Yes Virginia, It Will Scale: Using Data to Personalize High-volume Reference Interactions

2016 ◽  
Vol 42 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Lauren Reiter ◽  
J.P. Huffman
Keyword(s):  
High Volume ◽  
Using Data ◽  
Reference Interactions

scholarly journals Diagnosis of diseases using data mining

2017 ◽  
Vol 1 (4) ◽  
pp. 120-121 ◽  
Author(s):  
Elahe Gozali ◽  
Bahlol Rahimi ◽  
Malihe Sadeghi ◽  
Reza Safdari
Keyword(s):  
Data Mining ◽  
Success Rate ◽  
Science Studies ◽  
Review Paper ◽  
Medical Science ◽  
High Volume ◽  
Health Organizations ◽  
Using Data ◽  
Types Of Information ◽  
Diagnosis Of Diseases

Introduction: In the information age, data are the most important asset for health organizations. In the case of using data in useful and optimal manner, they can become financial resources for organization. Data mining is an appropriate method to transform this potential value into strategic information. Data mining means extraction of hidden information, recognition of hidden relationships and patterns, and in general, discovery of useful knowledge at high volume. The objective of this review paper was to evaluate using data mining in diagnoses of diseases. Methods: This research is a review paper conducted based on a structured review of the papers published in Science Direct, Pubmed, Google Scholar, SID, Magiran (between years 2005 and 2015) and books related to using data mining in medical science and using it in diagnose of diseases with related keywords. Results: Nowadays, data mining is used in many medical science studies, including diagnosis of diseases, discovering the hidden patterns in data, and so on. New ideas such as discovery of Knowledge from Discovery and Data Mining Database, which includes data mining techniques, have found more popularity and they has becomedesired research tool for researchers. Researchers can use them to identify patterns and relationshipsamong great number of variables. Using them, researchers have been able to predict theresults obtained from one disease by using information stores available in databases. Several studies have indicated that data mining is used widely in diagnosis of diseases based on types of information (medical images, characteristics of patients, and so on), such as tuberculosis, types of cancers, infectious diseases, and diagnosis of anomalies rarely diagnosed by human (spots and particular points within aye, which is the symptom of onset of blindness resulting from diabetes), determining type of behavior with patients, and predicting the success rate of surgical surgeries, determining the success rate of therapeutic methods in coping with incurable diseases, and so on. Conclusion: One of the most important challenging topics in healthcare is transformation of raw clinical data into meaningful information following continuous generation of great number of data. In current competitive environment, health organizations using technologies such as data mining to improve healthcare quality will achieve success faster. Many of research centers in Iran are faced with large volume of information, which is not analyzed at all or will be time-consuming due to using traditional methods, even in the case of using analysis and converting them to knowledge. In light of using data mining and its implementation, health organizations can transform the data into a powerful and competitive tool and take new steps in preventing, diagnosing, treating, and providing high-quality services for clients. 

Hydroacoustic fish stock assessment in the presence of dense aggregations of Chaoborus larvae

2005 ◽  
Vol 62 (2) ◽  
pp. 245-249 ◽  
Author(s):  
Tommi Malinen ◽  
Antti Tuomaala ◽  
Heikki Peltonen
Keyword(s):  
Stock Assessment ◽  
High Volume ◽  
Acoustic Properties ◽  
Fish Density ◽  
Fish Stock ◽  
Low Threshold ◽  
Using Data ◽  
Fish Stock Assessment ◽  
Turbid Lake ◽  
Analyze Data

A new method for eliminating reverberation due to Chaoborus larvae from hydroacoustic recordings is presented based on an assumption of a constant dependence between the area backscattering strength (sa) with a high-volume backscattering threshold (sv) and sa with a low sv threshold for fish. The idea was to analyze data with a threshold high enough to eliminate reverberation and then convert the estimate to coincide with the result that would have been achieved with a low threshold containing all backscattering from fish. The approach was validated with a secondary dataset, and the magnitude of overestimation of fish density by reverberation was evaluated using data from four surveys conducted in a clay-turbid lake, where small planktivorous fish, smelt (Osmerus eperlanus), and larvae of Chaoborus flavicans coexist in the water column. With the presented method, estimation of smelt density was possible even when Chaoborus density was >200 individuals·m–3. The analyses revealed that the overestimation of fish density could be as high as 50% if the reverberation is not taken into account. The presented method might also be applicable for eliminating reverberation due to other unwanted targets, because it is based on the acoustic properties of fish rather than those of unwanted targets.

Hengam a MapReduce-Based Distributed Data Warehouse for Big Data

2018 ◽  
Vol 8 (1) ◽  
pp. 16-35 ◽  
Author(s):  
Mohammadhossein Barkhordari ◽  
Mahdi Niamanesh
Keyword(s):  
Big Data ◽  
Information Retrieval ◽  
Data Warehouse ◽  
Data Exchange ◽  
Data Retrieval ◽  
High Volume ◽  
Data Locality ◽  
Distributed Data ◽  
Using Data ◽  
Distributed Data Warehouse

When working with a high volume of information that follows an exponential pattern, the authors confront big data. This huge amount of information makes big data retrieval and analytics important issues. There have been many attempts to solve data analytic problems using distributed platforms, but the main problem with the proposed methods is not observing the data locality. In this article, a MapReduce-based method called Hengam is proposed. In this method, data format unification helps nodes to have data independence. The unified format leads to an increase in the information retrieval speed and prevents data exchange betoen nodes. The proposed method was evaluated using data items from an ICT company and the information retrieval time was much better than that of other open-source distributed data warehouse software.

Prognosis of Patients with Metastatic Soft Tissue Sarcoma: Advances in Recent Years

2020 ◽  
Vol 43 (11) ◽  
pp. 613-619
Author(s):  
Jakob Lochner ◽  
Franka Menge ◽  
Nikolaos Vassos ◽  
Peter Hohenberger ◽  
Bernd Kasper
Keyword(s):  
Soft Tissue ◽  
Metastatic Disease ◽  
Medical Center ◽  
Treatment Strategies ◽  
Soft Tissue Sarcomas ◽  
High Volume ◽  
Prognostic Indicators ◽  
Metastatic Soft Tissue Sarcoma ◽  
Using Data ◽  
Clinical Records

<b><i>Objective:</i></b> The objective of this study was to investigate the prognosis of patients with metastatic soft tissue sarcomas (STS) and to define prognostic indicators for overall survival (OS). <b><i>Methods:</i></b> All patients who were treated at the Sarcoma Unit at the Mannheim University Medical Center between 2010 and 2016 and who developed metastatic disease deriving from a STS were included in this retrospective analysis. OS was investigated using data from clinical records and German registry offices. Clinical and pathological characteristics were recorded and analyzed. <b><i>Results:</i></b> A total number of 212 patients developed metastatic disease from STS during that period. Median OS after first documentation of metastatic disease was 24 months (95% CI 21–33). 1-, 2-, and 5-year OS rates were 70.0% (95% CI 64–77), 49.9% (95% CI 43–58), and 24.8% (95% CI 19–33), respectively. In multivariate analysis, significant predictors for mortality appeared to be gender, age, location and size of the primary tumor, histology, and disease-free interval. <b><i>Conclusion:</i></b> Being treated in a high-volume STS reference center in Germany, patients with metastatic disease could demonstrate an increased OS compared to former analyses. These data can be used as a benchmark for upcoming studies and highlight that further research on treatment strategies in this rare disease is urgently needed.

scholarly journals Typology of Customer Retention Schemes in the Private Sports Sector During COVID-19: Analyzing the Behavior of Martial Arts Instructors Using Latent Class Analysis

2021 ◽  
pp. 58
Author(s):  
◽  
Keyword(s):  
Latent Class Analysis ◽  
Martial Arts ◽  
Latent Class ◽  
Customer Retention ◽  
High Volume ◽  
Class Analysis ◽  
Technological Means ◽  
Using Data ◽  
The Impact ◽  
And Training

The COVID-19 pandemic brought new challenges in all aspects of life. It largely brought the sports sector to a halt: major events were postponed or canceled, while gyms and training centers were closed due to repeated lockdowns and social distancing rules and regulations. In the private sports sector, some instructors adopted technological means of maintaining contact with their students in an attempt to retain customers and maintain a high volume of cash flow. Our work focuses on the martial arts (MA) sector in Israel during two crucial periods in 2020: The first lockdown of March through June, when all sports activities were banned, and the period following it, when trainers were allowed to commence training under some regulations. Using data collected from 199 MA instructors, we test for their level and means of engagement with trainees during the lockdown, and the impact these had on customer retention in the period that followed. Using latent class analysis, we establish an empirically based typology of retention schemes (low contact, high contact, and maverick), and test whether these influenced the financial performance of MA studios. Our findings show that the financial damage and the return rate of trainees do not vary between the three types. We offer some insights into the uniqueness of the MA field, and how this may explain these counter-intuitive results.

Application to Bankruptcy Prediction in Banks

2010 ◽  
pp. 427-439
Author(s):  
Joaquín Ordieres-Meré ◽  
Ana González-Marcos ◽  
Manuel Castejón-Limas ◽  
Francisco J. Martínez-de-Pisón
Keyword(s):  
Data Mining ◽  
High Volume ◽  
Bankruptcy Prediction ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
High Volume Production ◽  
Data Driven Approach ◽  
Hidden Knowledge ◽  
Volume Production ◽  
Using Data

This chapter reports five experiences in successfully applying different data mining techniques in a hotdip galvanizing line. Engineers working in steelmaking have traditionally built mathematical models either for their processes or products using classical techniques. Their need to continuously cut costs down while increasing productivity and product quality is now pushing the industry into using data mining techniques so as to gain deeper insights into their manufacturing processes. The authors’ work was aimed at extracting hidden knowledge from massive data bases in order to improve the existing control systems. The results obtained, though small at first glance, lead to huge savings at such high volume production environment. The effective solutions provided by the use of data mining techniques along these projects encourages the authors to continue applying this data driven approach to frequent hard-to-solve problems in the steel industry.

Data Analysis Tools and Methodologies for Quick Yield Learning in a High Volume Manufacturing Environment

2000 ◽  
Author(s):  
L. Andrade ◽  
T. Taylor
Keyword(s):  
Data Analysis ◽  
Random Access ◽  
Final Analysis ◽  
High Volume ◽  
Analysis Tool ◽  
Analysis Tools ◽  
Minimal Cycle ◽  
Yield Learning ◽  
Efficient Data ◽  
Using Data

Abstract High volume products in manufacturing require fast yield learning, root cause identification, and verification that process or tool problems are fixed. Yield losses of 1% correspond to very large dollar losses. Therefore, it is important to have sophisticated data analysis tools that handle large volumes of data to drive higher yields. This paper will present our methodology for defining yields, assessing wafer yield signatures, and using data analysis tools to determine tools or processes which drive yield loss. A SAS based data analysis tool will be shown which can identify tool or process related problems causing abnormalities in parametrics and impacting yield. Case studies illustrating the usefulness of the tool are shown for a Synchronous Dynamic Random Access Memory (SDRAM) product from our wafer fab. In the final analysis, it is clear that an efficient data analysis approach utilizes resources most effectively and pinpoints yield problems with minimal cycle time.

scholarly journals A Study on Various Applications of Artificial Intelligence (AI) in the Field of Human Resource Management (HRM)

10.48175/605 ◽  
2020 ◽  
pp. 63-67
Author(s):  
Pritam Chattopadhyay
Keyword(s):  
Artificial Intelligence ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
New Technology ◽  
High Volume ◽  
Screening Process ◽  
Management Objectives ◽  
Using Data ◽  
Hr Managers

Candidate screening is a very important process in the entire recruitment process. Screening process helps HR’s with initial filtering of candidates and narrow down the received applications from many applications to few. Hiring teams have data pouring in from a variety of sources. In a fully digital HR ecosystem, it is difficult to process and analyze all these disparate data streams. AI can actually help transforming how HR managers view, select, and operate candidate screening. AI for recruiting is the application of artificial intelligence, such as the learning or problem-solving that a computer can do, to the recruitment function. This new technology is designed to streamline or automate some part of the recruiting workflow, especially repetitive, high-volume tasks. The promise of AI for improving quality of hire lies in its ability to using data to standardize the matching between candidates’ experience, knowledge, and skills and the requirements of the job. The benefits using AI are manifold; recruiters don’t have to sift through crowded job markets or endless candidate lists. This makes HR processes quite easy and faster. The research paper is basically focusing on various applications and implications of Artificial Intelligence (AI) in the field of human resource management. Objectives of the paper are mentioned below: • To study the various components of artificial intelligence and its various applications in the field of human resource management. • To understand various implications and impediments of practicing artificial intelligence at different strategic level of any company.

Polar Magnetic Field Reversals of the Sun in Maunder Minimum

2000 ◽  
Vol 179 ◽  
pp. 193-196
Author(s):  
V. I. Makarov ◽  
A. G. Tlatov
Keyword(s):  
Magnetic Field ◽  
Maunder Minimum ◽  
The Sun ◽  
Annual Rings ◽  
Field Reversal ◽  
Polar Magnetic Field ◽  
Pine Trees ◽  
Using Data ◽  
Magnetic Field Reversal

AbstractA possible scenario of polar magnetic field reversal of the Sun during the Maunder Minimum (1645–1715) is discussed using data of magnetic field reversals of the Sun for 1880–1991 and the14Ccontent variations in the bi-annual rings of the pine-trees in 1600–1730 yrs.

Morphology, Distribution and Identification of Micro-Constituents Along Grain Boundaries in Nickel-Base Alloys

1973 ◽  
Vol 31 ◽  
pp. 176-177
Author(s):  
D. E. Fornwalt ◽  
A. R. Geary ◽  
B. H. Kear
Keyword(s):  
Electron Microscope ◽  
Grain Boundaries ◽  
Volume Fraction ◽  
Rupture Life ◽  
Stress Rupture ◽  
High Volume ◽  
Precipitate Morphology ◽  
Stress Rupture Life ◽  
Nickel Base ◽  
Scanning Electron

A systematic study has been made of the effects of various heat treatments on the microstructures of several experimental high volume fraction γ’ precipitation hardened nickel-base alloys, after doping with ∼2 w/o Hf so as to improve the stress rupture life and ductility. The most significant microstructural chan§e brought about by prolonged aging at temperatures in the range 1600°-1900°F was the decoration of grain boundaries with precipitate particles.Precipitation along the grain boundaries was first detected by optical microscopy, but it was necessary to use the scanning electron microscope to reveal the details of the precipitate morphology. Figure 1(a) shows the grain boundary precipitates in relief, after partial dissolution of the surrounding γ + γ’ matrix.

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