Relevant bibliographies by topics / ANN Classifiers

Academic literature on the topic 'ANN Classifiers'

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Published: 6 September 2023

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Journal articles on the topic "ANN Classifiers"

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Mohamad, Mumtazimah, Wan Nor Shuhadah Wan Nik, Zahrahtul Amani Zakaria, and Arifah Che Alhadi. "An Analysis of Large Data Classification using Ensemble Neural Network." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 53. http://dx.doi.org/10.14419/ijet.v7i2.14.11155.

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In this paper, operational and complexity analysis are investigated for a proposed model of ensemble Artificial Neural Networks (ANN) multiple classifiers. The main idea to this is to employ more classifiers to obtain a more accurate prediction as well as to enhance the classification capabilities in case of larger data. The classification result analyzed between a single classifier and multiple classifiers followed by the estimates of upper bounds of converged functional error with the partitioning of the benchmark dataset. The estimates derived using the Apriori method shows that the proposed ensemble ANN algorithm with a different approach is feasible where such problems with a high number of inputs and classes can be solved with time complexity of O(n^k ) for some k, which is a type of polynomial. This result is in line with the significant performance achieved by the diversity rule applied with the use of reordering technique. As conclusion, an ensemble heterogeneous ANN classifier is practical and relevant to theoretical and experimental of combiners for the ensemble ANN classifier systems for a large dataset.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Potassium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 926–33. http://dx.doi.org/10.18137/cardiometry.2022.25.926933.

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Aim: The Motive of this research is to analyze, compare ventricular Cardiac Arrhythmia (CA) classification using potassium channel (k+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: D Noble Model For Human Ventricular Tissue (DNFHVT) is used for our classification. The DNFHVT is a mathematical model of action potential focusing on major ionic currents like K+,Na+ and Ca+.. Size of the sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. These data are imported to KNN and ANN classifiers to find better accuracy among them. The accuracy of novel ANN and KNN classifiers for 20 samples is obtained by alternating the cross fold validation. These results will be imported to Statistical Package for the Social Science (SPSS) software to identify the overall accuracy for each classifier. Results: The results are obtained from SPSS for novel ANN and KNN classifiers. ANN shows accuracy of 13.14% with standard deviation (1.6800) and Standard error mean (0.3757). Similarly KNN produces an accuracy value of 7.19% with standard deviation (1.6902) and Standard error mean (0.377). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 911–18. http://dx.doi.org/10.18137/cardiometry.2022.25.911918.

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Aim: Aim of this research is to analyze and compare ventricular Cardiac Arrhythmia (CA) classification using Sodium Channel (Na+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbour (KNN) classifiers. Materials and Methods: Ten Tusscher Human Ventricular Cell Model (THVCM) (data) is used for arrhythmias classification. THVCM has well defined sodium (Na+) channel dynamics. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier, K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifier to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Result: Ventricular normal, tachycardia and bradycardia data are fed into novel ANN and KNN classifiers. The results obtained from classifiers for 20 samples are fed to SPSS. In that ANN shows accuracy of 35.6% with standard deviation (3.17822) and Standard error mean (0.71067). Similarly KNN produces an accuracy value of 18.05% with standard deviation (1.19593) and Standard error mean (0.26739). Conclusion: As per the results, it clearly shows that the novel ANN has better accuracy for classification than KNN.
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Benmouna, Brahim, Raziyeh Pourdarbani, Sajad Sabzi, Ruben Fernandez-Beltran, Ginés García-Mateos, and José Miguel Molina-Martínez. "Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves." Remote Sensing 14, no. 24 (December 16, 2022): 6366. http://dx.doi.org/10.3390/rs14246366.

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Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400–1100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions.
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Chang, Mahmud, Shin, Nguyen-Quang, Price, and Prithiviraj. "Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection." AgriEngineering 1, no. 3 (September 4, 2019): 434–52. http://dx.doi.org/10.3390/agriengineering1030032.

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Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.
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Patgiri, Chayashree, and Amrita Ganguly. "Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method." Asian Pacific Journal of Health Sciences 9, no. 4 (June 20, 2022): 165–70. http://dx.doi.org/10.21276/apjhs.2022.9.4.33.

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Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the detection of sickle cells, supervised classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used. Here, geometric features of healthy and unhealthy RBCs are calculated and applied to these classifiers. In this approach, performance is found slightly greater in SVM classifier than the ANN classifier trained with scaled conjugate gradient back-propagation (BP) algorithm and with hidden layer of ten neurons. The proposed approach achieves a maximum of 99.2% accuracy with SVM classifier. The performance is also studied for seven different training algorithms in the ANN classifier by varying the numbers of hidden layer neurons. Comparative analysis of the performances of these algorithms shows that, resilient BP algorithm and 10 numbers of hidden neurons gave moderately better performance in ANN with 99% accuracy. ANN and SVM classifier with adaptive thresholding and active contour technique is an efficient approach for the classification of patients with SCA.
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (March 1, 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30676.

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<p>Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.</p>
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 3A (July 12, 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30687.

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<p>Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.</p>
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Masood, Ibrahim, Nadia Zulikha Zainal Abidin, Nur Rashida Roshidi, Noor Azlina Rejab, and Mohd Faizal Johari. "Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment." Applied Mechanics and Materials 465-466 (December 2013): 1149–54. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1149.

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Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control. Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically. In this research, full factorial design of experiment was utilized for investigating the effect of four design parameters, i.e., recognition window size, training data amount, training data quality and hidden neuron amount. The pattern recognition study focuses on bivariate correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shifts, μ = ± 0.75 ~ 3.00 standard deviations. Raw data was used as input representation for a generalized model ANN classifier. The findings suggested that: (i) the best performance for each pattern could be achieved by setting different design parameters through specific classifiers, which (ii) gave superior result (average RP = 98.85%) compared to an empirical design (average RP = 96.5%). This research has provided a new perspective in designing ANN pattern recognition scheme in the field of statistical process control.
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Wang, Daliang, and Xiaowen Guo. "Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance." Complexity 2021 (June 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/4251827.

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In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model.
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Dissertations / Theses on the topic "ANN Classifiers"

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Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.

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The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix against not helix (H/∼H), sheet against not sheet (S/∼S) and coil against not coil (C/∼C). The performance of these binary classifiers are compared using the overall accuracy in predicting the protein secondary structure for various window sizes. Our result indicate that hold-out cross validation achieved higher accuracy than 5-fold cross validation. The Naive Bayes classifier, using 5-fold cross validation achieved, the lowest accuracy for predicting helix against not helix. The classification tree classifiers, using 5-fold cross validation, achieved the lowest accuracies for both coil against not coil and sheet against not sheet classifications. The logistic regression classier accuracy is dependent on the window size; there is a positive relationship between the accuracy and window size. The logistic regression classier approach achieved the highest accuracy when compared to the classification tree and Naive Bayes classifiers for each classification task; predicting helix against not helix with accuracy 77.74 percent, for sheet against not sheet with accuracy 81.22 percent and for coil against not coil with accuracy 73.39 percent. It is noted that it is easier to compare classifiers if the classification process could be completely facilitated in R. Alternatively, it would be easier to assess these logistic regression classifiers if SPSS had a function to determine the accuracy of the logistic regression classifier.
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Joo, Hyonam. "Binary tree classifier and context classifier." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53076.

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Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel. Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption.
Master of Science
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Billing, Jeffrey J. (Jeffrey Joel) 1979. "Learning classifiers from medical data." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8068.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaf 32).
The goal of this thesis was to use machine-learning techniques to discover classifiers from a database of medical data. Through the use of two software programs, C5.0 and SVMLight, we analyzed a database of 150 patients who had been operated on by Dr. David Rattner of the Massachusetts General Hospital. C5.0 is an algorithm that learns decision trees from data while SVMLight learns support vector machines from the data. With both techniques we performed cross-validation analysis and both failed to produce acceptable error rates. The end result of the research was that no classifiers could be found which performed well upon cross-validation analysis. Nonetheless, this paper provides a thorough examination of the different issues that arise during the analysis of medical data as well as describes the different techniques that were used as well as the different issues with the data that affected the performance of these techniques.
by Jeffrey J. Billing.
M.Eng.and S.B.
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Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 53).
The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely rebuilding the forests. I implement these two strategies-incrementally growing existing trees and replacing old trees-in Spark Machine Learning(ML), a commonly used library for running ML algorithms in Spark. My implementation draws from existing methods in online learning literature, but includes several novel refinements. I evaluate the two implementations, as well as a variety of hybrid strategies, by recording their error rates and training times on four different datasets. My benchmarks show that the optimal strategy for incremental growth depends on the batch size and the presence of concept drift in a data workload. I find that workloads with large batches should be classified using a strategy that favors tree regrowth, while workloads with small batches should be classified using a strategy that favors incremental growth of existing trees. Overall, the system demonstrates significant efficiency gains when compared to the standard method of regrowing the random forest.
by Kathryn I. Siegel.
M. Eng.
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Palmer-Brown, Dominic. "An adaptive resonance classifier." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334802.

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Xue, Jinghao. "Aspects of generative and discriminative classifiers." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.

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Thesis (Ph.D.) - University of Glasgow, 2008.
Ph.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.
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Frankowsky, Maximilian, and Dan Ke. "Humanness and classifiers in Mandarin Chinese." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-224789.

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Mandarin Chinese numeral classifiers receive considerable at-tention in linguistic research. The status of the general classifier 个 gè re-mains unresolved. Many linguists suggest that the use of 个 gè as a noun classifier is arbitrary. This view is challenged in the current study. Relying on the CCL-Corpus of Peking University and data from Google, we investigated which nouns for living beings are most likely classified by the general clas-sifier 个 gè. The results suggest that the use of the classifier 个 gè is motivated by an anthropocentric continuum as described by Köpcke and Zubin in the 1990s. We tested Köpcke and Zubin’s approach with Chinese native speakers. We examined 76 animal expressions to explore the semantic interdepen-dence of numeral classifiers and the nouns. Our study shows that nouns with the semantic feature [+ animate] are more likely to be classified by 个 gè if their denotatum is either very close to or very far located from the anthropo-centric center. In contrast animate nouns whose denotata are located at some intermediate distance from the anthropocentric center are less likely to be classified by 个 gè.
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Lee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.

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Chungfat, Neil C. (Neil Caye) 1979. "Context-aware activity recognition using TAN classifiers." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87220.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (p. 73-77).
by Neil C. Chungfat.
M.Eng.
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Li, Ming. "Sequence and text classification : features and classifiers." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426966.

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Books on the topic "ANN Classifiers"

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Raudys, Šarūnas. Statistical and Neural Classifiers. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0359-2.

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Learning and using Japanese numbers. Lincolnwood, Ill., USA: Passport Books, 1996.

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Sharma, C. D. Classified catalogue code in theory and practice. 2nd ed. Jodhpur, India: Scientific Publishers, 1990.

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Learning kernel classifiers: Theory and algorithms. Cambridge, Mass: MIT Press, 2002.

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C, Carter Ruth, ed. Education and training for catalogers and classifiers. New York: Haworth Press, 1987.

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Cummings, James. Classified classics. Los Angeles: Price/Stern/Sloan, 1987.

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Daniels, B. J. Classified Christmas. Toronto, Ontario: Harlequin, 2007.

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(Army), Senior National Representatives. Defense, information exchange: Memorandum of understanding between the United States of America and other governments, signed at Washington, London, Paris and Bonn October 19, November 13 and 27, 1995 and January 9, 1996 with attachments and an understanding. Washington, D.C: Dept. of State, 2003.

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Xu, Dan, ed. Plurality and Classifiers across Languages in China. Berlin, Boston: DE GRUYTER, 2012. http://dx.doi.org/10.1515/9783110293982.

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Xu, Dan. Plurality and classifiers across languages in China. Berlin: De Gruyter Mouton, 2012.

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Book chapters on the topic "ANN Classifiers"

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Bakraouy, Zineb, Amine Baina, and Mostafa Bellafkih. "Agreement-Broker: Performance Analysis Using KNN, SVM, and ANN Classifiers." In Advances in Intelligent Systems and Computing, 868–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_82.

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Dutta, Munmi, Chayashree Patgiri, Mousmita Sarma, and Kandarpa Kumar Sarma. "Closed-Set Text-Independent Speaker Identification System Using Multiple ANN Classifiers." In Advances in Intelligent Systems and Computing, 377–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11933-5_41.

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Walse, Kishor H., Rajiv V. Dharaskar, and Vilas M. Thakare. "PCA Based Optimal ANN Classifiers for Human Activity Recognition Using Mobile Sensors Data." In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1, 429–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30933-0_43.

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Rashmi and Udayan Ghose. "Hybrid Entropy Method for Large Data Set Reduction Using MLP-ANN and SVM Classifiers." In Data Science and Analytics, 49–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5827-6_5.

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Niranjana Murthy, H. S., and M. Meenakshi. "Comparison between ANN-Based Heart Stroke Classifiers Using Varied Folds Data Set Cross-Validation." In Advances in Intelligent Systems and Computing, 693–99. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2012-1_74.

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Kim, Tae-Kyun, and Roberto Cipolla. "Multiple Classifier Boosting and Tree-Structured Classifiers." In Machine Learning for Computer Vision, 163–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28661-2_7.

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Yang, Ai-min, Yong-mei Zhou, and Min Tang. "A Classifier Ensemble Method for Fuzzy Classifiers." In Fuzzy Systems and Knowledge Discovery, 784–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_97.

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Jamnejad, Mohammad Iman, Sajad Parvin, Ali Heidarzadegan, and Mohsen Moshki. "A Meta Classifier by Clustering of Classifiers." In Nature-Inspired Computation and Machine Learning, 140–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13650-9_13.

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Cai, ChenWei, Dickson Keddy Wornyo, Liangjun Wang, and XiangJun Shen. "Building Weighted Classifier Ensembles Through Classifiers Pruning." In Communications in Computer and Information Science, 131–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8530-7_13.

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Ramos-Pollán, Raúl, Miguel Ángel Guevara-López, and Eugénio Oliveira. "Introducing ROC Curves as Error Measure Functions: A New Approach to Train ANN-Based Biomedical Data Classifiers." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 517–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_68.

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Conference papers on the topic "ANN Classifiers"

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Aswin, K. S., Manav Purushothaman, Polisetty Sritharani, and Angel T. S. "ANN and Deep Learning Classifiers for BCI applications." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917834.

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Karimi, M., M. Banejad, H. Hassanpour, and A. Moeini. "Classification of power system faults using ANN classifiers." In Energy Conference (IPEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/ipecon.2010.5697048.

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Ekbote, Juhi, and Mahasweta Joshi. "Indian sign language recognition using ANN and SVM classifiers." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276111.

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Omrani, Takwa, Adel Dallali, Belgacem Chibani Rhaimi, and Jaouhar Fattahi. "Fusion of ANN and SVM classifiers for network attack detection." In 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE, 2017. http://dx.doi.org/10.1109/sta.2017.8314974.

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Aiordachioaie, Dorel. "On Thermal Image Classification with ANN and Similarity Based Classifiers." In 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES). IEEE, 2022. http://dx.doi.org/10.1109/ciees55704.2022.9990680.

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Izquierdo, R., I. Parra, J. Munoz-Bulnes, D. Fernandez-Llorca, and M. A. Sotelo. "Vehicle trajectory and lane change prediction using ANN and SVM classifiers." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317838.

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Harini, V., Nayana N. Patil, and H. M. Rajashekar Swamy. "Comparison of Bayesian and ANN Classifiers for Crack Detection in Columns." In 2020 4th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2020. http://dx.doi.org/10.1109/iementech51367.2020.9270084.

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Mahima and N. B. Padmavathi. "Comparative study of kernel SVM and ANN classifiers for brain neoplasm classification." In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2017. http://dx.doi.org/10.1109/icicict1.2017.8342608.

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Akhmedova, Shakhnaz, and Eugene Semenkin. "ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm." In 12th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005571603100317.

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Sovierzoski, Miguel Antonio, Fernanda Isabel Marques Argoud, and Fernando Mendes de Azevedo. "Evaluation of ANN Classifiers During Supervised Training with ROC Analysis and Cross Validation." In 2008 International Conference on Biomedical Engineering And Informatics (BMEI). IEEE, 2008. http://dx.doi.org/10.1109/bmei.2008.251.

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Reports on the topic "ANN Classifiers"

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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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Umunoza Gasana, Emelyne, Dietrich von Rosen, and Martin Singull. Edgeworth-type expansion of the density of the classifier when growth curves are classified via likelihood. Linköping University Electronic Press, May 2023. http://dx.doi.org/10.3384/lith-mat-r-2023-02.

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In this paper, probabilities of misclassification of a two-step likelihood-based discriminant rule are established for the classification of growth curves. The defined two-step classifier considers the fact that the growth curves might not belong to any of the two predetermined populations. The distribution for the classifier is approximated via an Edgeworth-type expansion.
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Ostendorf, M., L. Atlas, R. Fish, O. Cetin, S. Sukittanon, and G. D. Bernard. Joint Use of Dynamical Classifiers and Ambiguity Plane Features. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada436824.

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Dube, Arindrajit, Ethan Kaplan, and Suresh Naidu. Coups, Corporations, and Classified Information. Cambridge, MA: National Bureau of Economic Research, April 2011. http://dx.doi.org/10.3386/w16952.

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Rangwala, Huzefa, and George Karypis. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada446086.

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Wu, Jin Chu, and Raghu N. Kacker. Standard Errors and Significance Testing in Data Analysis for Testing Classifiers. National Institute of Standards and Technology, July 2021. http://dx.doi.org/10.6028/nist.ir.8383.

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Nelson, Bruce, and Ammon Birenzvigo. Linguistic-Fuzzy Classifier for Discrimination and Confidence Value Estimation. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada426951.

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Hines, Paul C., and Carolyn M. Binder. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada573485.

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Hines, Paul C., and Carolyn M. Binder. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598331.

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Heisele, Bernd, Thomas Serre, Sayan Mukherjee, and Tomaso Poggio. Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada458821.

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