Relevant bibliographies by topics / EFFICIENT CLASSIFICATION

Academic literature on the topic 'EFFICIENT CLASSIFICATION'

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Published: 11 September 2023

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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 (March 31, 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 (May 1, 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 (April 20, 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 (January 5, 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 (May 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 two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.
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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 (May 18, 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 (January 1, 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 (March 30, 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 le framework des Filtres de Bloom a la representation de sous ensembles de labels sous forme de de vecteurs binaires sparses. Dans chacun des cas, des classifieurs binaires sont appris afin de prédire les representations des catégories/labels et un algorithme permettant de retrouver l'ensemble de catégories pertinentes a partir de la représentation prédite est proposée. Les méthodes proposées sont validées par des expérience sur des données de grandes échelles et donnent des performances supérieures aux méthodes classiquement utilisées pour la classification extreme
We propose in this thesis new methods to tackle classification problems with a large number of labes also called extreme classification. The proposed approaches aim at reducing the inference conplexity in comparison with the classical methods such as one-versus-rest in order to make learning machines usable in a real life scenario. We propose two types of methods respectively for single label and multilable classification. The first proposed approach uses existing hierarchical information among the categories in order to learn low dimensional binary representation of the categories. The second type of approaches, dedicated to multilabel problems, adapts the framework of Bloom Filters to represent subsets of labels with sparse low dimensional binary vectors. In both approaches, binary classifiers are learned to predict the new low dimensional representation of the categories and several algorithms are also proposed to recover the set of relevant labels. Large scale experiments validate the methods
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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 resource-constrained devices efficiently.Furthermore, these resource savings will help to minimize the environmental impact of NLP. This thesis considers two main questions.First, which deep learning model is optimal for intent classification?Which model can more accurately infer a written piece of text (here inference equals to hate-speech) in a short text environment. Second, can we make intent classification models to be simpler and more resource-efficient than deep learning?. Concerning the first question, the work here shows that intent classification in written language is still a complex problem for modern models.However, deep learning has shown successful results in every area it has been applied.The work here shows the optimal model that was used in short texts.The second question shows that we can achieve results similar to the deep learning models by more straightforward solutions.To show that, when combining classical machine learning models, pre-processing techniques, and a hyperdimensional computing approach. This thesis presents a research done for a more resource-efficient machine learning approach to intent classification. It does this by first showing a high baseline using tweets filled with hate-speech and one of the best deep learning models available now (RoBERTa, as an example). Next, by showing the steps taken to arrive at the final model with hyperdimensional computing, which minimizes the required resources.This model can help make intent classification faster and more resource-efficient by trading a few performance points to achieve such resource-saving.Here, a hyperdimensional computing model is proposed. The model is inspired by hyperdimensional computing and its called ``hyperembed,'' which shows the capabilities of the hyperdimensional computing paradigm.When considering resource-efficiency, the models proposed were tested on intent classification on short texts, tweets (for hate-speech where intents are to offend or not to), and questions posed to Chatbots. In summary, the work proposed here covers two aspects. First, the deep learning models have an advantage in performance when there are sufficient data. They, however, tend to fail when the amount of available data is not sufficient. In contrast to the deep learning models, the proposed models work well even on small datasets.Second, the deep learning models require substantial resources to train and run them while the models proposed here aim at trading off the computational resources spend to obtaining and running the model against the classification performance of the model.
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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 results shown that by using the reduced number of channels and frequency bands, it can achieve similar accuracies. The study also proposed a method used to select the appropriate features using the Relief F method. The experimental results of this study showed that the method could reduce and select appropriate features confidently and efficiently. Moreover, the Fuzzy Support Vector Machine (FSVM) is used to improve emotion classification accuracy, as it was found from this research that it performed better than the Support Vector Machine (SVM) in handling the outliers, which are typically presented in the EEG signals. Furthermore, the FSVM is treated as a black-box model, but some applications may need to provide comprehensible human rules. Therefore, the rules are extracted using the Classification and Regression Trees (CART) approach to provide human comprehensibility to the system. The FSVM and rule extraction experiments showed that The FSVM performed better than the SVM in classifying the emotion of interest used in the experiments, and rule extraction from the FSVM utilizing the CART (FSVM-CART) had a good trade-off between classification accuracy and human comprehensibility.
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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 learning approaches are not so dominant. As a matter of fact, most of the current state-of-the-art results in video classification are based on a hybrid approach where the hand-crafted descriptors are combined with deep features to obtain the best performance. This is due to several factors, such as the fact that video is a more complex data as compared to an image, therefore, more difficult to model and also that the video datasets are not large enough to train deep models with effective results. The pipeline for video classification can be broken down into three main steps: feature extraction, encoding and classification. While for the classification part, the existing techniques are more mature, for feature extraction and encoding there is still a significant room for improvement. In addition to these main steps, the framework contains some pre/post processing techniques, such as feature dimensionality reduction, feature decorrelation (for instance using Principal Component Analysis - PCA) and normalization, which can influence considerably the performance of the pipeline. One of the bottlenecks of the video classification pipeline is represented by the feature extraction step, where most of the approaches are extremely computationally demanding, what makes them not suitable for real-time applications. In this thesis, we tackle this issue, propose different speed-ups to improve the computational cost and introduce a new descriptor that can capture motion information from a video without the need of computing optical flow (which is very expensive to compute). Another important component for video classification is represented by the feature encoding step, which builds the final video representation that serves as input to a classifier. During the PhD, we proposed several improvements over the standard approaches for feature encoding. We also propose a new feature encoding approach for deep feature encoding. To summarize, the main contributions of this thesis are as follows3: (1) We propose several speed-ups for descriptor extraction, providing a version for the standard video descriptors that can run in real-time. We also investigate the trade-off between accuracy and computational efficiency; 
(2) We provide a new descriptor for extracting information from a video, which is very efficient to compute, being able to extract motion information without the need of extracting the optical flow; (3) We investigate different improvements over the standard encoding approaches for boosting the performance of the video classification pipeline.;(4) We propose a new feature encoding approach specifically designed for encoding local deep features, providing a more robust video representation.
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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 learning approaches are not so dominant. As a matter of fact, most of the current state-of-the-art results in video classification are based on a hybrid approach where the hand-crafted descriptors are combined with deep features to obtain the best performance. This is due to several factors, such as the fact that video is a more complex data as compared to an image, therefore, more difficult to model and also that the video datasets are not large enough to train deep models with effective results. The pipeline for video classification can be broken down into three main steps: feature extraction, encoding and classification. While for the classification part, the existing techniques are more mature, for feature extraction and encoding there is still a significant room for improvement. In addition to these main steps, the framework contains some pre/post processing techniques, such as feature dimensionality reduction, feature decorrelation (for instance using Principal Component Analysis - PCA) and normalization, which can influence considerably the performance of the pipeline. One of the bottlenecks of the video classification pipeline is represented by the feature extraction step, where most of the approaches are extremely computationally demanding, what makes them not suitable for real-time applications. In this thesis, we tackle this issue, propose different speed-ups to improve the computational cost and introduce a new descriptor that can capture motion information from a video without the need of computing optical flow (which is very expensive to compute). Another important component for video classification is represented by the feature encoding step, which builds the final video representation that serves as input to a classifier. During the PhD, we proposed several improvements over the standard approaches for feature encoding. We also propose a new feature encoding approach for deep feature encoding. To summarize, the main contributions of this thesis are as follows3: (1) We propose several speed-ups for descriptor extraction, providing a version for the standard video descriptors that can run in real-time. We also investigate the trade-off between accuracy and computational efficiency; 
(2) We provide a new descriptor for extracting information from a video, which is very efficient to compute, being able to extract motion information without the need of extracting the optical flow; (3) We investigate different improvements over the standard encoding approaches for boosting the performance of the video classification pipeline.;(4) We propose a new feature encoding approach specifically designed for encoding local deep features, providing a more robust video representation.
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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
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 87-96).
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 improved version of the k-ishNN algorithm originally designed by Shaul et. al. in [1] and a homomorphically encrypted implementation of a SVM classifier. We provide experimental demonstrations of the accuracy and practical efficiency of both of these algorithms.
by David Benjamin Stein.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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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 experiments, the convolutional neural network (CNN) architecture MobileNetV2 is used, as it is specifically designed to be computationally efficient. To incorporate prior knowledge, separate CNNs are used for the different subsets generated when partitioning the dataset based on region or shape. The separate CNNs are trained from scratch or initialized by pre-training on the full dataset. The results support the intuitive idea that performance initially increases with network size and indicate a network size where the improvement stops. Including shape information using the two investigated methods does not result in a significant improvement. Including region information using pretrained separate classifiers results in a small improvement for small complexities, for one of the regions in the experiments. In the end, none of the investigated methods of including prior knowledge are considered to yield an improvement large enough to justify the added implementational complexity. However, some other methods are suggested, which would be interesting to study in future work.
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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 defined. A novel, efficient code implementation is proposed in this thesis. It leverages on the power of GPUs parallel architecture in order to achieve performance up to 20× faster than earlier codes, enabling near real-time applications. The source code of the implementation is also released open-source. The scattering network is then applied on a complex dataset of textures to test the behaviour in a general classification task. Given the conceptual complexity of the database, this unspecialized model scores a mere 32.9 % of accuracy. Finally, the scattering network is applied to a classification task of the medical field. A dataset of images of skin lesions is used in order to train a model able to classify malignant melanoma against benign lesions. Malignant melanoma is one of the most dangerous skin tumor, but if discovered in early stage there are generous probabilities to recover. The trained model has been tested and an interesting accuracy of 70.5 % (sensitivity 72.2 %, specificity 70.0 %) has been reached. While not being values high enough to permit the use of the model in a real application, this result demonstrates the great capabilities of the scattering network representation.
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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. Manchester: 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. Brooks Air Force Base, TX: 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. Helsinki: 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. [Los Angeles, Calif.]: Natural History Museum of Los Angeles County, 1995.

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

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Balabanova, Evgeniya. Organizational behavior. ru: 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. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is addressed to students studying in the direction of "Management", as well as to students of sociology to study the courses "Sociology of Labor" and "Sociology of Management". It may also be of interest to a wide range of readers whose professional activity involves working with people.
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Burkov, Aleksey. Technical operation of electric ships. ru: 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. Designed for those who specializiruetsya in the field of the theory and practice of ship electric drives. Useful for the learning process in the system of higher Maritime education.
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Nursing productivity assessment and costing out nursing services. Philadelphia: J.B. Lippincott, 1988.

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Grigoryan, Ekaterina. Integrated quality management system at the enterprises of the military-industrial complex. ru: 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. 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-industrial complex (MIC), consisting in the most effective management of the enterprise, energy efficiency and resource conservation, is justified. The assessment of the use of quality management tools at the defense industry enterprises was carried out, the trends in the development of defense industry enterprises were substantiated, a marketing approach was applied to the classification of defense industry enterprises, in particular by market type, which allows identifying potential consumers of enterprises ' products, the degree of production diversification. Organizational and economic approaches to the formation of an integrated quality management system are presented. The procedure for creating an ISMC is considered. The methodology and criteria for evaluating the effectiveness of ISMC are substantiated. The presented material is of practical importance and can be useful to specialists in quality management, graduate students, researchers, teachers.
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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. Madison, Wis: 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, 658–65. Berlin, Heidelberg: 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, 39–66. Berkeley, CA: 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, 123–36. Berlin, Heidelberg: 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, 346–55. Berlin, Heidelberg: 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, 409–15. Berlin, Heidelberg: 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, 53–64. Cham: 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, 253–64. Berlin, Heidelberg: 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, 61–81. London: 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, 404–16. Berlin, Heidelberg: 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, 47–57. Berlin, Heidelberg: 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. Philadelphia, PA: 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. New York, NY, USA: 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, April 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. Fort Belvoir, VA: Defense Technical Information Center, July 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.), April 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 other features, such as the levee structure and riverbanks. Further tuning of training data or manual identification of regions of interest could yield significantly better results. The workflow also provides an orientation to each linear feature to support subsequent analyses of position relative to levee alignments. While the individual models fall short of immediate applicability, the procedure provides a feasible, automated scheme to assist in swale classification and characterization within mature alluvial valley systems similar to LMV.
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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. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 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 lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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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 solution. The duration of this project is one year, from May 1st, 2019, until April 30th, 2020, and is comprised of two primary research components: analyzing the properties and classification of limestone and conducting digital aerial mapping to calculate stockpile volumetrics. The scope of this technical report is specifically limited to the aerial mapping aspect of the project, which was carried out using drones. The project involved two phases, with drone flights taking place during each phase, spaced about six months apart. The first drone flight for data collection occurred on July 12th, 2019, while the second took place on December 15th, 2020. The project aims to utilize drone technology for stockpile volumetric calculation, providing a more efficient and cost-effective solution. The project will involve the use of advanced drone sensors and imaging technology to capture high-resolution data of the stockpile area. The data will then be processed using sophisticated software algorithms to generate accurate 3D models and volumetric calculations of the stockpile.
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 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 wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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Wang, Jianyong, and George Karypis. HARMONY: Efficiently Mining the Best Rules for Classification. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, July 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), December 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 & 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, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values (mg ⋅ m2/year): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline "Computer modelling in biology").
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