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Relevant bibliographies by topics / Supervised classifier

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Academic literature on the topic 'Supervised classifier'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Supervised classifier"

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HE, Ping, Xiao-Hua XU, and Ling CHEN. "Supervised Spectral Space Classifier." Journal of Software 23, no. 4 (2012): 748–64. http://dx.doi.org/10.3724/sp.j.1001.2012.04039.

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Dar, Basra Farooq, Malik Sajjad Ahmed Nadeem, Samina Khalid, Farzana Riaz, Yasir Mahmood, and Ghias Hameed. "Improving the Classification Ability of Delegating Classifiers Using Different Supervised Machine Learning Algorithms." Computer and Information Science 16, no. 3 (2023): 22. http://dx.doi.org/10.5539/cis.v16n3p22.

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Cancer Classification &amp; Prediction Is Vitally Important for Cancer Diagnosis. DNA Microarray Technology Has Provided Genetic Data That Has Facilitated Scientists Perform Cancer Research. Traditional Methods of Classification Have Certain Limitations E.G. Traditionally A Proposed DSS Uses A Single Classifier at A Time for Classification or Prediction Purposes. To Increase Classification Accuracy, Reject Option Classifiers Has Been Proposed in Machine Learning Literature. In A Reject Option Classifier, A Rejection Region Is Defined and The Samples Fall in That Region Are Not Classified b

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Rönnholm, P., S. Wittke, M. Ingman, et al. "UTILISING SIMULATED TREE DATA TO TRAIN SUPERVISED CLASSIFIERS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 633–39. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-633-2022.

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Abstract. The aim of our research was to examine whether simulated forest data can be utilized for training supervised classifiers. We included two classifiers namely the random forest classifier and the novel convolutional neural network classifier that utilizes feature images. We simulated tree parameters and created a feature vector for each tree. The original feature vector was utilised with random forest classifier. However, these feature vectors were also converted into feature images suitable for input into a YOLO (You Only Look Once) convolutional neural network classifier. The selecte

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Ren, Yunfei. "Sentiment Prediction by a Classifier." Applied and Computational Engineering 8, no. 1 (2023): 18–25. http://dx.doi.org/10.54254/2755-2721/8/20230060.

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In real life, there is far more unprocessed data than labeled data, which brings a large amount of data that cannot be directly used for machine learning training. Based on the tweet dataset processed by Natural Language Processing (NLP), this paper uses a variety of machine learning models for training and comparison. Moreover, different performances are analyzed and discussed. Since labeled datasets are difficult to obtain, the use of supervised learning will be limited. However, the number of unlabeled datasets is very large, which can provide a continuous training set for machine learning.

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Zhen, Hao, Yucheng Shi, Jidong J. Yang, and Javad Mohammadpour Vehni. "Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification." Applied Computing and Intelligence 3, no. 1 (2022): 13–26. http://dx.doi.org/10.3934/aci.2023002.

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<abstract> <p>Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to th

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Althobaiti, Maha, Udo Kruschwitz, and Massimo Poesio. "Combining Minimally-supervised Methods for Arabic Named Entity Recognition." Transactions of the Association for Computational Linguistics 3 (December 2015): 243–55. http://dx.doi.org/10.1162/tacl_a_00136.

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Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better r

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Zhou, Anwa, Zhibin Zhu, and Hao Fan. "A new semi-supervised PSVM classifier." Applied Mathematics and Computation 219, no. 8 (2012): 4006–12. http://dx.doi.org/10.1016/j.amc.2012.10.037.

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Kim, Geunhwan, YoungSang Hwang, Keunhwa Lee, and Youngmin Choo. "Generalization performance analysis of anomaly detection-based active sonar classifier using anomaly score landscape." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A343. http://dx.doi.org/10.1121/10.0027763.

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This study explores generalization performance of anomaly detection-based active sonar classifier using the deep neural network (NN). Despite its superior performance, NN remains challenging to interpret its decision due to black-box nature, which, consequently, makes it hard to trust the results. To address this, we employ the loss landscape method used to analyze the generalization performance of supervised learning-based NN. Owing to the inapplicability of the conventional loss landscapes to anomaly detection-based classifier, we propose a novel anomaly score landscape for the anomaly detec

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LONG, JUN, WENTAO ZHAO, FANGZHOU ZHU, and ZHIPING CAI. "ACTIVE LEARNING TO DEFEND POISONING ATTACK AGAINST SEMI-SUPERVISED INTRUSION DETECTION CLASSIFIER." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, supp01 (2011): 93–106. http://dx.doi.org/10.1142/s0218488511007362.

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Intrusion detection systems play an important role in computer security. To make intrusion detection systems adaptive to changing environments, supervised learning techniques had been applied in intrusion detection. However, supervised learning needs a large amount of training instances to obtain classifiers with high accuracy. Limited to lack of high quality labeled instances, some researchers focused on semi-supervised learning to utilize unlabeled instances enhancing classification. But involving the unlabeled instances into the learning process also introduces vulnerability: attackers can

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R, B. Alemayehu, Rout M, and C. Satapathy S. "Supervised Learning-Based Prediction and Analysis of Amharic Twitter Data." Indian Journal of Science and Technology 16, no. 47 (2023): 4561–68. https://doi.org/10.17485/IJST/v16i47.591.

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Abstract <strong>Objectives:</strong> This study aims to prepare a corpus and explore sentiment analysis in the Amharic language, which is increasingly used due to the growth of both the language and the Internet. <strong>Methods:</strong> The study acquired 23,646 Amharic tweets from Twitter using the Twitter API, cleaned and normalized the text through preprocessing, and manually annotated the data as positive, negative, or neutral by three annotators. The study utilized a multi-scale sentiment analysis approach to experimentally evaluate the classifier's performance and compa

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Dissertations / Theses on the topic "Supervised classifier"

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Breland, Adrienne E. "A supervised strain classifier." abstract and full text PDF (free order & download UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1453199.

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Gu, Shenkai. "Multi-objective and semi-supervised heterogeneous classifier ensembles." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/813278/.

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In the recent years, many applications in machine learning involve an increasingly large number of features and samples, which poses new challenges to many learning algorithms. To address these challenges, ensemble learning methods, which uses multiple base learners, have been proposed to achieve better predictive performance. This thesis covers a range of topics in ensemble classification, including multi-objective and semi-supervised heterogeneous classier ensembles. We first present an empirical study on heterogeneous classifier ensembles, which confirms that heterogeneous ensembles outperf

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Scalise, Damiano <1991&gt. "Global biome shifts under climate change scenarios: a supervised classifier modeling approach." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12480.

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Quantificare il potenziale di impatto dei diversi scenari di cambiamento climatico in relazione agli areali di distribuzione dei biomi terrestri (archetipi di comunità ecologiche prevalenti su larga scala) è un obiettivo importante sia per motivi di ricerca, sia nella sfera della sensibilizzazione pubblica. Negli ultimi due decenni diversi approcci sono emersi, ma, per via della grande varietà di metologie, i risultati non sono propriamente comparabili in termini quantitativi. Inoltre, fino ad oggi, nessuno studio ha potuto fornire un approccio coerentemente validato per la realizzazione di ma

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Watkins, Andrew B. "AIRS: a resource limited artificial immune classifier." Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-11052001-102048.

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Lindholm, Alexander. "A study about fraud detection and the implementation of SUSPECT - Supervised and UnSuPervised Erlang Classifier Tool." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-222774.

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Fraud detection is a game of cat and mouse between companies and people trying to commit fraud. Most of the work within the area is not published due to several reasons. One of the reasons is that if a company publishes how their system works, the public will know how to evade detection. This paper describes the implementation of a proof-of-concept fraud detection system. The prototype named SUSPECT uses two different methods for fraud detection. The first one being a supervised classifier in form of an artificial neural network and the second one being an unsupervised classifier in the form

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Shafi, Kamran Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "An online and adaptive signature-based approach for intrusion detection using learning classifier systems." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38991.

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This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, whic

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Bouzouita-Bayoudh, Inès. "Etude et extraction des règles associatives de classification en classification supervisée." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20217.

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Dans le cadre de cette thèse, notre intérêt se porte sur la précision de la classification et l'optimalité du parcours de l'espace de recherche. L'objectif recherché est d'améliorer la précision de classification en étudiant les différents types de règles et de réduire l'espace de recherche des règles. Nous avons proposé une approche de classification IGARC permettant de générer un classifieur formé d'une base de règles de classification génériques permettant de mieux classer les nouveaux objets grâce à la flexibilité de petites prémisses caractérisant ces règles. De plus cette approche manipu

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Duan, Haoyang. "Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31113.

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From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares t

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Dam, Hai Huong Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.

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Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the

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Alves, Matheus. "Social training : aprendizado semi supervisionado utilizando funções de escolha social." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169887.

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Dada a grande quantidade de dados gerados atualmente, apenas uma pequena porção dos mesmos pode ser rotulada manualmente por especialistas humanos. Isso é um desafio comum para aplicações de aprendizagem de máquina. Aprendizado semi-supervisionado aborda este problema através da manipulação dos dados não rotulados juntamente aos dados rotulados. Entretanto, se apenas uma quantidade limitada de exemplos rotulados está disponível, o desempenho da tarefa de aprendizagem de máquina (e.g., classificação) pode ser não satisfatória. Diversas soluções abordam este problema através do uso de uma ensemb

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Books on the topic "Supervised classifier"

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A, Landgrebe D., and United States. National Aeronautics and Space Administration., eds. Design of partially supervised classifiers for multispectral image data. School of Electrical Engineering, Purdue University, 1993.

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Bruyne, Steven De. Process, Data and Classifier Models for Accessible Supervised Classification Problem Solving. Academic & Scientific Publishers, 2010.

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Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kern

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Design of partially supervised classifiers for multispectral image data. School of Electrical Engineering, Purdue University, 1993.

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Vidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i

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Danny, Busch. Part A Annotated Guide, 6 Private Enforcement of the Market Abuse Regulation in European Law. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198811756.003.0006.

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This chapter discusses the role of the Market Abuse Regulation in private law. An infringement of the MAR has an important effect on the private law relations between the infringer and the investing public. As regulatory provisions of this nature are classified as public law, any failure to comply with the MAR will also affect the infringer’s relationship with the competent financial supervisor. In other words, the relevant financial supervisor can enforce these provisions under administrative law in the event of an infringement. This is essentially no different from the situation under of the

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Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described b

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Book chapters on the topic "Supervised classifier"

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Didaci, Luca, Gian Luca Marcialis, and Fabio Roli. "Semi-supervised Co-update of Multiple Matchers." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_16.

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Zanda, Manuela, and Gavin Brown. "A Study of Semi-supervised Generative Ensembles." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_25.

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Zhou, Zhi-Hua. "When Semi-supervised Learning Meets Ensemble Learning." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_53.

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Kleinberg, Eugene M. "A Mathematically Rigorous Foundation for Supervised Learning." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_6.

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Conversano, Claudio, Roberta Siciliano, and Francesco Mola. "Supervised Classifier Combination through Generalized Additive Multi-model." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_16.

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Mahmood, Amjad, Tianrui Li, Yan Yang, Hongjun Wang, and Mehtab Afzal. "Semi-supervised Clustering Ensemble for Web Video Categorization." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38067-9_17.

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Roli, Fabio. "Semi-supervised Multiple Classifier Systems: Background and Research Directions." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_1.

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Krasotkina, Olga, Oleg Seredin, and Vadim Mottl. "Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation." In Multiple Classifier Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20248-8_8.

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Smits, Paul C. "Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_27.

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Tatarchuk, Alexander, Eugene Urlov, Vadim Mottl, and David Windridge. "A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12127-2_17.

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Conference papers on the topic "Supervised classifier"

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Khalid, Nida, Aima Zahid, and Shibli Nisar. "A Password Strength Classifier Using Supervised Machine Learning." In 2024 21st International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2024. https://doi.org/10.1109/ibcast61650.2024.10876915.

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Yatsu, Naoya, Hiroki Shiraishi, Hiroyuki Sato, and Keiki Takadama. "Prototype Generation with the sUpervised Classifier System on kNN Matching." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611796.

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Lu, Lin, Xiaohua Xu, Ping He, Yue Ma, Qi Chen, and Ling Chen. "Supervised Lazy Random Walk Classifier." In 2013 10th Web Information System and Application Conference (WISA). IEEE, 2013. http://dx.doi.org/10.1109/wisa.2013.60.

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Yan, Yuesong, Jinsheng Cui, and Zhisong Pan. "Semi-supervised Fuzzy Relational Classifier." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.207.

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Aniyom, Ebenezer, Anthony Chikwe, and Jude Odo. "Hybridization of Optimized Supervised Machine Learning Algorithms for Effective Lithology." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/212019-ms.

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Abstract Lithology identification is an important aspect in reservoir characterization with one of its main purpose of well planning and drilling activities. A faster and more effective lithology identification could be obtained from an ensemble of optimized models using voting classifiers. In this study, a voting classifier machine learning model was developed to predict the lithology of different lithologies using an assembly of different classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier, K-Nearest Neighbor, and Multilayer Perceptron (MLP)

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Impedovo, Donato, Giuseppe Pirlo, and Donato Barbuzzi. "Supervised learning strategies in multi-classifier systems." In 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310470.

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Sheri, Ahmad M., Seungjong No, and Moongu Jeon. "Semi-Supervised Neural Classifier using Memristive Nanodevices." In Biomedical Engineering. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.764-149.

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Gong, Chen, Xiaojun Chang, Meng Fang, and Jian Yang. "Teaching Semi-Supervised Classifier via Generalized Distillation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/298.

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Semi-Supervised Learning (SSL) is able to build reliable classifier with very scarce labeled examples by properly utilizing the abundant unlabeled examples. However, existing SSL algorithms often yield unsatisfactory performance due to the lack of supervision information. To address this issue, this paper formulates SSL as a Generalized Distillation (GD) problem, which treats existing SSL algorithm as a learner and introduces a teacher to guide the learner?s training process. Specifically, the intelligent teacher holds the privileged knowledge that ?explains? the training data but remains unkn

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Zhang, Qingjiu, and Shiliang Sun. "Evolutionary classifier ensembles for semi-supervised learning." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596894.

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Lu, Yu-Ding, Hsin-Ying Lee, Hung-Yu Tseng, and Ming-Hsuan Yang. "Self-Supervised Audio Spatialization with Correspondence Classifier." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803494.

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Reports on the topic "Supervised classifier"

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de Dieu Niyigena, Jean, Innocent Ngaruye, Joseph Nzabanita, and Martin Singull. Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-02.

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Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then

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Forteza, Nicolás, and Sandra García-Uribe. A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach. Banco de España, 2023. http://dx.doi.org/10.53479/34613.

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Errors in the collection of household finance survey data may proliferate in population estimates, especially when there is oversampling of some population groups. Manual case-by-case revision has been commonly applied in order to identify and correct potential errors and omissions such as omitted or misreported assets, income and debts. We derive a machine learning approach for the purpose of classifying survey data affected by severe errors and omissions in the revision phase. Using data from the Spanish Survey of Household Finances we provide the best-performing supervised classification al

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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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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42425.

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Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispec

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext

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