An Approach to the Medical-Type Data Multiobjective Analysis

2021 ◽  
pp. 454-464
Author(s):  
Nailya S. Asfandiyarova ◽  
Olga V. Dashkevich ◽  
Liliya A. Demidova ◽  
Natalia V. Doroshina ◽  
Ekaterina I. Suchkova
Keyword(s):  
Multiobjective Analysis ◽  
Type Data

Pemerolehan bahasa anak berkebutuhan khusus (tunarungu) dalam memahami bahasa

2020 ◽  
Vol 2 (1) ◽  
pp. 5
Author(s):  
Nur Haliza ◽  
Eko Kuntarto ◽  
Ade Kusmana
Keyword(s):  
Special Needs ◽  
Hearing Impairment ◽  
Deaf Child ◽  
Deaf Children ◽  
Children With Special Needs ◽  
Speech Impairment ◽  
Descriptive Research ◽  
Total Communication ◽  
Type Data ◽  
Children With Hearing Loss

Children with hearing impairment are children with hearing loss who are classified into deaf and hard of hearing. The direct impact of disability is the obstruction of verbal / verbal communication, both speaking (expressive) and understanding the conversations of others (receptive). Obtaining the first language of a deaf child can be done with total communication. Total communication is the most effective communication system because in addition to using a form of communication orally or called oral, the activity of reading, writing, reading utterances, is also equipped with a form of cues. The purpose of this study was to determine the acquisition of language of children with special needs (deaf) in understanding language. Subjects in this study are children with special needs who experience speech impairment (hearing impairment) while the object of this study is focused on only one child, Mila Erdita, a 15-year-old child. This research refers to case studies with descriptive research type. Data collection techniques in this study will be done in three ways, namely; observation techniques, interview techniques, and documentation techniques. In this research, data processing that will be done is to describe the speech data of deaf children to see the acquisition of children's vocabulary. The results of this study indicate that deaf children can obtain a language of total communication using a form of communication orally or called oral, with the activities of reading, writing, reading utterances, also equipped with signs

Overpressure Identification Using The Eaton Method on The Gumai Formation of Geragai Graben, Jambi Sub Basin

2020 ◽  
Author(s):  
A. O. Marnila
Keyword(s):  
Pressure Data ◽  
Stratigraphic Sequence ◽  
Fine Grained ◽  
Marine Sedimentation ◽  
Plot Analysis ◽  
Significant Reversal ◽  
Porosity And Permeability ◽  
Upper Level ◽  
Type Data ◽  
Fine Grained Sand

Geragai graben is located in the South Sumatera Basin. It was formed by mega sequence tectonic process with various stratigraphic sequence from land and marine sedimentation. One of the overpressure indication zones in the Geragai graben is in the Gumai Formation, where the sedimentation is dominated by fine grained sand and shale with low porosity and permeability. The aim of the study is to localize the overpressure zone and to analyze the overpressure mechanism on the Gumai Formation. The Eaton method was used to determine pore pressure value using wireline log data, pressure data (RFT/FIT), and well report. The significant reversal of sonic and porosity log is indicating an overpressure presence. The cross-plot analysis of velocity vs density and fluid type data from well reports were used to analyze the causes of overpressure in the Gumai Formation. The overpressure in Gumai Formation of Geragai graben is divided into two zones, they are in the upper level and lower level of the Gumai Formation. Low overpressure have occurred in the Upper Gumai Formation and mild overpressure on the Lower Gumai Formation. Based on the analyzed data, it could be predicted, that the overpressure mechanism in the Upper Gumai Formation might have been caused by a hydrocarbon buoyancy, whereas in the Lower Gumai Formation, might have been caused by disequilibrium compaction as a result of massive shale sequence.

scholarly journals Applications of Clustering with Mixed Type Data in Life Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Shuang Yin ◽  
Guojun Gan ◽  
Emiliano A. Valdez ◽  
Jeyaraj Vadiveloo
Keyword(s):  
Life Insurance ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Financial Statements ◽  
Life Insurers ◽  
Insurance Portfolio ◽  
Policy Information ◽  
Spatial Attributes ◽  
Type Data ◽  
Significant Mortality

Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to examine and understand how actual death claims differ from what is expected—an early stage of developing a monitoring system crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset using only the insured’s characteristics and policy information without regard to known mortality. This clustering has the feature of efficiently handling categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contained observations of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders’ attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.

scholarly journals Neutrosophic ratio-type estimators for estimating the population mean

2021 ◽  
Author(s):  
Zaigham Tahir ◽  
Hina Khan ◽  
Muhammad Aslam ◽  
Javid Shabbir ◽  
Yasar Mahmood ◽  
...  
Keyword(s):  
Minimum Mean Square Error ◽  
Auxiliary Information ◽  
Population Parameter ◽  
Uncertain Information ◽  
Population Mean ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
The Mean ◽  
Type Data ◽  
First Time

AbstractAll researches, under classical statistics, are based on determinate, crisp data to estimate the mean of the population when auxiliary information is available. Such estimates often are biased. The goal is to find the best estimates for the unknown value of the population mean with minimum mean square error (MSE). The neutrosophic statistics, generalization of classical statistics tackles vague, indeterminate, uncertain information. Thus, for the first time under neutrosophic statistics, to overcome the issues of estimation of the population mean of neutrosophic data, we have developed the neutrosophic ratio-type estimators for estimating the mean of the finite population utilizing auxiliary information. The neutrosophic observation is of the form $${Z}_{N}={Z}_{L}+{Z}_{U}{I}_{N}\, {\rm where}\, {I}_{N}\in \left[{I}_{L}, {I}_{U}\right], {Z}_{N}\in [{Z}_{l}, {Z}_{u}]$$ Z N = Z L + Z U I N where I N ∈ I L , I U , Z N ∈ [ Z l , Z u ] . The proposed estimators are very helpful to compute results when dealing with ambiguous, vague, and neutrosophic-type data. The results of these estimators are not single-valued but provide an interval form in which our population parameter may have more chance to lie. It increases the efficiency of the estimators, since we have an estimated interval that contains the unknown value of the population mean provided a minimum MSE. The efficiency of the proposed neutrosophic ratio-type estimators is also discussed using neutrosophic data of temperature and also by using simulation. A comparison is also conducted to illustrate the usefulness of Neutrosophic Ratio-type estimators over the classical estimators.

Mining latent academic social relationships by network fusion of multi-type data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Feiyu Long ◽  
Nianwen Ning ◽  
Yunlei Zhang ◽  
Chenguang Song ◽  
Pengpeng Zhou ◽  
...  
Keyword(s):  
Social Relationships ◽  
Type Data

Research on Power Transformer Fault Forecast

2011 ◽  
Vol 204-210 ◽  
pp. 1553-1558
Author(s):  
Rui Rui Zheng ◽  
Ji Yin Zhao ◽  
Min Li ◽  
Bao Chun Wu
Keyword(s):  
Time Series ◽  
Fault Location ◽  
Power Transformer ◽  
Forecast Model ◽  
Ratio Method ◽  
Gray Model ◽  
Artificial Immune ◽  
Diagnosis Method ◽  
Type Data ◽  
Transformer Fault

To forecast power transformer fault, this paper proposed a integrated algorithm. Research found that discrete time series of power transformer dissolved gases concentration have 2 main types: the s type and the monotone increasing type. The gray verhulst model was chosen for forecasting the s type series, while the gray model predicted the monotone increasing type data. The two models combined a new integrated forecast model. The fault diagnosis method combines the improved three-ratio method and gray artificial immune algorithm, so it can diagnoses both single and multi power transformer faults, and give the fault location. Experiments show that the power transformer fault forecast algorithm is effective and reliable.

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