Relevant bibliographies by topics / Classification used machine learning

Contents

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

Academic literature on the topic 'Classification used machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Classification used machine learning"

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Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed sta
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Carpenter, Chris. "Dynamometer-Card Classification Uses Machine Learning." Journal of Petroleum Technology 72, no. 03 (2020): 52–53. http://dx.doi.org/10.2118/0320-0052-jpt.

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Hall, Brendon. "Facies classification using machine learning." Leading Edge 35, no. 10 (2016): 906–9. http://dx.doi.org/10.1190/tle35100906.1.

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There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist's toolbox, much of which used to be only available in proprietary (and expensive) software platf
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Hang, Weiqiang, and Timothy Banks. "Machine learning applied to pack classification." International Journal of Market Research 61, no. 6 (2019): 601–20. http://dx.doi.org/10.1177/1470785319841217.

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Pack or product classification is a quite common task in market research, particularly for sales tracking audits and related services. Electronic data sources have led to increased volumes, both in the sales volume being tracked and also the number of packs (or stock keeping units). The increase in packs needing to be classified presents a problem, in that, it needs to be done accurately and quickly. Traditional solutions using people for the classifications can be costly, due to the large number of people required to process the classifications in a timely and accurate manner. Reducing the ma
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Parhusip, Hanna Arini, Bambang Susanto, Lilik Linawati, Suryasatriya Trihandaru, Yohanes Sardjono, and Adella Septiana Mugirahayu. "Classification Breast Cancer Revisited with Machine Learning." International Journal on Data Science 1, no. 1 (2020): 42–50. http://dx.doi.org/10.18517/ijods.1.1.42-50.2020.

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The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The ori
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Butler, Brooks A., Spencer Wadsworth, Dallen Stark, et al. "Feature reduction of crowd noise used for machine learning classification." Journal of the Acoustical Society of America 146, no. 4 (2019): 2906. http://dx.doi.org/10.1121/1.5137086.

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Litman, D. J. "Cue Phrase Classification Using Machine Learning." Journal of Artificial Intelligence Research 5 (September 1, 1996): 53–94. http://dx.doi.org/10.1613/jair.327.

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Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets
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Nikmon, Marcel, Roman Budjač, Daniel Kuchár, Peter Schreiber, and Dagmar Janáčová. "Convolutional Networks Used to Classify Video and Audio Data." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 27, no. 45 (2019): 113–20. http://dx.doi.org/10.2478/rput-2019-0034.

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Abstract Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input
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B.Meena, Preeth, and Radha, P. "Disease Classification and Prediction using Ensemble Machine Learning Classification Algorithm." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 202–14. http://dx.doi.org/10.35940/ijrte.f5507.039621.

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In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge
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Punia, Sanjeev Kumar, Manoj Kumar, Thompson Stephan, Ganesh Gopal Deverajan, and Rizwan Patan. "Performance Analysis of Machine Learning Algorithms for Big Data Classification." International Journal of E-Health and Medical Communications 12, no. 4 (2021): 60–75. http://dx.doi.org/10.4018/ijehmc.20210701.oa4.

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In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data fro
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Dissertations / Theses on the topic "Classification used machine learning"

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Testi, Enrico. "Machine Learning for User Traffic Classification in Wireless Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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With the advent of Internet of Things telecommunications will play a crucial role in every day life. The rapidly growing demand for radio services by millions of user all over the world will make the radio spectrum an increasingly valuable resource. The modern standards of communications provide a static utilization of the radio spectrum resources, which results in its under-utilization. Therefore let us imagine a dynamic sharing of the radio resources, where every device can use a portion of such resources if and only if they are not utilized yet. In this regard, the Federal Communicati
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Feng, Zao. "Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/9463.

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Axén, Maja, and Jennifer Karlberg. "Binary Classification for Predicting Customer Churn." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171892.

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Predicting when a customer is about to turn to a competitor can be difficult, yet extremely valuable from a business perspective. The moment a customer stops being considered a customer is known as churn, a widely researched topic in several industries when dealing with subscription-services. However, in industries with non-subscription services and products, defining churn can be a daunting task and the existing literature does not fully cover this field. Therefore, this thesis can be seen as a contribution to current research, specially when not having a set definition for churn. A definitio
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Olofsson, Nina, and Nivin Fakih. "A Machine Learning Approach to Dialogue Act Classification in Human-Robot Conversations : Evaluation of dialogue act classification with the robot Furhat and an analysis of the market for social robots used for education." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175705.

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The interest in social robots has grown dramatically in the last decade. Several studies have investigated the potential markets for such robots and how to enhance their human-like abilities. Both of these subjects have been investigated in this thesis using the company Furhat Robotics, and their robot Furhat, as a case study. This paper explores how machine learning could be used to classify dialogue acts in human-robot conversations, which could help Furhat interact in a more human-like way. Dialogue acts are acts of natural speech, such as questions or statements. Several variables and thei
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Sousa, Beatriz Fernandes SimplÃcio. "Remote sensing and machine learning applied to soil use detection in caatinga bioma." Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=5201.

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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico<br>In order to manage adequately natural resources inside a fragile environment, just like Caatinga, one should know its properties and spatial distribution. This work proposes an approach to classify LANDSAT-5 satellite images. These images, corresponding to a semiarid environment located in Iguatu country, Ceara, Brazil, were classified aiming at detecting the Caatinga biome by two type of classifiers based on machinery learning: Multi Layer Perceptron (MLP) and Support Vector Machine (SVM). The static classifier of Maximum Likeli
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Brown, Ryan Charles. "Development of Ground-Level Hyperspectral Image Datasets and Analysis Tools, and their use towards a Feature Selection based Sensor Design Method for Material Classification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84944.

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Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in t
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Tang, Danny M. Eng Massachusetts Institute of Technology. "Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123130.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 129-131).<br>As machine learning permeates our society and manifests itself through commonplace technologies such as autonomous vehicles, facial recognition, and online store recommendations, it is necessary that the increasin
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Zaman, Bushra. "Remotely Sensed Data Assimilation Technique to Develop Machine Learning Models for Use in Water Management." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/584.

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Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spat
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Marquez, Astrid. "Use of multispectral data to identify farm intensification levels by applying emergent computing techniques." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6232.

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Concern about feeding an ever increasing population has long been one of humankind’s most pressing problems. This has been addressed throughout history by introducing into farming systems changes allowing them to produce more per unit of land area. However, these changes have also been linked to negative effects on the socio economic and environmental sphere, that have created the need for an integral understanding of this phenomenon. This thesis describes the application of learning machine methods to induct a relationship between the spectral response of farms’ land cover and their intensifi
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Andersson, Martin, and Marcus Mazouch. "Binary classification for predicting propensity to buy flight tickets. : A study on whether binary classification can be used to predict Scandinavian Airlines customers’ propensity to buy a flight ticket within the next seven days." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160855.

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A customers propensity to buy a certain product is a widely researched field and is applied in multiple industries. In this thesis it is showed that using binary classification on data from Scandinavian Airlines can predict their customers propensity to book a flight within the next coming seven days. A comparison between logistic regression and support vector machine is presented and logistic regression with reduced number of variables is chosen as the final model, due to it’s simplicity and accuracy. The explanatory variables contains exclusively booking history, whilst customer demographics
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Books on the topic "Classification used machine learning"

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Shuurmans, Dale Eric. Effective classification learning. University of Toronto, 1996.

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Buntine, Wray. Myths and legends in learning classification rules. NASA, Ames Research Center, Research Institute for Advanced Computer Science, 1990.

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1967-, Meira Wagner, ed. Demand-driven associative classification. Springer, 2011.

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Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3.

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Mohak, Shah, ed. Evaluating Learning Algorithms: A classification perspective. Cambridge University Press, 2011.

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Pattern classification using ensemble methods. World Scientific, 2010.

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A, Kulikowski Casimir, ed. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. M. Kaufmann Publishers, 1991.

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Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98675-3.

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Quiñonero-Candela, Joaquin, Ido Dagan, Bernardo Magnini, and Florence d’Alché-Buc, eds. Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11736790.

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Bacon, Simon. Machine learning for text classification of USENET newsgroups: A comparison of learning algorithms and dimensionality reduction techniques. The Author], 1997.

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Book chapters on the topic "Classification used machine learning"

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Fleury, L., and Y. Masson. "Comparison Between Some Indices Mainly Used in Machine Learning." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61159-9_12.

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Daouadi, Kheir Eddine, Rim Zghal Rebaï, and Ikram Amous. "Towards a Statistical Approach for User Classification in Twitter." In Machine Learning for Networking. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19945-6_3.

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Wong, Alex K. S., John W. T. Lee, and Daniel S. Yeung. "Use of Linguistic Features in Context-Sensitive Text Classification." In Advances in Machine Learning and Cybernetics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_73.

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Saif Eldin Mukhtar Heamida, Islam, and A. L. Samani Abd Elmutalib Ahmed. "The Classification Model Sentiment Analysis of the Sudanese Dialect Used Into the Internet Service in Sudan." In Enabling Machine Learning Applications in Data Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6129-4_26.

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Specht, Felix, and Jens Otto. "Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_11.

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AbstractCondition monitoring systems based on deep neural networks are used for system failure detection in cyber-physical production systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. This can result in a serious threat for production systems and employees. This work introduces an approach named CyberProtect to prevent misclassification caused by adversarial example attacks. The approach generates adversarial examples for retraining a deep neural network which results in a hardened variant of the deep neural network. The hardened deep neural network sustains a significant better classification rate (82% compared to 20%) while under attack with adversarial examples, as shown by empirical results.
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Alrabie, Sami, Mrhrez Boulares, and Ahmed Barnawi. "An Efficient Framework to Build Up Heart Sounds and Murmurs Datasets Used for Automatic Cardiovascular Diseases Classifications." In Enabling Machine Learning Applications in Data Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6129-4_2.

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Del Grossi, André A., Helen C. de Mattos Senefonte, and Vinícius G. Quaglio. "Prostate Cancer Biopsy Recommendation through Use of Machine Learning Classification Techniques." In Advances in Artificial Intelligence -- IBERAMIA 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12027-0_57.

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Bedi, Pradeep, S. B. Goyal, and Jugnesh Kumar. "Applied Classification Algorithms Used in Data Mining During the Vocational Guidance Process in Machine Learning." In Inventive Systems and Control. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1395-1_11.

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Wich, Maximilian, Edoardo Mosca, Adrian Gorniak, Johannes Hingerl, and Georg Groh. "Explainable Abusive Language Classification Leveraging User and Network Data." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86517-7_30.

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Franzen, Martina, Laure Kloetzer, Marisa Ponti, Jakub Trojan, and Julián Vicens. "Machine Learning in Citizen Science: Promises and Implications." In The Science of Citizen Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58278-4_10.

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AbstractThe chapter gives an account of both opportunities and challenges of human–machine collaboration in citizen science. In the age of big data, scientists are facing the overwhelming task of analysing massive amounts of data, and machine learning techniques are becoming a possible solution. Human and artificial intelligence can be recombined in citizen science in numerous ways. For example, citizen scientists can be involved in training machine learning algorithms in such a way that they perform certain tasks such as image recognition. To illustrate the possible applications in different areas, we discuss example projects of human–machine cooperation with regard to their underlying concepts of learning. The use of machine learning techniques creates lots of opportunities, such as reducing the time of classification and scaling expert decision-making to large data sets. However, algorithms often remain black boxes and data biases are not visible at first glance. Addressing the lack of transparency both in terms of machine action and in handling user-generated data, the chapter discusses how machine learning is actually compatible with the idea of active citizenship and what conditions need to be met in order to move forward – both in citizen science and beyond.
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Conference papers on the topic "Classification used machine learning"

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Bulbul, H. I., and Ö Unsal. "Comparison of Classification Techniques used in Machine Learning as Applied on Vocational Guidance Data." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.49.

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L .Vinagreiro, Michel Andre, Edson C. Kitani, Armando Antonio M. Lagana, and Leopoldo R. Yoshioka. "Using Multilinear Feature Space to Accelerate CNN Classification." In 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111109.

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Computer vision plays a crucial role in ADAS security and navigation, as most systems are based on deep CNN architectures the computational resource to run a CNN algorithm is demanding. Therefore, the methods to speed up computation have become a relevant research issue. Even though several works on acceleration techniques found in the literature have not yet been achieved satisfactory results for embedded real-time system applications. This paper presents an alternative approach based on the Multilinear Feature Space (MFS) method resorting to transfer learning from large CNN architectures. Th
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Dabetwar, Shweta, Stephen Ekwaro-Osire, and João Paulo Dias. "Damage Classification of Composites Using Machine Learning." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11851.

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Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite m
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Кривошеев, Николай, Nikolay Krivosheev, Владимир Спицын, and Vladimir Spicyn. "Machine Learning Methods for Classification Textual Information." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-266-269.

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A method for classifying textual information based on the apparatus of convolutional neural networks is considered. The text preprocessing algorithm is presented. Text preprocessing consists of: lemmatizing words, removing stop words, processing text characters, etc. The word-by-word conversion of the text into dense vectors is performed. Testing is carried out on the basis of the text data of "The 20 Newsgroups". This sample contains a collection of approximately 20,000 news stories in English, which is divided (approximately) evenly between 20 different categories. The accuracy of the best c
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Anika, Afra, Md Hasibur Rahman, Salekul Islam, Abu Shafin Mohammad Mahdee Jameel, and Chowdhury Rafeed Rahman. "A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification." In 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON). IEEE, 2019. http://dx.doi.org/10.1109/spicscon48833.2019.9065107.

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Rooks, Tyler F., Andrea S. Dargie, and Valeta Carol Chancey. "Machine Learning Classification of Head Impact Sensor Data." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12173.

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Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conduct
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Liu, Xinglu, Wan Wang, Wai Kin Victor Chan, Chiung Ying Kuan, and Junyoung Lee. "User Classification in Electronic Devices Using Machine Learning Methods." In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2019. http://dx.doi.org/10.1109/ieem44572.2019.8978567.

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Testi, Enrico, Elia Favarelli, and Andrea Giorgetti. "Machine Learning for User Traffic Classification in Wireless Systems." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553196.

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Shiling, Zhang. "The Study of PSO-SVM and PSO-GRNN Algorithm Used in the Fault Pattern Classification of Transformer." In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. ACM, 2020. http://dx.doi.org/10.1145/3383972.3384015.

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Cufoglu, Ayse, Mahi Lohi, and Kambiz Madani. "A Comparative Study of Selected Classification Accuracy in User Profiling." In 2008 Seventh International Conference on Machine Learning and Applications. IEEE, 2008. http://dx.doi.org/10.1109/icmla.2008.139.

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Reports on the topic "Classification used machine learning"

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

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collec
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Shabalina, A., A. Carpenter, M. Rahman, C. Tennant, and L. Vidyaratne. Machine Learning Based Cavity Fault Classification and Prediction. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1735851.

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Pilania, Ghanshyam, James E. Gubernatis, Turab Lookman, and Rampi Ramprasad. Materials Classification & Accelerated Property Predictions using Machine Learning. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1184607.

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Waldrop, Lauren, Carl Hart, Nancy Parker, Chris Pettit, and Scotland McIntosh. Utility of machine learning algorithms for natural background photo classification. Cold Regions Research and Engineering Laboratory (U.S.), 2018. http://dx.doi.org/10.21079/11681/27344.

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Downard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40439.

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The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from o
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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Davis, Benjamin, Esteban Guillen, and Larry Bacon. Applying Machine Learning to the Classification of DC-DC Converters ? NA-22 Final Report. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1735789.

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Hemphill, Geralyn M. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1329544.

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Davis, Benjamin. Applying Machine Learning to the Classification of DC-DC Converters: Real-world data collection processing & Validation. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1670255.

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Arnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200064.

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In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.
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