scholarly journals Who Is to Blame and What Is to Be Done: Analysis of Comments to Films about the Beslan School Siege

2021 ◽  
Vol 12 (3) ◽  
pp. 74-86
Author(s):  
N.M. Smirnov
Keyword(s):  
Random Forest ◽  
Study Design ◽  
Terrorist Attack ◽  
The State ◽  
Objective Analysis ◽  
Data Array ◽  
Textual Data ◽  
Forest Modeling

Objective. Analysis of YouTube comments to documentaries about the fifteenth anniversary of the Beslan school siege. Background. Amid reactivation of the Beslan discourse, demand for social reflection of the tragedy is increasing. It seems relevant to address nonreactive data to evaluate framing perception. Study design. Using random forest modeling we examine the content of YouTube comments to evaluate their specific characteristics. Data. Array of comments to Yuri Dud, Ksenia Sobchak and Novaya Gazeta films about Beslan (N=141,966, upload date: 02/29/2020, parsing loss<1% of the total). Measurements. Random forest modeling for textual data. Results. In the comments to all three films, blame is mainly reattributed to the state. The moral performatives are different: in the comments to Y. Dud and K. Sobchak’s films there are appeals for helping victims of the terrorist attack; in the case of Novaya Gazeta — to the punishment of the guilty. Conclusions. The Beslan tragedy turns out to be a serious point of social dissociation, there is neither rallying around the state nor a consensus on how to heal the trauma.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

scholarly journals Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

2015 ◽  
pp. 277 ◽  
Author(s):  
Elizabeth T Masters ◽  
Birol Emir ◽  
Jack Mardekian ◽  
Andrew Clair ◽  
Max Kuhn ◽  
...  
Keyword(s):  
Random Forest ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Forest Modeling

Contribution of the Police Academy in Szczytno to Counter-terrorist Activities

2020 ◽  
Vol Special Issue ◽  
pp. 101-116
Author(s):  
Waldemar Zubrzycki
Keyword(s):  
Higher Education ◽  
Police Officers ◽  
Terrorist Attack ◽  
Education Institution ◽  
The State ◽  
Police Academy ◽  
Teaching Staff ◽  
Accurate Identification ◽  
Legal Personality ◽  
Different Levels

One of the state entities which have the task of preventing and combating terrorism is the Police, while the Police Academy in Szczytno is their intellectual base. It is not only an organisational unit of the Police, but also a higher education institution with legal personality. This status entitles it to teach at different levels of civilian higher education, as well as conduct in-service training for police officers. Education in the area of terrorist threats and counteracting them is aimed, on the one hand, at making the students aware of the scale of such threats and their accurate identification, as well as the ways of responding to them by the state and its institutions, and, on the other hand, at learning specific reflexes that will allow anyone who finds themselves in an area threatened by a terrorist attack to quickly assess the resulting threat and to react immediately. The goals set in this way are reflected in the employment structure of the academy, which includes specialist organisational units, and among the scientific and teaching staff are experts, including practitioners, who in the past carried out the tasks of the state at different levels of the system for counteracting terrorist threats. They systematically participate in scientific undertakings, including research projects, scientific conferences and publication work, participate in the work of expert teams, both national and international, and cooperate extensively with external entities. The result of their multidimensional activities is shaping and deepening social awareness of terrorist threats and desired attitudes towards them, as well as appropriate preparation of police officers, but also other services, to safely perform tasks related to the elimination of terrorist threats.

scholarly journals Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jason Deglint ◽  
Farnoud Kazemzadeh ◽  
Daniel Cho ◽  
David A. Clausi ◽  
Alexander Wong
Keyword(s):  
Random Forest ◽  
Color Image ◽  
Image Sensor ◽  
Forest Modeling ◽  
Non Linear

scholarly journals Preliminary assessment of the pragmatic value of information in the classification problem based on deep neural networks

2021 ◽  
Vol 16 (93) ◽  
pp. 9-20
Author(s):  
Valery P. Meshalkin ◽  
◽  
Maxim I. Dli ◽  
Andrey Yu. Puchkov ◽  
Ekaterina I. Lobaneva ◽  
...  
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Data Processing ◽  
Value Of Information ◽  
Learning Algorithm ◽  
The State ◽  
Preliminary Assessment ◽  
Processing Scheme ◽  
Input Information ◽  
Controlled Object

A method is proposed for preliminary assessment of the pragmatic value of information in the problem of classifying the state of an object based on deep recurrent networks of long short-term memory. The purpose of the study is to develop a method for predicting the state of a controlled object while minimizing the number of used prognostic parameters through a preliminary assessment of the pragmatic value of information. This is an especially urgent task under conditions of processing big data, characterized not only by significant volumes of incoming information, but also by information rate and multiformatness. The generation of big data is now happening in almost all areas of activity due to the widespread introduction of the Internet of Things in them. The method is implemented by a two-level scheme for processing input information. At the first level, a Random Forest machine learning algorithm is used, which has significantly fewer adjustable parameters than a recurrent neural network used at the second level for the final and more accurate classification of the state of the controlled object or process. The choice of Random Forest is due to its ability to assess the importance of variables in regression and classification problems. This is used in determining the pragmatic value of the input information at the first level of the data processing scheme. For this purpose, a parameter is selected that reflects the specified value in some sense, and based on the ranking of the input variables by the level of importance, they are selected to form training datasets for the recurrent network. The algorithm of the proposed data processing method with a preliminary assessment of the pragmatic value of information is implemented in a program in the MatLAB language, and it has shown its efficiency in an experiment on model data.

scholarly journals Faces of security. Radicalization of prisoners as a challenge for the future of security of the Polish penitentiary institutions

2021 ◽  
Author(s):  
Witold Mazurek
Keyword(s):  
Theoretical Model ◽  
Terrorist Attack ◽  
The State ◽  
Contemporary Society ◽  
Fundamental Issue ◽  
Personal Safety ◽  
Negative Effects ◽  
State Security ◽  
Islamic Terrorism ◽  
The Moment

An issue that is essential to contemporary society is the question of state security and personal safety of the citizens. Therefore, the state has to deal with one of the most important threats, i.e. radicalization. The phenomenon of home-grown radicalism is known in Western European countries. The phenomenon of radicalization should not be identified solely with contemporary domestic Islamic terrorism. Radicalism is not limited to one ideology. The question about the process of acquiring extremist beliefs is one of the most important for political scientists, political psychologists, sociologists and criminologists. Wanting to deal with the etiology of the decision about a terrorist attack, the focus should be on the moment when potential terrorists start and undergo the process of radicalization. The article aims to signal the problem of radicalization in Polish penitentiary units. The authors do not provide the knowledge necessary to counteract this phenomenon in penitentiary units. However, they point to the definition problems, the theoretical model of radicalization, the place of radicalization in the European Union's policy. They also present the penitentiary unit as a place susceptible to radicalization. They formulate general remarks on combating radicalization among prisoners. They inform that the Prison Service should not be left alone in counteracting the radicalization of prisoners. It is an element of the state security system. It also cooperates with other entities in the rehabilitation of prisoners. The authors acknowledge that the way to prevent the negative effects of the radicalization process of prisoners is first of all training personnel in this field. It is also the fundamental issue to isolate prisoners who are a source of danger. In this area, the authors suggest that if the radical attitudes of prisoners are intensified, it is worth considering introducing legislative solutions facilitating faster and adequate operation of prison staff in such cases.

scholarly journals Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study (Preprint)

2021 ◽  
Author(s):  
Kristin Nicole Gmunder ◽  
Jose W Ruiz ◽  
Dido Franceschi ◽  
Maritza M Suarez
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Random Forest ◽  
Health System ◽  
Text Message ◽  
Mixed Effects ◽  
Descriptive Statistics ◽  
Phone Call ◽  
Patient Portal ◽  
Forest Modeling

BACKGROUND With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues. OBJECTIVE The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients. METHODS Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion. RESULTS Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider’s specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression. CONCLUSIONS The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider’s specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.

scholarly journals PigLeg: prediction of swine phenotype using machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8764 ◽  
Author(s):  
Siroj Bakoev ◽  
Lyubov Getmantseva ◽  
Maria Kolosova ◽  
Olga Kostyunina ◽  
Duane R. Chartier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Average Daily Gain ◽  
Nearest Neighbors ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Leg Weakness

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

Sign in / Sign up

Export Citation Format

Share Document