, Vanita Arora, Prabhu Babu, Ravibabu Mulaveesala

“…Among various adopted statistical post-processing techniques, pulse compression has been carried out using different methods, namely the offset removal with polynomial curve fitting and principal component analysis (PCA), which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. …”
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Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps by Torsten Mattfeldt, Hubertus Wolter, Danilo Trijic, Hans‐Werner Gottfried, Hans A. Kestler

“…Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. …”
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Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model by Godfrey A. Mills, Daniel K. Acquah, Robert A. Sowah

“…In this paper, we present a hybrid intrusion detection system that combines supervised and unsupervised learning models through an ensemble stacking model to increase the detection accuracy rates of attacks in networks while minimising false alarms. …”
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Election Prediction on Twitter: A Systematic Mapping Study by Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat

“…The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…This neural network automatically and through unsupervised learning identifies patterns, complex structures, and high-level features of the input data. …”
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A Physics-Informed Cold-Start Capability for xEV Charging Recommender System by Raik Orbay, Aditya Pratap Singh, Johannes Emilsson, Michele Becciani, Evelina Wikner, Victor Gustafson, Torbjorn Thiringer

“…It is shown that using 7 fundamental charging power transients would capture about 70&#x0025; of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.…”
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