The document discusses plans to analyze research professional networks by collecting data on researchers, publications, and relationships. It will construct a graph based on activity and analyze metrics like citation counts, h-index, g-index, and longevity to identify influential researchers and relationships. Future work involves implementing algorithms on the graph to find meaningful node values and influential researchers in the network.
This document provides an overview of data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting interesting and useful patterns from large databases. KDD is described as identifying valid and understandable patterns in data. The document outlines the differences between data, information, and knowledge, and discusses how data mining can be used to turn data into knowledge. It also summarizes some common applications of data mining such as in retail, finance, science, and recommender systems. Finally, it briefly discusses the roles of data warehouses and data cleaning in the data mining process.
Research professional activity network analysisSilicon
The document discusses network analysis of research professional activity. It outlines plans to collect data on researchers, publications, and relationships to construct a graph model. The plan is to define problems, propose techniques, simulate models, and analyze performance to understand relationships between professionals and their areas of work. Data will be collected from various publications and libraries to analyze activity and identify highly active researchers and suitable collaborators. Future work involves implementing graph algorithms on the model to find meaningful values for nodes representing individuals.
Introduction to Artificial Neural Network Qingkai Kong
This is the slides I created for the workshop at Berkeley D-Lab - Introduction to Artificial Neural Networks (ANN). It consists the basics of ANN, intuitive examples, and python implementation of the ANN. You can find rest of the materials (notebooks) at https://github.com/qingkaikong/20161202_ANN_basics.
The document discusses data mining and web mining. It defines data mining as extracting knowledge from large amounts of data, and notes that web mining applies data mining techniques to extract knowledge from web data, including web documents, hyperlinks, and usage logs. Web mining consists of three types: content mining, structure mining, and usage mining. Structure mining involves discovering structure information from the web, either at the intra-page or inter-page level by analyzing the links between pages. Web structure mining can help understand the relationships between different parts of a website.
The document discusses PageRank and HITS algorithms for web structure mining. It provides an overview of key concepts like hubs, authorities, and link analysis. It then explains PageRank in detail, including how it is calculated iteratively based on the prestige of inbound links. Finally, it provides an example calculation and discusses how additional inbound links can increase a page's PageRank.
This document provides an introduction to artificial neural networks. It discusses how neural networks can mimic the brain's ability to learn from large amounts of data. The document outlines the basic components of a neural network including neurons, layers, and weights. It also reviews the history of neural networks and some common modern applications. Examples are provided to demonstrate how neural networks can learn basic logic functions through adjusting weights. The concepts of forward and backward propagation are introduced for training neural networks on classification problems. Optimization techniques like gradient descent are discussed for updating weights to minimize error. Exercises are included to help understand implementing neural networks for regression and classification tasks.
This document outlines topics on error backpropagation training algorithms, Kohonen self-organizing maps, and Hopfield neural networks. It then lists several applications of artificial neural networks, including statistical pattern recognition, control of robotics and industrial processes, automatic synthesis of digital systems, adaptive telecommunications, image compression, radar classification, optimization problems, sentence understanding, and applying expertise to conceptual domains.
This document provides an overview of data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting interesting and useful patterns from large databases. KDD is described as identifying valid and understandable patterns in data. The document outlines the differences between data, information, and knowledge, and discusses how data mining can be used to turn data into knowledge. It also summarizes some common applications of data mining such as in retail, finance, science, and recommender systems. Finally, it briefly discusses the roles of data warehouses and data cleaning in the data mining process.
Research professional activity network analysisSilicon
The document discusses network analysis of research professional activity. It outlines plans to collect data on researchers, publications, and relationships to construct a graph model. The plan is to define problems, propose techniques, simulate models, and analyze performance to understand relationships between professionals and their areas of work. Data will be collected from various publications and libraries to analyze activity and identify highly active researchers and suitable collaborators. Future work involves implementing graph algorithms on the model to find meaningful values for nodes representing individuals.
Introduction to Artificial Neural Network Qingkai Kong
This is the slides I created for the workshop at Berkeley D-Lab - Introduction to Artificial Neural Networks (ANN). It consists the basics of ANN, intuitive examples, and python implementation of the ANN. You can find rest of the materials (notebooks) at https://github.com/qingkaikong/20161202_ANN_basics.
The document discusses data mining and web mining. It defines data mining as extracting knowledge from large amounts of data, and notes that web mining applies data mining techniques to extract knowledge from web data, including web documents, hyperlinks, and usage logs. Web mining consists of three types: content mining, structure mining, and usage mining. Structure mining involves discovering structure information from the web, either at the intra-page or inter-page level by analyzing the links between pages. Web structure mining can help understand the relationships between different parts of a website.
The document discusses PageRank and HITS algorithms for web structure mining. It provides an overview of key concepts like hubs, authorities, and link analysis. It then explains PageRank in detail, including how it is calculated iteratively based on the prestige of inbound links. Finally, it provides an example calculation and discusses how additional inbound links can increase a page's PageRank.
This document provides an introduction to artificial neural networks. It discusses how neural networks can mimic the brain's ability to learn from large amounts of data. The document outlines the basic components of a neural network including neurons, layers, and weights. It also reviews the history of neural networks and some common modern applications. Examples are provided to demonstrate how neural networks can learn basic logic functions through adjusting weights. The concepts of forward and backward propagation are introduced for training neural networks on classification problems. Optimization techniques like gradient descent are discussed for updating weights to minimize error. Exercises are included to help understand implementing neural networks for regression and classification tasks.
This document outlines topics on error backpropagation training algorithms, Kohonen self-organizing maps, and Hopfield neural networks. It then lists several applications of artificial neural networks, including statistical pattern recognition, control of robotics and industrial processes, automatic synthesis of digital systems, adaptive telecommunications, image compression, radar classification, optimization problems, sentence understanding, and applying expertise to conceptual domains.
Introduction to Neural networks (under graduate course) Lecture 7 of 9Randa Elanwar
This document provides an overview of neural network learning techniques including supervised, unsupervised, and reinforcement learning. It discusses the Hebbian learning rule, which updates weights based on the activation of connected neurons. Examples are provided to illustrate how the Hebbian rule can be used to train networks without error signals by detecting correlations in input-output patterns.
Artificial Neural Network in a Tic Tac Toe Symfony Console Application - Symf...aferrandini
This document discusses using artificial neural networks (ANNs) with PHP and the Symfony Console component. It covers ANN theory like activation functions, backpropagation, and learning types. It demonstrates how to build a Tic-Tac-Toe game with reinforcement learning and the Symfony Console. The document also provides instructions for installing the PHP FANN extension to interface with ANNs and code examples using this library.
Artificial Neural Network / Hand written character RecognitionDr. Uday Saikia
1. Overview
2.Development of System
3.GCR Model
4.Proposed model
5.Back ground Information
6. Preprocessing
7.Architecture
8.ANN(Artificial Neural Network)
9.How the Human Brain Learns?
10.Synapse
11.The Neuron Model
12.A typical Feed-forward neural network model
13.The neural Network
14.Training of characters using neural networks
15.Regression of trained neural networks
16.Training state of neural networks
17.Graphical user interface….
The document discusses data classification techniques. It begins with definitions of data classification and describes the learning and classification steps. It then discusses applications of data classification such as fraud detection and target marketing. Next, it covers decision tree induction as a classification technique, describing decision trees as flowchart structures and the parameters and algorithms used to construct them. Finally, it provides examples of decision tree construction and discusses future work and references.
The document discusses artificial neural networks. It describes their basic structure and components, including dendrites that receive input signals, a soma that processes the inputs, and an axon that transmits output signals. It also explains how neurons are connected at synapses to transfer signals between neurons. Finally, it mentions different types of activation functions that can be used in neural networks.
Brief and overall introduction to Artificial Neural Network (ANN).
-history of ANN
-learning technique (backpropagation)
-Generations of Neural net from 1st to 3rd
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
This document describes a technique for Sinhala handwritten character recognition using feature extraction and an artificial neural network. The methodology includes preprocessing, segmentation, feature extraction based on character geometry, and classification using an ANN. Features like starters, intersections, and zoning are extracted from segmented characters. The ANN was trained on these feature vectors and tested on 170 characters, achieving an accuracy of 82.1%. While the technique showed some success, the author notes room for improvement, such as making the system more font-independent and improving feature extraction and character separation.
Introduction Of Artificial neural networkNagarajan
The document summarizes different types of artificial neural networks including their structure, learning paradigms, and learning rules. It discusses artificial neural networks (ANN), their advantages, and major learning paradigms - supervised, unsupervised, and reinforcement learning. It also explains different mathematical synaptic modification rules like backpropagation of error, correlative Hebbian, and temporally-asymmetric Hebbian learning rules. Specific learning rules discussed include the delta rule, the pattern associator, and the Hebb rule.
This document discusses neural networks and their biological and technical underpinnings. It covers how natural neural networks operate using electrochemical signals and thresholds. It also discusses early artificial neural network models like McCulloch-Pitts networks and perceptrons. Perceptrons are defined as single-layer feedforward networks and can only represent linearly separable functions. The document introduces the concept of adding hidden layers to networks to increase their computational power and ability to represent more complex functions like XOR.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
This document provides an overview of artificial neural networks (ANN). It discusses the origin of ANNs from biological neural networks. It describes different ANN architectures like multilayer perceptrons and different learning methods like backpropagation. It also outlines some challenging problems that ANNs can help with, such as pattern recognition, clustering, and optimization. The summary states that while the paper gives a good overview of ANNs, more development is needed to show ANNs are better than other methods for most problems.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
Introduction to Neural networks (under graduate course) Lecture 7 of 9Randa Elanwar
This document provides an overview of neural network learning techniques including supervised, unsupervised, and reinforcement learning. It discusses the Hebbian learning rule, which updates weights based on the activation of connected neurons. Examples are provided to illustrate how the Hebbian rule can be used to train networks without error signals by detecting correlations in input-output patterns.
Artificial Neural Network in a Tic Tac Toe Symfony Console Application - Symf...aferrandini
This document discusses using artificial neural networks (ANNs) with PHP and the Symfony Console component. It covers ANN theory like activation functions, backpropagation, and learning types. It demonstrates how to build a Tic-Tac-Toe game with reinforcement learning and the Symfony Console. The document also provides instructions for installing the PHP FANN extension to interface with ANNs and code examples using this library.
Artificial Neural Network / Hand written character RecognitionDr. Uday Saikia
1. Overview
2.Development of System
3.GCR Model
4.Proposed model
5.Back ground Information
6. Preprocessing
7.Architecture
8.ANN(Artificial Neural Network)
9.How the Human Brain Learns?
10.Synapse
11.The Neuron Model
12.A typical Feed-forward neural network model
13.The neural Network
14.Training of characters using neural networks
15.Regression of trained neural networks
16.Training state of neural networks
17.Graphical user interface….
The document discusses data classification techniques. It begins with definitions of data classification and describes the learning and classification steps. It then discusses applications of data classification such as fraud detection and target marketing. Next, it covers decision tree induction as a classification technique, describing decision trees as flowchart structures and the parameters and algorithms used to construct them. Finally, it provides examples of decision tree construction and discusses future work and references.
The document discusses artificial neural networks. It describes their basic structure and components, including dendrites that receive input signals, a soma that processes the inputs, and an axon that transmits output signals. It also explains how neurons are connected at synapses to transfer signals between neurons. Finally, it mentions different types of activation functions that can be used in neural networks.
Brief and overall introduction to Artificial Neural Network (ANN).
-history of ANN
-learning technique (backpropagation)
-Generations of Neural net from 1st to 3rd
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
This document describes a technique for Sinhala handwritten character recognition using feature extraction and an artificial neural network. The methodology includes preprocessing, segmentation, feature extraction based on character geometry, and classification using an ANN. Features like starters, intersections, and zoning are extracted from segmented characters. The ANN was trained on these feature vectors and tested on 170 characters, achieving an accuracy of 82.1%. While the technique showed some success, the author notes room for improvement, such as making the system more font-independent and improving feature extraction and character separation.
Introduction Of Artificial neural networkNagarajan
The document summarizes different types of artificial neural networks including their structure, learning paradigms, and learning rules. It discusses artificial neural networks (ANN), their advantages, and major learning paradigms - supervised, unsupervised, and reinforcement learning. It also explains different mathematical synaptic modification rules like backpropagation of error, correlative Hebbian, and temporally-asymmetric Hebbian learning rules. Specific learning rules discussed include the delta rule, the pattern associator, and the Hebb rule.
This document discusses neural networks and their biological and technical underpinnings. It covers how natural neural networks operate using electrochemical signals and thresholds. It also discusses early artificial neural network models like McCulloch-Pitts networks and perceptrons. Perceptrons are defined as single-layer feedforward networks and can only represent linearly separable functions. The document introduces the concept of adding hidden layers to networks to increase their computational power and ability to represent more complex functions like XOR.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
This document provides an overview of artificial neural networks (ANN). It discusses the origin of ANNs from biological neural networks. It describes different ANN architectures like multilayer perceptrons and different learning methods like backpropagation. It also outlines some challenging problems that ANNs can help with, such as pattern recognition, clustering, and optimization. The summary states that while the paper gives a good overview of ANNs, more development is needed to show ANNs are better than other methods for most problems.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Hand Written Character Recognition Using Neural Networks Chiranjeevi Adi
This document discusses a project to develop a handwritten character recognition system using a neural network. It will take handwritten English characters as input and recognize the patterns using a trained neural network. The system aims to recognize individual characters as well as classify them into groups. It will first preprocess, segment, extract features from, and then classify the input characters using the neural network. The document reviews several existing approaches to handwritten character recognition and the use of gradient and edge-based feature extraction with neural networks. It defines the objectives and methods for the proposed system, which will involve preprocessing, segmentation, feature extraction, and classification/recognition steps. Finally, it outlines the hardware and software requirements to implement the system as a MATLAB application.
2. Plan of My work
Collect Information about researchers
Researcher Affiliation
Journal Details
Conference Details
Books Details (Authors and Editors)
Constructing a graph according to their activity.
Defining Problem .
Proposing/ Designing a new technique .
Simulation and Performance Analysis of proposed technique.
2 Anand Bihari
4. Introduction
Many research professional are involved in activities like Publish a Journal
paper, Conference paper, Books, Articles etc.
Two or more than two persons works together .
Our objective
Find the relationship between professionals.
How many professional are working in the same area.
Which person are more active in a particular area.
Which person are suitable for guidance in particular area.
4 Anand Bihari
5. Our Work
So here we analyze research professional activity as mining.
For analyzing research professional activity, we required three types of
Information like
Research Professional Data(Personal information)
Activity
Relationship between Research professional and their activity
We extract data from IEEE, Springer, Science Direct, ACM digital library etc.
5 Anand Bihari
6. Citation
A citation can represent many types of links, such as links between authors,
publication, journals and conferences.
When researchers refers to another author’s works in their own publication work,
they cite it.
A citation index is a compilation of all the cited references form articles
published during a particular year or period.
A citation index allows to determine the researcher impact of publication
according to the number of times it has been cited by other researcher.
Self citation are not included in such citation count.
6 Anand Bihari
8. Citation example(II)
Author Paper Paper cited by Paper citation value Author citation value
A N,Z 2
B M,W 2
P1 6
C 0
D P,Z 2
A N,Z 2
M D 1
P2 N 0 5
O D,W 2
P 0
W M,N,A,C,P 5
X N 1
P3 6
Y 0
8 Z 0 Anand Bihari
9. h-index
Invented by Jorge Hirsh at university of California in 2005.
A popular method to measure the centrality of academic papers.
h-index =number of your papers h that have been cited at least h times.
The use of h-index aims at identifying researchers with more papers and
relevant impact over a period of time.
For any general “set of papers” one can arrange these papers in decreasing
order of the number of citations they received.
The h-index is then the largest rank h = r such that the paper on this rank (and
hence also all papers on rank 1,…,h) has h or more citations.
Hence the papers on ranks h + 1, h + 2, … have not more than h citations.
9 Anand Bihari
10. h-index example
Author Paper Paper Citation Paper citation value Rank h-index of author
Value in desc. Order
A 2 2 1
B 2 2 2
P1 2
C 0 2 3
D 2 0 4
A 2 2 1
M 1 2 2
P2 N 0 1 3 2
O 2 0 4
P 0 0 5
W 5 5 1
X 1 1 2
P3 1
Y 0 0 3
10 Z 0 0 4 Anand Bihari
11. g-index
The g-index is introduced as an improvement of the h-index of Hirsh to
measure the global citation performance of a set of articles.
This set is ranked in decreasing order of the number of citations that they
received, the g-index is the (unique) largest number such that the top g articles
received (together) at least g2 citations.
The g-index of an author is greater or equal to h-index(g>=h).
11 Anand Bihari
12. g-index example(I)
Author Paper TC r ∑TC r2 h-index g-index
A 2 1 2 1
B 2 2 4 4
P1 2 2
C 2 3 6 9
D 0 4 6 16
A 2 1 2 1
M 2 2 4 4
P2 N 1 3 5 9 2 2
O 0 4 5 16
P 0 5 5 25
W 5 1 5 1
X 1 2 6 4
P3 1 2
Y 0 3 6 9
12 Z 0 4 6 16 Anand Bihari
13. g-index example(II)
TC stands for the total number of citations for each paper on rank r = 1,2,... .
∑TC stands for the cumulative number of citations to the papers on rank 1,...,r
(for each r).
The h-index of author P1 is h = 2 and the g-index g = 2 Indeed h = 2 is the
highest rank such that all papers on rank 1,...,h have at least 2 citations (and
hence the papers on rank 3 or higher have not more than 2 citations). Also g =
2 is the highest rank such that the top 2 papers have at least 22 = 4 citations
(here 4>= 4); on rank 3 we have 6 < 32 = 9 citations.
13 Anand Bihari
14. Longevity
Longevity reflects the length of one author’s academic life. We consider the
year when one author publish his/her first paper as the beginning year of his/her
academic life and the last paper as the end year. Then longevity can be defined
as
longevity(A)=YA(A’s last paper) – YA(A’s first paper)
Example:
let author A published first paper in 1997 and the last paper in 2011.
Therefore, longevity of Author A = 2011 – 1997 = 14
14 Anand Bihari
15. Future Work
After designing a graph , we will implement mathematical equation or graph
algorithm to find the meaningful value of a node.
Research person are taken as vertices.
Edge between them having some weight like activity between them.
Find more influence(Active) person in such area.
Find more influence person in the network.
For this we will collect data from a website known as www.arnetminer.org .
Learn Diversity, Sociability, Activity of researcher in social network.
15 Anand Bihari
16. Future Literature Survey
Titles Name of Journal/Conferences Publication
Year
Link creation and profile IEEE International Conference on 2010
alignment in the aNobii Social Computing / IEEE
social network International Conference on
Privacy, Security, Risk and Trust
Design and implementation of a International Conference on 2011
web structure Mining algorithm internet technology and secured
using breadth first search transactions
Strategy for academic search
application
16 Anand Bihari
17. References
Bing Liu “Web Data Mining ” Springer International Edition.
Articles “Evaluating Scientists: Citations, Impact Factor, h-Index, Online
Page Hits and What Else?” written by M Jagadesh Kumar Editor-in-Chief,
IETE Technical Review, Department of Electrical Engineering, IIT, Hauz Khas,
New Delhi-110 016, India
Springers articles “Theory and practise of the g-index” published by-LEO
EGGHE, Universiteit Hasselt (UHasselt), Campus Diepenbeek (Belgium) , Vol.
69, No. 1 (2006) 131–152.
Website : www.arnetminer.org, Wikipedia.
17 Anand Bihari