Friday, March 13, 2020

Top Interesting Books on Internet of Things (IoT)

Top 5 Data Science Trends for 2020
https://www.datasciencecentral.com/profiles/blogs/top-5-data-science-trends-for-2020

Trend 2. Rapid growth in the IoT
According to a report by IDC, it is expected that the investment in IoT technology would reach $1 trillion by the end of 2020, which is an exceptional growth of connected devices. Many of them are smart devices. We are already using many apps and devices that are functioning based on  IoT. Google Assistant or Microsoft Cortana allow us to automate the regular things based on IoT only., Businesses are investing in this technology, especially in smartphone development that uses IoT.

Internet of Things: Architectures, Protocols and Standards

Simone Cirani, Gianluigi Ferrari, Marco Picone, Luca Veltri
John Wiley & Sons, 30-Aug-2018 - Technology & Engineering - 408 pages
This book addresses researchers and graduate students at the forefront of study/research on the Internet of Things (IoT) by presenting state-of-the-art research together with the current and future challenges in building new smart applications (e.g., Smart Cities, Smart Buildings, and Industrial IoT) in an efficient, scalable, and sustainable way. It covers the main pillars of the IoT world (Connectivity, Interoperability, Discoverability, and Security/Privacy), providing a comprehensive look at the current technologies, procedures, and architectures.
https://books.google.co.in/books?id=iERsDwAAQBAJ

What is Data Science? - An Introduction to Data Science - New Developments


What is Data Science? - An Introduction to Data Science


Data driven or data analysis driven decision making is age old. But new data processing technology allows people to process data in ways that was not done before. Hence data will drive business decisions much more intensively in the next decade.


IT departments are not content anymore with just providing technology for processing data. The discipline and the profession of  IT is getting  involved in finding and understanding the relevance of new data sources, big and small.

The practice of business intelligence is  expanding to create to develop capabilities for analyzing and visualizing structured and unstructured data for their relevance for business decision making, and then building applications that can be run on a periodic basis which can be as small as even seconds to take crime or fraud prevention activities.

Data science is the name of this emerging discipline.

Data Science Tutorial 1 - Video

__________________________

__________________________
edureka!

More videos are available on YouTube on Data Science




Concise Visual Summary of Deep Learning Architectures
Basically neural network architectures
http://www.datasciencecentral.com/profiles/blogs/concise-visual-summary-of-deep-learning-architectures


http://www.datasciencecentral.com has number of articles on data science.


Data Science - New Developments

50 Years of Data Science
David Donoho
Journal of Computational and Graphical Statistics
Volume 26, 2017 - Issue 4
Pages 745-766  Published online: 19 Dec 2017
https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734

2020
The 2020 Data Science Dictionary—Key Terms You Need to Know
https://www.datasciencecentral.com/profiles/blogs/top-data-science-skills-for-2020-1

Trends in Artificial Intelligence and Data Science for 2020
https://www.datasciencecentral.com/profiles/blogs/trends-in-artificial-intelligence-and-data-science-for-2020-by

Top 5 Data Science Trends for 2020
https://www.datasciencecentral.com/profiles/blogs/top-5-data-science-trends-for-2020

Updated in 2020:  on  14 March 2020

7 June 2017, 2 September 2014


Tuesday, November 20, 2018

Fundamentals of the Artificial Intelligence - Notes - Toshinori Munakata


Book by Munakata is available with me.

Important Topics

What is artificial intelligence?

The Industrial Revolution, which started in England around 1760, has replaced human muscle power with the machine. Artificial intelligence (AI) aims at replacing human intelligence with the machine. The work on artificial intelligence started in the early 1950s, and the term  was coined in 1956.


AI can be more broadly defined as "the study of making computers do things that the human needs intelligence to do." This extended definition not only includes the first, mimicking human thought processes, but also covers the technologies that make the computer achieve intelligent tasks even if they do not necessarily simulate human thought processes.

But what is intelligent computation (AI) and what is not AI? 

Purely numeric computations, such as adding and multiplying numbers with incredible speed, are not AI. The category of pure numeric computations includes engineering problems such as solving a system of linear equations, numeric differentiation and integration, statistical analysis, and so on. Similarly, pure data recording and information retrieval are not AI. So processing of most business data and file processing, simple word processing and database handling are not AI.  

Two types of AI: A computer performing symbolic integration of (sin^2x)(e^-x)  is intelligent. 
Classes of problems requiring intelligence include inference based on knowledge,  reasoning with uncertain or incomplete information, various forms of perception and learning, and applications to problems such as control, prediction, classification, and optimization. 

A second type of intelligent computation is based on the mechanisms for biological processes used to arrive at a solution. The primary examples of this type or category are neural networks and genetic algorithms. These techniques are being used to compute many complex things using computers even though  the techniques do not appear intelligent, 

Although much practical AI is still best characterized as advanced computing rather than "intelligence," applications in everyday commercial and industrial settings have grown, especially since 1990.

As mentioned above, there are two fundamentally different major approaches in the field of AI. One is traditional symbolic AI. It is characterized by a high level of abstraction and a macroscopic view. Knowledge engineering systems and logic programming fall in this category. Symbolic AI covers areas such as knowledge based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. 

The second approach is based on low level, microscopic biological models and other computation procedures.  Neural networks and genetic algorithms are the prime examples of this latter approach.  These new evolving areas have shown application potential  from which many people expect significant practical applications in the future. There are relatively new AI techniques which include fuzzy systems, rough set theory, and chaotic systems or chaos for short. 

Neural networks:   A artificial neural network has neurons as the basic unit.  Neurons are interconnected by edges, forming a neural network. Similar to the brain, the network receives input, internal processes take place such as activations of the neurons, and the network yields output. 

Genetic algorithms: Computational models based on genetics and evolution theory and processes. The three basic ingredients are selection of solutions based on their fitness, reproduction of genes, and occasional mutation. The computer finds better and better solutions to problems mimicking the   
species evolution process.  

Fuzzy systems: It coverts discrete objects techniques like sets into continuous objects. In ordinary logic, proposition is either true or false, with nothing between, but fuzzy logic allows truthfulness in various degrees and truth a continuous variable. 

Rough Sets:  "Rough" sets means approximation sets. Given a set of elements and attribute values associated with these elements, some of which can be imprecise or incomplete, the theory is suitable 
to reasoning and discovering relationships in the data. 

Chaos: Nonlinear deterministic dynamical systems that exhibit sustained irregularity and extreme sensitivity to initial conditions. 


Further Reading 

For practical applications of AI, both in traditional and newer areas, the following 
five special issues provide a comprehensive survey. 

T. Munakata (Guest Editor), Special Issue on "Commercial and Industrial AI," 
Communications of the ACM, Vol. 37, No. 3, March, 1994. 

T. Munakata (Guest Editor), Special Issue on "New Horizons in Commercial and 
Industrial AI," Communications of the ACM, Vol. 38, No. 11, Nov., 1995. 

U. M. Fayyad, et al. (Eds.), Data Mining and Knowledge Discovery in Databases, 
Communications of the ACM, Vol. 39, No. 11, Nov., 1996. 

T. Munakata (Guest Editor), Special Section on "Knowledge Discovery," 
Communications of the ACM, Vol. 42, No. 11, Nov., 1999. 

U. M. Fayyad, et al. (Eds.), Evolving Data Mining into Solutions for Insights, , 
Communications of the ACM, Vol. 45, No. 8, Aug., 2002. 

Four books on traditional AI (Symbolic AI) 


G. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem 
Solving, 5th Ed., Addison-Wesley; 2005. 

S. Russell and P. Norvig, Artificial Intelligence: Modern Approach, 2nd Ed., 
Prentice-Hall, 2003. 

E. Rich and K. Knight, Artificial Intelligence, 2nd Ed., McGraw-Hill, 1991. 

P.H. Winston, Artificial Intelligence, 3rd Ed., Addison-Wesley, 1992. 



Artificial Neural Networks - Introduction



Author: Marek Libra

Posted under creative commons from Knol

The Artificial Neural Network (NN later) is a topic in artificial intelligence methods and techniques.  It was successfully applied in a wide range of problem domains like finance, engineering, medicine, geology, physics or control.

Neural networks are useful especially for solving problems of prediction, classification or control. They are also a good alternative to classical statistical approaches like regression analysis.

The artificial neural networks techniques were developed based on the model of biological neural networks. The biological neural networks are the basis of functioning of the nervous system of biological organisms. This inspiration is commonly known fact and it is mentioned in most of neural networks publications.

 The NN is built from a large number of simple processing units called artificial neurons (called
just neurons later).

The interface of an artificial neuron stays from n numeric inputs and one numeric output. Some models of neurons consider one next special input called bias. Each  input is evaluated by its numeric weight. The neuron can perform two operations: compute and adapt.


The compute operation transforms inputs to output. The compute operation takes numerical
inputs and computes their weighted sum. It performs a so called activation function to this sum
(a mathematical transformation) afterwards. The result of the activation function is set as a value
to the output interface.

The adapt operation, based on a pair of inputs and awaited outputs specified by the user,
tunes the weights of an NN for a better approximation of the computed output compared to
the awaited output for considered input.

The neurons in an NN are ordered and numerically signed (from N) according to the order.
    A lot of models of NNs are known. These models differs to each other by different usage of

    • the domain of numeric input, output and weights (real, integer or finite set like {0,1}),

    • the presence of bias (yes or no),

    • the definition of an activation function (sigmoid, hyperbolic tangents, discrete threshold,
      etc),

    • the topology of interconnected neurons (feed-forward or recurrent),

    • the ability to change the number of neurons or the network topology during the lifetime of
      the network,

    • the algorithm of the computation flow through the network over neurons,

    • the simulation time (discrete or continuous) or

    • the adaptation algorithm (none, back propagation, perceptron rule, genetic, simulated annealing etc.).

 A good taxonomy of NN models can be found i.e. in [1]

More detailed general descriptions, which are formal and well readable, can be found in [2] .

References

  • [1] Šíma and P. Orponen. General purpose computation with neural
  • [2] David M Skapura. Building Neural Networks. Addison-Wesley, 1995

Source Knol: /knol.google.com/   marek-libra/artificial-neural-networks/5rqq7q8930m0/12#
Knol Nrao - 5193


Further Reading

Simple mathematical steps in Neural Network problem solving



Mode detailed reading

Artificial Neural Networks: Mathematics of Backpropagation (Part 4)
October 28, 2014 in ml primers, neural networks
http://briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagation-part-4



Further Reading

Knols

  • Feed-Forward Neural Networks
  • Adaptation of Feed-Forward Neural Networks
  • The Perceptron Rule
  • The Back Propagation
  • My Knol Directory


Updated 21 November 2018,  8 June 2017, 8 May 2017, 28 April 2012.

Artificial Intelligence - Books List and Information



2013

Artificial Intelligence: The Basics

Kevin Warwick, Professor of Cybernetics Kevin Warwick
Routledge, 01-Mar-2013 - COMPUTERS
https://books.google.co.in/books?id=b16pAgAAQBAJ


2012

Artificial Intelligence: A Beginner's Guide

Blay Whitby
Oneworld Publications, 01-Dec-2012 - Computers - 192 pages
https://books.google.co.in/books?id=TKOfhnUhgS4C


2010

Artificial Intelligence: Foundations of Computational Agents

David L. Poole, Alan K. Mackworth
Cambridge University Press, 19-Apr-2010
https://books.google.co.in/books?id=B7khAwAAQBAJ


2008

Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More

Toshinori Munakata
Springer Science; Business Media, Jan 1, 2008 - 272 pages


This significantly updated 2nd edition thoroughly covers the most essential & widely employed material pertaining to neural networks, genetic algorithms, fuzzy systems, rough sets, & chaos. The exposition reveals the core principles, concepts, & technologies in a concise & accessible, easy-to-understand manner, & as a result, prerequisites are minimal. Topics & features: Retains the well-received features of the first edition, yet clarifies & expands on the topic Features completely new material on simulated annealing, Boltzmann machines, & extended fuzzy if-then rules tables

https://books.google.co.in/books?id=lei-Zt8UGSQC

Updated 21 November 2018,  26 June 2016, 27 June 2015

Internet of Things (IOT) and Industrial Internet of Things (IIoT) - Research Papers, Books and Articles - Bibliography


Top 5 Data Science Trends for 2020

https://www.datasciencecentral.com/profiles/blogs/top-5-data-science-trends-for-2020

Trend 2. Rapid growth in the IoT

According to a report by IDC, it is expected that the investment in IoT technology would reach $1 trillion by the end of 2020, which is an exceptional growth of connected devices. Many of them are smart devices. We are already using many apps and devices that are functioning based on  IoT. Google Assistant or Microsoft Cortana allow us to automate the regular things based on IoT only., Businesses are investing in this technology, especially in smartphone development that uses IoT.




Internet of Things: A Simple definition by Vermesan (2013): 

Internet of things is a network of physical objects
(Devayani Kulkarni's MS Thesis Internet of Things in Finnish Metal Industry, March 2018)


100+ Books on Internet of Things - IoT Books



IBM IoT - Products and Systems


Updated 2020 on 14 March 2020
20 November 2018

Monday, November 19, 2018

Big Data - Introduction




Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, a new platform of "big data" tools has arisen to handle sensemaking over large quantities of data, as in the Apache Hadoop Big Data Platform.


In 2012, Gartner updated its definition as follows: "Big data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization."

A 2016 definition states that "Big data represents the information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value".

A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd’s relational model."

(Source:  http://en.wikipedia.org/wiki/Big_data  )

Big Data Repositories


Big data repositories have existed in many forms for year built by corporations for their use with a special need. Commercial vendors historically offered parallel database management systems for big data beginning in the 1990s.

Teradata Corporation in 1984 marketed the parallel processing DBC 1012 system. Teradata systems were the first to store and analyze 1 terabyte of data in 1992. Hard disk drives were 2.5 GB in 1991 so the definition of big data continuously evolves according to Kryder's Law. Teradata installed the first petabyte class RDBMS based system in 2007. As of 2017, there are a few dozen petabyte class Teradata relational databases installed, the largest of which exceeds 50 PB. Systems up until 2008 were 100% structured relational data. Since then, Teradata has added unstructured data types including XML, JSON, and Avro.

In 2000, Seisint Inc. (now LexisNexis Group) developed a C++-based distributed file-sharing framework for data storage and query. The system stores and distributes structured, semi-structured, and unstructured data across multiple servers. Users can build queries in a C++ dialect called ECL.  In 2004, LexisNexis acquired Seisint Inc. and in 2008 acquired ChoicePoint, Inc.and their high-speed parallel processing platform. The two platforms were merged into HPCC (or High-Performance Computing Cluster) Systems and in 2011, HPCC was open-sourced under the Apache v2.0 License. Quantcast File System was available about the same time.

CERN and other physics experiments have collected big data sets and they analyzed via high performance computing (supercomputers). But big data movement presently uses  the commodity map-reduce architectures.

In 2004, Google published a paper on a process called MapReduce. The MapReduce concept provides a parallel processing model  to process huge amounts of data. With MapReduce, queries are split and distributed across parallel nodes and processed in parallel (the Map step). The results are then gathered and delivered as the output (the Reduce step). An implementation of the MapReduce framework was adopted by an Apache open-source project named Hadoop. Apache Spark was developed in 2012 in response to limitations in the MapReduce paradigm, as it adds the ability to set up many operations (not just map followed by reduce).

MIKE2.0 is an open approach to information management that acknowledges the need for revisions due to big data implications identified in an article titled "Big Data Solution Offering". The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records.

https://en.wikipedia.org/wiki/Big_data


Big Data - Dimensions


Big data - Four dimensions: Volume, Velocity, Variety, and Veracity (IBM document)
Examples of big data in enterprises

Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information.

12 terabytes of Tweets created each day has to analysed to get improved product sentiment analysis
Convert 350 billion annual meter readings to better predict power consumption

Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.

Examples:
Scrutinize 5 million trade events created each day to identify potential fraud
Analyze 500 million daily call detail records in real-time to predict customer churn faster

Variety: Big data is any type of data - structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together.

Monitor 100’s of live video feeds from surveillance cameras to target points of interest
Exploit the 80% data growth in images, video and documents to improve customer satisfaction


Veracity:  Establishing trust in big data presents a huge challenge as the variety and number of sources grows.



McKinsey Article on Big Data
http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation

28.2.2013





11 Feb 2016

Evolution of Big

http://www.ibmbigdatahub.com/infographic/evolution-big-data


https://hbr.org/2013/12/analytics-30

Analytics 1.0—the era of “business intelligence.”

Analytics 1.0 started gaining an objective, deep understanding of important business phenomena and giving managers the fact-based comprehension to go beyond intuition when making decisions. For the first time, data about production processes, sales, customer interactions, and more were recorded, aggregated, and analyzed.


Updated  20 November 2018,  11 Feb 2016, 28 Feb 2013