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Academic literature on the topic 'Hybrid classifier'

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Published: 11 March 2023

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Journal articles on the topic "Hybrid classifier"

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Demidova, Liliya, and Maksim Egin. "Data classification based on the hybrid intellectual technology." ITM Web of Conferences 18 (2018): 04001. http://dx.doi.org/10.1051/itmconf/20181804001.

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In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of the proposed hybrid intellectual data classification technology have been presented.
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YOUNG KOO, JA, and MYUNGHWAN KIM. "An improved hybrid classifier." International Journal of Remote Sensing 7, no. 3 (March 1986): 471–76. http://dx.doi.org/10.1080/01431168608954702.

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Zhiwen Yu, Le Li, Jiming Liu, and Guoqiang Han. "Hybrid Adaptive Classifier Ensemble." IEEE Transactions on Cybernetics 45, no. 2 (February 2015): 177–90. http://dx.doi.org/10.1109/tcyb.2014.2322195.

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Sharma, Richa, and Shailendra Narayan Singh. "An Efficient Hybrid Classifier for Prognosing Cardiac Disease." Webology 19, no. 1 (January 20, 2022): 5028–46. http://dx.doi.org/10.14704/web/v19i1/web19338.

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Machine learning (ML) is a powerful tool which empowers the practitioners for predictions upon any existing or real- time data. Here, the Machine first understands the valuable patterns from the dataset and then uses that information to make predictions on the unknown data. Further, classification is the commonly used machine learning approach (ML-Approach) to make such predictions. The objective of this work aims to design and development of an ensemble classifier for prognosing cardiovascular disease (heart disease). The developed classifier integrates Support Vector Machine (SVM), K–Nearest Neighbor (K-NN), and Weighted K-NN. The applicability of ensemble classifier is evaluated on the Cleveland Heart disease dataset. Some other classifiers such as Logistic Regression (LR), Sequential Minimal Optimization (SMO), K-NN+Weighted K-NN are also implemented on the same dataset to make the performance analysis. The results of this study depict the significant improvement in the Sensitivity and Specificity parameter.
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Kotsiantis, Sotiris. "A hybrid decision tree classifier." Journal of Intelligent & Fuzzy Systems 26, no. 1 (2014): 327–36. http://dx.doi.org/10.3233/ifs-120741.

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Yu, Zhiwen, Hantao Chen, Jiming Liuxs, Jane You, Hareton Leung, and Guoqiang Han. "Hybrid $k$ -Nearest Neighbor Classifier." IEEE Transactions on Cybernetics 46, no. 6 (June 2016): 1263–75. http://dx.doi.org/10.1109/tcyb.2015.2443857.

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Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms." Symmetry 13, no. 4 (April 7, 2021): 615. http://dx.doi.org/10.3390/sym13040615.

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The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training dataset are formed for the development of the kNN classifier: a variant that uses all objects from the original training dataset located inside the strip dividing the classes, and a variant that uses only those objects from the initial training dataset that are located inside the area containing all misclassified objects from the class dividing strip. In the second stage, the kNN classifier is developed using the new training dataset above-mentioned. The values of the parameters of the kNN classifier are determined during training to maximize the data classification quality. The data classification quality using the two-stage hybrid SVM-kNN classifier was assessed using various indicators on the test dataset. In the case of the improvement of the quality of classification near the class boundary defined by the SVM classifier using the kNN classifier, the two-stage hybrid SVM-kNN classifier is recommended for further use. The experimental results approve the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem. The experimental results obtained with the application of various datasets confirm the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem.
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Liu, Su Houn, Hsiu Li Liao, Shih Ming Pi, and Chih Chiang Kao. "Patent Classification Using Hybrid Classifier Systems." Advanced Materials Research 187 (February 2011): 458–63. http://dx.doi.org/10.4028/www.scientific.net/amr.187.458.

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Patents are distributed through hundreds of collections, divided up by general area. A hybrid classifier system thus can be a powerful solution to difficult patent classification problems. In this study, we present a system for classifying patent documents on a hybrid approach by combining multiple text classifiers (Naïve Bayes, KNN and Rocchio). Decisions made by various text classifiers can be combined by voting and sampling mechanisms in the system. A prototype system was developed and tested in a real world task. The results have indicated that the accuracy of the hybrid approach is more stable than that of any of the three individual text classifiers.
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Rangel-Díaz-de-la-Vega, Adolfo, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, and Itzamá López-Yáñez. "Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers." Applied Sciences 10, no. 8 (April 16, 2020): 2779. http://dx.doi.org/10.3390/app10082779.

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.
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Anagnostopoulos, Theodoros, and Christos Skourlas. "Ensemble majority voting classifier for speech emotion recognition and prediction." Journal of Systems and Information Technology 16, no. 3 (August 5, 2014): 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.

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Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one-against-all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.
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Dissertations / Theses on the topic "Hybrid classifier"

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Vishnampettai, Sridhar Aadhithya. "A Hybrid Classifier Committee Approach for Microarray Sample Classification." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1312341058.

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Nair, Sujit S. "Coarse Radio Signal Classifier on a Hybrid FPGA/DSP/GPP Platform." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/76934.

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The Virginia Tech Universal Classifier Synchronizer (UCS) system can enable a cognitive receiver to detect, classify and extract all the parameters needed from a received signal for physical layer demodulation and configure a cognitive radio accordingly. Currently, UCS can process analog amplitude modulation (AM) and frequency modulation (FM) and digital narrow band M-PSK, M-QAM and wideband signal orthogonal frequency division multiplexing (OFDM). A fully developed prototype of UCS system was designed and implemented in our laboratory using GNU radio software platform and Universal Software Radio Peripheral (USRP) radio platform. That system introduces a lot of latency issues because of the limited USB data transfer speeds between the USRP and the host computer. Also, there are inherent latencies and timing uncertainties in the General Purpose Processor (GPP) software itself. Solving the timing and latency problems requires running key parts of the software-defined radio (SDR) code on a Field Programmable Gate Array (FPGA)/Digital Signal Processor (DSP)/GPP based hybrid platform. Our objective is to port the entire UCS system on the Lyrtech SFF SDR platform which is a hybrid DSP/FPGA/GPP platform. Since the FPGA allows parallel processing on a wideband signal, its computing speed is substantially faster than GPPs and most DSPs, which sequentially process signals. In addition, the Lyrtech Small Form Factor (SFF)-SDR development platform integrates the FPGA and the RF module on one platform; this further reduces the latency in moving signals from RF front end to the computing component. Also for UCS to be commercially viable, we need to port it to a more portable platform which can be transitioned to a handset radio in the future. This thesis is a proof of concept implementation of the coarse classifier which is the first step of classification. Both fixed point and floating point implementations are developed and no compiler specific libraries or vendor specific libraries are used. This makes transitioning the design to any other hardware like GPPs and DSPs of other vendors possible without having to change the basic framework and design.
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Zimit, Sani Ibrahim. "Hybrid approach to interpretable multiple classifier system for intelligent clinical decision support." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631699.

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Data-driven decision support approaches have been increasingly employed in recent years in order to unveil useful diagnostic and prognostic patterns from data accumulated in clinical repositories. Given the diverse amount of evidence generated through everyday clinical practice and the exponential growth in the number of parameters accumulated in the data, the capability of finding purposeful task-oriented patterns from patient records is crucial for providing effective healthcare delivery. The application of classification decision support tool in clinical settings has brought about formidable challenges that require a robust system. Knowledge Discovery in Database (KDD) provides a viable solution to decipher implicit knowledge in a given context. KDD classification techniques create models of the accumulated data according to induction algorithms. Despite the availability of numerous classification techniques, the accuracy and interpretability of the decision model are fundamental in the decision processes. Multiple Classifier Systems (MCS) based on the aggregation of individual classifiers usually achieve better decision accuracy. The down size of such models is due to their black box nature. Description of the clinical concepts that influence each decision outcome is fundamental in clinical settings. To overcome this deficiency, the use of artificial data is one technique advocated by researchers to extract an interpretable classifier that mimics the MCS. In the clinical context, practical utilisation of the mimetic procedure depends on the appropriateness of the data generation method to reflect the complexities of the evidence domain. A well-defined intelligent data generation method is required to unveil associations and dependency relationships between various entities the evidence domain. This thesis has devised an Interpretable Multiple classifier system (IMC) using the KDD process as the underlying platform. The approach integrates the flexibility of MCS, the robustness of Bayesian network (BN) and the concept of mimetic classifier to build an interpretable classification system. The BN provides a robust and a clinically accepted formalism to generate synthetic data based on encoded joint relationships of the evidence space. The practical applicability of the IMC was evaluated against the conventional approach for inducing an interpretable classifier on nine clinical domain problems. Results of statistical tests substantiated that the IMC model outperforms the direct approach in terms of decision accuracy.
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Lou, Wan Chan. "A hybrid model of tree classifier and neural network for university admission recommender system." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1783609.

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Toubakh, Houari. "Automated on-line early fault diagnosis of wind turbines based on hybrid dynamic classifier." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10100/document.

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L'objectif principal de cette thèse est de développer un schéma générique et adaptatif basée sur les approches d'apprentissage automatique, intégrant des mécanismes de détection et d'isolation des défauts avec une force d’apparition progressive. Le but de ce schéma est de réaliser le diagnostic en ligne des défauts simple et multiple de type dérive dans les systèmes éoliens, et plus particulièrement dans le système du calage des pales et le convertisseur de puissance. Le schéma proposé est basé sur la décomposition du système éolien en plusieurs composantes. Ensuite, un classifieur est conçu et utilisé pour réaliser le diagnostic de défauts dans chaque composant. Le but de cette décomposition en composants est de faciliter l'isolation des défauts et d'augmenter la robustesse du schéma globale de diagnostic dans le sens que lorsque le classifier lié à un composant est défaillant, les classifieurs liées aux autres composants continuent à réaliser le diagnostic des défauts dans leurs composants. Ce schéma a aussi l'avantage de prendre en compte la dynamique hybride de l’éolienne
This thesis addresses the problem of automatic detection and isolation of drift-like faults in wind turbines (WTs). The main aim of this thesis is to develop a generic on-line adaptive machine learning and data mining scheme that integrates drift detection and isolation mechanism in order to achieve the simple and multiple drift-like fault diagnosis in WTs, in particular pitch system and power converter. The proposed scheme is based on the decomposition of the wind turbine into several components. Then, a classifier is designed and used to achieve the diagnosis of faults impacting each component. The goal of this decomposition into components is to facilitate the isolation of faults and to increase the robustness of the scheme in the sense that when the classifier related to one component is failed, the classifiers for the other components continue to achieve the diagnosis for faults in their corresponding components. This scheme has also the advantage to take into account the WT hybrid dynamics. Indeed, some WT components (as pitch system and power converter) manifest both discrete and continuous dynamic behaviors. In each discrete mode, or a configuration, different continuous dynamics are defined
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Rasheed, Sarbast. "A Multiclassifier Approach to Motor Unit Potential Classification for EMG Signal Decomposition." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/934.

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EMG signal decomposition is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference.
This thesis addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs either a one level classifier fusion scheme or a hybrid classifier fusion approach.
The hybrid classifier fusion approach is applied as a two-stage combination process that uses a new aggregator module which consists of two combiners: the first at the abstract level of classifier fusion and the other at the measurement level of classifier fusion such that it uses both combiners in a complementary manner. Both combiners may be either data independent or the first combiner data independent and the second data dependent. For the purpose of experimentation, we used as first combiner the majority voting scheme, while we used as the second combiner one of the fixed combination rules behaving as a data independent combiner or the fuzzy integral with the lambda-fuzzy measure as an implicit data dependent combiner.
Once the set of motor unit potential trains are generated by the classifier fusion system, the firing pattern consistency statistics for each train are calculated to detect classification errors in an adaptive fashion. This firing pattern analysis allows the algorithm to modify the threshold of assertion required for assignment of a motor unit potential classification individually for each train based on an expectation of erroneous assignments.
The classifier ensembles consist of a set of different versions of the Certainty classifier, a set of classifiers based on the nearest neighbour decision rule: the fuzzy k-NN and the adaptive fuzzy k-NN classifiers, and a set of classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers and its adaptive counterpart. The base classifiers, besides being of different kinds, utilize different types of features and their performances were investigated using both real and simulated EMG signals of different complexities. The feature sets extracted include time-domain data, first- and second-order discrete derivative data, and wavelet-domain data.
Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of candidate base classifiers and then chooses, from the base classifiers pool, subsets of specified number of classifiers to form candidate classifier ensembles. The system then selects the classifier ensemble having the maximum degree of agreement by exploiting a diversity measure for designing classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between the base classifier outputs, i. e. , to measure the degree of decision similarity between the base classifiers. This mechanism of choosing the team's classifiers based on assessing the classifier agreement throughout all the trains and the unassigned category is applied during the one level classifier fusion scheme and the first combiner in the hybrid classifier fusion approach. For the second combiner in the hybrid classifier fusion approach, we choose team classifiers also based on kappa statistics but by assessing the classifiers agreement only across the unassigned category and choose those base classifiers having the minimum agreement.
Performance of the developed classifier fusion system, in both of its variants, i. e. , the one level scheme and the hybrid approach was evaluated using synthetic simulated signals of known properties and real signals and then compared it with the performance of the constituent base classifiers. Across the EMG signal data sets used, the hybrid approach had better average classification performance overall, specially in terms of reducing the number of classification errors.
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McCool, Christopher Steven. "Hybrid 2D and 3D face verification." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16436/1/Christopher_McCool_Thesis.pdf.

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Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures). A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation: 1. the use of holistic difference vectors of the face, and 2. by dividing the 3D face into Free-Parts. The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined. This thesis also examines methods for performing classifier fusion score fusion. Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques. The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed. The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%.
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McCool, Christopher Steven. "Hybrid 2D and 3D face verification." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16436/.

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Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures). A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation: 1. the use of holistic difference vectors of the face, and 2. by dividing the 3D face into Free-Parts. The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined. This thesis also examines methods for performing classifier fusion score fusion. Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques. The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed. The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%.
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Al-Ani, Ahmed Karim. "An improved pattern classification system using optimal feature selection, classifier combination, and subspace mapping techniques." Thesis, Queensland University of Technology, 2002.

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Ala'raj, Maher A. "A credit scoring model based on classifiers consensus system approach." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13669.

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Managing customer credit is an important issue for each commercial bank; therefore, banks take great care when dealing with customer loans to avoid any improper decisions that can lead to loss of opportunity or financial losses. The manual estimation of customer creditworthiness has become both time- and resource-consuming. Moreover, a manual approach is subjective (dependable on the bank employee who gives this estimation), which is why devising and implementing programming models that provide loan estimations is the only way of eradicating the ‘human factor’ in this problem. This model should give recommendations to the bank in terms of whether or not a loan should be given, or otherwise can give a probability in relation to whether the loan will be returned. Nowadays, a number of models have been designed, but there is no ideal classifier amongst these models since each gives some percentage of incorrect outputs; this is a critical consideration when each percent of incorrect answer can mean millions of dollars of losses for large banks. However, the LR remains the industry standard tool for credit-scoring models development. For this purpose, an investigation is carried out on the combination of the most efficient classifiers in credit-scoring scope in an attempt to produce a classifier that exceeds each of its classifiers or components. In this work, a fusion model referred to as ‘the Classifiers Consensus Approach’ is developed, which gives a lot better performance than each of single classifiers that constitute it. The difference of the consensus approach and the majority of other combiners lie in the fact that the consensus approach adopts the model of real expert group behaviour during the process of finding the consensus (aggregate) answer. The consensus model is compared not only with single classifiers, but also with traditional combiners and a quite complex combiner model known as the ‘Dynamic Ensemble Selection’ approach. As a pre-processing technique, step data-filtering (select training entries which fits input data well and remove outliers and noisy data) and feature selection (remove useless and statistically insignificant features which values are low correlated with real quality of loan) are used. These techniques are valuable in significantly improving the consensus approach results. Results clearly show that the consensus approach is statistically better (with 95% confidence value, according to Friedman test) than any other single classifier or combiner analysed; this means that for similar datasets, there is a 95% guarantee that the consensus approach will outperform all other classifiers. The consensus approach gives not only the best accuracy, but also better AUC value, Brier score and H-measure for almost all datasets investigated in this thesis. Moreover, it outperformed Logistic Regression. Thus, it has been proven that the use of the consensus approach for credit-scoring is justified and recommended in commercial banks. Along with the consensus approach, the dynamic ensemble selection approach is analysed, the results of which show that, under some conditions, the dynamic ensemble selection approach can rival the consensus approach. The good sides of dynamic ensemble selection approach include its stability and high accuracy on various datasets. The consensus approach, which is improved in this work, may be considered in banks that hold the same characteristics of the datasets used in this work, where utilisation could decrease the level of mistakenly rejected loans of solvent customers, and the level of mistakenly accepted loans that are never to be returned. Furthermore, the consensus approach is a notable step in the direction of building a universal classifier that can fit data with any structure. Another advantage of the consensus approach is its flexibility; therefore, even if the input data is changed due to various reasons, the consensus approach can be easily re-trained and used with the same performance.
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Books on the topic "Hybrid classifier"

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Wozniak, Michal. Hybrid Classifiers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40997-4.

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Wozniak, Michal. Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer London, Limited, 2013.

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Wozniak, Michal. Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer, 2013.

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Wozniak, Michal. Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer Berlin / Heidelberg, 2016.

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Ferguson, Ben, and Hillel Steiner. Exploitation. Edited by Serena Olsaretti. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199645121.013.21.

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Exploitation is commonly understood as taking unfair advantage. This article discusses the various prominent accounts that have been offered of how an exchange, despite being Pareto improving and consensual, can nevertheless count as unfair or unjust and, hence, as presumptively impermissible. Does the wrongness of an exploitative transaction consist in its compounding a prior distributive injustice, or in its deliberately profiting from someone’s vulnerability, or in its commodification of that which should not be commodified? How should responsibility for exploitation be assigned, and can this avoid generating moral hazard? The accounts of exploitation analysed here are classified along two dimensions—historical vs. ahistorical and intentional vs. non-intentional—in their conceptions of unfairness, and the possibility of a hybrid account is explored.
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Marcus, Smith, and Leslie Nico. Part I The Nature of Intangible Property, 6 Equity and Debt Securities. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198748434.003.0006.

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This chapter discusses securities. Securities are an important and complex category of intangible. A ‘security’ is defined as a fungible financial instrument, offered for sale on identical terms to multiple investors on first issue, and thereafter generally traded in a market that facilitates its free transfer. Securities can broadly be classified into shares, debt securities, and hybrid securities. The chapter then looks at the legal incidents of securities, and how securities are allotted and held. Allotment describes the process whereby the issuer of securities agrees to issue those securities to a particular person and that person agrees to buy those securities. Meanwhile, the question of how securities are held has become an increasing complex area. Originally, securities were held in paper form. Such paper-based systems are increasingly becoming redundant but their operation remains important because they are relevant to the electronic systems by which securities are held today.
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Smalskys, Vainius, and Jolanta Urbanovič. Civil Service Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.160.

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Civil service consists of civil servants and their activity when implementing the assigned functions and decisions made by politicians. In other words, it is a system of civil servants who perform the assigned functions of public administration. The corpus of civil servants consists of people who work in central and local public administration institutions. The concept and scope of civil service in a particular country depends on the legal framework that defines the areas of public and private sectors and their relationship. In many countries, civil service consists of an upper level, a mid-level, and civil servants who work for coordinating, independent, and auxiliary institutions. However, the scope of civil service in different countries varies. When analyzing/comparing civil service systems of different countries, researchers often categorize them as Western European, continental European, Anglo-American, Anglo-Saxon, Eastern European, Scandinavian, Mediterranean, Asian, or African.All European Union member states can be classified into two groups: the career system—dominant in continental Europe, with the prevalence of traditional-hierarchical public administration, rational bureaucracy, and formalized operational rules—and the position system—dominant in Anglo-Saxon countries, with the prevalence of managerial principles, pragmatic administration, and charismatic leadership. Neither of the two models exists in pure form. If features of the career model dominate in the civil service of a country, it is identified as a country with the career CS model; if elements of the position model dominate the country is identified as a country with the position civil service model. An intermediate version of this model, characteristic of a number of countries, is the mixed/hybrid model.Many civil service researchers claim that in the case of two competing systems of civil service—closed (the career model) and open (the position model)—reforms of the open civil service system win. It has been argued that the organizing principles of the open, result-oriented civil service system (the position model), which is under the influence of “new public management,” will permanently “drive out” the closed, vertically integrated and formal procedure-oriented career model. Scholars argue that civil servants of the future will have to be at ease with more complexity and flexibility. They will have to be comfortable with change, often rapid change. At the same time, they will make more autonomous decisions and be more responsible, accountable, performance-oriented, and subject to new competency and skill requirements.
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Book chapters on the topic "Hybrid classifier"

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Woźniak, Michał. "Classifier Hybridization." In Hybrid Classifiers, 95–140. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40997-4_3.

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Frédéric, R., and G. Serge. "An Efficient Nearest Neighbor Classifier." In Hybrid Evolutionary Algorithms, 127–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_6.

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Jun, Goo, and Joydeep Ghosh. "Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis." In Multiple Classifier Systems, 42–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_5.

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Cohen, Shimon, and Nathan Intrator. "Forward and Backward Selection in Regression Hybrid Network." In Multiple Classifier Systems, 98–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_10.

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Tian, Qi, Jie Yu, and Thomas S. Huang. "Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis." In Multiple Classifier Systems, 42–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_5.

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Cohen, Shimon, and Nathan Intrator. "A hybrid projection based and radial basis function architecture." In Multiple Classifier Systems, 147–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_14.

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Cohen, Shimon, and Nathan Intrator. "Automatic Model Selection in a Hybrid Perceptron/Radial Network." In Multiple Classifier Systems, 440–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_44.

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Cohen, Shimon, and Nathan Intrator. "A Study of Ensemble of Hybrid Networks with Strong Regularization." In Multiple Classifier Systems, 227–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_23.

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Kim, Byung Joo. "A Classifier for Big Data." In Convergence and Hybrid Information Technology, 505–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32692-9_63.

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Van, Nguyen Duc, Nguyen Ngoc Doanh, Nguyen Trong Khanh, and Nguyen Thi Ngoc Anh. "Hybrid Classifier by Integrating Sentiment and Technical Indicator Classifiers." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 25–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77818-1_3.

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Conference papers on the topic "Hybrid classifier"

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Kitanovski, Ivan, Gjorgji Madzarov, and Dejan Gjorgjevikj. "Local hybrid SVMDT classifier." In 2011 19th Telecommunications Forum Telfor (TELFOR). IEEE, 2011. http://dx.doi.org/10.1109/telfor.2011.6143658.

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Bernardini, Flávia Cristina, Ronaldo C. Prati, and Maria Carolina Monard. "Evolving Sets of Symbolic Classifiers into a Single Symbolic Classifier Using Genetic Algorithms." In 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.158.

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Iqbal, Raja T., and Uvais Qidwai. "Boosted human-centric hybrid classifier." In the 43rd annual southeast regional conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1167350.1167373.

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Gulhane, Yogesh H., and S. A. Ladhake. "Hybrid Approach of Emotion Classifier." In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2018. http://dx.doi.org/10.1109/iceca.2018.8474546.

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Kabbai, Leila, Mehrez Abdellaoui, and Ali Douik. "Hybrid classifier using SIFT descriptor." In 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2013. http://dx.doi.org/10.1109/codit.2013.6689576.

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Radha, R., and R. R. Aparna. "Digit Recognition Using Hybrid Classifier." In 2014 World Congress on Computing and Communication Technologies (WCCCT). IEEE, 2014. http://dx.doi.org/10.1109/wccct.2014.18.

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Yamsaniwar, Sucheta, Surekha Tadse, Saikat Ranajit, and Rutvik Walde. "Glaucoma and Cataract Hybrid Classifier." In 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22). IEEE, 2022. http://dx.doi.org/10.1109/icetet-sip-2254415.2022.9791833.

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Barbosa, José Matheus Lacerda, Adriano Marabuco de Albuquerque Lima, Paulo Salgado Gomes de Mattos Neto, and Adriano Lorena Inácio de Oliveira. "Hybrid Swarm Enhanced Classifier Ensembles." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18263.

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Os Sistemas de Multi-Classificadores (MCSs) constituem um dos paradigmas mais competitivos para a obtenção de classificações precisas no campo do aprendizado de máquina. Este artigo busca avaliar se a utilização de algoritmos híbridos de enxames pode melhorar a performance dos MCSs por meio da otimização de pesos em combinações por voto majoritário ponderado. A metodologia proposta rendeu resultados competitivos em 25 conjuntos de dados de referência. Adotou-se a acurácia como função objetivo a ser maximizada pelas seguintes meta-heurísticas: otimização do exame de partículas (PSO), a colônia artificial de abelhas (ABC), e a alternativa híbrida das anteriores usando a técnica de multi enxames dinâmicos (DM-PSO-ABC).
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Lanaridis, A., V. Karakasis, and A. Stafylopatis. "Clonal Selection-Based Neural Classifier." In 2008 8th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2008. http://dx.doi.org/10.1109/his.2008.82.

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Hartono, Pitoyo, and Shuji Hashimoto. "Ensemble as a Piecewise Linear Classifier." In 2006 Sixth International Conference on Hybrid Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264915.

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Reports on the topic "Hybrid classifier"

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Grigsby, Claude C., Ryan M. Kramer, Michael A. Zmuda, Derek W. Boone, Tyler C. Highlander, and Mateen M. Rizki. Differential Profiling of Volatile Organic Compound Biomarker Signatures Utilizing a Logical Statistical Filter-Set and Novel Hybrid Evolutionary Classifiers. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada562341.

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

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Desai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Sakhare, Deborah Horton, and Darcy M. Bullock. National Mobility Analysis for All Interstate Routes in the United States. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317585.

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In November 2022, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 470 billion connected vehicle records covering all 50 states for the month of August 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone analytics graphics and electric/hybrid vehicle counts at a national level. This report illustrates several performance measures developed using this dataset for all interstate routes, both state wise and cross-country. State wise graphics are organized with 50 subdirectories containing graphics for each interstate in the 50 states. There are also a series of multi-state graphics for I-5, I-10, I-15, I-35, I-55, I-65, I-75, I-80, I-90, and I-95. Performance measures include absolute and normalized trip counts classified by type of trip (electric vehicle or hybrid vehicle or internal combustion engine vehicle), weekly heatmaps based on vehicle speed overlaid with hard-braking events and finally, speed profiles by interstate mile markers. Additional details on the directories and how to interpret these performance measures can found inside the document (after extracting the .zip file) titled “National_Mobility_Analysis_README.pdf”.
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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a sufficient number of solutions and algorithms for training, designing and testing various types of neural networks. Software architecture is presented using a procedural-object approach. Because there is no need to save intermediate result of the program (after learning entire perceptron is stored in the file), all the progress of learning is stored in the normal files on hard disk.
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Desai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Suryakant Sakhare, Deborah Horton, and Darcy M. Bullock. National Mobility Report for All Interstates–December 2022. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317591.

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In January 2023, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 503 billion connected vehicle records covering all 50 states for the month of December 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone/winter weather analytics graphics and electric/hybrid vehicle counts at a national level as well as visualize the mobility impacts of winter weather activity on the national interstate network. This report illustrates several performance measures developed using this dataset for all interstate routes, both state wise and cross-country. Statewide graphics are presented for each interstate in the 50 states. There are also a series of multi-state graphics for I-5, I-10, I-15, I-35, I-55, I-65, I-75, I-80, I-90, and I-95 and selected other routes that witnessed winter storm impacts. Performance measures include absolute and normalized trip counts classified by type of trip (electric vehicle or hybrid vehicle or internal combustion engine vehicle), weekly heatmaps based on vehicle speed (for the two-week period from December 12-25, 2022) and finally, speed profiles by interstate mile markers. Traffic tickers depicting miles of congestion as well as mile-hours of congestion for the national interstate network and selected multi-state routes are also included to provide a unified visual of nationwide mobility impact of recurring congestion as well as non-recurring congestion caused by winter weather. The generated performance measures can be found in the supplemental files.
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