Genetic algorithms (GA) apply evolutionary approaches to inductive learning problems. GA has been successfully applied to difficult problems like scheduling, traveling salesperson, network routing, and financial marketing. GA initialize a population of potential solutions and use genetic operators like crossover and mutation to create new solutions over multiple iterations, replacing weaker solutions with stronger ones according to a fitness function. This leads to increasingly better approximations of the optimal solution.
Iterative Determinant Method for Solving Eigenvalue Problemsijceronline
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 BI-OBJECTIVE MODEL FOR SVM WITH AN INTERACTIVE PROCEDURE TO IDENTIFY THE BE...ijaia
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
Joint contrastive learning with infinite possibilitiestaeseon ryu
Contrastive Learning은 두 이미지가 유사한지 유사하지 않은 지에 대해서 어떤 label이 없이 피쳐들을 배우게 하는 머신 learning 테크닉 중에 하나입니다 우리는 기존에 있는 Supervised learning과 조금 차이가 있는데 Supervised learning은 label cost가 들고
그다음에 Task specific 하기 때문에 generalizability가 조금 떨어질 수 있습니다 하지만 Contrastive Learning은 label이 없이 진행하기때문에 label cost가 없고 generalizability가 조금 더 좋을수 있습니다. 해당 논문은 보다 유용한 Contrastive Learning을 위한 Joint Contrastive Learning에 대해 제안을 하는대요 https://youtu.be/0NLq-ikBP1I
Iterative Determinant Method for Solving Eigenvalue Problemsijceronline
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 BI-OBJECTIVE MODEL FOR SVM WITH AN INTERACTIVE PROCEDURE TO IDENTIFY THE BE...ijaia
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
Joint contrastive learning with infinite possibilitiestaeseon ryu
Contrastive Learning은 두 이미지가 유사한지 유사하지 않은 지에 대해서 어떤 label이 없이 피쳐들을 배우게 하는 머신 learning 테크닉 중에 하나입니다 우리는 기존에 있는 Supervised learning과 조금 차이가 있는데 Supervised learning은 label cost가 들고
그다음에 Task specific 하기 때문에 generalizability가 조금 떨어질 수 있습니다 하지만 Contrastive Learning은 label이 없이 진행하기때문에 label cost가 없고 generalizability가 조금 더 좋을수 있습니다. 해당 논문은 보다 유용한 Contrastive Learning을 위한 Joint Contrastive Learning에 대해 제안을 하는대요 https://youtu.be/0NLq-ikBP1I
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
8 ijaems jan-2016-20-multi-attribute group decision making of internet public...INFOGAIN PUBLICATION
In this paper, an emergency group decision method is presented to cope with internet public opinion emergency with interval intuitionistic fuzzy linguistic values. First, we adjust the initial weight of each emergency expert by the deviation degree between each expert’s decision matrix and group average decision matrix with interval intuitionistic fuzzy numbers. Then we can compute the weighted collective decision matrix of all the emergencies based on the optimal weight of emergency expert. By utilizing the interval intuitionistic fuzzy weighted arithmetic average operator one can obtain the comprehensive alarm value of each internet public opinion emergency. According to the ranking of score value and accuracy value of each emergency, the most critical internet public emergency can be easily determined to facilitate government taking related emergency operations. Finally, a numerical example is given to illustrate the effectiveness of the proposed emergency group decision method.
Using Real Life Contexts in Mathematics Teaching is a conference presentation by Peter Galbraith for the Queensland Association of Mathematics Teachers in June 2013. It has now been generously shared with the Connect with Maths ~ Maths in Action~Applications and Modelling community as a resource.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
Solving linear equations from an image using anneSAT Journals
Abstract
Optical character recognition has a great impact in image processing application. This paper combines the concept of OCR and feed-forward artificial neural network to solve mathematical linear equations. We implement blob analysis and feature extraction to extract the individual characters to a captured image which having some mathematical equations. We are constructing 39 character set which having some numbers, alphabet and operators. Training of these character set is done by using supervised learning rule. If that image satisfying linear equation condition then our proposed algorithm solve this equation and generate the output. This paper tries to increase the recognition rate more than 87%. The result achieved from the training and testing on the network of the letter recognition is satisfactory.
Keywords: Artificial Neural Network, Linear Equation, Recognized rate, Optical Character Recognition.
On the Dynamics of Machine Learning Algorithms and Behavioral Game TheoryRikiya Takahashi
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016.
Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
Proposed algorithm for image classification using regression-based pre-proces...IJECEIAES
Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.
ESTIMATING PROJECT DEVELOPMENT EFFORT USING CLUSTERED REGRESSION APPROACHcscpconf
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis.
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Estimating project development effort using clustered regression approachcsandit
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a
challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the
complex and dynamic interaction of factors that impact software development. Heterogeneity
exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying
them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency
due to heterogeneity of the data. Using a clustered approach creates the subsets of data having
a degree of homogeneity that enhances prediction accuracy. It was also observed in this study
that ridge regression performs better than other regression techniques used in the analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
8 ijaems jan-2016-20-multi-attribute group decision making of internet public...INFOGAIN PUBLICATION
In this paper, an emergency group decision method is presented to cope with internet public opinion emergency with interval intuitionistic fuzzy linguistic values. First, we adjust the initial weight of each emergency expert by the deviation degree between each expert’s decision matrix and group average decision matrix with interval intuitionistic fuzzy numbers. Then we can compute the weighted collective decision matrix of all the emergencies based on the optimal weight of emergency expert. By utilizing the interval intuitionistic fuzzy weighted arithmetic average operator one can obtain the comprehensive alarm value of each internet public opinion emergency. According to the ranking of score value and accuracy value of each emergency, the most critical internet public emergency can be easily determined to facilitate government taking related emergency operations. Finally, a numerical example is given to illustrate the effectiveness of the proposed emergency group decision method.
Using Real Life Contexts in Mathematics Teaching is a conference presentation by Peter Galbraith for the Queensland Association of Mathematics Teachers in June 2013. It has now been generously shared with the Connect with Maths ~ Maths in Action~Applications and Modelling community as a resource.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
Solving linear equations from an image using anneSAT Journals
Abstract
Optical character recognition has a great impact in image processing application. This paper combines the concept of OCR and feed-forward artificial neural network to solve mathematical linear equations. We implement blob analysis and feature extraction to extract the individual characters to a captured image which having some mathematical equations. We are constructing 39 character set which having some numbers, alphabet and operators. Training of these character set is done by using supervised learning rule. If that image satisfying linear equation condition then our proposed algorithm solve this equation and generate the output. This paper tries to increase the recognition rate more than 87%. The result achieved from the training and testing on the network of the letter recognition is satisfactory.
Keywords: Artificial Neural Network, Linear Equation, Recognized rate, Optical Character Recognition.
On the Dynamics of Machine Learning Algorithms and Behavioral Game TheoryRikiya Takahashi
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016.
Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
Proposed algorithm for image classification using regression-based pre-proces...IJECEIAES
Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.
ESTIMATING PROJECT DEVELOPMENT EFFORT USING CLUSTERED REGRESSION APPROACHcscpconf
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis.
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Estimating project development effort using clustered regression approachcsandit
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a
challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the
complex and dynamic interaction of factors that impact software development. Heterogeneity
exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying
them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency
due to heterogeneity of the data. Using a clustered approach creates the subsets of data having
a degree of homogeneity that enhances prediction accuracy. It was also observed in this study
that ridge regression performs better than other regression techniques used in the analysis.
A presentation on HIV&AIDS awareness. It useful for the Life-Orientation Educators and even anyone beacuse it has a very crucial information that can help anyone.
This twelve-week online class will guide all types of medical professionals (doctors, nurses, researchers, aides, social workers, etc.) through the various skills needed to write and publish narratives--personal stories of their experiences (and those of others in the field). We will cover every step in the writing process, from brainstorming to researching to writing to revising, as well as the steps needed to pitch and publish an article or essay.
Our instructors--experienced writers of medical narratives and creative nonfiction--will communicate with participants through a combination of written lectures, written feedback, and email. In addition, the class will include three 1-hour phone conferences. Speakers will be Manoj Jain, Jason Lewis, and Ellen Ficklen.
# Registration includes a 4-issue subscription to Creative Nonfiction.
# Save $25 anytime when you register with a friend.
For registration please contact, online course coordinator Anjali Sachdeva at sachdeva@creativenonfiction.org or visit http://goo.gl/De2uQ
An ahp (analytic hierarchy process)fce (fuzzy comprehensive evaluation) based...ijcsa
In this paper the AHP (Analytic Hierarchy Process) and the FCE (Fuzzy Comprehensive Evaluation) are
applied to find the best coaches from different sports and to rank these great coaches.
First, we screen coaches’ information using three screening criterions. We rank the screened coaches
preliminarily by means of analytic hierarchy process (AHP). Second, we rank them by fuzzy comprehensive
evaluation method(FCE), and we determined the top5 coaches on basketball, football and hockey. Third,
we use the Topsis method to test the accuracy and reasonableness of the model, modify the model and then
reorder the original results to inspect the consistency of the results of the two models. Finally, we take
some other factors into account to optimize our model, which includes on the influence of time line horizon
and genders.
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
Radial Basis Function Neural Network (RBFNN), Induction Motor, Vector control...cscpconf
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
A Transportation Problem is
one of the
most
typical
problems being encountered in many situations
and
it
has
many
practical applic
ations. Many researches had been conducted
and
many methods
had been proposed to solve it. One of the most
difficult challenge in solving the problem deals with inputting a
very large volume of data. With the development of intelligent
technologies, compu
ters had already been used to solved this
problem. This paper presents a method using Genetic Algorithm
(GA) t
o provide a new tool that can quickly calculate the solution
to the Balanced Transportation Problem.
The test results are compared with selected o
ld methods to
confirm the effectiveness of the use of GA. A
mathematical model
was used to represent the GA and be applied to solve it. Finally,
the test results of the model were presented so show the
effectiveness.
Optimising Data Using K-Means Clustering AlgorithmIJERA Editor
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other.
Machine Learning techniques for the Task Planning of the Ambulance Rescue TeamFrancesco Cucari
The RoboCup Rescue simulation models an earthquake in an urban centre presented in the form of a map. The goal of this project is to develop a machine learning technique able to predict the expected time of death (ETD) of civilians and use it in the task planning of the ambulance team in order to save the maximum number of civilians.
HUDE 225Take Home Directions You are a psychologist working a.docxwellesleyterresa
HUDE 225
Take Home
Directions: You are a psychologist working at a local high-school, and the principal wants to create a pre-assessment of 9th grade students’ algebra ability, in order to identify those in need of remedial instruction.
A team of math teachers constructs the test, and pilots it with one class of students. After these data are collected, the principal asks you to perform an item analysis, in order to provide information about the suitability of the test.
Below is item-response data for 10 participants on 5 selected-response items from the test. All of these items are dichotomous and each are designed to tap the same ability: algebra. Additionally, all of the items feature four possible answer choices.
Your task is to compute all relevant CTT and IRT statistics that we have learned in classfor these particular items. You may use all course materials, and any computer programs (e.g., Excel, SPSS, JMP) or a hand calculator to assist you. Round your answers to two decimal places.
Also—you are the only psychologist in this particular school, so please do your own work. This activity is worth a total of 70 points.
Data:
Examinee
Items
Score
1
2
3
4
5
1
1
1
1
1
1
5
2
1
1
1
0
1
4
3
1
1
1
1
1
5
4
0
0
0
0
0
0
5
1
1
0
1
1
4
6
0
0
0
0
1
1
7
0
1
0
0
0
1
8
1
1
1
1
1
5
9
0
0
1
0
0
1
10
1
0
0
0
0
1
P (5 points)
Q (5 points)
Variance (5 points)
Standard deviation
(5points)
D (5 points)
Point-biserial correlation
(5 points)
Inter-Item Covariance Matrix (5 points: .5 point per covariance)
Item Number
1
2
3
4
5
1
2
3
4
5
Inter-Item Correlation Matrix (5points: .5 point per correlation)
Item Number
1
2
3
4
5
1
1
2
1
3
1
4
1
5
1
Test Statistics (6 points)
Average Score
Composite Variance
Composite SD
Cronbach’s Alpha
Standard Error of Measurement
Standard Error of Estimate
Item-Characteristic Curves (Paste below, 5 points):
(Note: Because of the small sample-size, your principal is only requiring a 1pl IRT model)
Test Information Function (Paste below- 1 point):
Item difficulty parameters (5 points):
Item
b
1
2
3
4
5
Item-Analysis Report: Based on the results of your item analysis, do you think this test is suitable for the purpose for which it was designed? Are there any possible revisions you might recommend? Explain your answer using relevant statistics you calculated above as support. Remember, students may be placed in remedial algebra based on their score on this test, so your report is important. (13 points).
Classical Test Theory and
Item Analysis
1
Review: Why do we measure?
In psychology and education, the
abilities and traits we are interested
in cannot be directly observed
Knowledge, cognitive skills, attitudes,
personality, etc.
So, we use measures to indirectly
assess students on these variables
2
A Classic Discovery
In 1904, Charles Spearman posited the following equation:
X = T ...
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
A computer game using temporal difference algorithm of Machine learning which improves the ability of the computer to learn and also explore the best next move for the game by greedy movement techniques and exploration method techniques for the future states of the game.
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightWaqas Tariq
Software is the most expensive element of virtually all computer based systems. For complex custom systems, a large effort estimation error can make the difference between profit and loss. Cost (Effort) Overruns can be disastrous for the developer. The basic input for the effort estimation is size of project. A number of models have been proposed to construct a relation between software size and Effort; however we still have problems for effort estimation because of uncertainty existing in the input information. Accurate software effort estimation is a challenge in Industry. In this paper we are proposing three software effort estimation models by using soft computing techniques: Particle Swarm Optimization with inertia weight for tuning effort parameters. The performance of the developed models was tested by NASA software project dataset. The developed models were able to provide good estimation capabilities.
Learning Strategy with Groups on Page Based Students' Profilesaciijournal
Most of students desire to know about their knowledge level to perfect their exams. In learning environment
the fields of study overwhelm on page with collaboration or cooperation. Students can do their exercises
either individually or collaboratively with their peers. The system provides the guidelines for students'
learning system about interest fields as Java in this system. Especially the system feedbacks information
about exam to know their grades without teachers. The participants who answered the exam can discuss
with each others because of sharing e mail and list of them.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
1. Genetic Algorithm
Genetic Algorithms (GA) apply an evolutionary
approach to inductive learning. GA has been
successfully applied to problems that are
difficult to solve using conventional techniques
such as scheduling problems, traveling
salesperson problem, network routing problems
and financial marketing.
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3. Genetic learning algorithm
• Step 1:
Initialize a population P of n elements
as a potential solution.
• Step 2:
Until a specified termination condition
is satisfied:
Use a fitness function to evaluate each
element of the current solution. If an element
passes the fitness criteria, it remains in P.
The population now contains m elements (m
<= n). Use genetic operators to create (n – m)
new elements. Add the new elements to the
population.
– 2a:
– 2b:
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4. Digitalized Genetic knowledge
representation
• A common technique for representing
genetic knowledge is to transform
elements into binary strings.
• For example, we can represent income
range as a string of two bits for assigning
“00” to 20-30k, “01” to 30-40k, and “11” to
50-60k.
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5. Genetic operator - Crossover
• The elements most often used for
crossover are those destined to be
eliminated from the population.
• Crossover forms new elements for the
population by combining parts of two
elements currently in the population.
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6. Genetic operator - Mutation
• Mutation is sparingly applied to elements
chosen for elimination.
• Mutation can be applied by randomly
flipping bits (or attribute values) within a
single element.
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7. Genetic operator - Selection
• Selection is to replace to-be-deleted
elements by copies of elements that pass
the fitness test with high scores.
• With selection, the overall fitness of the
population is guaranteed to increase.
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8. Step 1 of Supervised genetic learning
This step initializes a population P of
elements. The P referred to population
elements. The process modifies the
elements of the population until a
termination condition is satisfied, which
might be all elements of the population
meet some minimum criteria. An
alternative is a fixed number of iterations
of the learning process.
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9. Step 2 of supervised genetic learning
Step 2a applies a fitness function to
evaluate each element currently in the
population. With each iteration, elements
not satisfying the fitness criteria are
eliminated from the population. The final
result of a supervised genetic learning
session is a set of population elements
that best represents the training data.
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10. Step 2 of supervised genetic learning
Step 2b adds new elements to the
population to replace any elements
eliminated in step 2a. New elements are
formed from previously deleted elements
by applying crossover and mutation.
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11. An initial population for supervised
genetic learning example
Population
element
Income
Range
Life
Insurance
Promotion
Credit Card Sex
Insurance
Age
1
20-30k
No
Yes
Male
30-39
2
30k-40k
Yes
No
Female
50-59
3
?
No
No
Male
40-49
4
30k-40k
Yes
Yes
Male
40-49
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12. Question mark in population
A question mark in the population means
that it is a “don’t care” condition, which
implied that the attribute is not important to
the learning process.
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13. Training Data for Genetic Learning
Training
Instance
Income Range
Life Insurance
Promotion
Credit Card
Insurance
Sex
Age
1
30-40k
Yes
Yes
Male
30-39
2
30-40k
Yes
No
Female
40-49
3
50-60k
Yes
No
Female
30-39
4
20-30k
No
No
Female
50-59
5
20-30k
No
No
Male
20-29
6
30-40k
No
No
Male
40-49
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14. Goal and condition
• Our goal is to create a model able to
differentiate
individuals
who
have
accepted the life insurance promotion from
those who have not.
• We require that after each iteration of the
algorithm, exactly two elements from each
class (life insurance promotion=yes) & (life
insurance promotion=no) remain in the
population.
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15. Fitness Function
1. Let N be the number of matches of the input
attribute values of E with training instances
from its own class.
2. Let M be the number of input attribute value
matches to all training instances from the
competing classes.
3. Add 1 to M.
4. Divide N by M.
Note: the higher the fitness score, the smaller will
be the error rate for the solution.
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16. Fitness function for element 1 own
class of life insurance promotion = no
1. Income Range = 20-30k matches with
training instances 4 and 5.
2. No
matches
for
Credit
Card
Insurance=yes
3. Sex=Male
matches
with
training
instances 5 and 6.
4. No matches for Age=30-39.
5. ∴ N = 4
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17. Fitness function for element 1 of competing
class of life insurance promotion = yes
1. No matches for Income Range=20-30k
2. Credit Card Insurance=yes matches with
training instance 1.
3. Sex=Male matches with training instance 1.
4. Age=30-39 matches with training instances 1
and 3.
5. ∴M = 4
6. ∴F(1) = 4 / 5 = 0.8
7. Similarly F(2)=0.86, F(3)=1.2, F(4)=1.0
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18. Crossover operation for elements 1 & 2
Population element
#1
Income Range Life Insurance
Promotion
20-30k
No
Credit Card Insurance
Yes
Sex
Males
Age
30-39
Population element
#2
Credit Card Insurance
No
Sex
Female
Age
50-59
Population element
#1
Population element
#2
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Income Range Life Insurance Credit Card Insurance
Promotion
30-40k
Yes yes
Sex
Income Range Life Insurance Credit Card Insurance
Promotion
20-30k
No No
Sex
Age
Male
30-39
Age
Female
50-59
Income Range Life Insurance
Promotion
30-40k
Yes
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20. Application of the model(test phase)
• To use the model, we can compare a new
unknown instance (test data) with the elements
of the final population. A simple technique is to
give the unknown instance the same
classification as the population element to which
it is most similar.
• The algorithm then randomly chooses one of the
m elements and gives the unknown instance the
classification of the randomly selected element.
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21. Genetic Algorithms & unsupervised Clustering
Suppose there are P data instances within
the space where each data instance
consists of n attribute values. Suppose m
clusters are desired. The model will
generate k possible solutions. A specific
solution contains m n-dimensional points,
where each point is a best current
representative element for one of the m
clusters.
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22. For example, S2 represents one of the k possible solutions
and contains two elements E21 and E22.
E11
S1
a1 a2 a3
………………………………
E12
E21
an
L1
P
instances
L2
:
:
:
:
::
:
:
:
:
Lp
:
:
:
:
:
:
:
:
S2
:
:
:
:
:
:
Sk
E22
Ek1
Ek2
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Sotlutions
23. Crossover operation
A crossover operation is accomplished by
moving elements (n-dimensional points)
from solution Si to solution Sj. There are
several possibilities for implementing
mutation operations. One way to mutate
solution Si is to swap one or more point
coordinates of the elements within Si.
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24. Fitness function
An applicable fitness function for solution Sj is the
average Euclidean distance of the P instances in
the n-dimensional space from their closest
element within Sj. We take each instance I in P
and compute the Enclidean distance from I to
each of the m elements in Sj. Lower values
represent better fitness scores. Once genetic
learning terminates, the best of the k possible
solutions is selected as the final solution. Each
instance in the n-dimensional space is assigned
to the cluster associated with its closest element
in the final solution.
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25. Training data set for unsupervised GA
Instance
1
2
3
4
5
6
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X
1.0
1.0
2.0
2.0
3.0
5.0
Y
1.5
4.5
1.5
3.5
2.5
6.0
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26. Fitness function for unsupervised GA
We apply fitness function to the Training data. We
instruct the algorithm to start with a solution set
consisting of three plausible solutions (k=3).
With m=2, P=6, and k=3, the algorithm
generates the initial set of solutions. An element
in the solution space contains a single
representative data point for each cluster. For
example, the data points for solution S1 are
(1,0, 1.0) and (5.0,5.0).
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29. First Generation Solution
To compute the fitness score of 11.31 for solution
S1 the Euclidean distance between each
instance and its closest data point in S1 is
summed. To illustrate this, consider instance 1 in
training data. The Euclidean distance between
(1.0,1.0) and (1.0,1.5) is computed as 0.50. The
distance between (5.0,5.0) and (1.0,1.5) is 5.32.
The smaller value of 0.50 is represented in the
overall fitness score for solution S1. S2 is the
best first-generation solution.
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30. Second Generation Solution
The second generation is obtained by
performing a crossover between solutions
S1 and S3 with solution element (1.0,1.0)
in S1 exchanging places with solution
element (5.0,1.0) is S3. The result of the
crossover operation improves (decreases)
the fitness score for S3 while the score for
S1 increases.
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31. (Final) Third Generation Solution
The third generation is acquired by mutating
S1. The mutation interchanges the ycoordinate of the first element in S1 with
the x-coordinate of the second element.
The mutation results in an improved
fitness score for S1. Mutation and
crossover continue until a termination
condition is satisfied. If the third generation
is terminal, then the final solution is S2.
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32. Solution for Clustering
If S2 (3.0, 2.0) and (3.0, 5.0) is the final solution,
then computing the distances between S2 and
the following points are:
Instances 1, 3 and 5 forming one cluster and
instances 2 and 6 forming second cluster, and
instance 4 can be in either clusters.
Cluster 1 center (3.0, 2.0)
Instance X
Y
1.0
2.0
3.0
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1.5
1.5
2.5
Cluster 2 center (3.0, 5.0)
Instance X
Y
1.0
2.0
5.0
4.5
3.5
6.0
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33. General considerations for GA
• GA are designed to find globally optimized
solutions.
• The fitness function determines the
computation complexity of a genetic
algorithm.
• GA explain their results to the extent that
the fitness function is understandable.
• Transforming the data to a form suitable
for a genetic algorithm can be a challenge.
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34. Choosing a data mining technique
Given a set of data containing attributes and
values to be mined together with information
about the nature of the data and the problem to
be solved, determine an appropriate data
mining technique.
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35. Considerations for choosing data
mining techniques
• Is learning supervised or unsupervised?
• Do we require a clear explanation about the
relationships present in the data?
• Is there one set of input attributes and one set of
output attributes or can attributes interact with one
another in several ways?
• Is the input data categorical, numeric, or a
combination of both?
• If learning is supervised, is there one output attribute
or are there several output attributes? Are the output
attribute(s) categorical or numeric?
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36. Behavior of different data mining techniques
1. Neural networks is black-box structured, and is a poor
choice if an explanation about what has been learned is
required.
2. Association rule is a best choice when attributes are
allowed to play multiple roles in the data mining process.
3. Decision trees can determine attributes most predictive
of class membership.
4. Neural networks and clustering assume attributes to be
of equal importance.
5. Neural networks tend to outperform other models when
a wealth of noisy data are present.
6. Algorithms for building decision trees typically execute
faster than neural network or genetic learning.
7. Genetic algorithms is typically used for problems that
cannot be solved with traditional techniques.
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37. Review question 10
Given the following training data set
Training instance Income range
Credit card insurance
1
30-40k
Yes
2
30-40k
No
3
50-60k
No
4
20-30k
No
5
20-30k
No
6
30-40k
No
Sex
Male
Female
Female
Female
Male
Male
Age
30-39
40-49
30-39
50-59
20-29
40-49
Describe the steps needed to apply unsupervised
genetic learning to cluster the instances of the credit
card promotion database.
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38. Tutorial Question 10
Given the following training data set
Training instance Income range
1
30-40k
2
30-40k
3
50-60k
4
20-30k
5
20-30k
6
30-40k
Credit card insurance
Yes
No
No
No
No
No
Sex
Male
Female
Female
Female
Male
Male
Age
30-39
40-49
30-39
50-59
20-29
40-49
After transforming the input data into numeric such as yes=1, no=2, male=1, female=2,
20-29=1, 30-39=2, 40-49=3, 50-59=4, 20-30k=1, 30-40k=2, 40-50k=3, 50-60k=4, the
training data set becomes:
T(1)=(2,1,1,2)
T(2)=(2,2,2,3)
T(3)=(4,2,2,2)
T(4)=(1,2,2,4)
T(5)=(1,2,1,1)
T(6)=(2,2,1,3)
Assume there are two set of initial population for two clusters as:
Solution 1 of 2 clusters centers: K1(1,1,1,1), (4,2,2,4)
Solution 2 of 2 clusters centers: K2(4,4,4,4), (2,2,1,1)
Choose the best solution based on their fitness function score by use of unsupervised
genetic learning.
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39. Reading assignment
“Data Mining: A Tutorial-based Primer” by
Richard J Roiger and Michael W. Geatz,
published by Person Education in 2003,
pp.89-101.
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