Academic literature on the topic 'N-way classification'
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Journal articles on the topic "N-way classification"
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Durante, Caterina, Rasmus Bro, and Marina Cocchi. "A classification tool for N-way array based on SIMCA methodology." Chemometrics and Intelligent Laboratory Systems 106, no. 1 (2011): 73–85. http://dx.doi.org/10.1016/j.chemolab.2010.09.004.
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LIM, VALERIE P. C., SUSAN J. RICKARD LIOW, MICHELLE LINCOLN, YIONG HUAK CHAN, and MARK ONSLOW. "Determining language dominance in English–Mandarin bilinguals: Development of a self-report classification tool for clinical use." Applied Psycholinguistics 29, no. 3 (2008): 389–412. http://dx.doi.org/10.1017/s0142716408080181.
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ABSTRACTIn multilingual Asian communities, determining language dominance for clinical assessment and intervention is often complex. The aim of this study was to develop a self-report classification tool for identifying the dominant language in English–Mandarin bilinguals. Participants (N = 168) completed a questionnaire on language history and single-word receptive vocabulary tests (Peabody Picture Vocabulary Test type) in both languages. The results of a discriminant analysis on the self-report data revealed a reliable three-way classification into English-dominant, Mandarin-dominant, and balanced bilinguals. The vocabulary scores supported these dominance classifications, whereas the more typical variables such as age of first exposure, years of formal instruction, and years of exposure exerted only a limited influence. The utility of this classification tool in clinical settings is discussed.
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Kutsenko, Anton A. "Classification of Integrodifferential C∗-Algebras." Symmetry 13, no. 10 (2021): 1900. http://dx.doi.org/10.3390/sym13101900.
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The infinite product of matrices with integer entries, known as a modified Glimm–Bratteli symbol n, is a new, sufficiently simple, and very powerful tool for the characterization of approximately finite-dimensional (AF) algebras. This symbol provides a convenient algebraic representation of the Bratteli diagram for AF algebras in the same way as was previously performed by J. Glimm for more simple uniformly hyperfinite (UHF) algebras. We apply this symbol to characterize integrodifferential algebras. The integrodifferential algebra FN,M is the C∗-algebra generated by the following operators acting on L2([0,1)N→CM): (1) operators of multiplication by bounded matrix-valued functions, (2) finite-difference operators, and (3) integral operators. Most of the operators and their approximations studying in physics belong to these algebras. We give a complete characterization of FN,M. In particular, we show that FN,M does not depend on M, but depends on N. At the same time, it is known that differential algebras HN,M, generated by the operators (1) and (2) only, do not depend on both dimensions N and M; they are all ∗-isomorphic to the universal UHF algebra. We explicitly compute the Glimm–Bratteli symbols (for HN,M, it was already computed earlier) which completely characterize the corresponding AF algebras. This symbol n is an infinite product of matrices with nonnegative integer entries. Roughly speaking, all the symmetries appearing in the approximation of complex infinite-dimensional integrodifferential and differential algebras by finite-dimensional ones are coded by a product of integer matrices.
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Thomas, Hugo, Guillaume Gravier, and Pascale Sébillot. "One-shot relation retrieval in news archives: adapting N-way K-shot relation Classification for efficient knowledge extraction." Procedia Computer Science 246 (2024): 1060–69. http://dx.doi.org/10.1016/j.procs.2024.09.525.
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Son, Taeil, Jiyu Sun, Hyoung-Il Kim, et al. "A proposal for a novel and simple TNM staging for gastric cancer." Journal of Clinical Oncology 35, no. 4_suppl (2017): 21. http://dx.doi.org/10.1200/jco.2017.35.4_suppl.21.
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21 Background: Current TNM staging system for gastric cancer has controversies regarding N classification. We aimed to develop a simple and novel TNM staging system for gastric cancer by re-grouping N classification. Methods: We retrospectively reviewed 14260 patients treated for gastric cancer. To develop simple combinations of TNM staging with similar weighted value between T and N classification, N classification was restructured with different cutoffs. The optimal cutoffs for the number of metastatic lymph node which maximize the x2 statistic of log-rank test for survival differences among patients were selected. C-statistic was used to compare the discriminating performance of the proposed N classification with the current N classification in the TNM staging system. We performed validation with 2 external datasets from a hospital in Korea (n = 1500) and SEER (n = 11324). Results: We identified the new cutoffs of N classification as 1~4, 5~10, 11~24, and 25 or more for N1, N2, N3a, and N3b, respectively. We found survival of the new N3b classification was similar to M1, regardless of T classification. Thus, we stratified these groups of N3b and M1 disease as stage IV, simultaneously. Our new TNM staging had similar weighted value between T and N classification resulting in simple combinations. (Table) Survival curves of subgroups in the new TNM staging had higher x2 value than current staging system (x2: 8239 vs. 7023, respectively) and homogeneity among subgroups in the same stage increased. However, C-statistics (0.801, 95%CI: 0.795, 0.807) of new model showed similar discrimination power than that (0.797, 95%CI: 0.791, 0.803) in 7th TNM staging system. C-statistics were also similar in other hospital in Korea (0.805 vs. 0.802, respectively) and SEER database (0.709 vs. 0.706, respectively). Conclusions: This novel staging system by recalculating cut-offs of N classification provides exceptionally simple and practical way to stratify substages in TNM staging for gastric cancer. [Table: see text]
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Yan, Zhenguo, and Yue Wu. "A Neural N-Gram Network for Text Classification." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 3 (2018): 380–86. http://dx.doi.org/10.20965/jaciii.2018.p0380.
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Convolutional Neural Networks (CNNs) effectively extract local features from input data. However, CNN based on word embedding and convolution layers displays poor performance in text classification tasks when compared with traditional baseline methods. We address this problem and propose a model named NNGN that simplifies the convolution layer in the CNN by replacing it with a pooling layer that extracts n-gram embedding in a simpler way and obtains document representations via linear computation. We implement two settings in our model to extract n-gram features. In the first setting, which we refer to as seq-NNGN, we consider word order within each n-gram. In the second setting, BoW-NNGN, we do not consider word order. We compare the performance of these settings in different classification tasks with those of other models. The experimental results show that our proposed model achieves better performance than state-of-the-art models.
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Heese, Raoul, Jochen Schmid, Michał Walczak, and Michael Bortz. "Calibrated simplex-mapping classification." PLOS ONE 18, no. 1 (2023): e0279876. http://dx.doi.org/10.1371/journal.pone.0279876.
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We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n − 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.
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Zhao, Tianna, Yuanjian Zhang, and Duoqian Miao. "Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification." Mathematics 10, no. 11 (2022): 1847. http://dx.doi.org/10.3390/math10111847.
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Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.
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Badmus, Nofiu Idowu, and Rotimi -. "Formulating of Linear Model from One-Way Classification Model." Journal of Statistical Modelling and Analytics 6, no. 2 (2024): 1–10. https://doi.org/10.22452/josma.vol6no2.1.
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This study introduces a novel approach to formulating a linear regression model using a matrix method for Completely Randomized Design (CRD), a type of One-Way classification. In this approach, treatment is the sole classification, and the formulation utilizes response variables organized into rows and columns. The method yields the number of trials (n), slope, predictor, and regression parameters within the system. To ensure the normality of the response variable and select the appropriate error term distribution, we conducted normality tests (Shapiro-Wilk, Anderson-Darling, Cramér-von Mises, Lilliefors) and exploratory data analysis techniques (histogram, boxplot, QQ-plot). The formulation was validated through illustrations, and the results from the matrix method regression were compared to the ordinary least squares regression, yielding identical values for the regressors, and confirming the robustness of the proposed formulation. Furthermore, we evaluated the performance of machine learning linear regression model, which outperformed ordinary least squares regression in terms of mean absolute error, mean square error, and root mean square error, demonstrating the superior accuracy of the proposed approach.
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Gellis, Jason, and Robert Foley. "A novel system for classifying tooth root phenotypes." PLOS ONE 16, no. 11 (2021): e0251953. http://dx.doi.org/10.1371/journal.pone.0251953.
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Human root and canal number and morphology are highly variable, and internal root canal form and count does not necessarily co-vary directly with external morphology. While several typologies and classifications have been developed to address individual components of teeth, there is a need for a comprehensive system, that captures internal and external root features across all teeth. Using CT scans, the external and internal root morphologies of a global sample of humans are analysed (n = 945). From this analysis a method of classification that captures external and internal root morphology in a way that is intuitive, reproducible, and defines the human phenotypic set is developed. Results provide a robust definition of modern human tooth root phenotypic diversity. The method is modular in nature, allowing for incorporation of past and future classification systems. Additionally, it provides a basis for analysing hominin root morphology in evolutionary, ecological, genetic, and developmental contexts.
Book chapters on the topic "N-way classification"
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Di Nuzzo, Cinzia, and Salvatore Ingrassia. "Three-Way Spectral Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_13.
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AbstractIn this paper, we present a spectral clustering approach for clustering three-way data. Three-way data concern data characterized by three modes: n units, p variables, and t different occasions. In other words, three-way data contain a t × p observed matrix for each statistical observation. The units generated by simultaneous observation of variables in different contexts are usually structured as three-way data, so each unit is basically represented as a matrix. In order to cluster the n units in K groups, the spectral clustering application to three-way data can be a powerful tool for unsupervised classification. Here, one example on real three-way data have been presented showing that spectral clustering method is a competitive method to cluster this type of data.
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Essegbey, James. "Chapter 11. Catching and classifying fish among the Dwang." In Culture and Language Use. John Benjamins Publishing Company, 2024. http://dx.doi.org/10.1075/clu.23.11ess.
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The goal of this chapter is to discuss an endangered fishing practice, and the naming and uses of fish among the Dwang of the Bono East Region of Ghana. I discuss a (defunct) communal fish catching process known as kese /kə́sə́/, in which the poisonous plant Adenia cissampeloises, also called kese by the Dwang, was used to kill the fish and harvest them. I then turn to the Dwang names for six freshwater fish, which is a classification of sorts. I explore the semantics of the fish names, especially their classes, such as ka- for singular versus n- for plural (e.g., káwá/nkáwá), on the one hand, and ɔ- for singular versus a- for plural (e.g., ɔtʃwɪrɛ/atʃɪrɛ), among others. I discuss some medicinal and customary uses of the fish which have almost disappeared. The chapter is therefore a discussion of endangered indigenous knowledge of a plant and fish.
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Wardeh Maya, Coenen Frans, and Bench-Capon Trevor. "Arguing in Groups." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-619-5-475.
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We have previously introduced the notion of arguing from experience, whereby agents debate a classification problem using arguments based on association rules mined “on the fly” from their individual datasets. In this paper we extend PISA, which allows for n agents to argue about cases which have n possible classifications. By allowing any number of agents to participate all the agents supporting a given classification can form a collaborative group for the purposes of the dialogue. We describe how the system is organised, give an example, and report results which suggest that allowing groups in this way has a beneficial effect on the quality of the result.
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Kouneli, Maria. "Plural marking on mass nouns." In Gender and Noun Classification. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198828105.003.0011.
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Mass nouns are generally incompatible with plural morphology in number-marking languages. Greek mass nouns, though, can freely pluralize. Chapter 11 shows that the meaning of plural mass nouns in Greek is that of ‘spread over a surface in a disorderly way’. The author argues that plural morphology on mass nouns in the language is the spell-out of number features on the nominalizing head n, unlike plural morphology on count nouns, which spells out the head of the functional projection NumP. She extends this analysis to other languages with plural marking on mass nouns, and argues that plural morphology on mass nouns is never the spellout of features on Num, which can only have the meaning associated with regular plural morphology on count nouns cross-linguistically.
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Baues, Hans-Joachim. "On the Homotopy Classification of 2-Connected 6-Dimensional Polyhedra." In Homotopy Type and Homology. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198514824.003.0010.
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Abstract In this chapter we describe algebraic models which characterize the homotopy types of 2-connected 6-dimensional polyhedra. Such polyhedra are in the metastable range so that diverse features of ‘quadratic algebra’ are involved in the classification. We proceed in a similar way as in Chapter 8 where we classified (n −1)-connected (n + 3)-dimensional homotopy types which are in the stable range n≥ 4. We apply boundary invariants which, via the classification theorem 3.4.4, yield algebraic classifying data for 2-connected 6- dimensional homotopy types.
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Hackl, Werner O., Sabrina B. Neururer, Stefan Richter, et al. "Development of a Synthetic Oncology Pathology Dataset for Large Language Model Evaluation in Medical Text Classification." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250191.
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Background: Large Language Models (LLMs) offer promising applications in oncology pathology report classification, improving efficiency, accuracy, and automation. However, the use of real patient data is restricted due to legal and ethical concerns, necessitating privacy-compliant alternatives. Objectives: This study aimed to develop a synthetic oncology pathology dataset to serve as a benchmark for LLM evaluation, enabling reproducible and privacy-preserving AI research. Methods: A total of 227 synthetic pathology reports were generated using Microsoft Copilot, ChatGPT Plus, and Perplexity Pro to ensure structural and linguistic diversity. The dataset included cases of prostate (n=75), lung (n=78), and breast (n=74) cancer, evenly distributed between malignant (n=113) and benign (n=114) findings. Reports were reviewed and classified by three independent cancer registrars using a consensus-based validation process. Results & Conclusion: The dataset provides a structured, clinically relevant benchmark for evaluating LLM performance in pathology text classification. It enables AI model assessment without compromising data privacy, paving the way for scalable and ethical AI-driven oncology documentation.
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Hemion, Geoffrey. "Small curves." In The Classification of Knots and 3-Dimensional Spaces. Oxford University PressOxford, 1993. http://dx.doi.org/10.1093/oso/9780198596974.003.0019.
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Abstract How can we prove that there is a number such as N of the previous chapter, which gives us a limit on the size of possible conjugating homeomorphisms which are to be investigated? Again, this question is too difficult to answer directly. The trick which I used was to first prove that a ‘small’ closed curve c must exist on the surface S. Now the idea of smallness is easy to define for curves on S. Let c be some arbitrary curve on S. The curve will be assumed to be closed, in the sense that its starting point is the same as its endpoint. Put another way, c is a mapping of the circle (the I-sphere) into S. We can assume that c is in general position with respect to the set of curves {ζ1, ... , ζn}. Then c generally passes through the fundamental region Δ of S in a number of arcs. The size of c is defined to be this number. Thus we shall say that c is small if its size is small.
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Bachrach, Asaf, and Andrew Nevins. "Introduction: Approaching inflectional identity." In Inflectional Identity. Oxford University PressOxford, 2008. http://dx.doi.org/10.1093/oso/9780199219254.003.0001.
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Abstract The topic of this volume is inflectional identity. We group together under the term inflectional the morphological markers that participate in a “paradigmatically-related” alternation to express case, person, number, gender, or class distinctions. Identity covers and classifies a range of identity and similarity relations among the phonological form of these items. We refer, informally, to any n-way classification of verbal or nominal inflection as a paradigm, where the two (or more) dimensions could be tense and agreement (on verbs) or conjugation class and case (on nouns).
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Thomer, Andrea K., Morgan F. Wofford, Michael C. Lenard, Socorro Dominguez Vidana, and Simon J. Goring. "Revealing Earth science code and data-use practices using the Throughput Graph Database." In Recent Advancement in Geoinformatics and Data Science. Geological Society of America, 2023. http://dx.doi.org/10.1130/2022.2558(10).
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ABSTRACT The increased use of complex programmatic workflows and open data within the Earth sciences has led to an increase in the need to find and reuse code, whether as examples, templates, or code snippets that can be used across projects. The “Throughput Graph Database” project offers a platform for discovery that links research objects by using structured annotations. Throughput was initially populated by scraping GitHub for code repositories that reference the names or URLs of data archives listed on the Registry of Research Data Repositories (https://re3data.org). Throughput annotations link the research data archives to public code repositories, which makes data-relevant code repositories easier to find. Linking code repositories in a queryable, machine-readable way is only the first step to improving discoverability. A better understanding of the ways in which data is used and reused in code repositories is needed to better support code reuse. In this paper, we examine the data practices of Earth science data reusers through a classification of GitHub repositories that reference geology and paleontology data archives. A typology of seven reuse classes was developed to describe how data were used within a code repository, and it was applied to a subset of 129 public code repositories on GitHub. Code repositories could have multiple typology assignments. Data use for Software Development dominated (n = 44), followed by Miscellaneous Links to Data Archives (n = 41), Analysis (n = 22), and Educational (n = 20) uses. GitHub repository features show some relationships to the assigned typologies, which indicates that these characteristics may be leveraged to systematically predict a code repository’s category or discover potentially useful code repositories for certain data archives.
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Du Plessis, Andrew, and Terry Wall. "Stable topological type of finite mapgerms." In The Geometry of Topological Stability. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198535881.003.0008.
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Abstract This chapter forms a companion to chapter 7. Its objective is to classify a substantial class of germs up to some convenient equivalence relation (contact equivalence, alias 𝒦-equivalence, where this is feasible) and then to re-examine the classification from the viewpoint of ST-equivalence. We recall from chapter 6 that two germs are stably topologically equivalent (ST-equivalent for short) if they have stable unfoldings which are equivalent by homeomorphisms of source and target. A stratum defined by a 𝒦-invariant submanifold of some jet space is LST-invariant if the submanifold has a neighbourhood N such that germs with jet in the stratum are ST-distinct from others with jet in N. We define fine LSTinvariants as in Section 6.5; the definition is recalled in the introduction to Chapter 7. The background to these definitions, their importance, and the way they are applied to obtain necessary conditions for topological stability, are explained in Chapter 6. Applications of the results of this chapter to obtain sufficient conditions for topological stability are given in Chapters 10 and 11.
Conference papers on the topic "N-way classification"
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S, Tilak Bala, and Rajeswari M. "Innovative Financial Fraud Detection: Combining GCRNN and DiffPool with N-Way K-Shot Classification Techniques." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841755.
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Loebman, S., R. Roškar, Ž Ivezić, et al. "SDSS Observations of the Milky Way vs. N-body Models: A Comparison of Stellar Distributions in the Position-Velocity-Metallicity Space." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059055.
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Abbas, Syed Manzar, Khubaib Amjad Alam, and Kwang-Man Ko. "A Three-way Classification with Game-theoretic N-Soft Sets for Handling Missing Ratings in Context-aware Recommender Systems." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177701.
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Kojić, Nenad, Mladen Petrović, and Natalija Vugdelija. "Dynamic Generation of Website Content Based on User Segmentation Using Artificial Intelligence." In Sixth International Scientific Conference ITEMA Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2022. http://dx.doi.org/10.31410/itema.2022.17.
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The aim of this work is the segmentation of website users on the basis of artificial intelligence with the aim of dynamically modifying the content of the website for users, in accordance with the objectives of Web 4.0, and in this way enabling quick and optimal display of content following their needs. User classification will be based on click events on categories/subcategories and articles. Based on that information, using Kononen’s neural network, the user will be classified into one of the n categories to which the neural network was initially trained. Based on the detected type of the user’s classification, the content of the site is dynamically changed to the user, and the categories and products for which the majority of users of that type of classification have expressed greater interest are initially displayed and offered. The goal is to adapt the content of the site to the needs of the user and in this way the user can easily and quickly find the desired product.
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Wu, Junran, Shangzhe Li, Jianhao Li, Yicheng Pan, and Ke Xu. "A Simple yet Effective Method for Graph Classification." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/497.
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In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Intuitively, given a problem, a simpler data structure comes with a simpler algorithm. Here, we investigate the feasibility of improving graph classification performance while simplifying the learning process. Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data. Furthermore, we propose a novel message passing scheme, termed hierarchical reporting, in which features are transferred from leaf nodes to root nodes by following the hierarchical structure of coding trees. We then present a tree kernel and a convolutional network to implement our scheme for graph classification. With the designed message passing scheme, the tree kernel and convolutional network have a lower runtime complexity of O(n) than Weisfeiler-Lehman subtree kernel and other graph neural networks of at least O(hm). We empirically validate our methods with several graph classification benchmarks and demonstrate that they achieve better performance and lower computational consumption than competing approaches.
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Berghauser Pont, Meta, and Jesper Olsson. "Typology based on three density variables central to Spacematrix using cluster analysis." In 24th ISUF 2017 - City and Territory in the Globalization Age. Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/isuf2017.2017.5319.
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Since the publication of the book ‘Spacematrix. Space, density and urban form’ (Berghauser Pont and Haupt, 2010), the Spacematrix method has been linked back to its theoretical foundations by Steadman (2013), is further developed using the measure of accessible density to arrive at a density measure that more closely relates to the environment as experienced by people moving through the city (Berghauser Pont and Marcus, 2014) which then is used to arrive at a multi-scalar density typology (Berghauser Pont et al. 2017). This paper will take yet another step in the development of the Spacematrix method by including the measure of network density in the classification which until now was not used to its full potential. Important for successful classification is the ability to ascertain the fundamental characteristics on which the classification is to be based where the work of Berghauser Pont and Haupt (2010) will be followed addressing three key variables: Floor Space Index (FSI), Ground Space Index (GSI) and Network density (N) where especially the last was not fully included in the earlier work. Besides a typology based on these three variables, this paper will also result in a robust statistical method that can later be used on larger samples for city-scale comparisons. Two statistical methods are tested: hierarchical clustering and centroid-based clustering and besides a general discussion about their strong and weak points, the paper shows that the hierarchical method is more convincing in distinguishing differences in both building type and street pattern that is especially captured with Network density (N). As this method is not useful for large datasets we propose a combination of the two clustering methods as the way forward.
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Bigot, Fabien, and Stéphanie Mougin. "Spectral Fatigue Analysis of Plate Surface Hot-Spots: A Practical Solution to the Stress Direction Issue." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18490.
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Abstract Spectral Fatigue Analysis using coupled hydrodynamics and finite element models has now become a common practice for the fatigue strength assessment of offshore units, with established procedures given in Classification Rules. However, users are facing a practical issue that is almost never mentioned in the procedures. Indeed, many fatigue hot-spots are located on a plate surface, as opposed to plate edges. For such hot-spots, the finite element model results are the three components of the plane-stress stress tensor. Therefore, the outcome of the Spectral Fatigue Analysis is a set of three transfer functions (RAOs). On the other hand, our industry’s practice regarding the fatigue strength model is still the proven « design S-N curve » approach in combination with the Palmgren-Miner’s damage summation. As a consequence, today the engineer is left with no clear instruction about the proper way how to close this gap between the three stress RAOs on the one hand, and the single stress S-N curve on the other hand. If any advice is given, it is most often to consider the principal stresses, tentatively extending to spectral analysis the classification rule load cases approach. However, principal stress determination is a non-linear procedure that is not compatible with spectral analysis in frequency domain. Turning the spectral results into time domain to overcome this limitation is extremely costly and is not straightforward. Of course, a rational solution to this issue would be the adoption of a multiaxial fatigue damage criteria in lieu of the uniaxial S-N curve. But until such a multiaxial fatigue criteria is widely accepted in our industry, users have to square the circle, and force their stress tensor RAOs into the existing rule criteria. In this paper, a practical solution to reconcile plane stress results and conventional S-N curve criterion in spectral fatigue is proposed: the “facet approach “.
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Castro, Gustavo F. C. de, and Renato Tinós. "K-Nearest Neighbors based on the Nk Interaction Graph." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/eniac.2022.227174.
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The K-Nearest Neighbors (KNN) is a simple and intuitive nonparametric classification algorithm. In KNN, the K nearest neighbors are determined according to the distance to the example to be classified. Generally, the Euclidean distance is used, which facilitates the formation of hyper-ellipsoid clusters. In this work, we propose using the Nk interaction graph to return the K-nearest neighbors in KNN. The Nk interaction graph, originally used in clustering, is built based on the distance between examples and spatial density in small groups formed by k examples of the training dataset. By using the distance combined with the spatial density, it is possible to form clusters with arbitrary shapes. We propose two variations of the KNN based on the Nk interaction graph. They differ in the way in which the vertices associated with the N examples of the training dataset are visited. The two proposed algorithms are compared to the original KNN in experiments with datasets with different properties.
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Daver, D. I. "Innovations in Organizational Psychology: Inventory of Significant Work Beliefs." In V International Scientific Conference «MIP-V-2023: Modernization, Innovations, Progress». Krasnoyarsk Science and Technology City Hall, 2023. http://dx.doi.org/10.47813/mip.5.2023.9.136-141.
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Digitalization has changed the way we live and do business. Effective personnel management is impossible without taking into account the peculiarities caused by these changes. Having influenced the beliefs, values and meanings of the new generation, digitalization has set new challenges for the human resources management function. According to recent research on organizational development, there is a significant difference in effective methods of human resource management with an emphasis on characteristics of generations. According to the research of V.I. Pishchik, the meanings and values of generations are different, and, consequently, the methods of management must be adapted to these differences. In research to identify differences in beliefs of generations, we rely on the classification of N. Howe and W. Strauss, on a large study we conducted in 2022 to identify the beliefs of different generations about work, on the proven G. Triandis questionnaire, adapted by L. G .Pochebut. In this paper, beliefs are explored through values: benefit, socialization, hedonism, realization, altruism, reliability. This article presents the results of testing a new questionnaire developed using the semantic differential method. Factor and semantic analysis were used to interpret the results, indepth interviews were used for validation, and analogies were made with G. Triandis's questionnaire.
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Draayer, Bret, Gary W. Carhart, and Michael K. Giles. "Optical recognition via Bayesian classification: system performance." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thaa2.
Full textAbstract:
Given a collection of training images (TIs) to be recognized and a set of filters designed to represent the TIs, a recognition system based on the Bayes likelihood ratio test typically requires a calibration data set to provide an estimate of each probability distribution residing in the signal space. In order to make use of all the correlations evaluated, the dimension of the signal space should be set equal to the product of the number of filters (N f ) and the number of components required by the correlation metric (N m ). Responses are thus N-dimensional vectors (where N = N f N m ), and characterizing the response distributions (one per TI) by an N-dimensional variance analysis allows an unknown sample to be classified by correlating its response with each of the distributions. The calibration data not only provides descriptions of the distributions (used for classification), but also implicitly locates the boundaries between classification regions. Knowledge of the boundaries permits an evaluation of system performance by integrating classification errors over the entire signal space. A recognition system using the procedure outlined above was constructed and calibrated. Expected performance was calculated and compared to observed performance.
Reports on the topic "N-way classification"
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Teague, Judy, Gary Fleming, Kirsten Hazler, Lindsey Smart, and Tom Govus. Baseline vegetation mapping of National Capital Region parks in Maryland, Virginia, West Virginia, and the District of Columbia: Part 1?final report. National Park Service, 2024. http://dx.doi.org/10.36967/2304343.
Full textAbstract:
The National Park Service (NPS) National Capital Region (NCR), the Virginia Natural Heritage Program (VANHP), NatureServe, and Natural Heritage Programs in Maryland, Pennsylvania, and West Virginia, completed the vegetation classification and mapping of eleven parks, totaling 29,968 hectares (74,053 acres) in the NPS NCR. Upon the completion of the vegetation characterization and mapping, NatureServe finalized this process by conducting a thematic accuracy assessment of the resulting the vegetation map. Field observations were collected at 1,561 locations across the eleven parks to analyze the map classes observed in the field against those on the map. Overall map accuracy for 66 map classes was 80.59% (Kappa statistic of 79.23%). The final vegetation maps included a total of 110 map classes that were used to characterize 109 USNVC associations plus other land cover across the 11 parks. These 110 map classes can be separated into four categories: individual USNVC associations (n=80), composite map classes primarily comprised of another 30 USNVC associations (n=11), special feature map classes (n=9), and National Land Cover Database map classes (n=10). Of the 109 USNVC associations, 106 were identified during the classification effort and three were added during the mapping effort; 55 represented upland and 54 wetland or riparian vegetation.
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Ley, Matt, Tom Baldvins, David Jones, Hanna Pilkington, and Kelly Anderson. Vegetation classification and mapping: Gulf Islands National Seashore. National Park Service, 2023. http://dx.doi.org/10.36967/2299028.
Full textAbstract:
The Gulf Islands National Seashore (GUIS) vegetation inventory project classified and mapped vegetation on park-owned lands within the administrative boundary and estimated thematic map accuracy quantitatively. The project began in June 2016. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Ocean Springs, Mississippi where representatives gathered from GUIS, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. Primary imagery used for interpretation was 4-band (RGB and CIR) orthoimages from 2014 and 2016 with resolutions of 15 centimeters (cm) (Florida only) and 30 cm. Supplemental imagery with varying coverage across the study area included National Aerial Imagery Program 50 cm imagery for Mississippi (2016) and Florida (2017), 15 and 30 cm true color Digital Earth Model imagery for Mississippi (2016 and 2017), and current and historical true-color Google Earth and Bing Map imagery. National Oceanic Atmospheric Administration National Geodetic Survey 30 cm true color imagery from 2017 (post Hurricane Nate) supported remapping the Mississippi barrier islands after Hurricane Nate. The preliminary vegetation classification included 59 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 250 plots in 2016 and 29 plots in 2017 and 2018, as well as other observational data. The final vegetation classification includes 39 USNVC associations and 5 park special types; 18 forest and woodland, 7 shrubland, 17 herbaceous, and 2 sparse vegetation types were identified. The final GUIS map consists of 38 map classes. Land cover classes include four types: non-vegetated barren land / borrow pit, developed open space, developed low – high intensity, and water/ocean. Of the 34 vegetation map classes, 26 represent a single USNVC association/park special, six map classes contain two USNVC associations/park specials, and two map classes contain three USNVC associations/park specials. Forest and woodland associations had an abundance of sand pine (Pinus clausa), slash pine (Pinus elliottii), sand live oak (Quercus geminata), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), and saw palmetto (Serenoa repens). Shrubland associations supported dominant species such as eastern baccharis (Baccharis halimifolia), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), saw palmetto (Serenoa repens), and sand live oak (Quercus geminata). Herbaceous associations commonly included camphorweed (Heterotheca subaxillaris), needlegrass rush (Juncus roemerianus), bitter seabeach grass (Panicum amarum var. amarum), gulf bluestem (Schizachyrium maritimum), saltmeadow cordgrass (Spartina patens), and sea oats (Uniola paniculata). The final GUIS vegetation map consists of 1,268 polygons totaling 35,769.0 hectares (ha) or 88,387.2 acres (ac). Mean polygon size excluding water is 3.6 ha (8.9 ac). The most abundant land cover class is open water/ocean which accounts for approximately 31,437.7 ha (77,684.2 ac) or 87.9% of the total mapped area. Natural and ruderal vegetation consists of 4,176.8 ha (10,321.1 ac) or 11.6% of the total area. Within the natural and ruderal vegetation types, herbaceous types are the most extensive with 1945.1 ha (4,806.4 ac) or 46.5%, followed by forest and woodland types with 804.9 ha (1,989.0 ac) or 19.3%, sparse vegetation types with 726.9 ha (1,796.1 ac) or 17.4%, and shrubland types with 699.9 ha (1,729.5 ac) or 16.8%. Developed open space, which can include a matrix of roads, parking lots, park-like areas and campgrounds account for 153.8 ha (380.0 ac) or 0.43% of the total mapped area. Artificially non-vegetated barren land is rare and only accounts for 0.74 ha (1.82 ac) or 0.002% of the total area. We collected 701 AA samples to evaluate the thematic accuracy of the vegetation map. Final thematic accuracy, as a simple proportion of correct versus incorrect field calls, is 93.0%. Overall weighted map class accuracy is 93.6%, where the area of each map class was weighted in proportion to the percentage of total park area. This method provides more weight to larger map classes in the park. Each map class had an individual thematic accuracy goal of at least 80%. The hurricane impact area map class was the only class that fell below this target with an accuracy of 73.5%. The vegetation communities impacted by the hurricane are highly dynamic and regenerated quickly following the disturbance event, contributing to map class disagreement during the accuracy assessment phase. No other map classes fell below the 80% accuracy threshold. In addition to the vegetation polygon database and map, several products to support park resource management are provided including the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and the PLOTS database. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 16 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout the NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.
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Ley, Matt, Tom Baldvins, Hannah Pilkington, David Jones, and Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.
Full textAbstract:
The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Kountze, Texas where representatives gathered from BITH, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. The project acquired new 2014 orthoimagery (30-cm, 4-band (RGB and CIR)) from the Hexagon Imagery Program. Supplemental imagery for the interpretation phase included Texas Natural Resources Information System (TNRIS) 2015 50 cm leaf-off 4-band imagery from the Texas Orthoimagery Program (TOP), Farm Service Agency (FSA) 100-cm (2016) and 60 cm (2018) National Aerial Imagery Program (NAIP) imagery, and current and historical true-color Google Earth and Bing Maps imagery. In addition to aerial and satellite imagery, 2017 Neches River Basin Light Detection and Ranging (LiDAR) data was obtained from the United States Geological Survey (USGS) and TNRIS to analyze vegetation structure at BITH. The preliminary vegetation classification included 110 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 304 plots surveyed between 2016 and 2019 and 110 additional observation plots. The final vegetation classification includes 75 USNVC associations and 27 park special types including 80 forest and woodland, 7 shrubland, 12 herbaceous, and 3 sparse vegetation types. The final BITH map consists of 51 map classes. Land cover classes include five types: pasture / hay ground agricultural vegetation; non ? vegetated / barren land, borrow pit, cut bank; developed, open space; developed, low ? high intensity; and water. The 46 vegetation classes represent 102 associations or park specials. Of these, 75 represent natural vegetation associations within the USNVC, and 27 types represent unpublished park specials. Of the 46 vegetation map classes, 26 represent a single USNVC association/park special, 7 map classes contain two USNVC associations/park specials, 4 map classes contain three USNVC associations/park specials, and 9 map classes contain four or more USNVC associations/park specials. Forest and woodland types had an abundance of Pinus taeda, Liquidambar styraciflua, Ilex opaca, Ilex vomitoria, Quercus nigra, and Vitis rotundifolia. Shrubland types were dominated by Pinus taeda, Ilex vomitoria, Triadica sebifera, Liquidambar styraciflua, and/or Callicarpa americana. Herbaceous types had an abundance of Zizaniopsis miliacea, Juncus effusus, Panicum virgatum, and/or Saccharum giganteum. The final BITH vegetation map consists of 7,271 polygons totaling 45,771.8 ha (113,104.6 ac). Mean polygon size is 6.3 ha (15.6 ac). Of the total area, 43,314.4 ha (107,032.2 ac) or 94.6% represent natural or ruderal vegetation. Developed areas such as roads, parking lots, and campgrounds comprise 421.9 ha (1,042.5 ac) or 0.9% of the total. Open water accounts for approximately 2,034.9 ha (5,028.3 ac) or 4.4% of the total mapped area. Within the natural or ruderal vegetation types, forest and woodland types were the most extensive at 43,022.19 ha (106,310.1 ac) or 94.0%, followed by herbaceous vegetation types at 129.7 ha (320.5 ac) or 0.3%, sparse vegetation types at 119.2 ha (294.5 ac) or 0.3%, and shrubland types at 43.4 ha (107.2 ac) or 0.1%. A total of 784 AA samples were collected to evaluate the map?s thematic accuracy. When each AA sample was evaluated for a variety of potential errors, a number of the disagreements were overturned. It was determined that 182 plot records disagreed due to either an erroneous field call or a change in the vegetation since the imagery date, and 79 disagreed due to a true map classification error. Those records identified as incorrect due to an erroneous field call or changes in vegetation were considered correct for the purpose of the AA. As a simple plot count proportion, the reconciled overall accuracy was 89.9% (705/784). The spatially-weighted overall accuracy was 92.1% with a Kappa statistic of 89.6%. This method provides more weight to larger map classes in the park. Five map classes had accuracies below 80%. After discussing preliminary results with the parl, we retained those map classes because the community was rare, the map classes provided desired detail for management or the accuracy was reasonably close to the 80% target. When the 90% AA confidence intervals were included, an additional eight classes had thematic accruacies that extend below 80%. In addition to the vegetation polygon database and map, several products to support park resource management include the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and PLOTS database sampling data. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 15 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.
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Edwards, Mervyn, Matthias Seidl, and Alix Edwards. GB LSAV Approval Scheme: Non-ADS requirements D7.1. TRL, 2022. http://dx.doi.org/10.58446/dxiy5599.
Full textAbstract:
The UK government are committed to bringing forward legislation to allow the safe and secure deployment of self-driving vehicles. As part of the CAVPASS programme, TRL was commissioned to propose approaches to vehicle classification, and suitable technical requirements for aspects not related to the Automated Driving System (ADS). These included crashworthiness, occupant protection, protection of vulnerable Road Users (VRUs), and the lighting, braking and steering systems. The focus of this study was on Low-Speed Automated Vehicles (LSAVs). It involved selection and adaptation of existing pre- and post-deployment regulation to enable it to be applied to LSAVs. A main part was the adaptation of the technical regulations for M- and N-category vehicles, laid down in Great Britain’s Road Vehicles (Approval) Regulations 2020 (SI 2020 No. 818), which implements retained Regulation (EU) 2018/858. The study proposed the introduction of two new vehicle categories (for LSAVs with and without occupants, respectively) to allow approval of designs not compatible with the M- and N-category definitions, such as passenger shuttles with six seats and space for standing passengers, or goods vehicles without any seats. Technical clarifications for regulations were developed relating to references to the driver or driver’s seat, controls, warnings and tell-tales and relating to bi-directional vehicles in general. The study further found that a general permission to carry standing passengers in light vehicles could present unreasonable risks to occupants in braking maneuvers or collisions, but that it could be safe in some Operational Design Domains (ODDs). A concept was proposed which offers manufacturers a choice between two Crashworthiness Approval Levels (CALs). The less demanding CAL allows standing passengers but restricts the subsequent ODD of the vehicles. In summary, the study proposed a novel approach to link approval regulations to the vehicle’s ODD and a set of technical requirements for non-ADS-related aspects of passenger- and goods-carrying LSAVs, which could help enable the approval of new vehicle concepts.
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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Full textAbstract:
Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.