scholarly journals Number-based noun classification

2020 ◽  
Author(s):  
Maria Kouneli
Keyword(s):  
Theoretical Work ◽  
Classification Systems ◽  
Singular Number ◽  
Functional Projection ◽  
Number Marking ◽  
Descriptive Term ◽  
Complicated System ◽  
Different Types

Abstract Nilo-Saharan languages are well-known for their complicated system of nominal number marking, which features a variety of singulative and plural affixes (Dimmendaal 2000). Even though these systems have received some attention in the typological literature, there has been limited theoretical work on their implications for the morphosyntax of number cross-linguistically. The goal of this paper is to fill this gap, by providing an analysis of nominal number morphology in Kipsigis (Nilotic, Kenya), based on data from original fieldwork. First, I show that singulatives in Kipsigis are true allomorphs of singular number, unlike singulatives with a classifier function in languages like Ojibwe (Mathieu 2012). The descriptive term ‘singulative’ is therefore misleading, as it corresponds to two very different types of morphemes. Second, I claim that the tripartite system of number marking of Kipsigis and other Nilo-Saharan languages is due to the classification of nouns into morphosyntactic classes defined by the presence of inherent number features on little n; the interaction of these features with interpretable number features on the functional projection Num (Ritter 1991 a.o.) in the post-syntactic component gives rise to the exponence pattern that we observe. Finally, my analysis corroborates the existence of noun classification based on number, which has only been argued for Kiowa-Tanoan before (Harbour 2007). The existence of three number classes in Kipsigis can only be explained by reference to bivalent number features; number-based noun classification systems thus strongly support the view that number features are bivalent and not privative, which is also argued by Harbour (2007, 2011) for Kiowa.

scholarly journals Geological and Engineering Classification Systems of Mudrocks

2000 ◽  
Vol 5 ◽  
pp. 137 ◽  
Author(s):  
Amer Al-Rawas ◽  
Tariq Cheema ◽  
Mohammed Al-Aghbari
Keyword(s):  
Grain Size ◽  
Clay Content ◽  
Classification Systems ◽  
Research Attention ◽  
Slake Durability Index ◽  
Fine Grained ◽  
Durability Index ◽  
Different Types ◽  
Engineering Projects

Mudrocks are a diverse group of very fine-grained argillaceous sedimentary rocks that are frequently encountered in most types of engineering projects. Upon excavation, the release of overburden stress and changes in the moisture content may cause certain apparently well indurated mudrocks to slake, producing a soil-like material. Because many mudrocks are nondurable, they have gained a reputation as problematic soft rocks. Geologists and engineers are confronted with the problem of selecting adequate parameters for accurately evaluating engineering behavior of mudrocks. The use of a single parameter (e.g. grain-size) is never considered to be enough and a combination of several parameters are normally preferred for the classification of mudrocks. Compressive strength, slake durability index, plasticity characteristics, swelling potential, absorption and density are some of the parameters that had been used by several investigators in the past. In order to overcome this problem, considerable research attention has recently been devoted to the use of geological properties (grain size, clay content and clay composition, texture, fracture frequency, degree of lamination, etc.) in conjunction with the engineering characteristics for classification purposes. This paper describes the origin and occurrence of mudrocks, and their different types of classification tests and systems.

scholarly journals Autonomous Interpretation of the Microstructure of Steels and Special Alloys

2019 ◽  
Vol 949 ◽  
pp. 24-31 ◽  
Author(s):  
Bartłomiej Mulewicz ◽  
Grzegorz Korpala ◽  
Jan Kusiak ◽  
Ulrich Prahl
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Classification Systems ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Steel Microstructure ◽  
Different Types ◽  
Steel Grades

The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel microstructures. Our research focused on developing and implementation of Deep Convolutional Neural Networks (DCNN) for classification of different types of steel microstructure photographs received from the light microscopy at the TU Bergakademie, Freiberg. First, brief presentation of the idea of the system based on DCNN is given. Next, the results of tests of developed classification system on 8 different types (classes) of microstructure of the following different steel grades: C15, C45, C60, C80, V33, X70 and carbide free steel. The DCNN based classification systems require numerous training data and the system accuracy strongly depend on the size of these data. Therefore, created data set of numerous micrograph images of different types of microstructure (33283 photographs) gave the opportunity to develop high precision classification systems and segmentation routines, reaching the accuracy of 99.8%. Presented results confirm, that DCNN can be a useful tool in microstructure classification.

scholarly journals Classification of Distal Radius Physeal Fractures Not Included in the Salter-Harris System

2014 ◽  
Vol 8 (1) ◽  
pp. 219-224 ◽  
Author(s):  
Nikolaos K Sferopoulos
Keyword(s):  
Distal Radius ◽  
Classification Systems ◽  
Classification Schemes ◽  
Physeal Bar ◽  
Different Types ◽  
Salter Harris ◽  
Growth Abnormalities ◽  
Physeal Fractures

Introduction : The most commonly used classification for pediatric physeal fractures has been proposed by Salter and Harris. Among the most suitable classification schemes are those proposed by Ogden and Peterson who added several new types of injuries. The purpose of this study was to examine the value of both schemes to classify all different types of physeal injuries of the distal radius that are not included in the Salter-Harris system and to test a new nomenclature to classify and guide treatment for the whole spectrum of these injuries. Methods : A total of 292 children who were admitted for a physeal fracture of the distal radius that could not be classified according to the Salter-Harris system were identified from the hospital database. All radiographs were carefully examined and classified according to the existing classifications of Ogden and Peterson and a modified classification scheme. The results of the treatment were also evaluated. Results : Ninety-six physeal injuries could not be classified using the classification schemes of Ogden and Peterson. All injuries could be classified in five types using the new, modified nomenclature. Growth abnormalities of the distal radius were evaluated after an average follow-up time of 11 years. Growth arrest due to a physeal bar was detected only in one patient. Discussion : The proposed modified scheme is practical, incorporates all previous classification systems, allows classification of all physeal injuries of the distal radius that are not included in the Salter-Harris system and may assist comparison of treatment outcomes.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

scholarly journals Classification of Post-Fire Responses of Woody Plants to include Pyrophobic Communities

Fire ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 15 ◽  
Author(s):  
Lynda D. Prior ◽  
David M. J. S. Bowman
Keyword(s):  
Woody Plants ◽  
Seed Banks ◽  
Classification Systems ◽  
Woody Vegetation ◽  
General Category ◽  
Conifer Species ◽  
Soil Seed Banks ◽  
Obligate Seeder ◽  
In Fire

Developing standardised classification of post-fire responses is essential for globally consistent comparisons of woody vegetation communities. Existing classification systems are based on responses of species growing in fire-prone environments. To accommodate species that occur in rarely burnt environments, we have suggested some important points of clarification to earlier schemes categorizing post-fire responses. We have illustrated this approach using several Australasian conifer species as examples of pyrophobic species. In particular, we suggest using the term “obligate seeder” for the general category of plants that rely on seed to reproduce, and qualifying this to “post-fire obligate seeder” for the narrower category of species with populations that recover from canopy fire only by seeding; the species are typically fire-cued, with large aerial or soil seed banks that germinate profusely following a fire, and grow and reproduce rapidly in order to renew the seed bank before the next fire.

scholarly journals Interobserver agreement of the Paris and simplified classifications of superficial colonic lesions: a Western study

2021 ◽  
Vol 09 (03) ◽  
pp. E388-E394
Author(s):  
Francesco Cocomazzi ◽  
Marco Gentile ◽  
Francesco Perri ◽  
Antonio Merla ◽  
Fabrizio Bossa ◽  
...  
Keyword(s):  
Interobserver Agreement ◽  
Classification Systems ◽  
Sensitivity Analyses ◽  
Size Estimation ◽  
Rater Agreement ◽  
Staff Members ◽  
Video Clips ◽  
Paris Classification ◽  
Level Of Agreement

Abstract Background and study aims The Paris classification of superficial colonic lesions has been widely adopted, but a simplified description that subgroups the shape into pedunculated, sessile/flat and depressed lesions has been proposed recently. The aim of this study was to evaluate the accuracy and inter-rater agreement among 13 Western endoscopists for the two classification systems. Methods Seventy video clips of superficial colonic lesions were classified according to the two classifications, and their size estimated. The interobserver agreement for each classification was assessed using both Cohen k and AC1 statistics. Accuracy was taken as the concordance between the standard morphology definition and that made by participants. Sensitivity analyses investigated agreement between trainees (T) and staff members (SM), simple or mixed lesions, distinct lesion phenotypes, and for laterally spreading tumors (LSTs). Results Overall, the interobserver agreement for the Paris classification was substantial (κ = 0.61; AC1 = 0.66), with 79.3 % accuracy. Between SM and T, the values were superimposable. For size estimation, the agreement was 0.48 by the κ-value, and 0.50 by AC1. For single or mixed lesions, κ-values were 0.60 and 0.43, respectively; corresponding AC1 values were 0.68 and 0.57. Evaluating the several different polyp subtypes separately, agreement differed significantly when analyzed by the k-statistics (0.08–0.12) or the AC1 statistics (0.59–0.71). Analyses of LSTs provided a κ-value of 0.50 and an AC1 score of 0.62, with 77.6 % accuracy. The simplified classification outperformed the Paris classification: κ = 0.68, AC1 = 0.82, accuracy = 91.6 %. Conclusions Agreement is often measured with Cohen’s κ, but we documented higher levels of agreement when analyzed with the AC1 statistic. The level of agreement was substantial for the Paris classification, and almost perfect for the simplified system.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

scholarly journals Reliability of the classification of cartilage and labral injuries during hip arthroscopy

2020 ◽  
Vol 7 (3) ◽  
pp. 448-457
Author(s):  
Stephanie W Mayer ◽  
Tobias R Fauser ◽  
Robert G Marx ◽  
Anil S Ranawat ◽  
Bryan T Kelly ◽  
...  
Keyword(s):  
Femoral Head ◽  
Transition Zone ◽  
Central Compartment ◽  
Classification Systems ◽  
Cartilage Injury ◽  
Intraobserver Agreement ◽  
Acetabular Cartilage ◽  
Femoral Head Cartilage ◽  
Labral Tears

Abstract To determine interobserver and intraobserver reliabilities of the combination of classification systems, including the Beck and acetabular labral articular disruption (ALAD) systems for transition zone cartilage, the Outerbridge system for acetabular and femoral head cartilage, and the Beck system for labral tears. Additionally, we sought to determine interobserver and intraobserver agreements in the location of injury to labrum and cartilage. Three fellowship trained surgeons reviewed 30 standardized videos of the central compartment with one surgeon re-evaluating the videos. Labral pathology, transition zone cartilage and acetabular cartilage were classified using the Beck, Beck and ALAD systems, and Outerbridge system, respectively. The location of labral tears and transition zone cartilage injury was assessed using a clock face system, and acetabular cartilage injury using a five-zone system. Intra- and interobserver reliabilities are reported as Gwet’s agreement coefficients. Interobserver and intraobserver agreement on the location of acetabular cartilage lesions was highest in superior and anterior zones (0.814–0.914). Outerbridge interobserver and intraobserver agreement was >0.90 in most zones of the acetabular cartilage. Interobserver and intraobserver agreement on location of transition zone lesions was 0.844–0.944. The Beck and ALAD classifications showed similar interobserver and intraobserver agreement for transition zone cartilage injury. The Beck classification of labral tears was 0.745 and 0.562 for interobserver and intraobserver agreements, respectively. The Outerbridge classification had almost perfect interobserver and intraobserver agreement in classifying chondral injury of the true acetabular cartilage and femoral head. The Beck and ALAD classifications both showed moderate to substantial interobserver and intraobserver reliabilities for transition zone cartilage injury. The Beck system for classification of labral tears showed substantial agreement among observers and moderate intraobserver agreement. Interobserver agreement on location of labral tears was highest in the region where most tears occur and became lower at the anterior and posterior extents of this region. The available classification systems can be used for documentation regarding intra-articular pathology. However, continued development of a concise and highly reproducible classification system would improve communication.

Sign in / Sign up

Export Citation Format

Share Document