Academic literature on the topic 'EFFICIENT CLASSIFICATION'

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Journal articles on the topic "EFFICIENT CLASSIFICATION"

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E., Niranjan. "Efficient Classification of Images in Wireless Endoscopy." Journal of Advanced Research in Dynamical and Control Systems 12, no. 04-Special Issue (2020): 1650–55. http://dx.doi.org/10.5373/jardcs/v12sp4/20201646.

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SUN, H. W., K. Y. LAM, D. GOLLMANN, S. L. CHUNG, J. B. LI, and J. G. SUN. "Efficient Fingercode Classification." IEICE Transactions on Information and Systems E91-D, no. 5 (2008): 1252–60. http://dx.doi.org/10.1093/ietisy/e91-d.5.1252.

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N., SHOBHA RANI. "An Efficient Deep Classification for Malayalam Handwritten Document." Journal of Research on the Lepidoptera 51, no. 2 (2020): 01–12. http://dx.doi.org/10.36872/lepi/v51i2/301074.

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Naïve, Anna Fay E., and Jocelyn B. Barbosa. "Efficient Accreditation Document Classification Using Naïve Bayes Classifier." Indian Journal of Science and Technology 15, no. 1 (2022): 9–18. http://dx.doi.org/10.17485/ijst/v15i1.1761.

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Ruggieri, S. "Efficient C4.5 [classification algorithm]." IEEE Transactions on Knowledge and Data Engineering 14, no. 2 (2002): 438–44. http://dx.doi.org/10.1109/69.991727.

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Bruno, Antonio, Giacomo Ignesti, Ovidio Salvetti, Davide Moroni, and Massimo Martinelli. "Efficient Lung Ultrasound Classification." Bioengineering 10, no. 5 (2023): 555. http://dx.doi.org/10.3390/bioengineering10050555.

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A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of tw
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Zhao, Puning, and Lifeng Lai. "Efficient Classification with Adaptive KNN." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 11007–14. http://dx.doi.org/10.1609/aaai.v35i12.17314.

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In this paper, we propose an adaptive kNN method for classification, in which different k are selected for different test samples. Our selection rule is easy to implement since it is completely adaptive and does not require any knowledge of the underlying distribution. The convergence rate of the risk of this classifier to the Bayes risk is shown to be minimax optimal for various settings. Moreover, under some special assumptions, the convergence rate is especially fast and does not decay with the increase of dimensionality.
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Kumar, Prabhat, SS Patil, Hemamalini HC, RH Chaudhari, and Rajeev Kumar. "Efficient classification of sugarcane genomes." Journal of Pharmacognosy and Phytochemistry 10, no. 1S (2021): 227–32. http://dx.doi.org/10.22271/phyto.2021.v10.i1sd.13474.

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Yoshinaga, Naoki, and Masaru Kitsuregawa. "Efficient Classification with Conjunctive Features." Journal of Information Processing 20, no. 1 (2012): 228–37. http://dx.doi.org/10.2197/ipsjjip.20.228.

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Lee, YoonSeok, and Sung-Eui Yoon. "Memory-Efficient NBNN Image Classification." Journal of Computing Science and Engineering 11, no. 1 (2017): 1–8. http://dx.doi.org/10.5626/jcse.2017.11.1.1.

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Dissertations / Theses on the topic "EFFICIENT CLASSIFICATION"

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Cisse, Mouhamadou Moustapha. "Efficient extreme classification." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066594/document.

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Dans cette thèse, nous proposons des méthodes a faible complexité pour la classification en présence d'un très grand nombre de catégories. Ces methodes permettent d'accelerer la prediction des classifieurs afin des les rendre utilisables dans les applications courantes. Nous proposons deux methodes destinées respectivement a la classification monolabel et a la classification multilabel. La première méthode utilise l'information hierarchique existante entre les catégories afin de créer un représentation binaire compact de celles-ci. La seconde approche , destinée aux problemes multilabel adpate
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Monadjemi, Amirhassan. "Towards efficient texture classification and abnormality detection." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409593.

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Alonso, Pedro. "Faster and More Resource-Efficient Intent Classification." Licentiate thesis, Luleå tekniska universitet, EISLAB, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81178.

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Intent classification is known to be a complex problem in Natural Language Processing (NLP) research. This problem represents one of the stepping stones to obtain machines that can understand our language. Several different models recently appeared to tackle the problem. The solution has become reachable with deep learning models. However, they have not achieved the goal yet.Nevertheless, the energy and computational resources of these modern models (especially deep learning ones) are very high. The utilization of energy and computational resources should be kept at a minimum to deploy them on
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Chatchinarat, Anuchin. "An efficient emotion classification system using EEG." Thesis, Chatchinarat, Anuchin (2019) An efficient emotion classification system using EEG. PhD thesis, Murdoch University, 2019. https://researchrepository.murdoch.edu.au/id/eprint/52772/.

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Emotion classification via Electroencephalography (EEG) is used to find the relationships between EEG signals and human emotions. There are many available channels, which consist of electrodes capturing brainwave activity. Some applications may require a reduced number of channels and frequency bands to shorten the computation time, facilitate human comprehensibility, and develop a practical wearable. In prior research, different sets of channels and frequency bands have been used. In this study, a systematic way of selecting the set of channels and frequency bands has been investigated, and r
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Duta, Ionut Cosmin. "Efficient and Effective Solutions for Video Classification." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/369314.

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The aim of this PhD thesis is to make a step forward towards teaching computers to understand videos in a similar way as humans do. In this work we tackle the video classification and/or action recognition tasks. This thesis was completed in a period of transition, the research community moving from traditional approaches (such as hand-crafted descriptor extraction) to deep learning. Therefore, this thesis captures this transition period, however, unlike image classification, where the state-of-the-art results are dominated by deep learning approaches, for video classification the deep learnin
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Duta, Ionut Cosmin. "Efficient and Effective Solutions for Video Classification." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2669/1/Duta_PhD-Thesis.pdf.

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The aim of this PhD thesis is to make a step forward towards teaching computers to understand videos in a similar way as humans do. In this work we tackle the video classification and/or action recognition tasks. This thesis was completed in a period of transition, the research community moving from traditional approaches (such as hand-crafted descriptor extraction) to deep learning. Therefore, this thesis captures this transition period, however, unlike image classification, where the state-of-the-art results are dominated by deep learning approaches, for video classification the deep learnin
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Stein, David Benjamin. "Efficient homomorphically encrypted privacy-preserving automated biometric classification." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130608.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 87-96).<br>This thesis investigates whether biometric recognition can be performed on encrypted data without decrypting the data. Borrowing the concept from machine learning, we develop approaches that cache as much computation as possible to a pre-computation step, allowing for efficient, homomorphically encrypted biometric recognition. We demonstrate two algorithms: an imp
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Graham, James T. "Efficient Generation of Reducts and Discerns for Classification." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1175639229.

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Ekman, Carl. "Traffic Sign Classification Using Computationally Efficient Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157453.

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Traffic sign recognition is an important problem for autonomous cars and driver assistance systems. With recent developments in the field of machine learning, high performance can be achieved, but typically at a large computational cost. This thesis aims to investigate the relation between classification accuracy and computational complexity for the visual recognition problem of classifying traffic signs. In particular, the benefits of partitioning the classification problem into smaller sub-problems using prior knowledge in the form of shape or current region are investigated. In the experime
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Nurrito, Eugenio. "Scattering networks: efficient 2D implementation and application to melanoma classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12261/.

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Machine learning is an approach to solving complex tasks. Its adoption is growing steadily and the several research works active on the field are publishing new interesting results regularly. In this work, the scattering network representation is used to transform raw images in a set of features convenient to be used in an image classification task, a fundamental machine learning application. This representation is invariant to translations and stable to small deformations. Moreover, it does not need any sort of training, since its parameters are fixed and only some hyper-parameters must be d
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Books on the topic "EFFICIENT CLASSIFICATION"

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Antonacopoulos, A. Page segmentation and classification using the description of the background: A flexible and efficient approach for documents with complex and traditional layouts. UMIST, 1995.

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M, Darby Melody, and Armstrong Laboratory (U.S.). Human Resources Directorate., eds. Efficiency of classification: A revision of the Brogden table. Air Force Materiel Command, Armstrong Laboratory, Human Resources Directorate, 1997.

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Björkgren, Magnus A. Case-mix classification and efficiency measurement in long-term care of the elderly. STAKES, 2002.

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Brown, Brian Victor. Efficiency of two mass sampling methods for sampling phorid flies (Diptera: Phoridae) in a tropical biodiversity survey. Natural History Museum of Los Angeles County, 1995.

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Hoffman, Frances M. Nursing productivity assessment and costing out nursing services. Lippincott, 1988.

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Balabanova, Evgeniya. Organizational behavior. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1048688.

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The textbook presents the main classifications of people's behavior in the workplace and consistently examines groups of factors that affect labor behavior in the organization. These factors are grouped into individual-personal, organizational-managerial and institutional-cultural. Particular attention is paid to the contradictions between the economic and social efficiency of organizations. The results of modern research devoted to the search for a balance between the economic efficiency of management activities and the social well-being of employees are presented.&#x0D; Meets the requirement
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Burkov, Aleksey. Technical operation of electric ships. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1048423.

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The book investigates the issues related to improving the efficiency of technical operation of ship electric drives, developed their classification. Identified ship's drives, having low reliability, designed and implemented technical solutions to increase their reliability. The appropriateness of the integrated assessment within the tasks of a mathematical and physical modeling. Developed and implemented mathematical and physical models for studies of electric drives. The proposed method, an algorithmic software, and made payments of contactors for work in the proposed technical solutions.&#x0
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Nursing productivity assessment and costing out nursing services. J.B. Lippincott, 1988.

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Grigoryan, Ekaterina. Integrated quality management system at the enterprises of the military-industrial complex. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1095033.

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In modern conditions, an integrated quality management system (ISMC) that meets the requirements of several international standards and contributes to improving the efficiency of enterprise management, creating conditions for its sustainable development, as well as the competitiveness of the enterprise and its products is becoming more and more popular.&#x0D; The monograph considers theoretical and methodological approaches to quality management at the enterprise. The relevance of the application of an integrated quality management system, including at the enterprises of the military-industria
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W, Bruce John, ed. Land tenure, agrarian structure, and comparative land use efficiency in Zimbabwe: Options for land tenure reform and land redistribution. Land Tenure Center, University of Wisconsin- Madison, 1994.

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Book chapters on the topic "EFFICIENT CLASSIFICATION"

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Park, Sang-Hyeun, and Johannes Fürnkranz. "Efficient Pairwise Classification." In Machine Learning: ECML 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74958-5_65.

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Awad, Mariette, and Rahul Khanna. "Support Vector Machines for Classification." In Efficient Learning Machines. Apress, 2015. http://dx.doi.org/10.1007/978-1-4302-5990-9_3.

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Moed, M., and E. N. Smirnov. "Efficient AdaBoost Region Classification." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_10.

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Kiel, R. "An Efficient Application of a Rule-Based System." In Information and Classification. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-50974-2_35.

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Torgo, Luis. "Computationally Efficient Linear Regression Trees." In Classification, Clustering, and Data Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_45.

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Schönberger, Johannes L., Alexander C. Berg, and Jan-Michael Frahm. "Efficient Two-View Geometry Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24947-6_5.

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Shen, Jialie, John Shepherd, and Anne H. H. Ngu. "On Efficient Music Genre Classification." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11408079_24.

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Shukla, K. K., and Arvind K. Tiwari. "DWT-Based Power Quality Classification." In Efficient Algorithms for Discrete Wavelet Transform. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4941-5_5.

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Yin, Xiaoxin, and Jiawei Han. "Efficient Classification from Multiple Heterogeneous Databases." In Knowledge Discovery in Databases: PKDD 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564126_40.

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Grabocka, Josif, Erind Bedalli, and Lars Schmidt-Thieme. "Efficient Classification of Long Time-Series." In ICT Innovations 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37169-1_5.

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Conference papers on the topic "EFFICIENT CLASSIFICATION"

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"Efficient Gridding of Real Microarray Images." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001192501210130.

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Chevitarese, Daniel Salles, Daniela Szwarcman, Emilio Vital Brazil, and Bianca Zadrozny. "Efficient Classification of Seismic Textures." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489654.

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Kim, Myung, Ara Khil, and Joung Ryu. "Efficient Fuzzy Rules For Classification." In 2006 International Workshop on Integrating AI and Data Mining. IEEE, 2006. http://dx.doi.org/10.1109/aidm.2006.5.

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Sakurai, Yasushi, Lei Li, Rosalynn Chong, and Christos Faloutsos. "Efficient Distribution Mining and Classification." In Proceedings of the 2008 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2008. http://dx.doi.org/10.1137/1.9781611972788.58.

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Vargaftik, Shay, and Yaniv Ben-Itzhak. "Efficient multiclass classification with duet." In EuroSys '22: Seventeenth European Conference on Computer Systems. ACM, 2022. http://dx.doi.org/10.1145/3517207.3526970.

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Kuksa, Pavel P., and Vladimir Pavlovic. "Spatial Representation for Efficient Sequence Classification." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.1159.

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Chamansingh, Nicholas, and Patrick Hosein. "Efficient sentiment classification of Twitter feeds." In 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA). IEEE, 2016. http://dx.doi.org/10.1109/ickea.2016.7802996.

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Botwicz, Jakub, and Piotr Buciak. "Hardware Support for Efficient Data Classification." In EUROCON 2007 - The International Conference on "Computer as a Tool". IEEE, 2007. http://dx.doi.org/10.1109/eurcon.2007.4400607.

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Bhardwaj, Shweta, Mukundhan Srinivasan, and Mitesh M. Khapra. "Efficient Video Classification Using Fewer Frames." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00044.

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Hung, Che-Lun, Hsiao-Hsi Wang, Shih-Wei Guo, Yaw-Ling Lin, and Kuan-Ching Li. "Efficient GPGPU-Based Parallel Packet Classification." In 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2011. http://dx.doi.org/10.1109/trustcom.2011.186.

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Reports on the topic "EFFICIENT CLASSIFICATION"

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Deshwal, Pinky, Bhanu Prakash Ila2, Naveen Mehata Kondamudi1, and Anmol Gaurav. Software parts classification for agile and efficient product life cycle management. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2304p5417007.

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Johnson, Cecil D., Joseph Zeldner, and Dolores Scholarios. Developing New Test Selection and Weight Stabilization Techniques for Designing Classification Efficient Composites. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada298740.

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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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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Desa, Hazry, and Muhammad Azizi Azizan. OPTIMIZING STOCKPILE MANAGEMENT THROUGH DRONE MAPPING FOR VOLUMETRIC CALCULATION. Penerbit Universiti Malaysia Perlis, 2023. http://dx.doi.org/10.58915/techrpt2023.004.

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Stockpile volumetric calculation is an important aspect in many industries, including construction, mining, and agriculture. Accurate calculation of stockpile volumes is essential for efficient inventory management, logistics planning, and quality control. Traditionally, stockpile volumetric calculation is done using ground-based survey methods, which can be time-consuming, labour-intensive, and often inaccurate. However, with the recent advancements in drone technology, it has become possible to use drones for stockpile volumetric calculation, providing a faster, safer, and more accurate solu
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wave
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Wang, Jianyong, and George Karypis. HARMONY: Efficiently Mining the Best Rules for Classification. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada439469.

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Cheng, Cheng, and Melody Darby. Efficiency of Classification: A Revision of the Brogden Table. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada327930.

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Thompson, David, and Damon Hartley. Air classification of forest residue for tissue and ash separation efficiency. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1905857.

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Savosko, V., I. Komarova, Yu Lykholat, E. Yevtushenko, and T. Lykholat. Predictive model of heavy metals inputs to soil at Kryvyi Rih District and its use in the training for specialists in the field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4511.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining &amp; metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Centra
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