1) The document describes using a master-slave voting technique for supervised word sense disambiguation (WSD). It combines three approaches: Decision List as the master approach and Naive Bayes and AdaBoost as slave approaches.
2) Two experiments are conducted. The first combines Naive Bayes and Decision List, improving Naive Bayes' accuracy. The second combines AdaBoost and Decision List, further increasing accuracy.
3) A third experiment combines all three approaches, with Decision List as the master. As expected, this achieves the highest accuracy compared to the individual approaches.
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...cscpconf
In search based test data generation, the problem of test data generation is reduced to that of
function minimization or maximization.Traditionally, for branch testing, the problem of test data
generation has been formulated as a minimization problem. In this paper we define an alternate
maximization formulation and experimentally compare it with the minimization formulation. We
use a genetic algorithm as the search technique and in addition to the usual genetic algorithm
operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
Analysis of Textual Data Classification with a Reddit Comments DatasetAdamBab
McGill COMP551, Applied Machine Learning Course
The main objective of this project is to categorize comments from the American social
news aggregation, web content rating, and discussion website – Reddit.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...cscpconf
In search based test data generation, the problem of test data generation is reduced to that of
function minimization or maximization.Traditionally, for branch testing, the problem of test data
generation has been formulated as a minimization problem. In this paper we define an alternate
maximization formulation and experimentally compare it with the minimization formulation. We
use a genetic algorithm as the search technique and in addition to the usual genetic algorithm
operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
Analysis of Textual Data Classification with a Reddit Comments DatasetAdamBab
McGill COMP551, Applied Machine Learning Course
The main objective of this project is to categorize comments from the American social
news aggregation, web content rating, and discussion website – Reddit.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
Derric A. Alkis C
Abstract:
Delivering the customer to a high degree of confidence and the seller for more information about the products and the desire of customers through the use of modern technology and Machine Learning through comments left on the product to see and evaluate the comments added later and thus evaluate the product, whether good or bad.
The purpose research is to develop the decision model of Multi-Criteria Group Decision Making (MCGDM) into Interval Value Fuzzy Multi-Criteria Group Decision Making (IV-FMCGDM), while the specific purpose is to construct decision-making model of Adaptive Interval Value Fuzzy Analytic Hierarchy Process (AIV- FAHP) uses Triangular Fuzzy Number (TFN) and group decision aggregation functions using Interval Value Geometric Means Aggregation (IV-GMA). The novelty research is to study the concept of group decision making by improving the middle point on the Interval Value Triangular Fuzzy Number (IV TFN). It provides more accurate modeling, and better rating performance, and more effective linguistic representation. This research produced a new decision-making model and algorithm based on AIV-FAHP used to measure the quality of e-learning.
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstract...Deren Lei
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. However, the conventional reward ROUGE-L simply looks for exact n-grams matches between candidates and annotated references, which inevitably makes the generated sentences repetitive and incoherent. In this paper, we explore the practicability of utilizing the distributional semantics to measure the matching degrees. Our proposed distributional semantics reward has distinct superiority in capturing the lexical and compositional diversity of natural language.
SENSE DISAMBIGUATION TECHNIQUE FOR PROVIDING MORE ACCURATE RESULTS IN WEB SEARCHijwscjournal
As the web is increasing exponentially, so it is very much difficult to provide relevant information to the information seekers. While searching some information on the web, users can easily fade out in rich hypertext. The existing techniques provide the results that are not up to the mark. This paper focuses on the technique that helps in offering more accurate results, especially in case of Homographs. Homograph is a word that shares the same written form but has different meanings. The technique that shows how senses of words can play an important role in offering accurate search results, is described in following sections. While adopting this technique user can receive only relevant pages on the top of the search result.
WARRANTS GENERATIONS USING A LANGUAGE MODEL AND A MULTI-AGENT SYSTEMijnlc
Each argument begins with a conclusion, which is followed by one or more premises supporting the
conclusion. The warrant is a critical component of Toulmin's argument model; it explains why the premises
support the claim. Despite its critical role in establishing the claim's veracity, it is frequently omitted or left
implicit, leaving readers to infer. We consider the problem of producing more diverse and high-quality
warrants in response to a claim and evidence. To begin, we employ BART [1] as a conditional sequence tosequence language model to guide the output generation process. On the ARCT dataset [2], we fine-tune
the BART model. Second, we propose the Multi-Agent Network for Warrant Generation as a model for
producing more diverse and high-quality warrants by combining Reinforcement Learning (RL) and
Generative Adversarial Networks (GAN) with the mechanism of mutual awareness of agents. In terms of
warrant generation, our model generates a greater variety of warrants than other baseline models. The
experimental results validate the effectiveness of our proposed hybrid model for generating warrants.
Fast and Accurate Spelling Correction Using Trie and Damerau-levenshtein Dist...TELKOMNIKA JOURNAL
This research was intended to create a fast and accurate spelling correction system with the
ability to handle both kind of spelling errors, non-word and real word errors. Existing spelling correction
system was analyzed and was then applied some modifications to improve its accuracy and speed. The
proposed spelling correction system is then built based on the method and intuition used by existing
system along with the modifications made in previous step. The result is a various spelling correction
system using different methods. Best result is achieved by the system that uses bigram with Trie and
Damerau-Levenshtein distance with the word level accuracy of 84.62% and an average processing speed
of 18.89 ms per sentence.
Evaluation of subjective answers using glsa enhanced with contextual synonymyijnlc
Evaluation of subjective answers submitted in an exam is an essential but one of the most resource consuming educational activity. This paper details experiments conducted under our project to build a software that evaluates the subjective answers of informative nature in a given knowledge domain. The paper first summarizes the techniques such as Generalized Latent Semantic Analysis (GLSA) and Cosine Similarity that provide basis for the proposed model. The further sections point out the areas of improvement in the previous work and describe our approach towards the solutions of the same. We then discuss the implementation details of the project followed by the findings that show the improvements achieved. Our approach focuses on comprehending the various forms of expressing same entity and thereby capturing the subjectivity of text into objective parameters. The model is tested by evaluating answers submitted by 61 students of Third Year B. Tech. CSE class of Walchand College of Engineering Sangli in a test on Database Engineering.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
De-Noising Corrupted ECG Signals By Empirical Mode Decomposition (EMD) With A...IOSR Journals
The electrocardiogram (ECG) signals which are extensively used for heart disease diagnosis and patient monitoring are usually corrupted with various sources of noise. In this paper, an algorithm is developed to de-noise ECG signals based on Empirical Mode Decomposition (EMD) with application of Higher Order Statistics (HOS). The algorithm is applied on several ECG signals for different levels of Signal to Noise Ratio (SNR). The SNR improvement (SNRimp) and Percent Root mean square Difference (PRD (%)) are analyzed. The results show that the developed algorithm is a reasonable one to de-noise ECG signals.
Derric A. Alkis C
Abstract:
Delivering the customer to a high degree of confidence and the seller for more information about the products and the desire of customers through the use of modern technology and Machine Learning through comments left on the product to see and evaluate the comments added later and thus evaluate the product, whether good or bad.
The purpose research is to develop the decision model of Multi-Criteria Group Decision Making (MCGDM) into Interval Value Fuzzy Multi-Criteria Group Decision Making (IV-FMCGDM), while the specific purpose is to construct decision-making model of Adaptive Interval Value Fuzzy Analytic Hierarchy Process (AIV- FAHP) uses Triangular Fuzzy Number (TFN) and group decision aggregation functions using Interval Value Geometric Means Aggregation (IV-GMA). The novelty research is to study the concept of group decision making by improving the middle point on the Interval Value Triangular Fuzzy Number (IV TFN). It provides more accurate modeling, and better rating performance, and more effective linguistic representation. This research produced a new decision-making model and algorithm based on AIV-FAHP used to measure the quality of e-learning.
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstract...Deren Lei
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. However, the conventional reward ROUGE-L simply looks for exact n-grams matches between candidates and annotated references, which inevitably makes the generated sentences repetitive and incoherent. In this paper, we explore the practicability of utilizing the distributional semantics to measure the matching degrees. Our proposed distributional semantics reward has distinct superiority in capturing the lexical and compositional diversity of natural language.
SENSE DISAMBIGUATION TECHNIQUE FOR PROVIDING MORE ACCURATE RESULTS IN WEB SEARCHijwscjournal
As the web is increasing exponentially, so it is very much difficult to provide relevant information to the information seekers. While searching some information on the web, users can easily fade out in rich hypertext. The existing techniques provide the results that are not up to the mark. This paper focuses on the technique that helps in offering more accurate results, especially in case of Homographs. Homograph is a word that shares the same written form but has different meanings. The technique that shows how senses of words can play an important role in offering accurate search results, is described in following sections. While adopting this technique user can receive only relevant pages on the top of the search result.
WARRANTS GENERATIONS USING A LANGUAGE MODEL AND A MULTI-AGENT SYSTEMijnlc
Each argument begins with a conclusion, which is followed by one or more premises supporting the
conclusion. The warrant is a critical component of Toulmin's argument model; it explains why the premises
support the claim. Despite its critical role in establishing the claim's veracity, it is frequently omitted or left
implicit, leaving readers to infer. We consider the problem of producing more diverse and high-quality
warrants in response to a claim and evidence. To begin, we employ BART [1] as a conditional sequence tosequence language model to guide the output generation process. On the ARCT dataset [2], we fine-tune
the BART model. Second, we propose the Multi-Agent Network for Warrant Generation as a model for
producing more diverse and high-quality warrants by combining Reinforcement Learning (RL) and
Generative Adversarial Networks (GAN) with the mechanism of mutual awareness of agents. In terms of
warrant generation, our model generates a greater variety of warrants than other baseline models. The
experimental results validate the effectiveness of our proposed hybrid model for generating warrants.
Fast and Accurate Spelling Correction Using Trie and Damerau-levenshtein Dist...TELKOMNIKA JOURNAL
This research was intended to create a fast and accurate spelling correction system with the
ability to handle both kind of spelling errors, non-word and real word errors. Existing spelling correction
system was analyzed and was then applied some modifications to improve its accuracy and speed. The
proposed spelling correction system is then built based on the method and intuition used by existing
system along with the modifications made in previous step. The result is a various spelling correction
system using different methods. Best result is achieved by the system that uses bigram with Trie and
Damerau-Levenshtein distance with the word level accuracy of 84.62% and an average processing speed
of 18.89 ms per sentence.
Evaluation of subjective answers using glsa enhanced with contextual synonymyijnlc
Evaluation of subjective answers submitted in an exam is an essential but one of the most resource consuming educational activity. This paper details experiments conducted under our project to build a software that evaluates the subjective answers of informative nature in a given knowledge domain. The paper first summarizes the techniques such as Generalized Latent Semantic Analysis (GLSA) and Cosine Similarity that provide basis for the proposed model. The further sections point out the areas of improvement in the previous work and describe our approach towards the solutions of the same. We then discuss the implementation details of the project followed by the findings that show the improvements achieved. Our approach focuses on comprehending the various forms of expressing same entity and thereby capturing the subjectivity of text into objective parameters. The model is tested by evaluating answers submitted by 61 students of Third Year B. Tech. CSE class of Walchand College of Engineering Sangli in a test on Database Engineering.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
De-Noising Corrupted ECG Signals By Empirical Mode Decomposition (EMD) With A...IOSR Journals
The electrocardiogram (ECG) signals which are extensively used for heart disease diagnosis and patient monitoring are usually corrupted with various sources of noise. In this paper, an algorithm is developed to de-noise ECG signals based on Empirical Mode Decomposition (EMD) with application of Higher Order Statistics (HOS). The algorithm is applied on several ECG signals for different levels of Signal to Noise Ratio (SNR). The SNR improvement (SNRimp) and Percent Root mean square Difference (PRD (%)) are analyzed. The results show that the developed algorithm is a reasonable one to de-noise ECG signals.
Evaluation Of Analgesic And Anti Inflammatory Activity Of Siddha Drug Karuvil...IOSR Journals
The present study was carried out to validate the anti-inflammatory and analgesic activities of Karuvilanchi ver chooranam (KVC) (Root powder of Smilax zeylanica) in rodents. Analgesic study was carried out by using Eddy’s Hotplate method and acetic acid-induced writhing test and Anti inflammatory study was evaluated by Cotton pellet granuloma method and by plethysmometer method. The result of the analgesic activity evaluated using hot plate method revealed that the reaction time for mice was significantly increased in a dose dependent manner after one hour of oral administration. It was found that both KVC and Aspirin caused an inhibition on the writhing response induced by acetic acid. Doses of 250 and 500 mg/kg of the KVC and aspirin respectively, could completely block the writhing response exhibited about 61.51 and 72.51% inhibition. In acute inflammation model, the formalin induced paw oedema was significantly reduced by all the doses of KVC used when compared to control (P<0.05). The results of cotton pellet granuloma method indicated that KVC in both doses significantly reduced the weight of the cotton pellet granuloma with a dose dependent effect. From the result it can be concluded that the trial drug Karuvilanchi Ver Chooranam has potent analgesic and anti inflammatory properties which confirmed the traditional use
Simultaneous Triple Series Equations Involving Konhauser Biorthogonal Polynom...IOSR Journals
Biorthogonal polynomials are of great interest for Physicists.Spencer and Fano [9] used the biorthogonal polynomials (for the case k = 2) in carrying out calculations involving penetration of gamma rays through matter.In the present paper an exact solution of simultaneous triple series equations involving Konhauser-biorthogonal polynomials of first kind of different indices is obtained by multiplying factor technique due to Noble.[4] This technique has been modified by Thakare [10, 11] to solve dual series equations involving orthogonal polynomials which led to disprove a possible conjecture of Askey [1] that a dual series equation involving Jacobi polynomials of different indices can not be solved. In this paper the solution of simultaneous triple series equations involving generalized Laguerre polynomials also have been discussed as a charmfull particular case.
Dosimetric evaluation of the MLCs for irregular shaped radiation fieldsIOSR Journals
The three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiotherapy (IMRT), and image-guided radiotherapy (IGRT) are the most advanced techniques in radiotherapy, which use irregular fields–using multileaf collimators in a linear accelerator. The accuracy of these techniques depends on dosimetric characteristics of the multileaf collimators. There is an option for optimizing the jaws to the irregular MLC field to reduce the scattered radiation and intra- and inter-leaf radiation leakage beyond the field. In this study, ,80 leaf MLC system has been taken to compare and differentiate their characteristics with 6-MV, and 10-MV photon beams.
The MLC system in Elekta linear accelerator is used as a separate unit, that is, The dosimetric characteristics include dose rates, percentage depth doses, surface dose, dose in the build-up region, penumbra, and width of 50% dose levels
Assessment of Activity Concentration of The Naturally Occurring Radioactive M...IOSR Journals
The activity concentrations of potassium, Radium and thorium in soil samples from a mining site in yankandutse, Kaduna north western Nigeria were measured using gamma ray spectroscopy method. Activity concentration of potassium, Radium and thorium were determined. The activity concentrations of 40K, 226Ra and 232Th, respectively in Bq kg-1 in the soil samples ranged as follows: K-40 196.11±2.02 to 553.03±1.08 with average of 382.01, Ra-226 .1506±.03 to 5.67±.03 with average of 2.08 and Th-232 18.13±3.19 to 73.09±1.59 with average activity concentrations of 47.23 .The mean activity concentration of potassium and radium are below average but for thorium the activity concentration is above average.
Synthesis, Characterization and Application of Some Polymeric Dyes Derived Fr...IOSR Journals
In this study, Some Monoazo disperse dyes namely, 4-arylazoaminophenols (AAPs) were synthesized via diazotization and coupling reactions and later, polycondensation of these dyes with formaldehyde in the presence of aqueous oxalic acid was carried out. The resulting polymeric dyes namely, (4-arylazoaminophenol-formaldehyde)s (PAAP-F)s as well as their low-molecular weight precursors were characterized by yield, melting point, color, solubility, viscosimetry, Proton Nuclear Magnetic Resonance spectroscopy, UV-visible spectroscopy and Infra red spectroscopy. Their dyeing performance on nylon and polyester were assessed using standard methods. The products were obtained in good yield and had low melting points The dyes were found to be soluble in chloroform and acetone, some were found to dissolve in ethanol and methanol, and generally insoluble in water. The dyeing on nylon and polyester had yellow shades with moderate to good light and wash fastness. Their rubbing fastnesses on nylon and polyester were very good. Polymerizations of the monomeric dyes on dyed nylon and polyester have also been carried out. The dyeing properties of the monomeric and polymeric dyes were compared with the dyes polymerized in situ on nylon and polyester and the fastness properties were found to increase on polymerization and even better with the dyes polymerized inside the fibers
Bayesian Estimation of Above-Average Performance in Tertiary Institutions: A ...IOSR Journals
Bayesian approach for parameter estimation has the capacity to yield more precise estimates than methods based on sampling theory. There are several common Bayesian models; in this study we applied Empirical Bayes (EB) model called Beta-binomial model. The study is motivated by the need to beam searchlight on universities, faculties or fields of study with graduates who may not be eligible for further educational pursuits. This study provides means of assessment or a basis of evaluation of students’ performances among faculties or fields of study and overall performance of a university. This study uses Bayesian methods of inference to estimate the proportion of above-average performance of graduates from the various faculties in University of Lagos. The model adopted generated results which are of smaller variances compared with variances of sample Proportions, showing that the posterior proportions generated are more efficient estimators. This is further evidenced in narrow widths of the computed confidence intervals. The overall result shows that the proportion of above-average performance of graduates of University of Lagos, who are eligible for further educational pursuits (i.e. higher degrees), is approximately 72% of the university graduates
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
Opinion mining framework using proposed RB-bayes model for text classicationIJECEIAES
Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.
Query Aware Determinization of Uncertain Objectsnexgentechnology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Query aware determinization of uncertainnexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Sentiment Analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data.
Machine learning and reinforcement learningjenil desai
In this slides we have covered all the basics of the machine learning and reinforcement learning. We also covered how mario game can think by himself and complete the game.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
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J017256674
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. V (Mar – Apr. 2015), PP 66-74
www.iosrjournals.org
DOI: 10.9790/0661-17256674 www.iosrjournals.org 66 | Page
Supervised WSD Using Master- Slave Voting Technique
Boshra F. Zopon AL_Bayaty1
, Dr. Shashank Josh2
1
Bharati Vidyapeeth University, 2
Bharati Vidyapeeth University
1
Department of Computer Science,Yashwantrao Mohite College,
2
Department of Computer Engineering, Engineering College India, Pune, Pin no.411046
1
AL- Mustansiriyah University, Baghdad, Iraq
Abstract: The Word sense disambiguation approaches contain number of methods such as stacking, voting, in
this paper we combined three approaches, Decision List as Master approach and Naïve Bayes, Adaboost a
Slaves approaches, for combining models to increase the accuracy and WSD performance.
Keywords: Decision List, Naïve Bayes, Adaboost, Senseval-3, WSD, WordNet, Master-Slave technique, Voting,
combination.
I. Introduction
Number of systems can be used for the ability of an algorithm to continue operating despite
abnormalities in input, calculation etc, means it improves robustness. It there may be possibilities to create
independent module for WSD, in that case we act each module individually for better performance. If there is
combination of number WSD systems, the errors are find out and they are detected by a factor of 1/N. The main
task of WSD is to assign sense to word in context. The senses of a word can be typically taken from dictionary.
Various machine learning (ML) approaches are explained or evaluate to produce successful Word sense
disambiguation systems [1]
. But how the performance between different algorithms can measure still remains the
question. Decision List and Naïve Bayes are used improve the performance. This performance is improved by
collecting the voting. After collecting the voting accuracy of finding correct sense will get increased. Word
Sense Disambiguation is open problem, so the output of any approach will depend on your particular data.
Disambiguation means choosing one meaning from pre-specified set. The main idea is to determine similarity
between every meaning and the context.
II. Master – Slave Technique
In WSD there are two main methods voting and stacking, the voting method can be weighted or non-
weighted, the weighted approach done by adding more weight to the classifier which is selected by votes and
got more accuracy among some classifiers. Here in the figure below show you our suggestion which called
Master- Slave technique. In this model several classifier as slaves suns separately, and one or more can select by
the Master. The selection depends on the accuracy, in case found two classifiers got same results, the master has
control and decision to select according the reputation each one.
The Master-Slave technique [2]
is a technique to achieve improvement in WEB Search engine results,
by combination one or more of supervised classifiers, figure (1), shows the master-slave technique. In this
experiment we combine two supervised classifier, Decision List as master approach [3]
, and Naïve Bayes,
Adaboost as Slaves classifiers.
Figure 1: Mater – Slave technique
The Reputation
2. Supervised WSD Using Master- Slave Voting Technique
DOI: 10.9790/0661-17256674 www.iosrjournals.org 67 | Page
2.1 Decision List
A Decision List is an ordered list with conjunctive rule. It consists of sequence of tests, means for
output result or finally obtained result the tests applied for each input. This type of iteration can be done until
first test is applicable i.e. first test become classified as true or false, or negative or positive, assumes n Boolean
attributes to be considered, we denote the set of such variables as:
Vn= {x1, x2, xn} [4]
. Decision List was selected to be master approach in our model Master-slave technique.
2.2. Naïve Bayes
An advantage to use Naïve Bayes is that it requires data with, very small size to estimate the parameters
necessary for classification. The technique is based on Bayesian theorem.
Given set of variable , we construct the probability for the event cj from the set of
outcomes, . Her P is predictor and C is set of categorical levels, which act as dependent
variable. Using Byes rule
Where:
That means p belongs to cj. We can use Maximum A posteriori (MAP). Mainly is also known as decent
classifier, so the probability outputs from predict- probability are not be taken too seriously.
Naïve Bayes, indicates as a strong independence assumptions between the features. Naïve Bayes
requiring number of parameters, Bayes theorem is a technique for constructing classifiers. In that not a single
algorithm is used, but a combination of algorithm which based on common principle. For example- a fruit may
be considered to be orange, if it is orange, round and about 3 inch diameter, a naïve bayes classifier by
considering each of these features [5]
.
2.3. Adaboost
In some case, the weak classifier need combine in such a way to improve the accuracy and create
strong one. Ensemble method is an strategy to combine learning algorithms that have different methodology
together. An application to WSD combination method is Adaboost, which is a general approach to create and
contract a strong classifier from weak classifiers. The actual process carried out is as mentioned below(6)
.
Box (1): Adaboost Algorithm implemented
To make learning process easier members of training data are weighted equally. Adaboost Algorithm
treats it as an input. For X components, it is iterated y times one turn is allotted for each classifier. In case of
master – slave technique the algorithms are selected on the basis of the accuracy which is decision List and the
algorithms which is needs some boost in terms of accuracy are slaves, in this case Naïve bayes and Adaboost are
acting as a slaves, in such way combining improve the accuracy of these slaves.
2.4. Ensemble Methods
For ensemble methods use more can one learning algorithm to obtain predictive performance as
comparing constituent learning algorithms. Predictive performance means accuracy typically used in inductive
learners. Robustness over single estimator the original ensembles method is Bayesian averaging [7]
.
Some method for constructing ensembles manipulates the example to finalize multiple hypotheses such as:
Manipulate the set of input features.
Manipulate the output, to obtain the good ensemble of classifiers for obtaining the values.
For x =1; x< m; x++)
Fetch weight αx from classifier cx
))(()(
1
XxxsignxH
y
x
}
Where H(x) sign is function for linear combine of
weak learner to boost the performance.
3. Supervised WSD Using Master- Slave Voting Technique
DOI: 10.9790/0661-17256674 www.iosrjournals.org 68 | Page
Injecting randomness, used for generating ensembles of classifiers to inject randomness into the algorithm.
III. Experimental Setup
Experiments are conducted by using an approach to resolve word sense disambiguation. Input is
nothing but 10 nouns and 5 verbs along with WordNet repository to know POS. Innovative approach which is
based on Master-Slave model. Results are calculated on the basis of the said set up.
1. Data Set: five verbs and ten nouns are selected to perform the experiments of word sense disambiguation [8]
.
2. Data Source: WordNet 2.19]
referred to several the details related with a particular word like part of speech
(POS). This data source is used to resolve the disambiguation of various meanings related with given data
set.
3. Training Set: To train the algorithm to identify correct sense of given word context is used. This context is
in the form of snseval-3[10]
. This means with reference of the context given with respective word. Training
Phase plays important role in identification of correct meaning of a word from data set. Result of training
phase is to make disambiguation task much easier.
4. Testing Set: Thus the calculated meaning of a word is verified in this testing phase.
5. Algorithms: Algorithms are written in java [11]
which drives the meaning identification process. Master –
Slave Voting Algorithm, this is an extension of algorithm process mentioned above where two algorithms or
more are clubbed to deliver the maximum performance acting as a slave.
6. Attributes: Attributes is nothing but factors taken into the consideration for making the decision related with
word sense disambiguation. There are various stages of this attribute, means while deciding the weight
allotted for given meaning feature acts as attribute.
While dealing with Master-Slave model main task is to decide a particular algorithm as Master and
other/ others as a Slave. So in this decision making process overall accuracy or F-measure of all algorithm acts
as an attribute.
About topical and lexical context analysis, for example Suppose w-3, w-2, w-1, w, w+1, w+2, w+3 is
the context of words before and after given word w (to solve word sense disambiguation). All information
related with respective part of speech (-3≤POS≤3), and consider various combinations like (w-1, w+1), (w+1,
w+2), (w,w+1,w+2), (w-1, w, w+1, w+2, w+3), these could be many more combinations of these words
mentioned in a context. Together the set of all W, POS, is known as sample set for the attribute of given word
environment [12]
.
7. Combination algorithm applied: the steps of combination algorithm we implemented as below:
Input – data set of 15 words is used to disambiguation a word along with data source of WordNet and
context.
Process- java code is used to implement master- Slave model to improve the accuracy of an algorithm. Data
processing, classification and accuracy calculation is carried out by this code.
Output-Accuracy of this master- Slave model is decided by precision, recall and f-measure. Box1. Below
shows the steps of combination algorithm implemented.
Box (2): Master-Slave combination Algorithm implemented steps
Master – Slave model deals with combination of algorithms to improve the result. This combination helps to
increase the performance of an algorithm by boosting the accuracy of given algorithm. Algorithm is designed to
implement Master-Slave technique to improve the performance of Naïve Bayes and Adaboost algorithms.
Step1. Accuracy of Master X % is collected.
Step2. Accuracy of Slave y %
Step3. Collect voting to improve X by using factor
F= (X - f)/100.
Step4. Accuracy of Word=old Accuracy + F
Step5. Apply this factor for all words, X1, X2,
X3…, and X15.
Step6. Calculate precision, Recall, and f-measure.
4. Supervised WSD Using Master- Slave Voting Technique
DOI: 10.9790/0661-17256674 www.iosrjournals.org 69 | Page
IV. Methodology
Master – Slave model deals with combination of algorithm to improve the result. This combination helps to
increase the performance of an algorithm by boosting the accuracy of given algorithm.
To select Master and Slave experiment is conducted. After conducting the experiment and performing the
necessary literature survey related with it following options or approaches are considered.
1.Select the Master, generally an algorithm with high accuracy, good history and utilization.
2.Combine existing algorithms to improvise the accuracy.
3.Variable Factor selection: The Master- Slave Architecture adds a factor to boost the performance of system if
this factor is designed an fixed format, the value to be added will not be added differently for different words.
How to treat a word with accuracy=100%.
In the factor that we add is decided . Where x is the accuracy of an algorithm which is
lagging and all the time we add to ensure addition in the accuracy. Hence all the time performance of
the algorithm will get improved by referring Master- Slave model. This model is boosting the performance
ensures the rise in the overall accuracy provided selection of adequate algorithm, with high accuracy should be
made. Java code improved algorithm, which is written in Java [13]
, improves the accuracy by using delimiter
function which is mention at step 2.3. This function will internally invoke several programs to conduct voting
and find the correct sense.
V. The Experiments
5.1 First Experiment
The first combination deals with Naïve Bayes as classifier and Decision List as Master, experiment is
conducted by considering decision list as a master and Naïve Bayes algorithm as a slave, after completing this
experiment the accuracy of Naïve Bayes model (individual) got increased. To effect was possible only due to
decision list (which is acting as a master).
Table 1: Data Set of Words and Results of Naïve Bayes and Decision list Combination
Word POS
# First Combination
Sense Recall Precision F-Measure
Praise n 2 1000 500 1500
Name n 6 1000 764 2292
Worship v 3 1000 763 2289
Worlds n 8 1000 702 2106
Lord n 3 500 500 1500
Owner n 2 500 500 1500
Recompe-nse n 2 333 333 999
Trust v 6 1000 143 429
Guide v 5 1000 1000 3000
Straight n 3 1000 1000 3000
Path n 4 473 412 1236
Anger n 3 922 500 1500
Day n 10 250 250 750
Favored v 4 167 167 501
Help v 8 125 125 375
684.6667 510.6 1531.8
This table shows the F-measure which is calculated by knowing precision and recall with the help of
following formula.
Even after implementing Master-Slave model, the accuracy is not 100%.
5. Supervised WSD Using Master- Slave Voting Technique
DOI: 10.9790/0661-17256674 www.iosrjournals.org 70 | Page
Fig.2. The first combinationGraph
When we look at the performance of the combination that we have selected, we can observe the
considerable hike in the performance of Naïve Bayes algorithm.
This hike could be well interpreted by looking at the table of individual contribution of the Naïve Bayes
algorithm [14]
.
5.2 Second Experiment
Experiment conducted accuracy is increased, this combination experiment deals with Adaboost as slave
classifier, to improve the accuracy more and more. Experiment is conducted and it is observed that this
combination gives better result.
Table 2: Data Set of Words and Results of Adaboosts and Decision list Combination
Word POS
# Second Combination
Sense Recall Precision F-Measure
Praise n 2 899 1000 3000
Name n 6 1000 1000 3000
Worship v 3 996 1000 3000
Worlds n 8 141 1000 3000
Lord n 3 465 1000 3000
Owner n 2 942 1000 3000
Recompe-nse n 2 963 1000 3000
Trust v 6 167 167 501
Guide v 5 500 510 1530
Straight n 3 500 500 1500
Path n 4 333 333 999
Anger n 3 500 500 1500
Day n 10 111 1000 3000
Favored v 4 250 250 750
Help v 8 125 125 375
526.1333 692.3333 2077
This table shows the F-measure which is calculated by knowing precision and recall, and below the results
graph of the combination between Adaboost and decision list.
Fig.3. The second combinationGraph
0
1000
2000
3000
4000
5000
6000
1 3 5 7 9 11 13 15
First
combina
tion F-
Measure
First
combina
tion
Precision
First
combina
tion
Recall
0
1000
2000
3000
4000
5000
6000
1 3 5 7 9 11 13 15
Second
Combination
F-Measure
Second
Combination
Precision
Second
Combination
Recall
6. Supervised WSD Using Master- Slave Voting Technique
DOI: 10.9790/0661-17256674 www.iosrjournals.org 71 | Page
5.3 Third Experiment
Now after two experiments above, we combined the three approaches Naïve Bayes and Adaboost as
slaves with master approach which is Decision list, and as per the anticipation highest accuracy is received.
Table 3: Data Set of Words and Results of Naïve Bayes, Adaboosts and Decision list Combination
Word POS
# Third Combination
Sense Recall Precision F-Measure
Praise n 2 771 1000 3000
Name n 6 1000 1000 3000
Worship v 3 494 676 2028
Worlds n 8 142 1000 3000
Lord n 3 483 1000 3000
Owner n 2 848 1000 3000
Recompe-nse n 2 882 1000 3000
Trust v 6 167 167 501
Guide v 5 500 971 2913
Straight n 3 500 500 1500
Path n 4 333 333 999
anger n 3 500 500 1500
Day n 10 111 1000 3000
Favored v 4 250 250 750
Help v 8 125 125 375
473.7333 701.4667 2104.4
Fig.4. The third combination Graph
VI. Comparison Approaches of Master – Slave model combination
By looking to the graphs (2, 3, 4), and make Comparative analysis of three experiments of Master-
Slave model to observe rise in the performance of Naïve Bayes algorithm. So this model gives hike in the
individual performance of second and third combination experiments. The graph below
Fig. 5 Master-Slave thechinue
The Comperative Combination Recall Graph
0
1000
2000
3000
4000
5000
6000
1 3 5 7 9 11 13 15
Third
Experiment
Third
Combination
F-Measure
Third
Experiment
Third
Combination
Precision
Third
Experiment
Third
Combination
Recall
0
200
400
600
800
1000
1200
Praise
Name
Worship
Worlds
Lord
Owner
Recompe-nse
Trust
Guide
Straight
Path
anger
Day
Favored
Help
COMPARATIVE ANALYSIS OF RECALL
1st
Experiment
Recall
2nd
Experiment
Recall
3rd
Experiment
Recall
7. Supervised WSD Using Master- Slave Voting Technique
DOI: 10.9790/0661-17256674 www.iosrjournals.org 72 | Page
And table (7) at end of paper shows the comparative results of Master- Slave technique.
Fig.6. The Comperative Combination precision Graph
Fig.7. The Comperative Combination f-measure Graph
VII. Conclusion
In this paper, we presented Master- Slave technique suggested, in the first experiment, Decision list acts a
Master and Naïve Bayes act as slave. Individually each algorithm gives good values of precision and f-measure.
When they are combined together recall is enhanced which might be useful application like search engine which
requires more coverage of sample space, but word sense disambiguation it is less useful.
In the second experiment, we Decision list as a master and Adaboost as a slave. There is increase in
precision by (1.0733) and f-measure (3.2). Unlike to the first experiment recall is decreased. This is
enhancement in precision to resolve word sense disambiguation problem.
In the third experiment combination, the decision list as master, call the Naïve Bayes and Adaboost
together. It is observed that there in increases in precision and f-measure by (48.7367) And (146.2) respectively,
this combination gives all round performance for precision.
At final the Master – Slave technique worked well to increase the performance of Slave algorithms by
boosting the accuracy of the algorithms. Type of Slave, context will play very crucial role in the growth of f-
measure. These experiments motivate to consider number of Slaves and type of Slaves carefully to make the
disambiguation process more and more accuracy. Since the emphasis is more on precision and f-measure effort
are not highlighted in the values of recall.
Table 4: The Results of three approaches before combination
No
.
Approach Before Combination
Recall Precision F- measure
1 N.Bayes 305.73 628.6 1885.8
2 D. List 440.33 691.26 2073.8
3 Adaboost 459.2 652.73 1958.2
0
500
1000
1500
2000
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3000
COMPARATIVE ANALYSIS OF PRECISION
3rd
Experiment
Precision
2nd
Experiment
Precision
1st
Experiment
Precision
0
2000
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6000
8000
10000
Praise
Name
Worship
Worlds
Lord
Owner
Recompe-nse
Trust
Guide
Straight
Path
anger
Day
Favored
Help
COMPARATIVE ANALYSIS OF F-MEASURE
3rd
Experi
ment
F-
Measu
re
2nd
Experi
ment
F-
Measu
re