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FUZZY CONTROL OF A SERVOMECHANISM: PRACTICAL APPROACH USING MAMDANI AND TAKAG...ijfls
The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference
method and another controller based on the Takagi- Sugeno inference method, both will be designed for
application in a position control system of a servomechanism. Some comparations between the methods
mentioned above will be made with regard to the performance of the system in order to identify the
advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of
disturbances and nonlinearities of the system. Some results of simulation and practical application are
presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient
than controllers based on Mamdani method for this specific application.
Model extraction attacks on the bert based NLP models leads to potential risk of data being stolen. This presentation provides explanation on how models being extracted by the adversaries and naive defense strategies to prevent the model from being stolen.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
FUZZY CONTROL OF A SERVOMECHANISM: PRACTICAL APPROACH USING MAMDANI AND TAKAG...ijfls
The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference
method and another controller based on the Takagi- Sugeno inference method, both will be designed for
application in a position control system of a servomechanism. Some comparations between the methods
mentioned above will be made with regard to the performance of the system in order to identify the
advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of
disturbances and nonlinearities of the system. Some results of simulation and practical application are
presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient
than controllers based on Mamdani method for this specific application.
Model extraction attacks on the bert based NLP models leads to potential risk of data being stolen. This presentation provides explanation on how models being extracted by the adversaries and naive defense strategies to prevent the model from being stolen.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Optimal feature selection from v mware esxi 5.1 feature setijccmsjournal
A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which
suggest usage of different resources of the server. Feature selection algorithms have been used to extract
the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command.
Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index
respectively. The best cluster is further identified by the determined indices. The features of the best cluster
are considered into a set of optimal parameters.
Understanding how high powered ML models arrive at their predictions is an important aspect of Machine Learning, and SHAP is a powerful tool that enables practitioners to understand how different features combine to help a model arrive at a prediction.
This slidedeck is from a presentation given at pydata global on the theoretical foundations of SHAP as well as how to use its library. Link to the presentation can be found here: https://pydata.org/global2021/schedule/presentation/3/behind-the-black-box-how-to-understand-any-ml-model-using-shap/
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Optimization is considered to be one of the pillars of statistical learning and also plays a major role in the design and development of intelligent systems such as search engines, recommender systems, and speech and image recognition software. Machine Learning is the study that gives the computers the ability to learn and also the ability to think without being explicitly programmed. A computer is said to learn from an experience with respect to a specified task and its performance related to that task. The machine learning algorithms are applied to the problems to reduce efforts. Machine learning algorithms are used for manipulating the data and predict the output for the new data with high precision and low uncertainty. The optimization algorithms are used to make rational decisions in an environment of uncertainty and imprecision. In this paper a methodology is presented to use the efficient optimization algorithm as an alternative for the gradient descent machine learning algorithm as an optimization algorithm.
Fuzzy Logic approach in Gene Regulatory Network. These slides are made to present in my MSc. Bioinformatics Course II Semester, Jamia Millia Islamia, New Delhi.
It is mainly based on review paper of my teacher Dr. Khalid Raza.
Raza, Khalid. (2018). Fuzzy logic-based approaches for gene regulatory network inference. https://doi.org/10.1016/j.artmed.2018.12.004
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subse...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Introduction to Process Mining and its applicability to enterprise technology customer support. Please visit my blog at http://www.haimtoeg.com/?p=1881 for further discussion.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining: Understanding and Improving Desire Lines in Big DataWil van der Aalst
We are pleased to announce the lecture: “Process Mining: Understanding and Improving Desire Lines in Big Data”
in honour of doctor honoris causa Wil van der Aalst.
Wednesday May 30th - 10.00 a.m. - 12 a.m.,
Hasselt University, campus Diepenbeek (Agoralaan, building D) - auditorium H5
The Faculty of Business Economics of Hasselt University is pleased to invite you to the lecture
“Process Mining: Understanding and Improving Desire Lines in Big Data”.
This lecture is organised to honour prof. dr. Wil van der Aalst, on whom the degree of ‘doctor honoris causa’ will be conferred by Hasselt University, Faculty of Business Economics (promotor prof. Koen Vanhoof). Professor van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). Currently he is also an adjunct professor at Queensland University of Technology (QUT).His research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Many of his ideas have influenced researchers, software developers and standardization committees working on process support.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Optimal feature selection from v mware esxi 5.1 feature setijccmsjournal
A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which
suggest usage of different resources of the server. Feature selection algorithms have been used to extract
the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command.
Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index
respectively. The best cluster is further identified by the determined indices. The features of the best cluster
are considered into a set of optimal parameters.
Understanding how high powered ML models arrive at their predictions is an important aspect of Machine Learning, and SHAP is a powerful tool that enables practitioners to understand how different features combine to help a model arrive at a prediction.
This slidedeck is from a presentation given at pydata global on the theoretical foundations of SHAP as well as how to use its library. Link to the presentation can be found here: https://pydata.org/global2021/schedule/presentation/3/behind-the-black-box-how-to-understand-any-ml-model-using-shap/
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Optimization is considered to be one of the pillars of statistical learning and also plays a major role in the design and development of intelligent systems such as search engines, recommender systems, and speech and image recognition software. Machine Learning is the study that gives the computers the ability to learn and also the ability to think without being explicitly programmed. A computer is said to learn from an experience with respect to a specified task and its performance related to that task. The machine learning algorithms are applied to the problems to reduce efforts. Machine learning algorithms are used for manipulating the data and predict the output for the new data with high precision and low uncertainty. The optimization algorithms are used to make rational decisions in an environment of uncertainty and imprecision. In this paper a methodology is presented to use the efficient optimization algorithm as an alternative for the gradient descent machine learning algorithm as an optimization algorithm.
Fuzzy Logic approach in Gene Regulatory Network. These slides are made to present in my MSc. Bioinformatics Course II Semester, Jamia Millia Islamia, New Delhi.
It is mainly based on review paper of my teacher Dr. Khalid Raza.
Raza, Khalid. (2018). Fuzzy logic-based approaches for gene regulatory network inference. https://doi.org/10.1016/j.artmed.2018.12.004
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subse...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Introduction to Process Mining and its applicability to enterprise technology customer support. Please visit my blog at http://www.haimtoeg.com/?p=1881 for further discussion.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining: Understanding and Improving Desire Lines in Big DataWil van der Aalst
We are pleased to announce the lecture: “Process Mining: Understanding and Improving Desire Lines in Big Data”
in honour of doctor honoris causa Wil van der Aalst.
Wednesday May 30th - 10.00 a.m. - 12 a.m.,
Hasselt University, campus Diepenbeek (Agoralaan, building D) - auditorium H5
The Faculty of Business Economics of Hasselt University is pleased to invite you to the lecture
“Process Mining: Understanding and Improving Desire Lines in Big Data”.
This lecture is organised to honour prof. dr. Wil van der Aalst, on whom the degree of ‘doctor honoris causa’ will be conferred by Hasselt University, Faculty of Business Economics (promotor prof. Koen Vanhoof). Professor van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). Currently he is also an adjunct professor at Queensland University of Technology (QUT).His research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Many of his ideas have influenced researchers, software developers and standardization committees working on process support.
Process Mining - Chapter 8 - Mining Additional PerspectivesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 6 - Advanced Process Discovery_techniquesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Building Information Model (BIM) based process miningStijn van Schaijk
Master Thesis research into BIM based process mining. Enabling knowledge reassurance and fact-based problem discovery within the Architecture, Engineering, Construction and Facility Management Industry.
Process Mining - Chapter 11 - Analyzing Lasagna ProcessesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining: Data Science in Action - Wil van der Aalst, TU/e, DSC/e, HSEYandex
Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques, such as machine learning and data mining. Process mining seeks to find a connection between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications can include: analyzing treatment processes in hospitals, improving customer service processes in multinational companies, understanding browsing behavior of customers on a booking site, analyzing failures of a baggage handling system, or improving user interface of the X-ray machine. What all of these applications have in common is the need to relate dynamic behavior to process models. Not only does process mining provide a bridge between data mining and business process management, but it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.
Process Mining - Chapter 12 - Analyzing Spaghetti ProcessesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Nature Inspired Models And The Semantic WebStefan Ceriu
In this paper we present a series of nature inspired models used as alternative solutions for Semantic Web concerns. Some of the methods presented in this article perform better than classic algorithms by enhancing response time and computational costs. Others are just proof of concept, first steps towards new techniques that will improve their respective field. The intricate nature of the Semantic Web urges the need for faster, more intelligent algorithms and nature inspired models have been proven to be more than suitable for such complex tasks.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
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Phone : +91 97518 00789 / +91 72999 51536
Using genetic algorithms and simulation as decision support in marketing stra...infopapers
F.Stoica, L.F.Cacovean, Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning, Proceedings of the 9th WSEAS International Conference on SIMULATION, MODELLING AND OPTIMIZATION (SMO ‘09), Budapest Tech, Hungary, September 3-5, ISSN: 1790-2769 ISBN:978-960-474-113-7, pp. 435-439, 2009
An integrated event summarization approach for complex system managementVenkat Projects
An integrated event summarization approach for complex system management
Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization seeks to provide a comprehensible explanation of the event sequence on certain aspects. Previous methods have several limitations such as high time complexity, a low precision especially with the presence of noise and phase shifts, and providing a summary which is difficult for a human to understand. In this paper, we propose an integrated event summarization approach towards the understanding of the chaotic temporal data. Our approach focuses on two kinds of temporal relationships, the periodic pattern and the correlation pattern, hidden in event sequences of at most two event types. For the periodic patterns, we propose an event periodicity detection algorithm to discover them directly. For the correlation patterns, we make a simple statistical test based on low order statistics to check temporal dependency of events of two types and to eliminate event correlation candidate space dramatically, and then apply inter-arrival histograms to summarize an event sequence and capture the correlation patterns. In order to balance between accuracy and brevity, the minimum description length principle is used to guide the summarization process. Further, the event relationship network is built to describe discovered patterns. We conduct several groups of experiments on synthetic and real data. Experimental results show our approach is capable of producing usable event summarization, robust to noises, and scalable. The average compression ratio of event sequences reaches 99.7%.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A report on designing a model for improving CPU Scheduling by using Machine L...MuskanRath1
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
There are several methods to predict the length of the CPU bursts, such as the exponential averaging method, however, these methods may not give accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based on the best approach to estimate the length of the CPU bursts for processes. We will make use of Bayesian Theory for our model as a classifier tool that will decide which process will execute first in the ready queue. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. Furthermore, applying attribute selection techniques improves the performance in terms of space, time, and estimation.
Myanmar Named Entity Recognition with Hidden Markov Modelijtsrd
Named Entity Recognition is the process to detect Named Entities NEs in a file , document or from a corpus and to categorize them into certain Named entity classes like name of city, State, Country, organization, person, location, sport, river, quantity etc. This paper introduces the Named Entities Recognition NER for Myanmar language using Hidden Markov Model HMM .The main idea behind the use of HMM language independent and we can apply this system for any language domain. The corpus used by our NER system is also not domain specific. Khin Khin Lay | Aung Cho ""Myanmar Named Entity Recognition with Hidden Markov Model"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24012.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/24012/myanmar-named-entity-recognition-with-hidden-markov-model/khin-khin-lay
Similar to Process mining approaches kashif.namal@gmail.com (20)
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
2. Process Mining
A Process Managmenet technique that allows for the
analysis of business Process based on event logs.
Algorithms are applied to event log datasets to find
patterns and details contained in event logs recorded
by an information system
Objective is Effiecient and improve
3. Classification
Discovery
A discovery technique takes an event log and
produces a process model without using any a-priori
information.
Conformance checking
An existing process model is compared with an
event log of the same process.
Enhancement
The main idea is to extend or improve an existing process
model using information about the actual process
recorded in some event log.
4. Approach Used
Direct Algorithmic Approaches
Two-Phase Approaches
Computational Intelligence Approaches
Partial Approaches
5. Direct Algorithmic Approaches
Extracts footprint from the event log and uses this
footprint to directly construct a process model
Also called language-based regions
Extracted from the log and based on this relation a
Petri net is constructed
Alpha Algorithem is Example of Direct Approach
We apply an algorithm on the logs and derive directly
the process model
6. Two Phases Approach
Uses a two-step approach in which first a “low-level model” (e.g., a
transition system , Markov model) is constructed.
2nd step is that low-level model is converted into a “high-level model”
that can express concurrency and other (more advanced) control-flow
patterns.
Transition system is extracted from the log using a customizable
abstraction mechanism.
Transition system is converted into a Petri net using called statebased
regions The resulting model can be visualized as a Petri net, but can
also be converted into other notations (e.g., BPMN and EPCs).
Similar approaches can be envisioned using hidden Markov models.
Using an Expectation-Maximization(EM) algorithm such as the Baum–
Welch algorithm, the “most likely” Markov model can be derived from
a log.
Model is converted into highlevel model.
7. Hidden Morkov Model
Set of states: {s1, s2, s3…. sn }
Process moves from one state to another generating
a sequence of states : s1, s2….
Markov chain property: probability of each subsequent
state depends only on what was the previous state
8. Hidden Morkov Model
You are going to find robot mood that either rebot is
happy or sad by watching movie(W), sleeping S,
Crying C, Facebook F.
X=h if you happy X=s if unknown Y observation . w, s,
c or f .
We want to answer queries, such as:
P(X= h|Y =f) ?
P(X= s|Y =c) ?
10. Computational Intelligence
Approaches
Techniques originating from the field of computational intelligence form the
basis for the third family of process discovery approaches.
Examples of techniques are genetic programming, genetic algorithms,
simulated annealing, reinforcement learning, machine learning, neural
networks, fuzzy sets, rough sets, and swarm intelligence.
The log is not directly converted into a model but uses an iterative procedure to
mimic the process of natural evaluation.
Using genetic process mining approach starts with initial population of
individuals. Each individual corresponds to a randomly generated process
model. For each individual a fitness value is computed describing how well the
model fits with the log.
Populations evolve by selecting the fittest individuals and generating new
individuals using genetic operators such as crossover (combining parts of two
individuals) and mutation (random modification of an individual). The fitness
gradually increases from generation to generation. The process stops once an
individual of acceptable quality is found.
11. Machine Learning
Determine rules from data/facts
Improve performance with experience
Getting computers to program themselves
12. Sketch of an Induction Algorithm
Calculate for each attribute,
how good it classifies the elements of the training set
Classify with the best attribute
Repeat for each resutling subtree the first two steps
Stop this recursive process as soon as a termination
condition is satisfied
13. Partial Approaches
The approaches produce a complete end-to-end process
model.
It is also possible to focus on rules or frequent patterns
approach for mining of sequential patterns.
This approach is similar to the discovery of association
rules, however, now the order of events is taken into
account.
Here a sliding window is used to analyze how frequent an
“episode” ( partial order) is appearing.
Approaches exist to learn declarative (LTL-based)
languages like Declare.
14. PROLOG
PROLOG (=PROgramming in LOGic) is a
programming language based on Horn clauses
father(peter,mary).
father(peter,john).
mother(mary,mark).
mother(jane,mary).
grandfather(X,Z) :- father(X,Y), father(Y,Z).
grandfather(X,Z) :- father(X,Y), mother(Y,Z).
15. Heuristic miner
Heuristics Miner is a practical applicable mining algorithm
that can deal with noise, and can be used to express the
main behavior that is not all details and exceptions,
registered in an event log.
Extends alpha algorithm by considering the frequency of
traces in the log.
The Heuristics Miner Plug-in mines the control flow
perspective of a process model.
Considers the order of the events within a case.
these algorithms take frequencies of events and sequences
into account when constructing a process model
16. Steps
The construction of the dependency graph
For each activity, the construction of the input and output
expressions
The search for long distance dependency relations
1. Read a log
2. Get the set of tasks
3. Infer the ordering relations based on their frequencies
4. Build the net based on inferred relations
5. Output the net
17. Genetic Miner
Genetic miner uses a genetic algorithm to mine a petri
net representation of the process model from
execution traces.
A global search strategy (the quality or fitness of a
candidate model is calculated by comparing the
process model with all traces in the event log the
search process takes place at a global level. For a local
strategy there is no guarantee that the outcome of the
locally optimal steps
18. Steps
The first is to define the internal representation.
The second concern is to define the fitness measure.
The third concern relates to the genetic operators
(crossover and mutation)
Read event log
Build the initial population
Calculate fitness of the individuals in the population
Stop and return the fittest individuals
Create next population
19.
20. Fuzzy miner
Process Mining is a technique for extracting process
models from execution logs.
People have an idealized view of reality.
Real-life processes turn out to be less structured than
people tend to believe.
Model spaghetti-like
21. Output
Phase I: Fuse similar behaving attributes
Phase II: Generate Meta rules
Phase III: Generate frequent fuzzy itemsets
Phase IV: Make fuzzy association rules.