Recognizing Patterns in Trace Elements

1977 ◽  
Vol 31 (2) ◽  
pp. 87-94 ◽  
Author(s):  
J. R. McGill ◽  
B. R. Kowalski
Keyword(s):  
Pattern Recognition ◽  
Trace Elements ◽  
Scientific Community ◽  
Trend Detection ◽  
Massive Data ◽  
Scientific Interest ◽  
Major Aspect ◽  
Data Manipulation ◽  
Computer Based ◽  
Data Explosion

There is a growing awareness in the scientific community that as methods of analysis become more efficient, a massive data explosion is ensuing. Reducing data to meaningful trends has become a major aspect of research because of the bulk of information involved and because scientific interest is focusing on more and more subtle phenomena. One aid to data reduction and trend detection is pattern recognition, which is a collection of computer-based data manipulation techniques that have proved useful in a number of problems. This paper is intended as an illustrative introduction to these techniques, with the aim of giving the reader an intuitive feel for the process of a real application of the methods. A problem in archaeology, solved by neutron activation analysis, is discussed.

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

scholarly journals Design of Hybrid Method for Automatic Sentiment Classification System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3616-3620
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Classification System ◽  
Scientific Community ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Learning Approach ◽  
Business World ◽  
Major Aspect ◽  
Machine Learning Approach

The Developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that focuses on opinions and sentiments as a major aspect of their central goal. This work introduces hybrid approach that includes lexicon based approach and machine learning approach for extracting aspects and sentiments

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data and Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Research on Data Mining Algorithm Based on Pattern Recognition

2019 ◽  
Vol 34 (06) ◽  
pp. 2059015 ◽  
Author(s):  
Xuelong Zhang
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Rapid Development ◽  
Future Trend ◽  
Support Vector ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Rapid Improvement ◽  
Training Time ◽  
Database Technology

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.

scholarly journals A Computer Aided Approach for River Styles—Inspired Characterization of Large Basins: A Structured Procedure and Support Tools

Geosciences ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 231 ◽  
Author(s):  
Andrea Nardini ◽  
Santiago Yépez ◽  
Maria Dolores Bejarano
Keyword(s):  
Scientific Community ◽  
Emerging Countries ◽  
Python Language ◽  
Software Packages ◽  
Support Tools ◽  
Computer Based ◽  
Large Systems ◽  
Type Of Exercise

This paper presents a systematic procedure for developing a characterization and classification of river reaches inspired by the River Styles Framework, through which insight can be gained about the understanding of river behavior. Our procedure takes advantage of several computer based “tools”, i.e., algorithms implemented in software packages of various types, from “simple” Excel sheets to sophisticated algorithms in Python language, in general all supported by Geographic Information Systems (GIS). The main potentially useful, existing tools for this specific aim are discussed here, revealing their strengths and weaknesses. New, complementary or alternative tools that have been developed in the project feeding this paper are presented, which can contribute to the scientific community and stakeholders of the topic. The main result of our research is a structured and practical guide (a ToolBox Manual) that can support practitioners and researchers wishing to characterize and classify large rivers, based on the River Styles Framework. The main contribution is that this set of ideas, solutions, and tools, makes this type of exercise significantly more transparent and at the same time much less subjective. Moreover, the procedure is applicable to large systems and does not require more information than that generally available also in developing or emerging countries.

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