Implementation of Data Mining Algorithm With R

2019 ◽  
pp. 92-125
Author(s):  
C. Deisy ◽  
Mercelin Francis
Keyword(s):  
Text Mining ◽  
Data Storage ◽  
Analytical Techniques ◽  
Graphical Analysis ◽  
Machine Learning Algorithms ◽  
Social Media Analytics ◽  
Data Mining Algorithm ◽  
Clustering And Classification ◽  
Decision Making Processes ◽  
Free Open Source

R is a programming language that uses command-line scripting for graphical and statistical analysis and representation and finally generating a report. It is a free, open source, powerful, and highly extensible tool for data analysis. It consists of a large repository of intermediate tools for statistical and graphical analysis of data which utilizes conditional loops and user-defined functions with input and output capabilities. Statistical and analytical techniques are developed with R for various decision-making processes like forecasting, social media analytics, text mining, and so on. The chapter focuses on the basics of R, data storage elements, and its manipulation. It also highlights the usage of the machine learning algorithms for prediction, clustering, and classification. Applications like text mining are implemented to extract various patterns or rules based on the scenario. Illustrations are explained providing a base for developing many applications applying the basic concepts of R.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Machine Learning-based Whitefly Feature Identification and Counting

2021 ◽  
Author(s):  
Kai-Chao Yao ◽  
Shih-Feng Fu ◽  
Wei-Tzer Huang ◽  
Cheng-Chun Wu
Keyword(s):  
Machine Learning ◽  
Data Storage ◽  
Ccd Camera ◽  
Machine Learning Algorithms ◽  
Feature Identification ◽  
Matching Algorithm ◽  
Vision Development ◽  
Counting Data ◽  
Image Conversion ◽  
Pyramid Matching

This article uses LabVIEW, a software program to develop a whitefly feature identification and counting technology, and machine learning algorithms for whitefly monitoring, identification, and counting applications. In addition, a high-magnification CCD camera is used for on-demand image photography, and then the functional programs of the VI library of LabVIEW NI-DAQ and LabVIEW NI Vision Development Module are used to develop image recognition functions. The grayscale-value pyramid-matching algorithm is used for image conversion and recognition in the machine learning mode. The built graphical user interface and device hardware provide convenient and effective whitefly feature identification and sample counting. This monitoring technology exhibits features such as remote monitoring, counting, data storage, and statistical analysis.

EXPRESS: The Future of Marketing Analytics and Public Policy

2021 ◽  
pp. 074391562110423
Author(s):  
Brennan Davis ◽  
Dhruv Grewal ◽  
Steve Hamilton
Keyword(s):  
Public Policy ◽  
Policy Research ◽  
Analytical Techniques ◽  
Future Research ◽  
Social Media Analytics ◽  
Special Issue ◽  
Marketing Analytics ◽  
Future Of Marketing ◽  
And Robotics

The purpose of this special issue is to encourage the emerging role of analytics in marketing and public policy research. We draw attention to a multitude of comprehensive data sources and analytical techniques that tackle important public policy and marketing issues. We highlight six key domains that provide fruitful avenues for such pursuit: retail analytics, social media analytics, marketing mix analytics, services including healthcare, nonprofits and politics, and artificial intelligence and robotics. We also offer an overview of the various articles and commentaries that are included in this special issue, and we encourage future research building on the underlying analytics approaches, substantive findings, and theoretical discoveries.

Semantic Analysis and Text Summarization in Socio-Technical Systems

2017 ◽  
pp. 243-281 ◽  
Author(s):  
Nina Rizun
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Technical System ◽  
Synergy Effect ◽  
Decision Making Processes ◽  
Multi Level ◽  
Technical Systems

In this chapter, the authors present the results of the development the text-mining methodology for increasing the reliability of the functioning of Socio-technical System (STS). Taking into account revealed strengths and weaknesses of Discriminant and Probabilistic approaches of Latent Semantic Relations analysis in of the abstracting and summarization projection, the Methodology of Two-level Single Document Summarization was developed. The Methodology assumes the following elements of novelty: based on obtaining a multi-level topical framework of the document (abstracting); uses the synergy effect of consistent usage the combination of two approaches for identification of conceptually significant elements of the text (summarization). The examples demonstrating the basic workability of proposed Methodology were presented. Such approaches should help human to increase the quality of supporting the decision-making processes of STS in real time.

scholarly journals Plasma-Assisted Chemical Vapor Deposition of F-Doped MnO2 Nanostructures on Single Crystal Substrates

Nanomaterials ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1335
Author(s):  
Lorenzo Bigiani ◽  
Chiara Maccato ◽  
Alberto Gasparotto ◽  
Cinzia Sada ◽  
Elza Bontempi ◽  
...  
Keyword(s):  
Chemical Vapor Deposition ◽  
Data Storage ◽  
Vapor Deposition ◽  
Oxygen Deficiency ◽  
Chemical Vapor ◽  
Magnetic Ordering ◽  
Analytical Techniques ◽  
Structural Features ◽  
Optical Absorption Spectroscopy ◽  
Magnetic Characteristics

MnO2 nanostructures were fabricated by plasma assisted-chemical vapor deposition (PA-CVD) using a fluorinated diketonate diamine manganese complex, acting as single-source precursor for both Mn and F. The syntheses were performed from Ar/O2 plasmas on MgAl2O4(100), YAlO3(010), and Y3Al5O12(100) single crystals at a growth temperature of 300 °C, in order to investigate the substrate influence on material chemico-physical properties. A detailed characterization through complementary analytical techniques highlighted the formation of highly pure and oriented F-doped systems, comprising the sole β-MnO2 polymorph and exhibiting an inherent oxygen deficiency. Optical absorption spectroscopy revealed the presence of an appreciable Vis-light harvesting, of interest in view of possible photocatalytic applications in pollutant degradation and hydrogen production. The used substrates directly affected the system structural features, as well as the resulting magnetic characteristics. In particular, magnetic force microscopy (MFM) measurements, sensitive to the out-of-plane magnetization component, highlighted the formation of spin domains and long-range magnetic ordering in the developed materials, with features dependent on the system morphology. These results open the door to future engineering of the present nanostructures as possible magnetic media for integration in data storage devices.

scholarly journals Signal-piloted processing and machine learning based efficient power quality disturbances recognition

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252104
Author(s):  
Saeed Mian Qaisar
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Data Storage ◽  
Computational Cost ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Processing Load ◽  
Suggested Approach ◽  
Power Quality Disturbances

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

scholarly journals Monitoring bias and fairness in machine learning models: A review

2021 ◽  
Author(s):  
Marley Bacelar
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Economic Sectors ◽  
Improve Accuracy ◽  
Private And Public ◽  
Decision Making Processes ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Machine Learning Models

Introduction Machine learning algorithms are quickly gaining traction in both the private and public sectors for their ability to automate both simple and complex decision-making processes. The vast majority of economic sectors, including transportation, retail, advertisement, and energy, are being disrupted by widespread data digitization and the emerging technologies that leverage it. Computerized systems are being introduced in government operations to improve accuracy and objectivity, and AI is having an impact on democracy and governance [1]. Numerous businesses are using machine learning to analyze massive quantities of data, from calculating credit for loan applications to scanning legal contracts for errors to analyzing employee interactions with customers to detect inappropriate behavior. New tools make it easier than ever for developers to design and deploy machine-learning algorithms [2] [3].

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

scholarly journals A Review of Machine Learning Algorithms for Cloud Computing Security

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1379
Author(s):  
Umer Ahmed Butt ◽  
Muhammad Mehmood ◽  
Syed Bilal Hussain Shah ◽  
Rashid Amin ◽  
M. Waqas Shaukat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Storage ◽  
Response Times ◽  
Edge Computing ◽  
Machine Learning Algorithms ◽  
Information Storage ◽  
Future Research ◽  
Computer Algorithms ◽  
Advantages And Disadvantages

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.

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