scholarly journals Deep learning multidimensional projections

2020 ◽  
Vol 19 (3) ◽  
pp. 247-269 ◽  
Author(s):  
Mateus Espadoto ◽  
Nina Sumiko Tomita Hirata ◽  
Alexandru C Telea
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Projection Methods ◽  
Multidimensional Data ◽  
Sample Data ◽  
Out Of Sample ◽  
Reduction Methods ◽  
High Dimensional Datasets ◽  
Sample Set ◽  
Learning Data

Dimensionality reduction methods, also known as projections, are often used to explore multidimensional data in machine learning, data science, and information visualization. However, several such methods, such as the well-known t-distributed stochastic neighbor embedding and its variants, are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct any such projections. We train a deep neural network based on sample set drawn from a given data universe, and their corresponding two-dimensional projections, compute with any user-chosen technique. Next, we use the network to infer projections of any dataset from the same universe. Our approach generates projections with similar characteristics as the learned ones, is computationally two to four orders of magnitude faster than existing projection methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high-dimensional datasets from machine learning.

Big Data Processing

2018 ◽  
pp. 151-165
Author(s):  
Abhishek Mukherjee ◽  
Chetan Kumar ◽  
Leonid Datta
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Programming Model ◽  
Programming Algorithm ◽  
Efficient Manner ◽  
Dedicated Servers ◽  
Server Maintenance ◽  
Text Mining Application ◽  
Learning Data

This chapter is a description of MapReduce, which serves as a programming algorithm for distributed computing in a parallel manner on huge chunks of data that can easily execute on commodity servers thus reducing the costs for server maintenance and removal of requirement of having dedicated servers towards for running these processes. This chapter is all about the various approaches towards MapReduce programming model and how to use it in an efficient manner for scalable text-based analysis in various domains like machine learning, data analytics, and data science. Hence, it deals with various approaches of using MapReduce in these fields and how to apply various techniques of MapReduce in these fields effectively and fitting the MapReduce programming model into any text mining application.

scholarly journals Python the game changer in the field of Machine Learning, Data Science and IoT: A Review

2021 ◽  
Vol 9 (8) ◽  
pp. 1827-1837
Author(s):  
Prithwish Parial
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Experience ◽  
Object Oriented Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
High Level ◽  
Learning Data ◽  
Game Changer ◽  
Oriented Programming Language

Abstract: Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.

An Interview with Dr. Michael Zeller, Winner of ACM SIGKDD 2020 Service Award

2021 ◽  
Vol 22 (2) ◽  
pp. 6-7
Author(s):  
Michael Zeller
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Investment Company ◽  
Service Award ◽  
Virtual Conference ◽  
Learning Data

Michael Zeller, Ph.D. is the recipient of the 2020 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Zeller was honored for his years of service and many accomplishments as the secretary and treasurer for ACM SIGKDD, the organizing body of the annual KDD conference. Zeller is also head of AI strategy and solutions at Temasek, a global investment company seeking to make a difference always with tomorrow in mind. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 1999, what he learned from the first-ever virtual conference, his work at Temasek, and what excites him about the future of machine learning, data science and artificial intelligence.

scholarly journals A Comprehensive Review on IoT and Its Applications

2021 ◽  
Vol 9 (8) ◽  
pp. 2846-2853
Author(s):  
Dr. S V Viraktamath
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Data Science ◽  
Sensor Data ◽  
Complex Information ◽  
Huge Data ◽  
Complex Information System ◽  
High Level ◽  
And Control ◽  
Learning Data

Abstract: The IoT (Internet of Things) is one of the leading and advantageous technologies in the 21st century, which can give the high level implementation feasibility in the field of wireless telecommunications. The IoT can also be defined as a smart and interconnected network in a highly dynamic infrastructure. It also provides the feature of from anywhere at any time. The main aim of internet of things is to create a huge and complex information system by combining various trending technologies like sensor data, Artificial Intelligence, Machine Learning, Data science, Networking, big data and Clouds. The biggest deal in IoT is to collect the huge data and data security in maintaining the data confidentiality and providing the privacy for every entity. As a result of all these aspects, new difficulties are entering in improving and implementing the current technologies. There are many such technologies by which many more difficulties are entering. They all must be investigated further. This is a special issue which examines the most recent contributions of IoT platform as well as in the progress of the trending technologies. A statement from IoT is that, monitor and control any technology from anywhere, anytime, wireless, fastest. Keyword: Wireless, IoT, Machine learning, Cloud.

Tools, Technologies, and Methodologies to Support Data Science

2021 ◽  
pp. 50-72
Author(s):  
Ricardo A. Barrera-Cámara ◽  
Ana Canepa-Saenz ◽  
Jorge A. Ruiz-Vanoye ◽  
Alejandro Fuentes-Penna ◽  
Miguel Ángel Ruiz-Jaimes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Information Systems ◽  
Data Science ◽  
Smart Phones ◽  
Predictive Analysis ◽  
Biomedical Equipment ◽  
Abstract Knowledge ◽  
Learning Data

Various devices such as smart phones, computers, tablets, biomedical equipment, sports equipment, and information systems generate a large amount of data and useful information in transactional information systems. However, these generate information that may not be perceptible or analyzed adequately for decision-making. There are technology, tools, algorithms, models that support analysis, visualization, learning, and prediction. Data science involves techniques, methods to abstract knowledge generated through diverse sources. It combines fields such as statistics, machine learning, data mining, visualization, and predictive analysis. This chapter aims to be a guide regarding applicable statistical and computational tools in data science.

Impact investment is carrying firm momentum into 2021

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Empirical Studies ◽  
Government Policies ◽  
Content Type ◽  
Listed Firms ◽  
Climate Action ◽  
President Elect ◽  
Institutional Shareholders ◽  
Learning Data

Significance Government policies, demographic change and shifting businesses priorities are raising the profile of ESG issues and driving demand for investment. Furthermore, a growing number of empirical studies are finding that investing with impact does not compromise returns. Impacts US President-elect Joe Biden promises more climate action; ESG-focused ETF inflows surged in November and should maintain momentum. Institutional shareholders will gradually embed ESG factors into their monitoring of the corporate governance of publicly listed firms. Tech advances, such as in machine learning, data science, satellite imagery and equipment-mounted sensors, will enhance ESG risk analysis. Investors will monitor the risk of green or social washing, whereby a firm or bond issuer overstates the ESG impact of projects funded.

An Interview with Dr. Shipeng Yu, Winner of ACM SIGKDD 2021 Service Award

2021 ◽  
Vol 23 (2) ◽  
pp. 1-2
Author(s):  
Shipeng Yu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Professional Network ◽  
Service Award ◽  
Years Of Service ◽  
Learning Data

Shipeng Yu, Ph.D. is the recipient of the 2021 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Yu was honored for his years of service and many accomplishments as general chair of KDD 2017 and currently as sponsorship director for SIGKDD. Dr. Yu is Director of AI Engineering, Head of the Growth AI team at LinkedIn, the world's largest professional network. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 2006, the benefits and drawbacks of virtual conferences, his work at LinkedIn, and KDD's place in the field of machine learning, data science and artificial intelligence.

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