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The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder‐decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder‐decoder structure is formed with labels and words. This improves the original paper classification method based on encoder‐decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder‐decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results. [ABSTRACT FROM AUTHOR]

With the continuous evolution of technology, XR products are expected to witness significant breakthroughs and advancements in these aspects, ultimately enhancing the overall user experience. However, careful consideration and optimization of these characteristics based on the intended purpose and usage scenarios of the product remain an area of ongoing research. The primary goal of this paper is to provide a comprehensive reference framework for researchers and product developers to facilitate further research and development in XR technology. By analyzing and discussing the existing literature, this review aims to synthesize the current understanding and state‐of‐the‐art progress in XR technology, serving as a valuable resource for researchers and developers seeking to innovate in this rapidly growing field. [ABSTRACT FROM AUTHOR]

Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article. Hybrid spectral similarity algorithms are tested on forensic analysis of paper data. We compared the classification capabilities of various hybrid spectral similarity algorithms on hyperspectral data of 40 different paper samples. The overall accuracy (OA), kappa K̂, Z‐score of kappa (ZK̂), and the 95% confidence interval of kappa (CI(K̂)) are used for comparison. The SID‐SAM and SID‐SCA produced an overall accuracy of 88% and 87%, respectively, which is highest among the hybrid spectral similarity measures tested. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods. [ABSTRACT FROM AUTHOR]

To conclude, we believe that this special issue accurately portrays the scientific features of the CLADAG community nowadays and supports the CLADAG mission of facilitating the exchange of ideas in Classification and Data Analysis. This special issue of Statistical Analysis and Data Mining contains a selection of the papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG), scheduled for September 9-11, 2021 in Florence, Italy. The articles cover a broad range of data analysis topics: gender gap analysis, income clustering, structural equation modeling, multivariate nonparametric methods, and classifier selection. [Extracted from the article]

Seizures are the most common neurological emergency in the neonatal period and in contrast to those in infancy and childhood, are often provoked seizures with an acute cause and may be electrographic‐only. Hence, neonatal seizures may not fit easily into classification schemes for seizures and epilepsies primarily developed for older children and adults. A Neonatal Seizures Task Force was established by the International League Against Epilepsy (ILAE) to develop a modification of the 2017 ILAE Classification of Seizures and Epilepsies, relevant to neonates. The neonatal classification framework emphasizes the role of electroencephalography (EEG) in the diagnosis of seizures in the neonate and includes a classification of seizure types relevant to this age group. The seizure type is determined by the predominant clinical feature. Many neonatal seizures are electrographic‐only with no evident clinical features; therefore, these are included in the proposed classification. Clinical events without an EEG correlate are not included. Because seizures in the neonatal period have been shown to have a focal onset, a division into focal and generalized is unnecessary. Seizures can have a motor (automatisms, clonic, epileptic spasms, myoclonic, tonic), non‐motor (autonomic, behavior arrest), or sequential presentation. The classification allows the user to choose the level of detail when classifying seizures in this age group. [ABSTRACT FROM AUTHOR]

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics. [ABSTRACT FROM AUTHOR]

Summary: Since it is common for the users of a web page to access it through a wide variety of devices—including desktops, laptops, tablets and phones—web developers rely on responsive web design (RWD) principles and frameworks to create sites that are useful on all devices. A correctly implemented responsive web page adjusts its layout according to the viewport width of the device in use, thereby ensuring that its design suitably features the content. Since the use of complex RWD frameworks often leads to web pages with hard‐to‐detect responsive layout failures (RLFs), developers employ testing tools that generate reports of potential RLFs. Since testing tools for responsive web pages, like ReDeCheck, analyse a web page representation called the Document Object Model (DOM), they may inadvertently flag concerns that are not human visible, thereby requiring developers to manually confirm and classify each potential RLF as a true positive (TP), false positive (FP), or non‐observable issue (NOI)—a process that is time consuming and error prone. The conference version of this paper presented Viser, a tool that automatically classified three types of RLFs reported by ReDeCheck. Since Viser was not designed to automatically confirm and classify two types of RLFs that ReDeCheck's DOM‐based analysis could surface, this paper introduces Verve, a tool that automatically classifies all RLF types reported by ReDeCheck. Along with manipulating the opacity of HTML elements in a web page, as does Viser, the Verve tool also uses histogram‐based image comparison to classify RLFs in web pages. Incorporating both the 25 web pages used in prior experiments and 20 new pages not previously considered, this paper's empirical study reveals that Verve's classification of all five types of RLFs frequently agrees with classifications produced manually by humans. The experiments also reveal that Verve took on average about 4 s to classify any of the RLFs among the 469 reported by ReDeCheck. Since this paper demonstrates that classifying an RLF as a TP, FP, or NOI with Verve, a publicly available tool, is less subjective and error prone than the same manual process done by a human web developer, we argue that it is well‐suited for supporting the testing of complex responsive web pages. [ABSTRACT FROM AUTHOR]

This EAACI position paper aims at providing a state-of-the-art overview on nonallergic rhinitis ( NAR). A significant number of patients suffering from persistent rhinitis are defined as nonallergic noninfectious rhinitis ( NANIR) patients, often denominated in short as having NAR. NAR is defined as a symptomatic inflammation of the nasal mucosa with the presence of a minimum of two nasal symptoms such as nasal obstruction, rhinorrhea, sneezing, and/or itchy nose, without clinical evidence of endonasal infection and without systemic signs of sensitization to inhalant allergens. Symptoms of NAR may have a wide range of severity and be either continuously present and/or induced by exposure to unspecific triggers, also called nasal hyperresponsiveness ( NHR). NHR represents a clinical feature of both AR and NAR patients. NAR involves different subgroups: drug-induced rhinitis, (nonallergic) occupational rhinitis, hormonal rhinitis (including pregnancy rhinitis), gustatory rhinitis, senile rhinitis, and idiopathic rhinitis ( IR). NAR should be distinguished from those rhinitis patients with an allergic reaction confined to the nasal mucosa, also called 'entopy' or local allergic rhinitis ( LAR). We here provide an overview of the current consensus on phenotypes of NAR, recommendations for diagnosis, a treatment algorithm, and defining the unmet needs in this neglected area of research. [ABSTRACT FROM AUTHOR]

The paper by G. Zammarchi I et al i . exploits Markov chain to analyse web usability of a University website using eye tracking methodology. This special issue of I Statistical Analysis and Data Mining i collects papers presented at the 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Cassino, Italy, 11-13 September 2019. [Extracted from the article]

The International League Against Epilepsy ( ILAE) presents a revised operational classification of seizure types. The purpose of such a revision is to recognize that some seizure types can have either a focal or generalized onset, to allow classification when the onset is unobserved, to include some missing seizure types, and to adopt more transparent names. Because current knowledge is insufficient to form a scientifically based classification, the 2017 Classification is operational (practical) and based on the 1981 Classification, extended in 2010. Changes include the following: (1) 'partial' becomes 'focal'; (2) awareness is used as a classifier of focal seizures; (3) the terms dyscognitive, simple partial, complex partial, psychic, and secondarily generalized are eliminated; (4) new focal seizure types include automatisms, behavior arrest, hyperkinetic, autonomic, cognitive, and emotional; (5) atonic, clonic, epileptic spasms, myoclonic, and tonic seizures can be of either focal or generalized onset; (6) focal to bilateral tonic-clonic seizure replaces secondarily generalized seizure; (7) new generalized seizure types are absence with eyelid myoclonia, myoclonic absence, myoclonic-atonic, myoclonic-tonic-clonic; and (8) seizures of unknown onset may have features that can still be classified. The new classification does not represent a fundamental change, but allows greater flexibility and transparency in naming seizure types. [ABSTRACT FROM AUTHOR]

The International League Against Epilepsy ( ILAE) Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances that have taken place since the last ratified classification in 1989. As a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies, and communication around the world. The new classification originates from a draft document submitted for public comments in 2013, which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type, where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome, where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis, as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century. [ABSTRACT FROM AUTHOR]

This paper introduces machine learning approaches on adding the stylus‐touch to the capacitive touch screen technology. The proposed schemes can discriminate the stylus‐touch from finger‐touch as well as no‐touch by means of classification algorithms using support vector machine and anomaly detection. The high frequency pulses are sent from a stylus to a touch screen and the receiver classifies the received sample sequences into three classes of no‐touch, finger‐touch, and stylus‐touch. In addition, some possible applications of data transmission and user authentication are demonstrated. [ABSTRACT FROM AUTHOR]

Making annotations on books and learning materials is part of students' everyday life. Although there are many computer-based annotation systems, many people prefer to print digital documents and make annotations on physical paper. We introduce a classification scheme for computer-based annotation systems and provide an overview of different systems using this scheme. During our investigations, we identified several shortcomings in existing annotation systems: limitation to single document formats, no capturing of relations to reflect lateral reading, loss of context information an annotation was created in and limited sharing capabilities among collaborative users. To overcome these shortcomings, we introduce the concepts of Anchor Points, Artefacts and Spaces. Anchor Points define locations in arbitrary documents. Artefacts capture annotations on multiple Anchor Points in several documents, allowing students to map their current working context that typically contains several open documents and a relationship between them. Spaces are repositories for the learning materials and annotations that are automatically replicated among collaborative mobile users. [ABSTRACT FROM AUTHOR]

Hypersensitivity diseases are not adequately coded in the International Coding of Diseases ( ICD)-10 resulting in misclassification, leading to low visibility of these conditions and general accuracy of official statistics. To call attention to the inadequacy of the ICD-10 in relation to allergic and hypersensitivity diseases and to contribute to improvements to be made in the forthcoming revision of ICD, a web-based global survey of healthcare professionals' attitudes toward allergic disorders classification was proposed to the members of European Academy of Allergy and Clinical Immunology ( EAACI) (individuals) and World Allergy Organization ( WAO) (representative responding on behalf of the national society), launched via internet and circulated for 6 week. As a result, we had 612 members of 144 countries from all six World Health Organization ( WHO) global regions who answered the survey. ICD-10 is the most used classification worldwide, but it was not considered appropriate in clinical practice by the majority of participants. The majority indicated the EAACI- WAO classification as being easier and more accurate in the daily practice. They saw the need for a diagnostic system useful for nonallergists and endorsed the possibility of a global, cross-culturally applicable classification system of allergic disorders. This first and most broadly international survey ever conducted of health professionals' attitudes toward allergic disorders classification supports the need to update the current classifications of allergic diseases and can be useful to the WHO in improving the clinical utility of the classification and its global acceptability for the revised ICD-11. [ABSTRACT FROM AUTHOR]

Comments on J. R. L. Allen's paper on the classification of cross-stratified units in sedimentology. Morphological features of cross-stratification; Non-genetic terminology.

In the absence of an internationally agreed classification of haemorrhoids, evaluation of the ever increasing number of therapeutic options available is impossible. A new classification is proposed which relates symptoms to our knowledge of haemorrhoidal development, the use of which should provide a more accurate way of comparing treatments and in the evaluation of new techniques. The evidence for such a classification is presented in detail. [ABSTRACT FROM AUTHOR]

Various types of foreign fibres may be mixed in the planting, transportation, and production processes of cotton, which not only cause equipment to be out of control, but also leads to a decrease in the quality of cotton textile products and economic losses. The machine vision based detection method for cotton foreign fibres is widely used. Based on existing related research, we construct a classification dataset for cotton foreign fibres in practical scenarios, named the CF2113‐10 dataset. The authors design a basic foreign fibre classification network called CottonNet that balances performance and efficiency. The classification accuracy on the validation set reached 94.2%. In order to enhance the high‐level feature extraction ability, this paper improves the feature fusion method of residual networks and proposes CottonNet‐Res, which improves the classification accuracy to 95.1%. Finally, a classification model based on feature difference fitting, CottonNet‐Fusion, is proposed to address the classification problem of foreign fibre images sampled in complex environments. The classification accuracy of foreign fibre images sampled in ordinary scenes has improved to 97.4%, while the images sampled in complex environments maintain an accuracy of 90.3%. [ABSTRACT FROM AUTHOR]

Accurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two‐stage substation equipment classification method based on dual‐scale attention. Leveraging the region proposal technique from Faster‐regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual‐scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two‐stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi‐layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine‐grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1‐score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual‐scale attention and the two‐stage classification module in improving the overall performance of the classification network. [ABSTRACT FROM AUTHOR]

The classification of scientific citations according to their quality and function, established in a previous paper, is strengthened by tests of its reproducibility and universality. The method is then applied to articles in various specialties of theoretical physics published in various journals, and conclusions are drawn about differences by specialty and by geographical areas. Specifically, it is shown for the sample investigated (a) that the number of papers referred to per article is much lower for Soviet journals than for European, US, or Japanese journals; (b) that this number is much lower for solid state physics than for high energy or nuclear physics; (c) that US journals have a higher percentage of conceptual citations, and a much higher percentage of organic citations than Soviet journals; (d) that the Soviet Journal of Nuclear Physics has citation patterns markedly different than the other two Soviet journals investigated and is rather similar to that of the US journals; (e) that high energy physics shows a significantly higher percentage of organic citations and a somewhat higher percentage of evolutionary citations than either nuclear physics or solid state physics. Some speculations are presented to "explain" these effects. [ABSTRACT FROM AUTHOR]

Visibility graph methods allow time series to mine non‐Euclidean spatial features of sequences by using graph neural network algorithms. Unlike the traditional fixed‐rule‐based univariate time series visibility graph methods, a symmetric adaptive visibility graph method is proposed using orthogonal signals, a method applicable to in‐phase and quadrature (I/Q) orthogonal signals for adaptive graph mapping for radio modulated signals in automatic modulation classification tasks. The method directly models the intra‐channel and inter‐channel graph relations of I/Q signals using two different types of convolutional kernels. It captures non‐Euclidean spatial feature information of I/Q signals using a graph neural network combining graph sampling aggregation and graph differentiable pooling as a feature extractor. Extensive experimental results on two benchmark datasets and a simulated dataset containing channel fading show that the proposed Quadrature Signal Symmetric Adaptive Visibility Graph (QSSAVG) method in this paper outperforms the benchmark method in terms of classification accuracy and is also more robust against channel fading and noise variations. [ABSTRACT FROM AUTHOR]

Building point clouds classification is an important task for a Smart City. This paper proposes a method based on multi‐scale and multi‐level cloth simulation to classify buildings in airborne LiDAR point clouds. First, the point clouds are preprocessed to denoise, filter ground and normalise height. The height distribution of point clouds in the whole area is then counted. Point clouds are segmented with similar heights on the top of the internal buildings by using the watershed algorithm based on height hierarchical labelling. Finally, the multi‐scale cloth simulation is used to crudely extract the seed points on the top of the buildings. These seed points are grown to realise the complete classification of airborne building point clouds. Multiple sets of data are selected for experimental verification. The results show that this method has higher classification accuracy and better applicability with a correct classification rate of 96.83%, 95.94% and 94.36%, and a recall rate of 98.18%, 96.32% and 92.33% in point level. [ABSTRACT FROM AUTHOR]

This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. Worked examples are included for both cases. This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. [ABSTRACT FROM AUTHOR]

The performance of face recognition (FR) has reached a plateau for public benchmark datasets, such as labeled faces in the wild (LFW), celebrities in frontal‐profile in the wild (CFP‐FP), and the first manually collected, in‐the‐wild age database (AgeDB), owing to the rapid advances in convolutional neural networks (CNNs). However, the effects of faces under various fine‐grained conditions on FR models have not been investigated, owing to the absence of relevant datasets. This paper analyzes their effects under different conditions and loss functions using K‐FACE, a recently introduced FR dataset with fine‐grained conditions. We propose a novel loss function called MixFace, which combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness was experimentally demonstrated using various benchmark datasets. [ABSTRACT FROM AUTHOR]

The unavailability of labeled data has always been the main limitation of data‐driven solutions for monitoring the health state of full‐scale structures. In this area, domain adaptation (DA) solutions have occasionally been proposed in recent years, which allow the sharing of data sets between distinct but similar systems. This paper presents a novel computational methodology to evaluate the condition state of historical buildings subjected to continuous monitoring. The DA method, specifically transfer component analysis, is used to maintain correlations between two data domains with low relevance, thereby improving the accuracy of classification models. Additionally, it is shown that the kernelized Bayesian transfer learning can enhance classification accuracy beyond what is achievable with a support vector machine. The paper is completed with a real‐world application to the classification of data sets from two Italian Baroque churches, both characterized by imposing oval masonry domes, but equipped with very different monitoring systems. [ABSTRACT FROM AUTHOR]

There are many different types of nuclei in a tumor tissue. We can identify the specific nuclei and their distribution in the tissue to reflect the current cancer state of histopathological images. However, due to the existence of cellular heterogeneity, the recognition of nuclei in histopathological images has always been a problem of computer vision. In the paper, we use the transfer learning of Deep Convolutional Neural Network to classify nuclei, and found that adjusting the size of the nuclear image to a certain size can improve the accuracy of the nuclei classification model, while not significantly reducing the nuclei classification efficiency. Through further research, it was confirmed that the environment around the nuclei can bring great help to the model classification. Based on the principle, we design a feature fusion model. We extract features from nuclei image different sizes by CNN, fuse the features, and then use fully connected layer to classify the features. Experiments have proved that the feature fusion model has a considerable improvement in accuracy compared to the normal classification model. [ABSTRACT FROM AUTHOR]

In this paper, the tumor affected images are detected and classified from non‐tumor affected brain magnetic resonance imaging (MRI) using Adaptive Neuro Fuzzy Inference System (ANFIS) classification process. The proposed work for brain tumor detection consists of a noise reduction module, decomposition module, feature extraction module, classification module, and segmentation module. The noisy brain images are filtered by the proposed Vector Index Filtering algorithm and the filtered images are further decomposed using Fast Wavelet Transform. Later, the index features are computed from each decomposed sub‐band image and these computed index features are classified either as normal or abnormal brain images using the ANFIS classification process. Further, the tumor regions are segmented in classified abnormal brain images using morphological functions. This paper uses the Brain Tumor Image Segmentation Challenge—BRATS 2016 dataset for analyzing the performance of the proposed brain tumor detection and segmentation system in terms of sensitivity (97.9%), specificity (98.6%), accuracy (99.1%), error rate (0.09%), F1‐score (96.8%), and Geometrics mean (98.5%). Comparisons are made between the proposed method's experimental results and other state of the art methods. The proposed methods using machine learning approach, stated in this paper are well suited for detecting tumor regions in brain images. [ABSTRACT FROM AUTHOR]

When building classification models of complex systems with many classes, the traditional chemometric approaches such as discriminant analysis or soft independent modeling of class analogy often fail. Some people resort to advanced deep neural network, but this is only an option if there is access to very many samples. Another alternative often used is to build hierarchical models where subclasses are sort of peeled off one or a few at a time. Such approaches often outperform classical classification as well as deep neural network on small multi‐class problems. The downside though is that it is very cumbersome to build such hierarchies of models. It requires substantial work of a skilled person. In this paper, we develop a fully automated approach for building hierarchical models and test the performance on a number of classification problems. In this paper, we develop a fully automated approach for building hierarchical models and test the performance on a number of classification problems. [ABSTRACT FROM AUTHOR]

This paper examines the classification of the landscape, both biota and terrain, in Wagiman, a language of northern Australia. There is considerable debate as to the comparative roles of cognitive and cultural factors in the analysis of landscape terminologies. Any analysis of terminologies necessarily involves consideration of meaning. There are many approaches to the analysis of meaning and analysis of Wagiman landscape classification requires at least two approaches. One approach involves necessary and sufficient conditions for connotation, and the other involves prototypes. The comparative roles of cognitive and cultural factors vary depending upon the approach to meaning, with cognitive factors playing a greater role in connotation and cultural factors playing a greater role in prototypes. The current research literature on Australian languages examines behavioural and morphological oppositions, material make‐up, shape, size as cognitive factors and affordance and human usage as cultural factors. This paper provides evidence that there is another cultural factor which plays a central role in prototype classification, mental maps of the landscape involving zonal oppositions. Prototypes for many terms do not have an individuated reference but rather have an unindividuated reference to typical zones of occurrence. [ABSTRACT FROM AUTHOR]

A challenge unique to classification model development is imbalanced data. In a binary classification problem, class imbalance occurs when one class, the minority group, contains significantly fewer samples than the other class, the majority group. In imbalanced data, the minority class is often the class of interest (e.g., patients with disease). However, when training a classifier on imbalanced data, the model will exhibit bias towards the majority class and, in extreme cases, may ignore the minority class completely. A common strategy for addressing class imbalance is data augmentation. However, traditional data augmentation methods are associated with overfitting, where the model is fit to the noise in the data. In this tutorial we introduce an advanced method for data augmentation: generative adversarial networks (GANs). The advantages of GANs over traditional data augmentation methods are illustrated using the Breast Cancer Wisconsin study. To promote the adoption of GANs for data augmentation, we present an end‐to‐end pipeline that encompasses the complete life cycle of a machine learning project along with alternatives and good practices both in the paper and in a separate video. Our code, data, full results and video tutorial are publicly available in the paper's GitHub repository (https://github.com/yuxiaohuang/research/tree/master/gwu/accepted/sam_2021). [ABSTRACT FROM AUTHOR]

This paper carried out a classification study on the quality of different types of apples. In order to achieve a high‐precision classification of high‐quality apples in various dimensions, shorten the classification time, improve efficiency, and make it feasible for practical applications. This paper proposes an automatic recognition and classification model OB‐Net (for apple appearance quality) based on a dual‐branch structure. The model consists of two branches, an O branch and a B branch. The O branch extracts the shape and contour features of the apple surface defect by decomposing the feature map into high and low frequencies. The B branch extracts the shape size and texture characteristics of the apple surface defect by fusing channel attention and spatial attention mechanisms; this is done to further increase the feature distance between different types of defects. Experimental results show that the classification accuracy rates of rot, insect bites, russeting, scratches, and intact apples are 95.65%, 98.17%, 94.62%, 92.02% and 97.67%, respectively; the overall classification and recognition accuracy rate reaches 95.64%. Finally, the three aspects of the feature map, heat map and category probability statistics map extracted from the OB‐Net network model prove the validity of the network model in the classification of apple appearance quality. [ABSTRACT FROM AUTHOR]

This article focuses on the identification of research papers by physical units appearing the periodical "American Documentation" in 1963. It was hoped that through the identification of the units, specification of ranges and coincidence of units papers in reasonably broad research fields could be identified. This approach would eliminate those papers not containing the specified units in the specified ranges and coincidences. For example, it may be hypothesized that a nuclear physicist should request papers reporting experimental determinations of neutron cross sections for incident neutron energies between 1 and 20 MeV.

This paper discusses, in a multiclass classification setting, the issue of the choice of the so‐called categorical classifier, which is the procedure or criterion that transforms the probabilities produced by a probabilistic classifier into a single category or class. The standard choice is the Bayes Classifier (BC), but it has some limits with rare classes. This paper studies the classification performance of the BC versus two alternatives, that are the Max Difference Classifier (MDC) and Max Ratio Classifier (MRC), through an extensive simulation and some case studies. The results show that both MDC and MRC are preferable to BC in a multiclass setting with imbalanced data. [ABSTRACT FROM AUTHOR]

Summary: Text classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context‐aware text classification system to improve text quality. We use a content‐aware recommendation system to extract the data from well‐known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique on classification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance. [ABSTRACT FROM AUTHOR]

Segmenting scientific abstracts into discourse categories like background, objective, method, result, and conclusion is useful in many downstream tasks like search, recommendation and summarization. This task of classifying each sentence in the abstract into one of a given set of discourse categories is called sequential sentence classification. Existing machine learning‐based approaches to this problem consider the content of only the abstract to obtain the neural representation of each sentence, which is then labelled with a discourse category. But this ignores the semantic information offered by the discourse labels themselves. In this paper, we propose LIHT, Label Informed Hierarchical Transformers – a method for sequential sentence classification that explicitly and hierarchically exploits the semantic information in the labels to learn label‐aware neural sentence representations. The hierarchical model helps to capture not only the fine‐grained interactions between the discourse labels and the words in the abstract at the sentence level but also the potential dependencies that may exist in the label sequence. Thus, LIHT generates label‐aware contextual sentence representations that are then labelled with a conditional random field. We evaluate LIHT on three publicly available datasets, namely, PUBMED‐RCT, NICTA‐PIBOSO and CSAbstract. The incremental gain in F1‐score in all the three cases over the respective state‐of‐the‐art approaches is around 1%. Though the gains are modest, LIHT establishes a new performance benchmark for this task and is a novel technique of independent interest. We also perform an ablation study to identify the contribution of each component of LIHT in the observed performance, and a case study to visualize the roles of the different components of our model. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this umbrella review was to determine the most effective nursing interventions for the prevention and management of suicidal behavior. In order to do so, the review identifies interventions from the Nursing Interventions Classifications taxonomy with evidence to this end. Methods: A systematic search was conducted for systematic reviews included in the PubMed, CINAHL, Cochrane, Scopus, Web of Knowledge, and Joanna Briggs Institute databases between January 1, 2011 and May 1, 2020. Findings: The review is made up of 21 systematic reviews. In order to carefully analyze the interventions described, these were coded into categories, grouping the interventions into those based on psychological therapy, interventions related to pharmacotherapy, interventions related to the professional relationship between health professional and patient, and interventions aimed at the general public. Conclusions: Nursing interventions for suicide risk management are prevalent in the scientific literature and are shown to be effective for patients with suicidal ideation or suicide attempts. Prevention and treatment of suicidal behavior require the provision of coping tools, behavioral interventions that directly address suicidal thoughts and behaviors, and support through therapeutic partnerships, among others. Implications for nursing practice: This paper synthesizes the most current evidence on the most effective Nursing Interventions Classifications interventions for the treatment and prevention of suicidal behavior. It provides nursing practitioners with a comprehensive review of the therapeutic interventions with the best evidence and is useful for the development of clinical guidelines and protocols, as well as for the development of health policies and plans. [ABSTRACT FROM AUTHOR]

This paper uses Deep Learning to classify if a display panel with defects in the manufacturing line can be repaired. Both tabular data and images are fused together to make predictions, with separate feature extraction undertaken for each of the modalities. The model's predictions achieve high Average Precision as well as robust Precision values in the high Recall regions, which makes it practical for deployment. We also demonstrate superior results with multi‐modal data compared to only tabular data. [ABSTRACT FROM AUTHOR]

Background: The performance of nearest‐neighbour classification is highly sensitive to the quality of data. In order to reduce the impact of the inevitable existence of irrelevant features in data, this paper employs the information learned by neural networks to implement a feature weighting technique and associated weighted multi‐functional nearest‐neighbour classification method. Method: The non‐iterative neural networks are easy to implement while enjoying remarkable computational efficiency. In this paper, four non‐iterative neural networks (ELM, E‐ELM, RAWN and RVFL) are employed to evaluate the significance of features to decisions learned and stored in the parameters of neural networks. Moreover, the bias of the significance of features is comforted by using cross‐validation. The resulting feature significance is converted into feature weights to further implement weighted multi‐functional nearest‐neighbour classification. Result: The experimental results demonstrate that the proposed neuro‐weighted feature weighting strategy can effectively reduce the impact of irrelevant features and enhance the performance of multi‐functional nearest‐neighbour classification. Contribution: The proposed algorithm explores an avenue to efficiently utilize the learned knowledge of neural networks to reduce the role of irrelevant feature in nearest‐neighbour classification. [ABSTRACT FROM AUTHOR]

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non‐Glioma image based on the optimized set of features. Furthermore, energy‐based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy. [ABSTRACT FROM AUTHOR]

Key points: While the importance of citation context has long been recognized, simple citation counts remain as a crude measure of importance.Providing citation context should support the publication of careful science instead of headline‐grabbing and salami‐sliced non‐replicable studies.Machine learning has enabled the extraction of citation context for the first time, and made the classification of citation types at scale possible. [ABSTRACT FROM AUTHOR]

ABSTRACT During the last few decades, several multi-criteria decision analysis methods have been proposed to help in selecting the best compromise alternatives. Among them, analytic hierarchy process (AHP) and its applications have attracted much attention from academics and practitioners. However, since the early 1980s, critics have raised questions regarding its proper use. One of them concerns the unacceptable changes in the ranks of the alternatives, called rank reversal, upon changing the structure of the decision. Several modifications were suggested to preserve ranks. In this paper, a classification scheme and a comprehensive literature review are presented in order to uncover, classify and interpret the current research on AHP methodologies and rank reversals. On the basis of the scheme, 61 scholarly papers from 18 journals are categorized into specific areas. The specific areas include the papers on the topics of adding/deleting alternatives and the papers published in adding/deleting criteria. The scholarly papers are also classified by (1) year of publication, (2) journal of publication, (3) authors' geographic location and (4) using the AHP in association with other methods. It is hoped that the paper can meet the needs of researchers and practitioners for convenient references of AHP methodologies and rank reversals and hence promote the future of rank reversal research. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

High penetrations of renewable energy are crucial for low‐carbon power systems. However, the higher volatility of renewable power generation pushes real‐time operations closer to equipment limits. It is thus important to utilize flexibilities in the system through corrective security‐constrained economic dispatch (SCED) that allows generators to take corrective adjustments after contingencies. The corrective SCED problem, containing a large number of contingencies, and corresponding post‐contingency decisions and constraints, is very large in scale and difficult to solve using purely model‐based methods within the strict time limits of real‐time markets. To accelerate the solution process, this paper develops a novel interpretable data‐driven contingency classification method. Historical data and their potentially useful patterns are utilized in interpretable data‐driven decision tree classifiers. To directly consider continuous features, such as net load values, and to consider imbalanced datasets without much additional complexity, Improved Strong Optimal Classification Trees (ISOCTs) are developed with new branching threshold constraints and category weights in the objective function. ISOCTs are then embedded into a hybrid model‐based and data‐driven framework to guarantee the accuracy of the real‐time active contingency set and the resulting security of dispatch decisions. Numerical testing results demonstrate the classification accuracy, computational efficiency, and interpretability of the proposed approach. [ABSTRACT FROM AUTHOR]

Aim: This paper introduces the Surveillance of Cerebral Palsy in Europe (SCPE) classification of events contributing to postneonatally acquired cerebral palsy, presents its interrater reliability, and describes the cases identified in the SCPE database. Method: The development of the classification, based on literature review and expert discussions, resulted in six main categories and 19 subcategories. The first chronological event designated as the primary event was mainly reported. Interrater reliability was assessed through online exercise providing 24 clinical vignettes representing single/complex pathways. Percent agreement and Gwet's AC1 index of reliability were estimated. Primary events were described using data of 221 children born between 2008 and 2012. Results: Thirty‐nine professionals (21 registries) participated in the reliability exercise. Substantial overall agreement was reached (0.75), with some contrast between complex (0.48, moderate agreement) and single events involved (0.89, almost perfect). The distribution of primary events showed that 32.1% were infections (category A), 23.1% head injuries (B), 15.4% related to surgery or medical interventions (C), 13.1% cerebrovascular accidents (D), 9.1% hypoxic brain damaging events of other origins (E), and 7.2% miscellaneous (F). Interpretation: This classification allows all the events involved to be recorded while consistently reporting the primary event, and may be used in different settings. What this paper adds: A standardized classification enables the description of the events contributing to postneonatal cerebral palsy (CP).The first chronological event in complex pathway leading to CP is coded.Category choice and coding of the primary event identify preventable situations.The detailed 2‐level classification is easy to use in various settings.Substantial overall interrater reliability shows that main categories can be consistently differentiated. [ABSTRACT FROM AUTHOR]

Summary: In the present scenario, wireless body area network (WBAN) use case has gone beyond medical monitoring (e.g., E‐Healthcare) and has become a vital part of our daily lives. WBANs, applied in E‐Healthcare systems, extract the physiological parameters through the implanted (in‐body) and/or wearable (on‐body) sensors applied to the human body and route them to the remote server in a systematized and well‐grounded manner. Thus, routing is the most primitive and non‐trivial task in WBANs for prompt and proper delivery of sensed data. The design of routing protocol is crucial for efficacious communication and high network performance. Therefore, the target of this paper is to present an all‐encompassing taxonomy of the state‐of‐the‐art routing protocols in WBANs. Firstly, this paper provides an insight into WBANs and their related projects. Next, WBAN routing challenges are explored, and after clustering these challenges based on their operating mechanism, the routing protocols are categorized into eight categories, which correspond to cluster‐ and tree‐based, cross‐layer‐based, fuzzy‐based, interference‐based, posture‐based, QoS‐based, SDN‐based, and temperature‐based routing protocols. Furthermore, comparison tables highlighting the goals, strengths, limitations, and novelty of each protocol, along with a qualitative comparison table of the routing protocols for each category based on their features are furnished. This exhaustive survey can serve as a starting point for WBAN researchers, as open research issues have been highlighted in the discussion section. [ABSTRACT FROM AUTHOR]

Epigenetic regulation of genes through posttranslational regulation of proteins is a well‐explored approach for disease treatment, particularly in cancer chemotherapy. Histone deacetylases have shown significant potential as effective drug targets in therapeutic studies aiming to restore epigenetic normality in oncology. Besides their role in modifying histones, histone deacetylases can also catalyze the deacetylation of various nonhistone proteins and participate in the regulation of multiple biological processes. This paper provides a review of the classification, structure, and functional characteristics of the four classes of human histone deacetylases. The increasing abundance of structural information on HDACs has led to the gradual elucidation of structural differences among subgroups and subtypes. This has provided a reasonable explanation for the selectivity of certain HDAC inhibitors. Currently, the US FDA has approved a total of six HDAC inhibitors for marketing, primarily for the treatment of various hematological tumors and a few solid tumors. These inhibitors all have a common pharmacodynamic moiety consisting of three parts: CAP, ZBG, and Linker. In this paper, the structure–effect relationship of HDAC inhibitors is explored by classifying the six HDAC inhibitors into three main groups: isohydroxamic acids, benzamides, and cyclic peptides, based on the type of inhibitor ZBG. However, there are still many questions that need to be answered in this field. In this paper, the structure‐functional characteristics of HDACs and the structural information of the pharmacophore model and enzyme active region of HDAC is are considered, which can help to understand the inhibition mechanism of the compounds as well as the rational design of HDACs. This paper integrates the structural‐functional characteristics of HDACs as well as the pharmacophore model of HDAC is and the structural information of the enzymatic active region, which not only contributes to the understanding of the inhibition mechanism of the compounds, but also provides a basis for the rational design of HDAC inhibitors. [ABSTRACT FROM AUTHOR]

We consider ancient noncollapsed mean curvature flows in R4$\mathbb {R}^4$ whose tangent flow at −∞$-\infty$ is a bubble‐sheet. We carry out a fine spectral analysis for the bubble‐sheet function u that measures the deviation of the renormalized flow from the round cylinder R2×S1(2)$\mathbb {R}^2 \times S^1(\sqrt {2})$ and prove that for τ→−∞$\tau \rightarrow -\infty$ we have the fine asymptotics u(y,θ,τ)=(y⊤Qy−2tr(Q))/|τ|+o(|τ|−1)$u(y,\theta ,\tau)= (y^\top Qy -2\textrm {tr}(Q))/|\tau | + o(|\tau |^{-1})$, where Q=Q(τ)$Q=Q(\tau)$ is a symmetric 2 × 2‐matrix whose eigenvalues are quantized to be either 0 or −1/8$-1/\sqrt {8}$. This naturally breaks up the classification problem for general ancient noncollapsed flows in R4$\mathbb {R}^4$ into three cases depending on the rank of Q. In the case rk(Q)=0$\mathrm{rk}(Q)=0$, generalizing a prior result of Choi, Hershkovits and the second author, we prove that the flow is either a round shrinking cylinder or R×$\mathbb {R}\times$2d‐bowl. In the case rk(Q)=1$\mathrm{rk}(Q)=1$, under the additional assumption that the flow either splits off a line or is self‐similarly translating, as a consequence of recent work by Angenent, Brendle, Choi, Daskalopoulos, Hershkovits, Sesum and the second author we show that the flow must be R×$\mathbb {R}\times$2d‐oval or belongs to the one‐parameter family of 3d oval‐bowls constructed by Hoffman‐Ilmanen‐Martin‐White, respectively. Finally, in the case rk(Q)=2$\mathrm{rk}(Q)=2$ we show that the flow is compact and SO(2)‐symmetric and for τ→−∞$\tau \rightarrow -\infty$ has the same sharp asymptotics as the O(2) × O(2)‐symmetric ancient ovals constructed by Hershkovits and the second author. The full classification problem will be addressed in subsequent papers based on the results of the present paper. [ABSTRACT FROM AUTHOR]

In recent years, the interest in the development of highly concentrated electrolyte solutions for battery applications has increased enormously. Such electrolyte solutions are typically characterized by a low flammability, a high thermal and electrochemical stability and by the formation of a stable solid electrolyte interphase (SEI) in contact to electrode materials. However, the classification of concentrated electrolyte solutions in terms of the classical scheme "strong" or "weak" has been controversially discussed in the literature. In this paper, a comprehensive theoretical framework is presented for a more general classification, which is based on a comparison of charge transport and mass transport. By combining the Onsager transport formalism with linear response theory, center‐of‐mass fluctuations and collective translational dipole fluctuations of the ions in equilibrium are related to transport properties in a lithium‐ion battery cell, namely mass transport, charge transport and Li+ transport under anion‐blocking conditions. The relevance of the classification approach is substantiated by showing that i) it is straightforward to classify highly concentrated electrolytes and that ii) both fast charge transport and fast mass transport are indispensable for achieving fast Li+ transport under anion‐blocking conditions. [ABSTRACT FROM AUTHOR]

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This paper presents a taxonomy (hierarchical organization) of hydrological processes; specifically, runoff generation processes in natural watersheds. Over 130 process names were extracted from a literature review of papers describing experimental watersheds, perceptual models, and runoff processes in a range of hydro‐climatic environments. Processes were arranged into a hierarchical structure, and presented as a spreadsheet and interactive diagram. For each process, additional information was provided: a list of alternative names for the same process, a classification into hydrological function (e.g., flux, storage, release) and a unique identifier similar to a hashtag. We hope that the proposed hierarchy will prompt collaboration and debate in the hydrologic community into naming and organizing processes, towards a comprehensive taxonomy. The taxonomy provides a method to label and search hydrological knowledge, thereby facilitating synthesis and comparison of processes across watersheds. [ABSTRACT FROM AUTHOR]

World's science and technologies have been challenged by the COVID‐19 pandemic. Each and every community across the globe are trying to find a real‐time novel method for accurate treatment and cure of COVID‐19 infected patients. The most important lead to take from this pandemic is to detect the infected patients as soon as possible and provide them an accurate treatment. At present, the worldwide methodology to detect COVID‐19 is reverse transcription‐polymerase chain reaction (RT‐PCR). This technique is costly and time taking. For this reason, the implementation of a novel method is required. This paper includes the use of deep learning analysis to develop a system for identifying COVID‐19 patients. Proposed technique is based on convolution neural network (CNN) and deep neural network (DNN). This paper proposes two models, first is designing DNN on the basis of fractal feature of the images and second is designing CNN using lungs x‐ray images. To find the infected area (tissues) of the lungs image using CNN architecture, segmentation process has been used. Developed CNN architecture gave results of classification with accuracy equal to 94.6% and sensitivity equal to 90.5% which is much better than the proposed DNN method, which gave accuracy 84.11% and sensitivity 84.7%. The outcome of the presented model shows 94.6% accuracy in detecting infected regions. Using this method the growth of the infected regions can be monitored and controlled. The designed model can also be used in post‐COVID‐19 analysis. [ABSTRACT FROM AUTHOR]