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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.
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Wu, Jingjing, Yanxia Zhang, Meixia Qu, Bin Jiang, and Wenyu Wang. "Automatic Classification of Spectra with IEF-SCNN." Universe 9, no. 11 (2023): 477. http://dx.doi.org/10.3390/universe9110477.
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Telescopes such as the Large Sky Area Multi-Object Spectroscopic Telescope and the Sloan Digital Sky Survey have produced an extensive collection of spectra, challenging the feasibility of manual classification in terms of accuracy and efficiency. To overcome these limitations, machine learning techniques are increasingly being utilized for automated spectral classification. However, these approaches primarily treat spectra as frequency domain signals, and lack robustness in low signal-to-noise ratio (S/N) scenarios and for small datasets of rare celestial objects. Moreover, they frequently neglect nuanced expert astronomical understanding. In this study, we draw inspiration from the human spectral discrimination process and propose a new model called the Image-EFficientNetV2-Spectrum Convolutional Neural Network (IEF-SCNN). IEF-SCNN combines spectral images using EfficientNetV2 with one-dimensional (1D) spectra through a 1DCNN. This integration effectively incorporates astronomical expertise into the classification process. Specifically, we plot the spectrum as an image and then classify it in a way that incorporates an attention mechanism. This attention mechanism mimics human observation of images for classification, selectively emphasizing relevant information while ignoring irrelevant details. Experimental data demonstrate that IEF-SCNN outperforms existing models in terms of the F1-score and accuracy metrics, particularly for low S/N (<6) data. Using progressive learning and an attention mechanism, the model trained on 12,000 M-class stars with an S/N below 6 achieved an accuracy of 87.38% on a 4000-sample test set. This surpasses traditional models (support vector machine with 83.15% accuracy, random forest with 65.40%, and artificial neural network with 84.40%) and the 1D stellar spectral CNN (85.65% accuracy). This research offers a foundation for the development of innovative methods for the automated identification of specific celestial objects, and can promote the creation of user-friendly software for astronomers who may not have computational expertise.
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Stankovic, Milena, Claudio Moraga, and Radomir Stankovic. "An improved spectral classification of Boolean functions based on an extended set of invariant operations." Facta universitatis - series: Electronics and Energetics 31, no. 2 (2018): 189–205. http://dx.doi.org/10.2298/fuee1802189s.
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Boolean functions expressing some particular properties often appear in engineering practice. Therefore, a lot of research efforts are put into exploring different approaches towards classification of Boolean functions with respect to various criteria that are typically selected to serve some specific needs of the intended applications. A classification is considered to be strong if there is a reasonably small number of different classes for a given number of variables n and it it desir able that classification rules are simple. A classification with respect to Walsh spectral coefficients, introduced formerly for digital system design purposes, appears to be useful in the context of Boolean functions used in cryptography, since it is in a way compatible with characterization of cryptographically interesting functions through Walsh spectral coefficients. This classification is performed in terms of certain spectral invariant operations. We show by introducing a new spectral invariant operation in the Walsh domain, that by starting from n?5, some classes of Boolean functions can be merged which makes the classification stronger, and from the theoretical point of view resolves a problem raised already in seventies of the last century. Further, this new spectral invariant operation can be used in constructing bent functions from bent functions represented by quadratic forms.
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CHIANG, CHENG-CHIN, and HSIN-CHIA FU. "ON THE CLASSIFICATION CAPABILITY OF A DYNAMIC THRESHOLD NEURAL NETWORK." International Journal of Neural Systems 05, no. 02 (1994): 103–14. http://dx.doi.org/10.1142/s0129065794000128.
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This paper proposes a new type of neural network called the Dynamic Threshold Neural Network (DTNN) which is theoretically and experimentally superior to a conventional sigmoidal multilayer neural network in classification capability, Given a training set containing 4k+1 patterns in ℜn, to successfully learn this training set, the upper bound on the number of free parameters for a DTNN is (k+1)(n+2)+2(k +1), while the upper bound for a sigmoidal network is 2k(n+1)+(2k+1). We also derive a learning algorithm for the DTNN in a similar way to the derivation of the backprop learning algorithm. In simulations on learning the Two-Spirals problem, our DTNN with 30 neurons in one hidden layer takes only 3200 epochs on average to successfully learn the whole training set, while the single-hidden-layer feedforward sigmoidal neural networks have never been reported to successfully learn the given training set even though more hidden neurons are used.
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Awaysheh, Abdullah, Jeffrey Wilcke, François Elvinger, Loren Rees, Weiguo Fan, and Kurt Zimmerman. "Identifying free-text features to improve automated classification of structured histopathology reports for feline small intestinal disease." Journal of Veterinary Diagnostic Investigation 30, no. 2 (2017): 211–17. http://dx.doi.org/10.1177/1040638717744002.
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The histologic evaluation of gastrointestinal (GI) biopsies is the standard for diagnosis of a variety of GI diseases (e.g., inflammatory bowel disease [IBD] and alimentary lymphoma [ALA]). The World Small Animal Veterinary Association (WSAVA) Gastrointestinal International Standardization Group proposed a reporting standard for GI biopsies consisting of a defined set of microscopic features. We compared the machine classification accuracy of free-text microscopic findings with those represented in the WSAVA format with a diagnosis of IBD and ALA. Unstructured free-text duodenal biopsy pathology reports from cats ( n = 60) with a diagnosis of IBD ( n = 20), ALA ( n = 20), or normal ( n = 20) were identified. Biopsy samples from these cases were then scored following the WSAVA guidelines to create a set of structured reports. Three supervised machine-learning algorithms were trained using the structured and then the unstructured reports. Diagnosis classification accuracy for the 3 algorithms was compared using the structured and unstructured reports. Using naive Bayes and neural networks, unstructured information-based models achieved higher diagnostic accuracy (0.90 and 0.88, respectively) compared to the structured information-based models (0.74 and 0.72, respectively). Results suggest that discriminating diagnostic information was lost using current WSAVA microscopic guideline features. Addition of free-text features (number of plasma cells) increased WSAVA auto-classification performance. The methodologies reported in our study represent a way of identifying candidate microscopic features for use in structured histopathology reports.
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Sirotova, Mariana, and Krzysztof Rubacha. "Educational Reinforcement Strategy by Teachers with Diverse Ethical Orientations." Przegląd Badań Edukacyjnych 2, no. 43 (2023): 165–74. http://dx.doi.org/10.12775/pbe.2023.033.
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The study included teachers representing three ethical orientations: care orientation (T), justice orientation (S) and undefined orientation (N). The classification of the participants into groups was based on the result of the ethical orientation test by which they were tested. The reinforcement standard they used was then diagnosed: collectivist versus individualistic. Using the one-way ANOVA, reinforcement averages were compared. Group T and N participants were more likely to use an individualistic rather than collectivistic reinforcement standard in comparison to Group S participants.
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Kurebayashi, Yusuke, and Junichi Otaki. "Classification of Neurocognition in Japanese Patients with Schizophrenia: A Cluster Analysis." Perspectives in Psychiatric Care 2024 (May 9, 2024): 1–9. http://dx.doi.org/10.1155/2024/3920137.
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Objectives. Cognitive functions in almost all domains are lower in patients with schizophrenia than those in healthy controls, with the severity of impairment differing between domains. Treatments are being developed to improve cognitive impairment in patients with schizophrenia. However, the pattern of cognitive impairment must be clarified to facilitate treatment. Therefore, this study aimed to classify the patterns of cognitive impairment in individuals and provide treatment suggestions. Methods. Patients with schizophrenia were recruited from two psychiatric hospitals in Japan. Demographic and psychopathological symptoms were assessed using the Positive and Negative Symptoms Scale for Schizophrenia and neurocognitive functions, using the CogHealth battery. The following domains were assessed: processing speed, visual attention, working memory, visual learning, and spatial attention. The scores were standardised and assigned as the same-aged average score. Hierarchical cluster analysis using Ward’s method was performed based on CogHealth scores. Subsequently, one-way analysis of variance (ANOVA) and Tukey’s multiple comparisons were performed to compare the variables in each cluster. Results. In total, 133 participants were classified into four clusters: Cluster 1 (n = 16), with severe cognitive impairment and psychiatric symptoms and the longest stay; Cluster 2 (n = 44), with moderate cognitive impairment and psychiatric symptoms; Cluster 3 (n = 42), with preserved cognitive function, except for spatial perception, and mild psychiatric symptoms; and Cluster 4 (n = 31), with only memory and spatial perception impairment and mild psychiatric symptoms. Implications. The clusters indicate that impairment may occur in all or selective domains. Selective domain impairments may be in spatial perception or in spatial perception and memory. Therefore, it is recommended that treatments for cognitive dysfunction are developed into four subsets considering an individual’s cognitive features.
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Rıfkı Çora, Ahmet, and Ersin Çelik. "Relationship between peripheral arterial disease severity determined by the Glass classification and triglyceride-glucose index; novel association and novel classification system." Investigación Clínica 63, no. 4 (2022): 363–75. http://dx.doi.org/10.54817/ic.v63n4a04.
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Peripheral arterial disease is a serious clinical manifestation caused by atherosclerosis. It is one common cause of morbidity and mortality worldwide. It is commonly seen in males, and its (prevelance) increases with age. It is most prevalent with smoking, hypertension, diabetes mellitus and hyperlip-idemia. Novel studies investigate the relationship between triglyceride-glucose index (TyG) and cardiovascular diseases. Studies investigating the association of this index and peripheral arterial disease and disease severity are generally done by using The Trans-Atlantic Inter-Society Consensus (TASC) classification. We aimed to study this association by using the new Global Limb Anatomic Staging System (GLASS) classification. Two hundred patients between 25 to 90 years old diagnosed with peripheral arterial disease and admitted to the hospital for peripheral arterial angiography between July 2021 and December 2021, were evaluated retrospectively with blood parameters and angiographic images. Patients were divided into two groups: moderate (group 1; n=58) and severe (group 2; n=142) according to the GLASS classification. No statistical differences were observed for comorbidities and repeated interventional pro-cedure rates (p=0.164). Triglyceride values were found to be statistically dif-ferent between groups (p=0.040). TyG was found higher in group 2 (p= 0.04). According to the binary logistic regression model, only TyG was found to have a significant effect as a diagnostic factor (p=0.011). TyG was also significantly correlated with the Rutherford (p=0.012) and GLASS classification severity (p<0.001). Peripheral arterial disease and disease severity could be easily moni-tored with simple calculable TyG. In this way, precautions could be taken, and morbidities could be prevented.
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Chilikov, A. A. "Regarding Classification of n-sharing of Multivariate Mappings over Finite Fields and One NSUCrypto'2019 Olympiad Problem." Mathematics and Mathematical Modeling, no. 1 (September 24, 2022): 31–51. http://dx.doi.org/10.24108/mathm.0122.0000262.
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A problem of great importance that arises in designing and implementation of a cryptosystem is countering side channel attacks. Often an appropriate mathematical algorithm, implemented on a specific physical device to work in the physical environment, becomes vulnerable to such attacks.The “function sharing” technique is a prospective and efficient way to avoid this problem. In the paper we investigate “non-complete sharing” of Boolean functions and mappings, and functions and mappings over finite fields and provide a complete description of the set of functions with n variables, which have sharing.The main findings are the following: introducing and investigating a new concept of “weak” non-complete n-sharing, establishing its connection with “weak” and “classical” n-sharing, and substantiating its advantages from the algebraic point-of-view as well as establishing and proving a criterion for the existence of weak non-complete n-sharing for an arbitrary function. The results also include an explicit description of a set of functions which have weak sharing in terms of algebraic normal form, obtaining the precise and simple descriptions for the boundary (“border”) cases: n = 2, n=m and binary fields. Applying these results to the AES S-box allows complete solving the problem, i.e. a complete answer to the question of a representability of the S-box of the AES cipher as a sharing is available. We believe that the same way can be successful for other cryptographic algorithms.
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Jaime, Xavier A., Jay P. Angerer, Chenghai Yang, et al. "Exploring Effective Detection and Spatial Pattern of Prickly Pear Cactus (Opuntia Genus) from Airborne Imagery before and after Prescribed Fires in the Edwards Plateau." Remote Sensing 15, no. 16 (2023): 4033. http://dx.doi.org/10.3390/rs15164033.
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Over the past century, prickly pear (PP) cactus (e.g., genus Opuntia; subgenus Platyopuntia) has increased on semi-arid rangelands. Effective detection of cacti abundance and spatial pattern is challenging due to the inherent heterogeneity of rangeland landscapes. In this study, high-resolution multispectral imageries (0.21 m) were used to test object-based (OB) feature extraction, random forest (RF) machine learning, and spectral endmember (n-D) classification methods to map PP and evaluate its spatial pattern. We trained and tested classification methods using field-collected GPS location, plant cover, and spectrometry from 288 2 m radius polygons before a prescribed burn and 480 samples after the burn within a 69.2-ha burn unit. The most accurate classification method was then used to map PP distribution and quantify abundance before and after fire. As a case study, we assessed the spatial pattern of mapped PP cover, considering topoedaphic setting and burn conditions. The results showed that the endmember classification method, spectral angle mapper (SAM), outperformed the RF and OB classifications with higher kappa coefficients (KC) (0.93 vs. 0.82 and 0.23, respectively) and overall accuracies (OA) (0.96 vs. 0.91 and 0.49) from pre-fire imagery. KC and OA metrics of post-fire imagery were lower, but rankings among classification methods were similar. SAM classifications revealed that fire reduced PP abundance by 46.5%, but reductions varied by soil type, with deeper soils having greater decreases (61%). Kolmogorov-Smirnov tests indicated significant changes before and after fire in the frequency distribution of PP cover within deeper soils (D = 0.64, p = 0.02). A two-way ANOVA revealed that the interaction of season (pre- vs. post-fire) and soils significantly (p < 0.00001) influenced the spatial pattern of PP patches. Fire also reduced the size and shape of PP patches depending on the topoedaphic settings. This study provides an innovative and effective approach for integrating field data collection, remote sensing, and endmember classification methods to map prickly pear and assess the effects of prescribed fire on prickly pear spatial patterns. Accurate mapping of PP can aid in the design and implementation of spatially explicit rangeland management strategies, such as fire, that can help reduce and mitigate the ecological and economic impacts of prickly pear expansion.
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Tegmark, Max, and Tailin Wu. "Pareto-Optimal Data Compression for Binary Classification Tasks." Entropy 22, no. 1 (2019): 7. http://dx.doi.org/10.3390/e22010007.
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The goal of lossy data compression is to reduce the storage cost of a data set X while retaining as much information as possible about something (Y) that you care about. For example, what aspects of an image X contain the most information about whether it depicts a cat? Mathematically, this corresponds to finding a mapping X → Z ≡ f ( X ) that maximizes the mutual information I ( Z , Y ) while the entropy H ( Z ) is kept below some fixed threshold. We present a new method for mapping out the Pareto frontier for classification tasks, reflecting the tradeoff between retained entropy and class information. We first show how a random variable X (an image, say) drawn from a class Y ∈ { 1 , … , n } can be distilled into a vector W = f ( X ) ∈ R n − 1 losslessly, so that I ( W , Y ) = I ( X , Y ) ; for example, for a binary classification task of cats and dogs, each image X is mapped into a single real number W retaining all information that helps distinguish cats from dogs. For the n = 2 case of binary classification, we then show how W can be further compressed into a discrete variable Z = g β ( W ) ∈ { 1 , … , m β } by binning W into m β bins, in such a way that varying the parameter β sweeps out the full Pareto frontier, solving a generalization of the discrete information bottleneck (DIB) problem. We argue that the most interesting points on this frontier are “corners” maximizing I ( Z , Y ) for a fixed number of bins m = 2 , 3 , … which can conveniently be found without multiobjective optimization. We apply this method to the CIFAR-10, MNIST and Fashion-MNIST datasets, illustrating how it can be interpreted as an information-theoretically optimal image clustering algorithm. We find that these Pareto frontiers are not concave, and that recently reported DIB phase transitions correspond to transitions between these corners, changing the number of clusters.
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Prokopowicz, Piotr, and Dariusz Mikołajewski. "Fuzzy Approach to Computational Classification of Burnout—Preliminary Findings." Applied Sciences 12, no. 8 (2022): 3767. http://dx.doi.org/10.3390/app12083767.
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There is a common belief that medical professions generate more work-related stress and earlier job burnout. We tested two groups: study group 1: medical (physical therapists, n = 30), and study group 2: non-medical (informaticians, n = 30). The purpose of this study was to find new, more reliable models for calculating work-related stress and burnout in the two aforementioned different professional groups. In the paper, we focused on a new model of algorithm based on AI methods that extends the interpretability of the scale of results obtained using the MBI test. The outcomes of the Maslach Burnout Inventory (MBI) were analysed in both study groups. These became the starting point for the development of three different fuzzy models, from which, after comparison, the one best suited to the study groups and the way they were evaluated was selected. Among the patients participating in the study, the following results were obtained: MBI values expressed as median values were significantly higher in group 2 than in group 1. The computational analysis showed that the contribution of the different parts of the MBI test to the final score was unequal in both groups. AI allowed for optimal selection of the model parameters for the study group, from which an algorithm was created to optimise the selection of tools or their parameters. A computational tool can do this faster, more accurately, and more efficiently, becoming an important supporting tool. In the medical context, the main benefit of the results presented in this paper is the definition of an evaluation model that transforms the MBI test scores into a universal percentage scale while preserving the properties of the guidelines underlying the MBI. An additional advantage of the proposed solution is the readability and flexibility resulting from the linguistic rules underlying the model.
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Gulzar, Zameer, and A. Anny Leema. "Course Recommendation Based on Query Classification Approach." International Journal of Web-Based Learning and Teaching Technologies 13, no. 3 (2018): 69–83. http://dx.doi.org/10.4018/ijwltt.2018070105.
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This article describes how with a non-formal education, a scholar has to choose courses among various domains to meet the research aims. In spite of this, the availability of large number of courses, makes the process of selecting the appropriate course a tedious, time-consuming, and risky decision, and the course selection will directly affect the performance of a scholar. The best approach to solve such problems and to produce desirable results is to use a “recommendation system.” Recommender systems at the core employ information retrieval techniques and the ongoing effort of such information retrieval systems is to deliver the most relevant information to the learner. Therefore, if a recommender system is able to recognize the intent and requirements that a user express in the form of queries, it can generate more valid recommendations. This article presents an N-Gram classification technique which can be used to generate course recommendations to scholars depend on the requirements and domain of interest. This way of personalization can improve the quality of research and learning experience by recommending courses which are otherwise overlooked by scholars, as it takes the time to go through the curriculum and finding the best possible match.
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RIESEN, KASPAR, and HORST BUNKE. "GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1053–81. http://dx.doi.org/10.1142/s021800140900748x.
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Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propose an approach transforming graphs into n-dimensional real vectors by means of prototype selection and graph edit distance computation. This approach allows one to build graph kernels in a straightforward way. It is not only applicable to graphs, but also to other kind of symbolic data in conjunction with any kind of dissimilarity measure. Thus it is characterized by a high degree of flexibility. With several experimental results, we prove the robustness and flexibility of our new method and show that our approach outperforms other graph classification methods on several graph data sets of diverse nature.
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Cuesta-Frau, David, Antonio Molina-Picó, Borja Vargas, and Paula González. "Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy." Entropy 21, no. 10 (2019): 1013. http://dx.doi.org/10.3390/e21101013.
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Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear I R n mapping into I R , where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.
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Benjumea, J. C., J. Núnez, and A. F. Tenorio. "Minimal linear representations of the low-dimensional nilpotent Lie algebras." MATHEMATICA SCANDINAVICA 102, no. 1 (2008): 17. http://dx.doi.org/10.7146/math.scand.a-15048.
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The main goal of this paper is to compute a minimal matrix representation for each non-isomorphic nilpotent Lie algebra of dimension less than $6$. Indeed, for each of these algebras, we search the natural number $n\in\mathsf{N}\setminus\{1\}$ such that the linear algebra $\mathfrak{g}_n$, formed by all the $n \times n$ complex strictly upper-triangular matrices, contains a representation of this algebra. Besides, we show an algorithmic procedure which computes such a minimal representation by using the Lie algebras $\mathfrak{g}_n$. In this way, a classification of such algebras according to the dimension of their minimal matrix representations is obtained. In this way, we improve some results by Burde related to the value of the minimal dimension of the matrix representations for nilpotent Lie algebras.
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Lovera, Fernando Andres, Yudith Coromoto Cardinale, and Masun Nabhan Homsi. "Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification." Electronics 10, no. 22 (2021): 2739. http://dx.doi.org/10.3390/electronics10222739.
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The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.
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Fernández-Trincado, José G., Leonardo Chaves-Velasquez, Angeles Pérez-Villegas, et al. "Dynamical orbital classification of selected N-rich stars with Gaia Data Release 2 astrometry." Monthly Notices of the Royal Astronomical Society 495, no. 4 (2020): 4113–23. http://dx.doi.org/10.1093/mnras/staa1386.
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ABSTRACT We have used the galaxy modelling algorithm gravpot16, to explore the most probable orbital elements of a sample of 64 selected N-rich stars across the Milky Way. We use the newly measured proper motions from Gaia Data Release 2 with existing line-of-sight velocities from the second generation of the Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) and spectrophotometric distance estimations from starhorse. We adopted a set of high-resolution particle simulations evolved in the same steady-state Galactic potential model with a bar, in order to identify the groups of N-rich stars that have a high probability of belonging to the bulge/bar, disc and stellar halo component. We find that the vast majority of the N-rich stars show typically maximum height from the Galactic plane below 3 kpc, and develop eccentric orbits (e &gt; 0.5), which means that these stars appear to have bulge/bar-like and/or halo-like orbits. We also show that ∼66 per cent of the selected N-rich stars currently reside in the inner Galaxy inside the corotation radius, whilst ∼ 14 per cent are in halo-like orbits. Among the N-rich stars in the inner Galaxy, ∼ 27 per cent share orbital properties in the boundary between bulge/bar and disc, depending on the bar pattern speeds. Our dynamical analysis also indicates that some of the N-rich stars are likely to be halo interlopers, which suggests that halo contamination is not insignificant within the bulge area.
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Grotkamp, Sabine L., Wolfgang M. Cibis, Elisabeth A. M. Nüchtern, Gert von Mittelstaedt, and Wolfgang K. F. Seger. "Personal Factors in the International Classification of Functioning, Disability and Health: Prospective Evidence." Australian Journal of Rehabilitation Counselling 18, no. 1 (2012): 1–24. http://dx.doi.org/10.1017/jrc.2012.4.
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The goal of this study was to propose a systematic classification of relevant personal factors for describing the background of an individual's life and way of living. The German Society of Social Medicine and Prevention constituted an ICF working group consisting of members from Medical Advisory Boards of Statutory Health Insurances (n = 6) and other institutions (n = 12) in 2009. A two-tier consensus building approach was utilised to construct and document the personal factors, with an initial team of experts compiling the personal factors and a second group of experts, who had not participated in developing the initial proposal, validating the process. The consensus process resulted in personal factors classified into 72 categories and arranged in six chapters as follows: general factors normally unchangeable (chapter 1); a person's inherent physical and mental constitution (chapters 2 and 3); more modifiable factors, such as attitudes, basic skills and behaviour patterns (chapter 4); life situation and socioeconomic/sociocultural factors (chapter 5); and other health factors e.g., prior interventions (chapter 6). We believe the personal factors from this effort to be a good basis for a wider global dialogue on their operationalisation.
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Ihsanto, Eko, Kalamullah Ramli, Dodi Sudiana, and Teddy Surya Gunawan. "An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks." Applied Sciences 10, no. 2 (2020): 483. http://dx.doi.org/10.3390/app10020483.
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Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( S n ), specificity ( S p ), and positive predictivity ( P p ), and accuracy ( A c c ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.
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Thoma, Louise M., Hege Grindem, David Logerstedt, et al. "Coper Classification Early After Anterior Cruciate Ligament Rupture Changes With Progressive Neuromuscular and Strength Training and Is Associated With 2-Year Success: The Delaware-Oslo ACL Cohort Study." American Journal of Sports Medicine 47, no. 4 (2019): 807–14. http://dx.doi.org/10.1177/0363546519825500.
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Background: Some athletes demonstrate excellent dynamic stability after anterior cruciate ligament (ACL) rupture and return to sport without ACL reconstruction (ACLR) (copers). Others demonstrate persistent instability despite rehabilitation (noncopers) and require surgical stabilization. Testing to determine coper classification can identify potential copers early after rupture. It is unclear how coper classification changes after a brief intervention and how early classification relates to long-term outcomes. Purpose: (1) To evaluate the consistency of early coper classification (potential coper vs noncoper) before and after progressive neuromuscular and strength training (NMST) among athletes early after acute ACL rupture and (2) to evaluate the association of early coper classification with 2-year success after ACL rupture. Study Design: Cohort study; Level of evidence, 2. Methods: This was a prospective analysis from the Delaware-Oslo ACL Cohort Study, composed of athletes consecutively enrolled early after ACL rupture. Participants (n = 271) were tested and classified as potential copers or noncopers according to established criteria before and after a 10-session NMST program. Success 2 years after ACLR or nonoperative rehabilitation was defined as meeting or exceeding sex- and age-matched norms for knee function, no ACL graft rupture, and ≤1 episode of giving way within the previous year. The McNemar test evaluated changes in coper classification pre- to posttraining. Logistic regression adjusted for baseline characteristics was used to evaluate the association of early coper classification and surgical status with 2-year success. Results: Of 300 athletes enrolled, 271 (90%) completed the posttraining data collection, and 219 (73%) returned for the 2-year follow-up. The coper classifications were different between time points: nearly half of those classified initially as noncopers became potential copers ( P < .001). At the 2-year follow-up, 66% of the ACLR group and 74% of the nonoperative group were successful. Athletes who were potential copers posttraining and chose ACLR or nonoperative rehabilitation had 2.7 (95% CI, 1.3-5.6) and 2.9 (95% CI, 1.2-7.2) times the odds of success, respectively, as compared with noncopers who chose ACLR. Conclusion: Coper classification improved after NMST; more athletes became potential copers. Athletes who were potential copers after NMST were more likely to succeed 2 years later regardless of whether they had surgery, strongly supporting the addition of NMST before ACLR. Persistent noncopers fared poorly, indicating that more intensive rehabilitation may be needed.
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Kustiawan, Rendi, Adiwijaya Adiwijaya, and Mahendra Dwifebri Purbolaksono. "A Multi-label Classification on Topic of Hadith Verses in Indonesian Translation using CART and Bagging." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 2 (2022): 868. http://dx.doi.org/10.30865/mib.v6i2.3787.
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Hadith is a source of law for Muslims after the al-qur'an, in which there are instructions in the form of words, actions, attitudes, and others. Hadith must be studied and practiced by Muslims, then used as a way of life after the al-qur'an. Classifying hadith is a way to make it easier for Muslims to learn hadith by looking at the text pattern in the translation of Bukhari hadith based on three classes or categories based on suggestions, prohibitions, and information. The classification carried out is a multi-label classification. The classification process uses N-gram and TF-IDF as feature extraction, CART and bagging as classification methods, and hamming loss as evaluation methods. Bagging is used to cover the shortcomings of CART, namely, the CART model is less stable, which, if there is a slight change in the training data, will have a significant effect on the resulting learning model. Several testing methods were carried out to obtain the best hammer loss value in this study. Based on several tests that have been carried out, the best hamming loss value is 0.1914 or 80.86%. These results indicate that the use of bagging can help increase accuracy by 5%.
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Khaled, Alzamel, and Alajmi Manayer. "Few-shot Learning Approach for Arabic Scholarly Paper Classification using SetFit Framework." WSEAS TRANSACTIONS ON COMMUNICATIONS 23 (December 27, 2024): 89–95. https://doi.org/10.37394/23204.2024.23.12.
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Focus on the few-shot approach has increased recently for TC as it is competitive with fine-tuning models that need a large dataset [14]. In NLP, the process of using PTMs to classify new data is preferable to the expensive process of training a model from scratch. This can be considered a kind of TL, i.e., it focuses on reusing knowledge of PTMs to solve different problems, as long as the pre-training data is appropriately comparable. Transferring knowledge allows the model to circumvent the lack of data and enable FSL as a low-cost solution. To clarify, the term shot refers to a single example that is used for training, and the number of examples available for training is equal to N in N-shot learning. The focus of this study is on few-shot classification, which involves distinguishing between N classes using K examples of each. In this approach, N-way-K shot classification implies that each task involves N classes with K examples. In FSL, the model is able to predict a new class based on a few new examples [11] by transferring knowledge and contrasting examples. Such contrastive learning [5] has shown its effectiveness in different studies of various NLP tasks [20]. However, as far as we know, no previous studies have applied contrastive learning to standard Arabic for multi-class classification. This study aims to apply few-shot learning using a Siamese Network-based model(SN-XLM-RoBERTa [6]) to classify MSA texts in predefined classes labelled with the most common ministries’ names. For this study, we extracted a new dataset from an AI-powered research tool. The model was fine-tuned by K examples per class. We experimented with various K values, including 10, 20, 50, 100, and 200. The results show that the accuracy in distinguishing between 6 classes using 200 examples of each is 91.076%. Moreover, the results indicated that employing few-shot learning, as in SN-XLM-RoBERTa, in classifying MSA texts can be a promising solution in case of an insufficient dataset or uncertain labelling. Few-Shot Learning (FSL) may contribute to the research domain by automating the classification process.
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Kaķol, Jerzy, Stephen A. Saxon, and Aaron R. Todd. "THE ANALYSIS OF WARNER BOUNDEDNESS." Proceedings of the Edinburgh Mathematical Society 47, no. 3 (2004): 625–31. http://dx.doi.org/10.1017/s001309150300066x.
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AbstractIn answer to Jarchow’s 1981 text, we recently characterized when $C_{\textrm{c}}(X)$ is a $df$-space, finding along the way attractive analytic characterizations of when the Tychonov space $X$ is pseudocompact. Analogues now reveal how exquisitely Warner boundedness lies between these two notions. To illustrate, $X$ is pseudocompact, $X$ is Warner bounded or $C_{\textrm{c}}(X)$ is a $df$-space if and only if for each sequence $(\mu_{n})_{n}\subset C_{\textrm{c}}(X)'$ there exists a sequence $(\varepsilon_{n})_{n}\subset(0,1]$ such that $(\varepsilon_{n}\mu_{n})_{n}$ is weakly bounded, is strongly bounded or is equicontinuous, respectively. Our characterizations and proofs add to and simplify Warner’s.AMS 2000 Mathematics subject classification: Primary 46A08; 46A30; 54C35
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Şahin Mantı, Arzu, and Özgür İlke Ulusoy. "An Evaluation of the Fracture Resistance of Teeth with Simulated External Cervical Resorption Cavities Categorized Using Three-Dimensional Classification." Journal of Clinical Medicine 13, no. 8 (2024): 2159. http://dx.doi.org/10.3390/jcm13082159.
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(1) Background: External cervical resorption causes dental hard tissue destruction that may reduce the fracture resistance of affected teeth. By using a compressive strength test, this study aimed to evaluate the fracture resistance of teeth with simulated external cervical resorption cavities that have different three-dimensional classifications. (2) Methods: In total, 170 teeth with simulated external cervical resorptions were divided into 16 experimental groups (n = 10) and 1 control group (n = 10) based on the three-dimensional classification: 1Ap, 1Bp, 1Cp, 1Dp, 2Ap, 2Bp, 2Cp, 2Dp, 3Ap, 3Bp, 3Cp, 3Dp, 4Ap, 4Bp, 4Cp, 4Dp and a control group. Defects were restored with mineral trioxide aggregate. The fracture resistances of the samples were statistically analyzed using two-way repeated ANOVA and the Bonferroni correction for multiple comparisons at a significance level of p < 0.05. (3) Results: The lowest resistance to fracture was observed in samples with vertical height level “4” and circumferential spread of “D” (p < 0.001). In the groups with circumferential spreads “B”, “C” and “D”, there were significant differences between the samples with vertical height levels “1”, “2”, “3” and “4” regarding fracture resistance (p < 0.001). (4) Conclusions: The circumferential spread and vertical height of the external cervical resorption influenced the fracture resistance of the affected teeth.
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Zeeshan, Tayyaba, Zeeshan Qamar, Badr Bamousa, et al. "Effect of Salvadora persica chewing sticks on maxillary central incisors crown-root angulation in Western-Asian population & correlation with Collum angle on 3D CBCT images." Edelweiss Applied Science and Technology 8, no. 6 (2024): 4512–17. http://dx.doi.org/10.55214/25768484.v8i6.2981.
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The main aim of the study was to identify the Collum angle classification for maxillary central incisors among individuals use vs not-use chewing sticks which could have beneficial role for immediate implant placement in respective individuals. Additionally, to identify the root-crown positioning and angulation in the Western Asian Saudi Arabian population for chewing sticks user’s vs non-users in order to develop guideline for the prosthetic procedures using Cone beam computed tompography (CBCT). A total number of 380 CBCT scans were included in the study for both of the groups based on the inclusion criterion of the patients reporting to the Dental OPD of Riyadh Elm University. The data was analyzed using 3D planner dental imaging software. Initially the teeth were classified based on root position in relation to facial plate of alveolar bone using Jung et al. classification. Additionally, the Collum angle and the root angulation of the maxillary central incisors was evaluated. The data was analyzed using one-way Anova with post-hoc Tuckey-Kramer test for pair-wise comparison. As per the Jung et al. classification, majority of the roots were observed with buccal placement. Based on the Collum angle classification majority of the maxillary central incisors were observed with facial placement (n=170), specially subtype I (n=78). But the root angulation was observed to be independent of Collum angle. Thus, it can be concluded, unlike root angulation, Collum angle has a correlation with root positioning. The angle for the individuals using chewing sticks of Salvadora Persica was similar to the individuals not using the miswak. Collum angle is correlated in all classified position of root. But the root angulation is independent of classification varies between miswak and non-miswak users.
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Yang, Shuang, Chunmei Shu, Haiyou Hu, Guanghui Ma, and Min Yang. "Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features." Computational and Mathematical Methods in Medicine 2022 (May 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/9726181.
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The objective of this study was to explore the image classification and case characteristics of pigmented nevus (PN) diagnosed by dermoscopy under deep learning. 268 patients were included as the research objects and they were randomly divided into observation group ( n = 134 ) and control group ( n = 134 ). Image recognition algorithm was used for feature extraction, segmentation, and classification of dermoscopic images, and the image recognition and classification algorithm were studied as the performance and accuracy of fusion classifier were compared. The results showed that the classifier was optimized, and the linear kernel accuracy was 85.82%. The PN studied mainly included mixed nevus, junctional nevus, intradermal nevus, and acral nevus. The sensitivity under collaborative training was higher than that under feature training and fusion feature training, and the differences among three trainings were significant ( P < 0.05 ). The sensitivity of the observation group was 88.65%, and the specificity was 90.26%, while the sensitivity and the specificity of the control group were 85.65% and 84.03%, respectively; there were significant differences between the two groups ( P < 0.05 ). In conclusion, dermoscopy under deep learning could be applied as a diagnostic way of PN, which helped improve the accuracy of diagnosis. The dermoscopic manifestations of PN showed a certain corresponding relationship with the type of cases and could provide auxiliary diagnosis in clinical practice. It could be applied clinically.
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MORRISON, SCOTT, DAVID PENNEYS, EMILY PETERS, and NOAH SNYDER. "SUBFACTORS OF INDEX LESS THAN 5, PART 2: TRIPLE POINTS." International Journal of Mathematics 23, no. 03 (2012): 1250016. http://dx.doi.org/10.1142/s0129167x11007586.
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We summarize the known obstructions to subfactors with principal graphs which begin with a triple point. One is based on Jones's quadratic tangles techniques, although we apply it in a novel way. The other two are based on connections techniques; one due to Ocneanu, and the other previously unpublished, although likely known to Haagerup.We then apply these obstructions to the classification of subfactors with index below 5. In particular, we eliminate three of the five families of possible principal graphs called "weeds" in the classification from S. Morrison and N. Snyder, Subfactors of index less than 5, part 1: the principal graph odometer, to appear in Comm. Math. Phys.
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Wei, Zhenbo, Yu Zhao, and Jun Wang. "Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue." Transactions of the ASABE 60, no. 4 (2017): 1037–44. http://dx.doi.org/10.13031/trans.9488.
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Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were &gt;0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.
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Seo, Suin, Sung-Il Cho, Wonjeong Yoon, and Cheol Min Lee. "Classification of Smoking Cessation Apps: Quality Review and Content Analysis." JMIR mHealth and uHealth 10, no. 2 (2022): e17268. http://dx.doi.org/10.2196/17268.
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Background Many people use apps for smoking cessation, and the effectiveness of these apps has been proven in several studies. However, no study has classified these apps and only few studies have analyzed the characteristics of these apps that influence their quality. Objective The purpose of this study was to analyze the content and the quality of smoking cessation apps by type and identify the characteristics that affect their overall quality. Methods Two app marketplaces (App Store and Google Play) were searched in January 2018, and the search was completed by May 2020. The search terms used were “stop smoking,” “quit smoking,” and “smoking cessation.” The apps were categorized into 3 types (combined, multifunctional, and informational). The tailored guideline of Clinical Practice Guideline for Treating Tobacco Use and Dependence was utilized for evaluating app content (or functions), and the Mobile App Rating Scale (MARS) was used to evaluate the quality. Chi-square test was performed for the general characteristics, and one-way analysis of variance was performed for MARS analysis. To identify the general features of the apps that could be associated with the MARS and content scores, multiple regression analysis was done. All analyses were performed using SAS software (ver. 9.3). Results Among 1543 apps, 104 apps met the selection criteria of this study. These 104 apps were categorized as combined type (n=44), functional type (n=31), or informational type (n=29). A large amount of content specified in the guideline was included in the apps, most notably in the combined type, followed by the multifunctional and informational type; the MARS scores followed the same order (3.64, 3.26, and 3.0, respectively). Regression analysis showed that the sector in which the developer was situated and the feedback channel with the developer had a significant impact on both the content and MARS scores. In addition, problematic apps such as those made by unknown developers or copied and single-function apps were shown to have a large market share. Conclusions This study is the first to evaluate the content and quality of smoking cessation apps by classification. The combined type had higher-quality content and functionality than other app types. The app developer type and feedback channel with the app developer had a significant impact on the overall quality of the apps. In addition, problematic apps and single-function apps were shown to have a large market share. Our results will contribute to the use and development of better smoking cessation apps after considering the problems identified in this study.
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Struja, Tristan, Andreas Eckart, Alexander Kutz, et al. "Metabolomics and Their Ability to Distinguish Thyroid Disorders: A Retrospective Pilot Study." Hormone and Metabolic Research 51, no. 04 (2019): 256–60. http://dx.doi.org/10.1055/a-0850-9691.
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AbstractEarly diagnosis of thyroid disorders is key to further treatment. We assessed the ability of a high-throughput proton NMR metabolomic profile to distinguish disease type amongst of Graves’ disease (n=87), Hashimoto’s thyroiditis (n=17), toxic goiter (n=11), and autoimmune thyroiditis [i. e., subacute thyroiditis (n=4), postpartum thyroiditis (n=1)]. This observational study was conducted investigating patients presenting with a thyroid disorder at a Swiss hospital endocrine referral center and an associated endocrine outpatient clinic. The main outcome was diagnosis of thyroid disorder based on classical parameters. Blood draws took place as close as possible to treatment initiation. We performed one-way ANOVA and partial least squares discriminant analysis (PLS-DA) as multivariate classification and feature ranking method. One-way ANOVA analysis yielded following significantly different metabolites, triglycerides in small VLDL, triglycerides in very small VLDL, and triglycerides in large LDL (FDR=0.04). There was no distinct separation of any of the 4 diagnoses by PLS-DA. We did not find a metabolomic biomarker combination capable of predicting diagnosis. Preanalytical issues might have influenced our results. We strongly suggest replicating our work in another cohort.
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Hjorth, Greg, and Alexander S. Kechris. "Analytic equivalence relations and Ulm-type classifications." Journal of Symbolic Logic 60, no. 4 (1995): 1273–300. http://dx.doi.org/10.2307/2275888.
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Our main goal in this paper is to establish a Glimm-Effros type dichotomy for arbitrary analytic equivalence relations.The original Glimm-Effros dichotomy, established by Effros [Ef], [Ef1], who generalized work of Glimm [G1], asserts that if an Fσ equivalence relation on a Polish space X is induced by the continuous action of a Polish group G on X, then exactly one of the following alternatives holds:(I) Elements of X can be classified up to E-equivalence by “concrete invariants” computable in a reasonably definable way, i.e., there is a Borel function f: X → Y, Y a Polish space, such that xEy ⇔ f(x) = f(y), or else(II) E contains a copy of a canonical equivalence relation which fails to have such a classification, namely the relation xE0y ⇔ ∃n∀m ≥ n(x(n) = y(n)) on the Cantor space 2ω (ω = {0,1,2, …}), i.e., there is a continuous embedding g: 2ω → X such that xE0y ⇔ g(x)Eg(y).Moreover, alternative (II) is equivalent to:(II)′ There exists an E-ergodic, nonatomic probability Borel measure on X, where E-ergodic means that every E-invariant Borel set has measure 0 or 1 and E-nonatomic means that every E-equivalence class has measure 0.
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Cheng, Kai, Juanle Wang, and Xinrong Yan. "Mapping Forest Types in China with 10 m Resolution Based on Spectral–Spatial–Temporal Features." Remote Sensing 13, no. 5 (2021): 973. http://dx.doi.org/10.3390/rs13050973.
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The comprehensive application of spectral, spatial, and temporal (SST) features derived from remote sensing images is a significant technique for classifying and mapping forest types. Facing limitations in the availability of detailed forest type identification processes for large regions, a forest type classification framework based on SST features was developed in this study. The advantages of Sentinel-2 and Landsat series imagery were used to extract SST forest type classification features, using red-edge bands, a gray-level co-occurrence matrix, and harmonic analysis, with the assistance of the Google Earth Engine platform. Considering four representative Chinese geographic regions—middle and high latitudes, complex mountainous areas, cloudy and rainy areas, and the N–S climate transition zone—our method was proven to be effective, with overall classification accuracies > 85%. The scheme to assess the importance of SST features for forest classification in various regions was designed using the Gini criterion in the random forest algorithm and revealed that spectral features were more effective in classifying forest types with complex compositions. Temporal features were found to be favorable for identifying forest types with obvious evergreen and deciduous growth patterns, while spatial features produced better classification results for forest types with different spatial structures, such as needle- or broad-leaved forests. The findings of this study can provide a reference for feature selection in remote sensing forest type classification processes, and identifying forest types in this way could provide support for the accurate and sustainable management of forest resources.
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Johnston, Briana P., Mauricio X. S. Oliveira, Carly A. Becker, et al. "22 Influence of Cell-Mediated Immune Response Classification on Performance of Heat-Stressed Dairy Cattle." Journal of Animal Science 101, Supplement_1 (2023): 18–19. http://dx.doi.org/10.1093/jas/skad068.021.
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Abstract The objective was to evaluate the influence of cell-mediated immune response classification on performance of dairy cattle experiencing conditions of heat stress on pasture. On d 0, pregnant, lactating dairy cows (n = 27; 18 Holstein and 9 Jersey) received a subcutaneous sensitization dose of killed Candida albicans (CA; 0.5 mg) with Quil-A adjuvant (0.75 mg) in 2.5 mL of PBS. Tail skin fold thickness (SFT) was measured with a Harpenden caliper on d 14 immediately following an intradermal injection of killed CA (0.1 mg in 0.5 mL of PBS) in the right tail skin fold and a control injection (PBS) in the left tail skin fold. On d 15, change in SFT was measured and used to assign cell-mediated immune response classifications (CMIR) as High (HR; &gt;0.5 SD above the mean; n = 9), Intermediate (IR; within 0.5 SD above or below the mean; n = 10), or Low (LR; &gt;0.5 SD below the mean; n = 8). Cows from each CMIR were assigned to 1 of 3 environmental conditions (EC): No Abatement (no heat abatement provided; n = 9), Shade (portable shade structure; n = 9), or Sprinklers (polyvinyl chloride sprinkler system; n = 9) for a 38-d period (July 1, 2019 to August 8, 2019). Daily Milk Yield (DMY), Reticulorumen Temperature (RT), Heat Stress Score (HSS; 0 = respiration rate of &lt; 80 breaths/min to 4 = moribund, labored breathing), and instances of Mouth Open (MO), Tongue Out (TO), and Drool were recorded daily. Respiration rate (RR; breaths/min) was recorded 3 times/day (morning, midday, and late afternoon) and averaged on Monday, Wednesday, and Friday weekly. Body Condition Score (BCS; 1 = emaciated to 5 = obese) was recorded weekly. Data were analyzed using MIXED procedures of SAS specific for repeated measures with CMIR, EC, Breed, Time and 2-way interactions as fixed effects and Pen as random. Low exhibited the lowest HSS and BCS, but the greatest MO and TO instances (P &lt; 0.02). High exhibited the greatest instances of Drool (P &lt; 0.02). Heat stress score, MO, and Drool were greatest in the No Abatement group (P &lt; 0.01). Respiration rate was greatest in the No Abatement and Shade groups relative to Sprinklers (P &lt; 0.01). Daily milk yield, RT, and Drool were increased whereas HSS and RR were decreased in Holstein relative to Jersey cows (P &lt; 0.01). Interactions (P &lt; 0.05) included: CMIR by EC for DMY, HSS, RR, MO, TO, and Drool; CMIR by Breed for RT, DMY, and BCS; EC by Breed for RT, DMY, HSS, and RR; and EC by Time for DMY, HSS, BCS, RR, MO, and TO. There was an effect of Time for all variables (P &lt; 0.05). Preliminary results suggest an influence of cell-mediated immune response classification on performance of dairy cattle experiencing conditions of heat stress and warrant future studies with an increased number of animals.
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Nischal, Lakhotia, Harod Omprakash, and Manoranjitham T. "Detecting and Classifying Toxic Language in Twitter using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 644–48. https://doi.org/10.35940/ijeat.E9714.069520.
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Today international on-line content material has turned out to be a first-rate part due to growth in the use of net. Individuals of various societies and instructive foundation can speak through this platform. Therefore, for automatic detection of poisonous content, we need to distinguish between hate speech and offensive language. Here a way to robotically stumble on and classify tweets on Twitter into 3 commands: hateful, offensive and easy is proposed. We do not forget n-grams as functions and by way of passing their time period frequency-inverse document frequency (TFIDF) values to numerous system gaining knowledge of fashions using Twitter dataset, we perform comparative evaluation of the models. We work towards classification and comparison of different classifiers using the combination of best feature from each type of feature extraction and determining which model works best for the purpose of classification of tweets into hate-speech, offensive language or neither.
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Tan, Jun, Jiamin Yuan, Xiaoyong Fu, and Yilin Bai. "Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network." PLOS ONE 19, no. 10 (2024): e0302800. http://dx.doi.org/10.1371/journal.pone.0302800.
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Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classification technique is applied to identify colorectal cancer. But visually categorizing polyps is difficult since different polyps have different lighting conditions. Different from previous works, this article presents Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), a polyp classification technique that combines Convolutional Neural Network (CNN) and Scattering Wavelet Transform (SWT) to improve polyp classification performance. This method concatenates simultaneously learnable image filters and wavelet filters on each input channel. The scattering wavelet filters can extract common spectral features with various scales and orientations, while the learnable filters can capture image spatial features that wavelet filters may miss. A network architecture for ESWCNN is designed based on these principles and trained and tested using colonoscopy datasets (two public datasets and one private dataset). An n-fold cross-validation experiment was conducted for three classes (adenoma, hyperplastic, serrated) achieving a classification accuracy of 96.4%, and 94.8% accuracy in two-class polyp classification (positive and negative). In the three-class classification, correct classification rates of 96.2% for adenomas, 98.71% for hyperplastic polyps, and 97.9% for serrated polyps were achieved. The proposed method in the two-class experiment reached an average sensitivity of 96.7% with 93.1% specificity. Furthermore, we compare the performance of our model with the state-of-the-art general classification models and commonly used CNNs. Six end-to-end models based on CNNs were trained using 2 dataset of video sequences. The experimental results demonstrate that the proposed ESWCNN method can effectively classify polyps with higher accuracy and efficacy compared to the state-of-the-art CNN models. These findings can provide guidance for future research in polyp classification.
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Stausberg, J., and D. Nasseh. "Evaluation of a Binary Semi-supervised Classification Technique for Probabilistic Record Linkage." Methods of Information in Medicine 55, no. 02 (2016): 136–43. http://dx.doi.org/10.3414/me14-01-0087.
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SummaryBackground: The process of merging data of different data sources is referred to as record linkage. A medical environment with increased preconditions on privacy protection demands the transformation of clear-text attributes like first name or date of birth into one-way encrypted pseudonyms. When performing an automated or privacy preserving record linkage there might be the need of a binary classification deciding whether two records should be classified as the same entity. The classification is the final of the four main phases of the record linkage process: Preprocessing, indexing, matching and classification. The choice of binary classification techniques in dependence of project specifications in particular data quality has not extensively been studied yet.Objectives: The aim of this work is the introduction and evaluation of an automatable semi-supervised binary classification system applied within the field of record linkage capable of competing or even surpassing advanced automated techniques of the domain of unsupervised classification.Methods: This work describes the rationale leading to the model and the final implementation of an automatable semi-supervised binary classification system and the comparison of its classification performance to an advanced active learning approach out of the domain of unsupervised learning. The performance of both systems has been measured on a broad variety of artificial test sets (n = 400), based on real patient data, with distinct and unique characteristics.Results: While the classification performance for both methods measured as F-measure was relatively close on test sets with maximum defined data quality, 0.996 for semi-supervised classification, 0.993 for unsupervised classification, it incrementally diverged for test sets of worse data quality dropping to 0.964 for semi-supervised classification and 0.803 for unsupervised classification.Conclusions: Aside from supplying a viable model for semi-supervised classification for automated probabilistic record linkage, the tests conducted on a large amount of test sets suggest that semi-supervised techniques might generally be capable of outperforming unsupervised techniques especially on data with lower levels of data quality.
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MAS, M., G. MAYOR, and J. TORRENS. "t–OPERATORS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 01 (1999): 31–50. http://dx.doi.org/10.1142/s0218488599000039.
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This paper looks over a class of operators introduced in ([2]), called t–operators. Introduced in order to be applied to fuzzy preorders, their properties lead them to be also appropriate in some fields like aggregation problems and expert systems. We characterize these operators as a special combination of a t-norm and a t-conorm on [0, 1] in a similar way of uninorms in ([5]). We study duality and self duality on t–operators with respect to a strong negation N. We also give a classification of continuous t–operators through ordinal sums. Finally, we obtain from some t–operators (those idempotent) a special kind of E.A.F. by extending them to E=∪n≥1[0,1]n.
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Fakhrudin, Abdul Daffa, and Putu Harry Gunawan. "Arrhythmia Classification Using CNN-SVM from ECG Spectrogram Representation." Eduvest - Journal of Universal Studies 4, no. 12 (2024): 11245–54. https://doi.org/10.59188/eduvest.v4i12.49993.
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Arrhythmia, a critical subset of cardiovascular diseases and a leading cause of morbidity and mortality, is caused by irregular heartbeats that disrupt the normal rhythm of the heart. Detecting arrhythmias accurately is essential for timely diagnosis and treatment, which can be achieved through electrocardiogram (ECG) signals. This study presents a hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for arrhythmia classification, leveraging spectrogram representations of ECG signals. The CNN extracts spatial and temporal features from the spectrograms, while the SVM classifies five arrhythmia classes: Normal (N), Supra-ventricular premature (S), Ventricular escape (V), Fusion of ventricular and normal (F), and Unclassified (Q). Preprocessing techniques such as wavelet denoising and Short-Time Fourier Transform (STFT) were applied to improve signal quality and facilitate robust feature extraction. The proposed model was trained and evaluated on the MIT-BIH Arrhythmia Database, achieving a weighted F1-score of 0.985, demonstrating its ability to handle the imbalanced dataset effectively. Class-wise metrics highlighted high precision, recall, and F1-scores for majority classes and commendable performance for underrepresented classes, despite the inherent imbalance. These findings underscore the hybrid model's potential for arrhythmia classification by integrating the feature extraction strengths of CNNs with the precise classification capabilities of SVMs. Future research could address dataset imbalance through augmentation techniques and explore the model’s generalizability by testing on larger and more diverse datasets, paving the way for its application in real-world clinical scenarios.
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Golubitsky, Yu. "Small Industry, Gimmicks and Moscow Statistics, or How the Small Russian Enterprise Came into the World (Russian Physiological Sketch, 1830-1840)." Voprosy Ekonomiki, no. 8 (August 20, 2009): 123–29. http://dx.doi.org/10.32609/0042-8736-2009-8-123-129.
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The article considers business practices of Moscow small industry in the XIX century, basing upon physiological sketches of N. Polevoy and I. Kokorev, statistical data and the classification of professions are also presented. The author claims that the heroes of the analyzed sketches are the forefathers of Moscow small businesses and shows what a deep similarity their occupations and a way of life bear to the present-day routine existence of small enterprises.
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Fleming, Allison R., and Michael J. Leahy. "Using the International Classification of Functioning to Conceptualize Quality of Life Among Rehabilitation Services Recipients." Rehabilitation Research, Policy, and Education 28, no. 1 (2014): 2–23. http://dx.doi.org/10.1891/2168-6653.28.1.2.
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Researchers and policy makers have proposed that quality of life (QOL) is an important and useful way to measure the impact of services, although practical application of QOL in rehabilitation has been limited. In this study, a comprehensive framework (the International Classification of Functioning [ICF]) is used to compare the relationship between QOL and function in key life areas in a sample of adults with disabilities receiving vocational services (n = 224). Results of a multiple regression analysis indicated that level of education, duration of disability, difficulty with social relationships and inclusion, the impact of the disability or health condition on the person or his or her family, and relational support and attitudes of family, friends, and acquaintances showed significant relationships with QOL. This study highlights the role of the social impact of disability on QOL and provides support for the use of the ICF for conceptualizing disability and its impact in a way that is inclusive of personal and environmental factors.