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Periodic Table's Properties Using unsupervised Chemometric methods: Undergraduate Analytical Chemistry Laboratory Exercise

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JOURNAL OF CHEMICAL EDUCATION 2024年第3期102卷 1237-1244页

作者： de Quental, Adrian Gabriel Pereira do Nascimento, Ayrton Lucas Firmino de Morais, Camilo de Lelis Medeiros Neves, Ana Carolina de Oliveira das Neves, Luiz Seixas de Lima, Kassio Michell Gomes Univ Fed Rio Grande do Norte Lab Quim Biolog & Quimiometria Inst Quim BR-59072970 Natal Brazil Univ Estadual Ceara Ctr Educ Ciencias & Tecnol Regiao Inhamuns BR-63660000 Taua Brazil Univ Fed Rio Grande do Norte Inst Quim BR-59072970 Natal Brazil

An application of unsupervised chemometrics methods using periodic table properties for an analytical chemistry laboratory exercise is presented. Chemometric techniques such as hierarchical clustering analysis (HCA), k-means, and principal component analysis (PCA) were applied to a multivariate data set of chemical properties of the elements (atomic radius, electronegativity, ionization energy, electronic affinity, thermal conductivity, density, entropy, and specific heat). The exercise was carried out by undergraduate students attending a chemometric analysis class during the fifth semester of their third year at our educational institution. The theory of HCA, k-means, and PCA is discussed, and multivariate analysis procedures (data set construction, preprocessing, dendrogram, k-means clustering, scores, and loadings) were carried out using the well-liked programming language R, a widely used programming language designed for data analysis and statistics, within the user-friendly RStudio integrated development environment. The unsupervised algorithms were able to find "natural" clustering from the periodic table using the data structure without any prior knowledge of the class assignment of the samples. This analytical chemistry laboratory exercise with chemometric techniques can also be used in a wide range of laboratory activities such as water analysis, food analysis, drug analysis, and physical chemistry.

关键词： Graduate education Analytical chemistry Hands-onlearning Chemometric unsupervised methods Periodic table

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unsupervised Learning methods for Voltage Regulation in Smart Grids 21

Unsupervised Learning Methods for Voltage Regulation in Smar...

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21st IEEE International Conference on Environment and Electrical Engineering / 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe)

作者： Abegaz, Brook W. Loyola Univ Engn Program Chicago IL 60626 USA

ISBN: (纸本)9781665436137

A voltage regulator is a power electronic circuit that provides a stable operating voltage by reducing variations on the terminal voltage, current, frequency, temperature, and related factors of generation units. In this paper, various unsupervised learning methods are used to improve the stability of such voltage regulators using a model-based design. The results show that implementations of some of the clustering algorithms provide better reliability and stability for feedback-based voltage regulators than other methods namely model predictive controller (MPC), Gaussian mixture model (GMM), self-organizing mapping (SOM), and hierarchical clustering (HC) methods. Specifically, the K-Means clustering approach (KM) provided superior stability, albeit a slower response of the output voltage of the voltage regulators. The clustering approach could be applied to improve the efficiency of voltage regulation methods in smart grids and related cyber-physical systems.

关键词： Learning Algorithms Smart Grids Voltage Regulation unsupervised methods

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A Comparative Evaluation of Supervised and unsupervised methods for Detecting Outliers 2

A Comparative Evaluation of Supervised and Unsupervised Meth...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Rajeswari, A. M. Yalini, S. K. Janani, R. Rajeswari, N. Deisy, C. Thiagarajar Coll Engn Madurai Tamil Nadu India

ISBN: (纸本)9781538619742

Outlier refers to a sample which is deviated with the rest of the samples in the dataset. Such outliers can be categorized as global outliers, collective outliers, and contextual outliers based on the behaviour and the degree of its deviation from the normal samples. methods like supervised, unsupervised and semi supervised are employed to find these kinds of outliers. This paper sheds light on the layout and performance analysis of supervised and unsupervised outlier detection methods in determining the aforementioned outliers. To understand the performance of these methods in predicting outliers from few benchmarks and real-time data sets, the data mining tools like Rapid Miner and R is used.

关键词： Outliers Outlier Detection Supervised methods unsupervised methods Outlier Types

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Persian Sentiment Lexicon Expansion Using unsupervised Learning methods 9

Persian Sentiment Lexicon Expansion Using Unsupervised Learn...

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9th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Akhoundzade, Reza Devin, Kourosh Hashemi Sharif Univ Technol Tehran Iran Ferdowsi Univ Mashhad Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728150758

Sentiment analysis, is a subfield of natural language processing that aims at opinion mining to analyze thoughts, orientation and, evaluation of users within some texts. The solution to this problem includes two main steps: extracting aspects and determining users' positive or negative sentiments with respect to the aspects. Two main challenges of sentiment analysis in the Persian language are lack of comprehensive tagged data sets and use of colloquial language in texts. In this paper we propose, a system to specify and extract sentiment words using unsupervised methods in the Persian language that also support colloquial words. Additionally, we also proposed and implemented a state-of-art technique to expand Persian sentiment lexicon. Our proposed method utilized neural network (Word2Vec model) with the help of rule-based methods. F1 measure for sentiment words extraction in our proposed method is 0.58.

关键词： sentiment analysis learning methods aspect extraction unsupervised methods topic modeling neural networks rule-based methods precision recall

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Small Data Challenges in Big Data Era: A Survey of Recent Progress on unsupervised and Semi-Supervised methods

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第4期44卷 2168-2187页

作者： Qi, Guo-Jun Luo, Jiebo Futurewei Technol Bellevue WA 98004 USA Univ Rochester6927 Dept Comp Sci Rochester NY 14627 USA

Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect. To address it, many efforts have been made on training sophisticated models with few labeled data in an unsupervised and semi-supervised fashion. In this paper, we will review the recent progresses on these two major categories of methods. A wide spectrum of models will be categorized in a big picture, where we will show how they interplay with each other to motivate explorations of new ideas. We will review the principles of learning the transformation equivariant, disentangled, self-supervised and semi-supervised representations, all of which underpin the foundation of recent progresses. Many implementations of unsupervised and semi-supervised generative models have been developed on the basis of these criteria, greatly expanding the territory of existing autoencoders, generative adversarial nets (GANs) and other deep networks by exploring the distribution of unlabeled data for more powerful representations. We will discuss emerging topics by revealing the intrinsic connections between unsupervised and semi-supervised learning, and propose in future directions to bridge the algorithmic and theoretical gap between transformation equivariance for unsupervised learning and supervised invariance for supervised learning, and unify unsupervised pretraining and supervised finetuning. We will also provide a broader outlook of future directions to unify transformation and instance equivariances for representation learning, connect unsupervised and semi-supervised augmentations, and explore the role of the self-supervised regularization for many learning problems.

关键词： Data models Task analysis Training Adaptation models Gallium nitride Big Data Supervised learning unsupervised methods semi-supervised methods domain adaptation transformation equivariance and invariance disentangled representations generative models auto-encoders generative adversarial networks auto-regressive models flow-based generative models transformers self-supervised methods teach-student models instance discrimination and equivariance

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EVALUATION OF unsupervised DEEP CLUSTERING methods FOR CROP CLASSIFICATION USING SAR IMAGE SEQUENCES

EVALUATION OF UNSUPERVISED DEEP CLUSTERING METHODS FOR CROP ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Torres, D. Lobo La Rosa, L. E. Cue Oliveira, D. A. B. Feitosa, R. Q. Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn Rio De Janeiro Brazil IBM Res Sao Paulo Brazil

ISBN: (纸本)9781665403696

Reliable crop mapping is an essential tool for agricultural monitoring and food security. In the tropics, where cloud cover massively affects optical imagery, synthetic-aperture radar (SAR) imagery emerged as a cost-effective alternative for discriminating crops in large scale agricultural regions. Recently, unsupervised deep clustering approaches have emerged as a competitive alternative in several different applications with labeling restrictions. This paper explores this literature and evaluates the feasibility of such methods applied to crop classification in a tropical region from multi-temporal SAR image sequences. We focus on the k-Means-related deep clustering methods, specifically, on Deep Embedding Clustering and Deep K-Means. We report experiments conducted on a public dataset from a tropical region with highly complex crop dynamics. In our experiments the tested unsupervised approaches managed to deliver nearly 78% of supervised counterparts for this task(1).

关键词： crop monitoring deep clustering multi-temporal SAR images sequences unsupervised methods

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Adversarial neighbor perception network with feature distillation for anomaly detection

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Su, Yuting Su, Enqi Wang, Weiming Jing, Peiguang Ma, Dubuke Wang, Fu Lee Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong 999077 Peoples R China Univ Michigan Ann Arbor MI 48109 USA

Anomaly detection has become a research hotspot in the field of intelligent manufacturing, which has attracted strong attention from academia and industry. Although the unsupervised methods based on reconstruction have shown promising results in anomaly detection, they still have the problems that the representation of abnormal features is not significantly different and the representation of normal features is not accurate enough. To solve these problems, we propose an adversarial neighbor perception network with feature distillation (ANP-FD) for anomaly detection, which includes multi-scale neighbor perception, robust distillation recovery, and co-attention adversarial detection modules. First, the multi-scale features pass through the neighbor awareness to predict accurate spatial location information to improve the reconstruction accuracy. Then, the abnormal features are eliminated by a robust trainable distillation structure, which expands the representation difference of abnormal features during reconstruction. In addition, the co-attention adversarial detection can accurately detect and locate anomalies in the multi-scale feature space. The experimental results on the MVTec, BTAD, MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate that our proposed method achieves better performance than current state-of-the-art (SOTA) approaches. Especially, the proposed method achieves 99.5 AUROC% and 94.5 AUPRO% on the MVTec. We also achieve superior performance in few-shot scenarios. Code: https://***/thesusu/ANP-FD.

关键词： Anomaly detection unsupervised methods Neighbor perception Spatial location Feature distillation

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Q-Seg: Quantum Annealing-Based unsupervised Image Segmentation

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2024年第5期44卷 27-39页

作者： Venkatesh, Supreeth Mysore Macaluso, Antonio Nuske, Marlon Klusch, Matthias Dengel, Andreas German Res Ctr Artificial Intelligence D-66123 Saarbrucken Germany

We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixelwise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-wave advantage device, offering superior scalability over existing quantum approaches and outperforming several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime.

关键词： Image Segmentation Quantum Computing Annealing Quantum Annealing Image Edge Detection Optimization Measurement Image Segmentation unsupervised Image Segmentation Computer Vision unsupervised methods Segmentation Method Solution Space Edge Detection Earth Observation Quantum Computing Optimization Task Segmentation Problem Segmentation Quality Image Segmentation methods Flood Mapping Multi Object Tracking Special Case Training Data Intensity Values Similarity Measure Edge Weights Minimum Cut Noisy Labels Grid Graph Gradient Boosting Decision Tree Real Devices Synthetic Aperture Radar Binary Segmentation Specific Use Case Segmentation Task

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Design of a Distance Learning Supervision System Based on Time-Series Data of Learning Behaviors

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2025年第2期29卷 337-348页

作者： Zhu, Yan Yangzhou Polytech Coll Sch Informat Engn 458 Wenchang West Rd Yangzhou 225002 Jiangsu Peoples R China

This paper proposes a remote learning monitoring method based on learning behavior time series data to effectively monitor learning progress of students. This method integrates multi-scale feature extraction, a variational information bottleneck module, and a variational autoencoder to enhance feature diversity and clustering performance. Tests indicate that the proposed multi-scale full convolution algorithm model achieves a Precision of 0.887, an F1 score of 0.922, an area under the curve of 0.883, and a Recall of 0.960, outperforming benchmark algorithms such as Naive Bayes and chaotic lightning search algorithms in leak prediction. The improved unsupervised algorithm achieves a Precision of 0.888, a Recall of 0.944, an F1 score of 0.915, and an Accuracy of 0.861, surpassing benchmark algorithms. This study offers a high-precision solution for remote learning monitoring, which holds practical value in enhancing teaching quality, addressing learning challenges of students, and providing theoretical support for optimizing the learning environment. Future research will focus on further optimizing algorithm models.

关键词： prediction of dropping out of school temporal data of learning behavior variable scoring information bottleneck variable scoring self-encoder unsupervised methods

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Exploring the unifying concept of spondyloarthritis: a latent class analysis of the REGISPONSER registry

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RHEUMATOLOGY 2024年第11期63卷 3098-3105页

作者： Michelena, Xabier Sepriano, Alexandre Zhao, Sizheng Steven Lopez-Medina, Clementina Collantes-Estevez, Eduardo Font-Ugalde, Pilar Juanola, Xavier Marzo-Ortega, Helena Vall dHebron Hosp Univ Vall dHebron Barcelona Hosp Campus Rheumatol Unit Barcelona Spain Univ Leeds Leeds Teaching Hosp NHS Trust Leeds Inst Rheumat & Musculoskeletal Med Rheumatol Unit Leeds England Univ Nova Lisboa NOVA Med Sch Rheumatol Unit Lisbon Portugal Univ Manchester Ctr Epidemiol Versus Arthrit Div Musculoskeletal & Dermatol Sci Rheumatol Unit Manchester Lancs England Univ Cordoba Reina Sofia Univ Hosp Maimonides Inst Res Biomed Cordoba IMIB Rheumatol Unit Cordoba Spain Bellvitge Univ Hosp Rheumatol Unit LHospitalet Llobregat Spain Univ Leeds Leeds Teaching Hosp NHS Trust Chapel Allerton Hosp Rheumatol UnitNIHR Leeds BRCLeeds Teaching Hosp Second floor Leeds LS7 4SA England Univ Leeds Chapel Allerton Hosp Leeds Inst Rheumat & Musculoskeletal Med Second floor Leeds LS7 4SA England

Objectives The aim of our study was to identify the potential distinct phenotypes within a broad SpA *** We conducted a cross-sectional study using the REGISPONSER registry, which has data from 31 specialist centres in Spain, including patients with SpA who have fulfilled the ESSG criteria. A latent class analysis (LCA) was performed to identify the latent classes underlying SpA according to a set of predefined clinical and radiographic features, independently of expert *** In a population of 2319 SpA patients, a five-classes LCA model yielded the best fit. Classes named 'Axial with spine involvement' and 'Axial with isolated SI joint involvement' showed a primarily axial SpA phenotype defined by inflammatory back pain and high HLA-B27 prevalence. Patients in class 'Axial + peripheral' showed a similar distribution of manifest variables to previous classes but also had a higher likelihood of peripheral involvement (peripheral arthritis/dactylitis) and enthesitis, therefore representing a mixed (axial and peripheral) subtype. Classes 'Peripheral + psoriasis' and 'Axial + peripheral + psoriasis' were indicative of peripheral SpA (and/or PsA) with high likelihood of psoriasis, peripheral involvement, dactylitis, nail disease, and low HLA-B27 prevalence, while class 'Axial + peripheral + psoriasis' also exhibited increased probability of axial involvement both clinically and *** The identification of five latent classes in the REGISPONSER registry with significant overlap between axial and peripheral phenotypes is concordant with a unifying concept of SpA. Psoriasis and related features (nail disease and dactylitis) influenced the phenotype of both axial and peripheral manifestations.

关键词： spondyloarthritis axial spondyloarthritis peripheral spondyloarthritis unsupervised methods

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