Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat

The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.

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Impact of Big Data over Telecom Industry

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v6i2.1958 ◽

2018 ◽

Vol 6 (2) ◽

Author(s):

Muhammad Waqar Khan ◽

Muhammad Asghar Khan ◽

Muhammad Alam ◽

Wajahat Ali

Keyword(s):

Big Data ◽

Data Science ◽

Cell Phones ◽

Smart Phones ◽

World Population ◽

Huge Amount ◽

Scale Down ◽

Telecom Industry ◽

Telecom Sector ◽

Theoretical Computing

During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.

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Making waves in Breedbase: An integrated spectral data storage and analysis pipeline for plant breeding programs

tppj ◽

10.1002/ppj2.20012 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Jenna Hershberger ◽

Nicolas Morales ◽

Christiano C. Simoes ◽

Bryan Ellerbrock ◽

Guillaume Bauchet ◽

...

Keyword(s):

Plant Breeding ◽

Spectral Data ◽

Data Storage ◽

Analysis Pipeline ◽

Breeding Programs

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Applying the Fuzzy Multi-Attribute Decision Model in Plant Breeding Programs

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)33639-x ◽

2001 ◽

Vol 34 (26) ◽

pp. 93-98 ◽

Cited By ~ 1

Author(s):

M. Calin ◽

C. Leonte

Keyword(s):

Plant Breeding ◽

Decision Model ◽

Breeding Programs

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Integrating Biotechnology into Woody Plant Breeding Programs

Genetic Manipulation of Woody Plants ◽

10.1007/978-1-4613-1661-9_27 ◽

1988 ◽

pp. 433-449 ◽

Cited By ~ 9

Author(s):

D. E. Riemenschneider ◽

B. E. Haissig ◽

E. T. Bingham

Keyword(s):

Plant Breeding ◽

Woody Plant ◽

Breeding Programs

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NIRS Calibration of Aflatoxin in Maize

Australian Journal of Chemistry ◽

10.1071/ch18316 ◽

2018 ◽

Vol 71 (11) ◽

pp. 868 ◽

Cited By ~ 2

Author(s):

Ross E. Darnell ◽

Jagger J. Harvey ◽

Glen P. Fox ◽

Mary T. Fletcher ◽

James Wainaina ◽

...

Keyword(s):

Near Infrared ◽

False Negative ◽

Field Trials ◽

Aflatoxin Contamination ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Method ◽

Breeding Programs ◽

Wet Chemistry ◽

Learning Techniques

The aim of this study is to determine the value of near-infrared spectroscopy (NIRS) as a diagnostic tool for aflatoxin contamination, specifically to rapidly predict levels of aflatoxin, either quantitatively or qualitatively, in ground maize. Maize was collected from inoculated field trials conducted across four sites in Kenya. Inoculated and uninoculated maize ears were harvested, milled, and prepared for NIRS scanning and wet chemistry-based aflatoxin quantification. Several statistical and machine learning techniques were compared. Absorbance at a single bandwidth explained 34 % of the variation in levels of aflatoxin using a regression model while a partial least-squares (PLS) method showed that NIR measurements could explain 42 % of the variation in aflatoxin levels. To compare various methods for their ability to classify samples with high (>100 ppb) levels of aflatoxin, various additional procedures were applied. The k-nearest neighbour classification method yielded sensitivity and specificity values of 0.75 and 0.52 respectively, compared with the support vector machine method with estimates of 0.81 and 0.68, whereas PLS could achieve values of 0.82 and 0.72 respectively. The corresponding false positive and false negative values are still unacceptable for NIRS to be used with confidence, as ~18 % of contaminated ground maize samples would be accepted and 28 % of good maize would be discarded or declared contaminated or downgraded. However, such calibrations could be useful in breeding programs without access to wet chemistry analysis, seeking to rank entries semiquantitatively.

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Big Data Analytics Enabled Smart Financial Services: Opportunities and Challenges

Big Data Analytics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-72413-3_2 ◽

2017 ◽

pp. 15-39 ◽

Cited By ~ 3

Author(s):

Vadlamani Ravi ◽

Sk Kamaruddin

Keyword(s):

Big Data ◽

Financial Services ◽

Data Analytics ◽

Big Data Analytics

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Data Science and Big Data Analytics in Financial Services

Web Services ◽

10.4018/978-1-5225-7501-6.ch067 ◽

2019 ◽

pp. 1301-1329

Author(s):

Suren Behari ◽

Aileen Cater-Steel ◽

Jeffrey Soar

Keyword(s):

Big Data ◽

Data Analysis ◽

Financial Services ◽

Data Science ◽

Big Data Analytics ◽

Disruptive Innovation ◽

Big Data Analysis ◽

Future Trends ◽

The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

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Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

10.4018/978-1-7998-8089-9.ch010 ◽

2022 ◽

pp. 187-204

Author(s):

María A. Pérez-Juárez ◽

Javier M. Aguiar-Pérez ◽

Miguel Alonso-Felipe ◽

Javier Del-Pozo-Velázquez ◽

Saúl Rozada-Raneros ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Financial Institutions ◽

Financial Services ◽

Essential Role ◽

Digital Natives ◽

Digital Immigrants ◽

Artificial Intelligence Techniques ◽

New Challenges

A lot of millennials have been educated in gamified schools where they played Kahoot several times per week, and where applications like Classcraft made them feel like the protagonists of a videogame in which they had to accumulate points to be able to level up. All those that were educated in a gamified environment feel it is natural and logical that gamification is used in all areas. For this reason, gamification is increasingly becoming important in different fields including financial services, bringing new challenges. Gamification allows financial institutions to provide personalized and compelling experiences. Big data and artificial intelligence techniques are called to play an essential role in the gamification of financial services. This chapter aims to explore the possibilities of using artificial intelligence and big data techniques to support gamified financial services which are essential for digital natives but also increasingly important for digital immigrants.

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