This document introduces the concept of automata. It discusses that automata theory studies abstract computing devices and was originally proposed to model brain function but proved useful for other purposes like designing digital circuits. Automata can model systems that have a finite number of states. There are two important notations used in automata theory - grammars and regular expressions. Automata are essential for studying the limits of computation in terms of what problems are decidable and what problems are intractable. The central concepts of automata theory include alphabets, strings, concatenation of strings, reverse of strings, and Kleene closure.
An Entity–relationship model (ER model) describes the structure of a database with the help of a diagram, which is known as Entity Relationship Diagram (ER Diagram). An ER model is a design or blueprint of a database that can later be implemented as a database. The main components of E-R model are: entity set and relationship set
An Entity–relationship model (ER model) describes the structure of a database with the help of a diagram, which is known as Entity Relationship Diagram (ER Diagram). An ER model is a design or blueprint of a database that can later be implemented as a database. The main components of E-R model are: entity set and relationship set
A REVIEW OF APPLICATIONS OF THEORY OF COMPUTATION AND AUTOMATA TO MUSICDr. Michael Agbaje
Theory of Computation and Automata is a theoretical branch of computer science. It established its roots during 20th Century when mathematicians began developing theoretically and literally machines which mimic certain features of man, completing calculations more quickly and reliably. The word automaton is closely related to the word "automation", meaning automatic processes carrying out the production of specific processes. Automata theory deals with the logic of computation with respect to simple machines, referred to as automata. Through automata, computer scientists are able to understand how machines compute functions and solve problems and more importantly, what it means for a function to be defined as computable or for a question to be described as decidable (Stanford(2004),Cristopher(2013))
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Uncertainty classification of expert systems a rough set approachEr. rahul abhishek
In this paper, we discussed about the un certainity classifications of the Expert Systems using a Rough Set Approach. It is a Softcomputing technique using this we classified the types of Expert Systems. An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human. In the 80's a third part appeared: a dialog interface to communicate with users. This ability to conduct a conversation with users was later called "conversational". Rough set theory is a technique deals with uncertainty.
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques ijsc
Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.
Ontology engineering of automatic text processing methodsIJECEIAES
Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
A New Method Based on MDA to Enhance the Face Recognition PerformanceCSCJournals
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONA...ijiert bestjournal
In Natural Scene Image,Text detection is important tasks which are used for many content based image analysis. A maximally stable external region based method is us ed for scene detection .This MSER based method incl udes stages character candidate extraction,text candida te construction,text candidate elimination & text candidate classification. Main limitations of this method are how to detect highly blurred text in low resolutio n natural scene images. The current technology not focuses on any t ext extraction method. In proposed system a Conditi onal Random field (CRF) model is used to assign candidat e component as one of the two classes (text& Non Te xt) by Considering both unary component properties and bin ary contextual component relationship. For this pur pose we are using connected component analysis method. The proposed system also performs a text extraction usi ng OCR
In order to achieve the wide range of the robotic application it is necessary to provide iterative motions
among points of the goals. For instance, in the industry mobile robots can replace any components between
a storehouse and an assembly department. Ammunition replacement is widely used in military services.
Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if
it is necessary to observe control points in the secret place. The paper deals with path planning programme
for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the
design of modelling programme. The programme is needed as environment modelling to obtain the
simulation data. The simulation data give the possibility to conduct the wide analyses for selected
algorithm. Analysis means the simulation data interpretation and comparison with other data obtained
using the motion-planning. The results of the careful analysis were considered for optimal path planning
algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for
steady covered space. The results described in this work can be extended in a number of directions, and
applied to other algorithms.
ONLINE BANGLA HANDWRITTEN COMPOUND WORD RECOGNITION BASED ON SEGMENTATIONcscpconf
In this paper I propose a scheme for “Online Bangla Handwritten Compound Word Recognition” based on segmentation of word into its constituent characters with more accuracy. The goal of this Paper is to develop a system for segmentation of Bengali Compound Word into its constituent characters or basic strokes and then to recognize each character individually based on stroke generation, thus the recognizer can recognize the entire word. I
achieved the correct segmentation rate of 87% and the overall recognition rate of 73% on a dataset of 4200 Bangla Compound Words.
Discovering Novel Information with sentence Level clustering From Multi-docu...irjes
Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).
A REVIEW OF APPLICATIONS OF THEORY OF COMPUTATION AND AUTOMATA TO MUSICDr. Michael Agbaje
Theory of Computation and Automata is a theoretical branch of computer science. It established its roots during 20th Century when mathematicians began developing theoretically and literally machines which mimic certain features of man, completing calculations more quickly and reliably. The word automaton is closely related to the word "automation", meaning automatic processes carrying out the production of specific processes. Automata theory deals with the logic of computation with respect to simple machines, referred to as automata. Through automata, computer scientists are able to understand how machines compute functions and solve problems and more importantly, what it means for a function to be defined as computable or for a question to be described as decidable (Stanford(2004),Cristopher(2013))
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Uncertainty classification of expert systems a rough set approachEr. rahul abhishek
In this paper, we discussed about the un certainity classifications of the Expert Systems using a Rough Set Approach. It is a Softcomputing technique using this we classified the types of Expert Systems. An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human. In the 80's a third part appeared: a dialog interface to communicate with users. This ability to conduct a conversation with users was later called "conversational". Rough set theory is a technique deals with uncertainty.
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques ijsc
Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.
Ontology engineering of automatic text processing methodsIJECEIAES
Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
A New Method Based on MDA to Enhance the Face Recognition PerformanceCSCJournals
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
ROBUST TEXT DETECTION AND EXTRACTION IN NATURAL SCENE IMAGES USING CONDITIONA...ijiert bestjournal
In Natural Scene Image,Text detection is important tasks which are used for many content based image analysis. A maximally stable external region based method is us ed for scene detection .This MSER based method incl udes stages character candidate extraction,text candida te construction,text candidate elimination & text candidate classification. Main limitations of this method are how to detect highly blurred text in low resolutio n natural scene images. The current technology not focuses on any t ext extraction method. In proposed system a Conditi onal Random field (CRF) model is used to assign candidat e component as one of the two classes (text& Non Te xt) by Considering both unary component properties and bin ary contextual component relationship. For this pur pose we are using connected component analysis method. The proposed system also performs a text extraction usi ng OCR
In order to achieve the wide range of the robotic application it is necessary to provide iterative motions
among points of the goals. For instance, in the industry mobile robots can replace any components between
a storehouse and an assembly department. Ammunition replacement is widely used in military services.
Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if
it is necessary to observe control points in the secret place. The paper deals with path planning programme
for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the
design of modelling programme. The programme is needed as environment modelling to obtain the
simulation data. The simulation data give the possibility to conduct the wide analyses for selected
algorithm. Analysis means the simulation data interpretation and comparison with other data obtained
using the motion-planning. The results of the careful analysis were considered for optimal path planning
algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for
steady covered space. The results described in this work can be extended in a number of directions, and
applied to other algorithms.
ONLINE BANGLA HANDWRITTEN COMPOUND WORD RECOGNITION BASED ON SEGMENTATIONcscpconf
In this paper I propose a scheme for “Online Bangla Handwritten Compound Word Recognition” based on segmentation of word into its constituent characters with more accuracy. The goal of this Paper is to develop a system for segmentation of Bengali Compound Word into its constituent characters or basic strokes and then to recognize each character individually based on stroke generation, thus the recognizer can recognize the entire word. I
achieved the correct segmentation rate of 87% and the overall recognition rate of 73% on a dataset of 4200 Bangla Compound Words.
Discovering Novel Information with sentence Level clustering From Multi-docu...irjes
Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).
1. Introduction to Concept of Automata
Abhineet Anand
Assistant Professor
Dept. of Computer Science And Engineering,
College of Engineering Studies.
University of Petroleum and Energy Studies, Dehradun.
January 22, 2013
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 1 / 12
2. Outline
1 Automata:The Methods and The Madness
2 Structural Representations
3 Automata and Complexity
4 The Central Concept of Automata Theory
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 2 / 12
3. Automata:The Methods and The Madness
Automata Theory is the study of abstract computing devices, or
”machine”.
Before there were computers, in 1930’s, A. Turing studded an abstract
machine that had all the capabilities of today’s computers, at least as
far as in what they could compute.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 3 / 12
4. Automata:The Methods and The Madness
Automata, originally proposed to model brain function, turned out to
be extremely useful for a variety of other purposes, like
Software for designing and checking the behavior of digital circuits.
The ”lexical Analyzer” of a typical complier.
Software for scanning large bodies of text.
Software for verifying system of all types that have a finite number of
distinct states, such as communication protocol or protocols for secure
exchange of information.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 4 / 12
5. Develop feeling about Automata
There are system or component that may be viewed as being at all
times in one of a finite number of ”states”.
The purpose of a state is to remember the relevant portion of the
system’s history.
Since there are only a finite number of states, the entire history
generally cannot be remembered, so the system must be designed
carefully, to remember what is important and forget what is not.
The advantage of having only a finite number of states is that we can
implement the system with a fixed set of resources.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 5 / 12
6. Example : A Finite Automata modeling an ON/OFF switch
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 6 / 12
7. Structural Representations
There are two important Notation that plays an important role in the study
of automata and their applications.
Grammers: are useful models when designing software that
processes data with a recursive structure.
The best-known example is a ”parser”, the component of a complier
that deals with the recursively nested features of a typical
programming language, such as expression - arithmetic, conditional,
and so on.
Regular Expression: also denote the structure of data.
The pattern of string they describe are exactly the same as what can
be described by finite automata.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 7 / 12
8. Automata and Complexity
Automata are essential for the study of the limits of computation. There
are two important issues:
What can a computer do at all? - Decidability.
What can a computer do efficiently? - Intractability.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 8 / 12
9. The Central Concept of Automata Theory
Alphabet: An alphabet is a finite, nonempty set of symbols.
Conventionally, Σ is used to denote.
Examples :
Σ = {0, 1}, the binary alphabet.
Σ = {a , b , c , ....., Z }, the set of all lower-case letters.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 9 / 12
10. The Central Concept of Automata Theory
String: A string is a finite sequence of symbols chosen from some
alphabets. Example:
01101 is a string from the binary alphabet Σ = {0, 1}.
The Empty String
is the string with zero occurrences of symbols. This string denoted by
ϸ , is a string that may be chosen from any alphabet.
Length of String
Power of an Alphabet
If Σ is an alphabet, it can express the set of all string of a certain length
from the alphabet.
If Σ = {a , b , c }, then Σ1 = {a , b , c }
Σ2 = {aa , ab , ac , ba , bb , bc , ca , cb , cc } and so on.
Concatenation of String
Let X and Y be strings. Then XY denotes the concatenation of X and Y,
which is the string formed by making a copy of X and following it by a
copy of Y.
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 10 / 12
11. The Central Concept of Automata Theory
Reverse of the String
If Σ is a set of Alphabet then reverse of the alphabet may be denoted
by ΣR .
Kleen Clousure
Given an alphabet, if it is to define a language in which any string of
letter from Σ ia a word, even the null string. Example:
if Σ = {a }
then Σ∗ = {ϸ, a , aa , aaa , aaaa , .....}
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 11 / 12
12. THANK YOU
Abhineet Anand (UPES, Dehradun) Introduction:Concept of Automata January 22, 2013 12 / 12