Manuscripts Listed by Traditional Classification

In article the legislative base, which is the foundation of functioning of the state system of medical-social examination, is considered and analyzed. The questions of legal regulation of the state activity in the sphere of social policy concerning disabled people are discussed. The methods of sociological research and logical analysis of literature and official normatively-legal papers, being the basis of activity of the system of medico-social examination and sphere of giving to the invalids the equal with other citizens possibilities in realization of constitutional rights and freedoms, public welfare and establishment, are applied to the invalids as the measures of government support. In conclusions the emphasis is placed on need of carrying out radical restructurings for system of medico-social examination. It is offered to modify the existing classification of indexes of health and indexes, related to the health taking into account the socio-economic, climatic and other features; to strength the control of execution of government programs in the medico-social sphere; to modify the traditional classification of groups of disability; to change a way of features accounting of disabled people with various functional violations proceeding from a complex assessment of dysfunction of the neuro-physiological and psycho-physiological statuses; to use the innovative technologies of diagnostics, treatment, rehabilitation in correction of the functional violations with taking in mind not only the nosologic group of disease, but by an individual approach.

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Semantics of Voids within Data: Ignorance-Aware Machine Learning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040246 ◽

2021 ◽

Vol 10 (4) ◽

pp. 246

Author(s):

Vagan Terziyan ◽

Anton Nikulin

Keyword(s):

Spatial Data ◽

Information Science ◽

Nearest Neighbor ◽

Geographical Information ◽

Two Dimensional ◽

Prototype Selection ◽

Data Space ◽

Important Concern ◽

Traditional Classification ◽

Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

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Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

Remote Sensing ◽

10.3390/rs13040547 ◽

2021 ◽

Vol 13 (4) ◽

pp. 547

Author(s):

Wenning Wang ◽

Xuebin Liu ◽

Xuanqin Mou

Keyword(s):

Classification Accuracy ◽

Data Augmentation ◽

Classification Problem ◽

Classification Performance ◽

Spectral Structure ◽

Limited Sample ◽

Sample Classification ◽

Training Samples ◽

Traditional Classification ◽

Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

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Phylogenetic Core Groups: a promising concept in search of a consistent methodological framework

Microbiome ◽

10.1186/s40168-021-01023-y ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Alberto Pascual-García

Keyword(s):

Microbial Communities ◽

Microbial Ecology ◽

Environmental Conditions ◽

The Novel ◽

Methodological Framework ◽

Operational Taxonomic Units ◽

Traditional Classification ◽

Taxonomical Classification ◽

Novel Concept

AbstractIn this comment, we analyse the conceptual framework proposed by Aguirre de Cárcer (Microbiome 7:142, 2019), introducing the novel concept of Phylogenetic Core Groups (PCGs). This notion aims to complement the traditional classification in operational taxonomic units (OTUs), widely used in microbial ecology, to provide a more intrinsic taxonomical classification which avoids the use of pre-determined thresholds. However, to introduce this concept, the author frames his proposal in a wider theoretical framework based on a conceptualization of selection that we argue is a tautology. This blurs the subsequent formulation of an assembly principle for microbial communities, favouring that some contradictory examples introduced to support the framework appear aligned in their conclusions. And more importantly, under this framework and its derived methodology, it is not possible to infer PCGs from data in a consistent way. We reanalyse the proposal to identify its logical and methodological flaws and, through the analysis of synthetic scenarios, we propose a number of methodological refinements to contribute towards the determination of PCGs in a consistent way. We hope our analysis will promote the exploration of PCGs as a potentially valuable tool, helping to bridge the gap between environmental conditions and community composition in microbial ecology.

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Planning and Control in Construction of LW3-1 Jacket

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.467.277 ◽

2013 ◽

Vol 467 ◽

pp. 277-281

Author(s):

Shao Ning Han ◽

Hong Wang ◽

Hang Wu ◽

Pi Song Sun ◽

Yu Hui Xue ◽

...

Keyword(s):

Quality Control ◽

Process Control ◽

Deep Water ◽

Construction Technology ◽

Structural Dimension ◽

Planning And Scheduling ◽

Comprehensive Management ◽

Planning And Control ◽

Traditional Classification ◽

And Control

According to Chinese traditional classification, large-sized jackets weight over 10,000 tons and stand in over 100-meter water. Large-sized jackets are different from the normal in construction tonnage, structural dimension and quality control requirements as well as construction difficulty. More complicated construction technology and programme, higher grade of materials, stricter control requirements on node weld and higher risks all need matched stricter, more meticulous and more comprehensive management in planning and scheduling. Combined with construction of LW3-1 deep-water jacket, this paper describes the planning, tracking progress, process control and optimization.

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An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Algorithms ◽

10.3390/a11090139 ◽

2018 ◽

Vol 11 (9) ◽

pp. 139 ◽

Cited By ~ 5

Author(s):

Ioannis Livieris ◽

Andreas Kanavos ◽

Vassilis Tampakas ◽

Panagiotis Pintelas

Keyword(s):

Supervised Learning ◽

Predictive Models ◽

Learning Algorithm ◽

Learning Algorithms ◽

Classification Problem ◽

Classification Methods ◽

Training Algorithm ◽

Traditional Classification ◽

Supervised Learning Algorithms ◽

Significant Research

Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

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Imbalanced Data Classification Based on Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.443.741 ◽

2013 ◽

Vol 443 ◽

pp. 741-745

Author(s):

Hu Li ◽

Peng Zou ◽

Wei Hong Han ◽

Rong Ze Xia

Keyword(s):

Real World ◽

Imbalanced Data ◽

Data Classification ◽

Comprehensive Analysis ◽

Classification Method ◽

Classification Methods ◽

Real World Data ◽

Minority Class ◽

Imbalanced Data Classification ◽

Traditional Classification

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

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Management of Health-Care Associated Pneumonia (HCAP)

Italian Journal of Medicine ◽

10.4081/itjm.2012.87 ◽

2013 ◽

pp. 87-90

Author(s):

Alessia Rosato ◽

Claudio Santini

Keyword(s):

Health Care ◽

Treatment Approach ◽

Multidrug Resistant ◽

Outpatient Setting ◽

Community Acquired Pneumonia ◽

Traditional Classification ◽

Hospital Acquired Pneumonia ◽

Health Care Associated Pneumonia ◽

Hospital Acquired

Introduction The traditional classification of Pneumonia as either community acquired (CAP) or hospital acquired (HAP) reflects deep differences in the etiology, pathogenesis, approach and prognosis between the two entities. Health-Care Associated Pneumonia (HCAP) develops in a heterogeneous group of patients receiving invasive medical care or surgical procedures in an outpatient setting. For epidemiology and outcomes, HCAP closely resembles HAP and possibly requires an analogous therapeutic regimen effective against multidrug-resistant pathogens. Materials and methods We reviewed the pertinent literature and the guidelines for the diagnosis and management of HCAP to analyze the evidence for the recommended approach. Results Growing evidence seems to confirm the differences in epidemiology and outcome between HCAP and CAP but fails to confirm any real advantage in pursuing an aggressive treatment for all HCAP and CAP patients. Discussion Further investigations are needed to establish the optimal treatment approach according to the different categories of patients and the different illness severities. Keywords Health Care Associated Pneumonia (HCAP); Community Acquired Pneumonia (CAP); Hospital Acquired Pneumonia (HAP); Multidrug-resistant (MDR) Pathogens

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Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

Computational Intelligence and Neuroscience ◽

10.1155/2021/6400045 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Shambhavi Mishra ◽

Tanveer Ahmed ◽

Vipul Mishra ◽

Manjit Kaur ◽

Thomas Martinetz ◽

...

Keyword(s):

Adaptive Filtering ◽

Stock Prices ◽

High Frequency ◽

Time Windows ◽

Stock Index ◽

Level Data ◽

Traditional Classification ◽

Regression Problems ◽

The Common ◽

Online Prediction

This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

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Liar's war: Protecting civilians from disinformation during armed conflict

International Review of the Red Cross ◽

10.1017/s1816383121000473 ◽

2021 ◽

pp. 1-24

Author(s):

Eian Katz

Keyword(s):

Armed Conflict ◽

Legal Framework ◽

International Criminal Law ◽

International Humanitarian Law ◽

International Human Rights Law ◽

Humanitarian Law ◽

Human Rights Law ◽

Human Needs ◽

Traditional Classification

Abstract Disinformation in armed conflict may pose several distinctive forms of harm to civilians: exposure to retaliatory violence, distortion of information vital to securing human needs, and severe mental suffering. The gravity of these harms, along with the modern nature of wartime disinformation, is out of keeping with the traditional classification of disinformation in international humanitarian law (IHL) as a permissible ruse of war. A patchwork set of protections drawn from IHL, international human rights law and international criminal law may be used to limit disinformation operations during armed conflict, but numerous gaps and ambiguities undermine the force of this legal framework, calling for further scholarly attention and clarification.

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