“ In recent news, we found that there is an AI-based tool for recommending new recipes, with new flavors after crunching thousands of existing recipes.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Data Science Isn't a Fad: Let's Keep it That WayMelinda Thielbar
First presented at the February 2013 Research Triangle Analysts meeting, this presentation discusses the technical side of making data science a field that's here to last. This presentation focuses on the "science" aspect of data science and how it drives value to an organization.
International Journal of Database and Analytics(IJDA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Database and Analytics . The journal focuses on all technical and practical aspects of Database and Analytics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced Database and Analytics and establishing new collaborations in these areas.
Big Data Analytics on Hadoop RainStor InfographicRainStor
A look at how RainStor's compression helps solve the Cost, Complexity and Compliance Risk challenges of managing big data on Hadoop. RainStor runs natively on Hadoop, integrates with YARN and Hue. Can be accessed through Hive, Pig or MapReduce.
The Danger of Big Data by Kerry Bodine - Forrester research
Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
International Journal of Database and Analytics(IJDA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Database and Analytics .
Where Open Source Meets Audit Analytics - ISACA North America CACS 2017Andrew Clark
Open source software is taking the computer science community and IT departments by storm. The breadth of options, the timeliness of updates, the price, and the sense of community are all contributing factors to the rise of open source computing. For many years audit analytics has been confined to the Computer Assisted Auditing Techniques, CAAT, software vendors ACL, IDEA and now Arbutus. However, these software programs require extensive training to use effectively, are not very flexible, and in most cases fail to provide the outcome auditors are expecting. Moving to an open source platform based around the python ecosystem allows for true customization of analytics, and provides a common language to interact with your IT department. By using the same set of tools, an auditing department can move from rudimentary AP duplicate tests all the way to advanced classification and clustering machine learning tests. Although the barrier to entry for open source software is higher than for most CAATs, with cross-functional collaboration, a truly customized, sustainable, and highly effective analytics program can be created.
During the presentation, Andrew will explain what open source software is and why it matters; give an overview of the Python and R programming languages; provide an overview of the appealing attributes and downsides of each language; explain why open source languages should be considered instead of traditional CAATs; demonstrate how machine learning can be effectively applied to audit analytics; and most importantly, provide a real world example of how to begin applying these technologies in your organization. By developing a cursory understanding of the vast and exciting landscape of audit data science, your appetite will be whetted to not only find ways to employ these new technologies in your department, but to strive to become a data evangelist for your organization.
In our last paper we compared two alternate machine-learning techniques from
the Apache Mahout stable, namely: Apache Sparks’, spark-itemsimilarity, and its
counterpart Apache Hadoop’s MapReduce. We saw how Apache Spark was better
both qualitatively as well as quantitatively even for moderately sized sites.
In this paper, we look at how we can further optimize the efficiency of these runs
without compromising on quality. We determine how the two algorithms we
studied last time perform when run on all data available and when run only with
success data. In the e-commerce domain, success data is defined, as a subset of
the total data, which we heuristically believe, does not include noise.
Data Science deals with the extraction of valuable insights from an incredible number of sources in an endless number of formats. This session will go through a typical workflow using practical tools and tricks. This will give you a basic understanding of Data Science in the Cloud. The examples will show the steps that are needed to build and deploy a model to predict traffic collisions with weather data.
Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
We show how online reviews analyzed and viewed through the lens of behavioral economics can provide insights into consumers’ perceptions and emotions. We web scraped and analyzed 7,000 online automotive reviews across six countries. We found that 1) negative events have a greater impact than positive events on consumers’ brand evaluations, and 2) the emotions experienced by consumers differ by car models. By analyzing big data through the lens of behavioral economics, marketers can better understand how to market and manage their brands.
Data Science Isn't a Fad: Let's Keep it That WayMelinda Thielbar
First presented at the February 2013 Research Triangle Analysts meeting, this presentation discusses the technical side of making data science a field that's here to last. This presentation focuses on the "science" aspect of data science and how it drives value to an organization.
International Journal of Database and Analytics(IJDA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Database and Analytics . The journal focuses on all technical and practical aspects of Database and Analytics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced Database and Analytics and establishing new collaborations in these areas.
Big Data Analytics on Hadoop RainStor InfographicRainStor
A look at how RainStor's compression helps solve the Cost, Complexity and Compliance Risk challenges of managing big data on Hadoop. RainStor runs natively on Hadoop, integrates with YARN and Hue. Can be accessed through Hive, Pig or MapReduce.
The Danger of Big Data by Kerry Bodine - Forrester research
Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
International Journal of Database and Analytics(IJDA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Database and Analytics .
Where Open Source Meets Audit Analytics - ISACA North America CACS 2017Andrew Clark
Open source software is taking the computer science community and IT departments by storm. The breadth of options, the timeliness of updates, the price, and the sense of community are all contributing factors to the rise of open source computing. For many years audit analytics has been confined to the Computer Assisted Auditing Techniques, CAAT, software vendors ACL, IDEA and now Arbutus. However, these software programs require extensive training to use effectively, are not very flexible, and in most cases fail to provide the outcome auditors are expecting. Moving to an open source platform based around the python ecosystem allows for true customization of analytics, and provides a common language to interact with your IT department. By using the same set of tools, an auditing department can move from rudimentary AP duplicate tests all the way to advanced classification and clustering machine learning tests. Although the barrier to entry for open source software is higher than for most CAATs, with cross-functional collaboration, a truly customized, sustainable, and highly effective analytics program can be created.
During the presentation, Andrew will explain what open source software is and why it matters; give an overview of the Python and R programming languages; provide an overview of the appealing attributes and downsides of each language; explain why open source languages should be considered instead of traditional CAATs; demonstrate how machine learning can be effectively applied to audit analytics; and most importantly, provide a real world example of how to begin applying these technologies in your organization. By developing a cursory understanding of the vast and exciting landscape of audit data science, your appetite will be whetted to not only find ways to employ these new technologies in your department, but to strive to become a data evangelist for your organization.
In our last paper we compared two alternate machine-learning techniques from
the Apache Mahout stable, namely: Apache Sparks’, spark-itemsimilarity, and its
counterpart Apache Hadoop’s MapReduce. We saw how Apache Spark was better
both qualitatively as well as quantitatively even for moderately sized sites.
In this paper, we look at how we can further optimize the efficiency of these runs
without compromising on quality. We determine how the two algorithms we
studied last time perform when run on all data available and when run only with
success data. In the e-commerce domain, success data is defined, as a subset of
the total data, which we heuristically believe, does not include noise.
Data Science deals with the extraction of valuable insights from an incredible number of sources in an endless number of formats. This session will go through a typical workflow using practical tools and tricks. This will give you a basic understanding of Data Science in the Cloud. The examples will show the steps that are needed to build and deploy a model to predict traffic collisions with weather data.
Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
We show how online reviews analyzed and viewed through the lens of behavioral economics can provide insights into consumers’ perceptions and emotions. We web scraped and analyzed 7,000 online automotive reviews across six countries. We found that 1) negative events have a greater impact than positive events on consumers’ brand evaluations, and 2) the emotions experienced by consumers differ by car models. By analyzing big data through the lens of behavioral economics, marketers can better understand how to market and manage their brands.
Use of Hough Transform and Homography for the Creation of Image Corpora for S...IJCI JOURNAL
. In the context of smart agriculture, developing deep learning models demands large and highquality datasets for training. However, the current lack of such datasets for specific crops poses a significant
challenge to the progress of this field. This research proposes an automated method to facilitate the
creation of training datasets through automated image capture and pre-processing. The method’s efficacy
is demonstrated through two study cases conducted in a Cannabis Sativa cultivation setting. By leveraging
automated processes, the proposed approach enables to create large-volume and high-quality datasets,
significantly reducing human effort. The results indicate that the proposed method not only simplifies
dataset creation but also allows researchers to concentrate on other critical tasks, such as refining image
labeling and advancing artificial intelligence model creation. This work contributes towards efficient and
accurate deep learning applications in smart agriculture.
A Study on the Applications and Impact of Artificial Intelligence in E Commer...ijtsrd
Trends in computer science show that various aspects of Artificial Intelligence are emerging, and other trends show that these advances are being applied to create intelligent in formation systems. In recent days artificial intelligence is changing the ways in which computers are usable as problem solving tools. The talent of humans is thus smartly creating and operating tools are indeed a feature of human based brainpower. This technology is now adapted by various E Commerce websites in order to identify the customer preference, pervious purchases, frequent checks etc. Google and Microsoft are also investing in artificial intelligence through various forms in order to enhance better customer service. The main aim of the study is to analysis and explores the various applications and impact of artificial intelligence in E Commerce industry. This study analyses and concludes that by replacement of human expert with artificial intelligence systems in E Commerce industry can significantly speedup and cheapens the production or service process. Prof. Lakshmi Narayan. N | Naveena. N "A Study on the Applications and Impact of Artificial Intelligence in E-Commerce Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26374.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26374/a-study-on-the-applications-and-impact-of-artificial-intelligence-in-e-commerce-industry/prof-lakshmi-narayan-n
Classifier Model using Artificial Neural NetworkAI Publications
When it comes to AI and ML, precision in categorization is of the utmost importance. In this research, the use of supervised instance selection (SIS) to improve the performance of artificial neural networks (ANNs) in classification is investigated. The goal of SIS is to enhance the accuracy of future classification tasks by identifying and selecting a subset of examples from the original dataset. The purpose of this research is to provide light on how useful SIS is as a preprocessing tool for artificial neural network-based classification. The work aims to improve the input dataset to ANNs by using SIS, which may help with problems caused by noisy or redundant data. The ultimate goal is to improve ANNs' ability to identify data points properly across a wide range of application areas.
FutureLinks - AI-powered Digital Research AssistantJulius Kühn
utureLinks is a multi-disciplinary, AI and Machine Learning-powered aggregator for academic papers, and journals. It will provide a personalized and customized experience to researchers, students and scholars giving them access to millions of journals from disciplines of their choosing. Future Links will not just be the one-stop-platform for researchers, but it will also spur readership and exposure of journals.
Behavioural Modelling Outcomes prediction using Casual FactorsIJMER
Generating models from large data sets—and deter-mining which subsets of data to
mine—is becoming increasingly automated. However choosing what data to collect in the first place
requires human intuition or experience, usually supplied by a domain expert. This paper describes a
new approach to machine science which demonstrates for the first time that non-domain experts can
collectively formulate features, and provide values for those features such that they are predictive of
some behavioral outcome of interest. This was accomplished by building a web platform in which
human groups interact to both respond to questions likely to help predict a behavioral outcome and
pose new questions to their peers. This results in a dynamically-growing online survey, but the result
of this cooperative behavior also leads to models that can predict user's outcomes based on their
responses to the user-generated survey questions. Here we describe two web-based experiments that
instantiate this approach: the first site led to models that can predict users' monthly electric energy
consumption; the other led to models that can predict users' body mass index. As exponential
increases in content are often observed in successful online collaborative communities, the proposed
methodology may, in the future, lead to similar exponential rises in discovery and insight into the
causal factors of behavioral outcomes
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Welocme to ViralQR, your best QR code generator.ViralQR
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Using ai in advance science venkat vajradhar - medium
1. 6/25/2020 Using AI in Advance science - venkat vajradhar - Medium
https://medium.com/@pvvajradhar/using-ai-in-advance-science-26a48b3ecad1 1/3
Using AI in Advance science
venkat vajradhar
Nov 5, 2019 · 3 min read
“ In recent news, we found that there is an AI-based tool for recommending new
recipes, with new flavors after crunching thousands of existing recipes.
AI services based tool for recommending new recipes, with new flavors after
crunching thousands of existing recipes. Although the work is interesting, such an
approach is particularly interesting when applied to innovation.
For example, a recent paper from researchers at Carnegie Mellon University and the
Hebrew University of Jerusalem highlights an AI solutions-driven approach to patent
mine databases and research mine databases, for reusable ideas in new problem-
solving.
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The key to this approach is to find similarities that integrate disparate methods and
problems. Before training the deep learning algorithm, they used crowdsourcing to
understand how people create new similarities and my intellectual databases for
potential innovations.
fter decades of effort, this is the first time anyone has gained computational
traction on a problem like this,” the authors say. “If you can search for
similarities, you can accelerate innovation. If you can accelerate the rate of innovation,
it will solve many other problems.”
Suffice it to say that finding similarities is not an easy thing for a computer to do,
because it is one of the things we humans do without always understanding how to do
it. Previous attempts to automate the job required first creating a craftsmanship data
structure, which is very time consuming and therefore not scalable.
The team turned to Mechanical Turk to recruit a large number of volunteers for similar
products through the Product Innovation Website Quirky. They did this by looking at a
range of products with similar purposes or ways of doing their job.
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These insights are provided in the algorithm before being loosely set in an additional
dataset of product descriptions. Researchers have developed the algorithm with some
similarities of its own. Interestingly, this proves to be good at work and goes beyond
just surface similarities to find similarities between different products. When tested,
new product suggestions are rated as the most innovative ideas.
The team believes that this process can be easily used as part of the innovation process
by allowing companies to uncover previously hidden connections between patents or
research papers. Both datasets are enormous and growing exponentially. This is a
problem that is well suited to autonomy practices, so it will be interesting to see what
comes next for the team and their system.
Researchers applied a related approach to derive relationships between words and subjects
before learning about 3.3 million scientific materials from papers published between 1922
and 2018 on Materials Science.
This approach was able to capture the basic knowledge of the nature and structure of
chemicals and their properties, as well as some new chemical compounds similar to
“A
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thermoelectric materials. Not examined before.
The team believes that this approach is an effective and effective way to explore new
avenues for scientific research and therefore accelerates the advancement of
knowledge in various fields.
Arti cial Intelligence Science Machine Learning
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