Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
Raffael Marty gave a presentation on big data visualization. He discussed using visualization to discover patterns in large datasets and presenting security information on dashboards. Effective dashboards provide context, highlight important comparisons and metrics, and use aesthetically pleasing designs. Integration with security information management systems requires parsing and formatting data and providing interfaces for querying and analysis. Marty is working on tools for big data analytics, custom visualization workflows, and hunting for anomalies. He invited attendees to join an online community for discussing security visualization.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
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.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
The document discusses various evaluation metrics that can be used for binary classification and click prediction, including AUC, RIG, LogLoss, precision, recall, and F1. It notes that AUC ignores predicted probabilities and considers type 1 and type 2 errors equally. RIG is bad for comparing models with different data distributions but can be used to compare multiple models trained on the same data. The document also provides a reference for more information on offline and online predictive model performance evaluations.
A birthday attack is a type of cryptographic attack that exploits the mathematics behind the birthday problem in probability theory. This attack can be used to abuse communication between two or more parties. The attack depends on the higher likelihood of collisions found between random attack attempts and a fixed degree of permutations
Machine learning models involve a bias-variance tradeoff, where increased model complexity can lead to overfitting training data (high variance) or underfitting (high bias). Bias measures how far model predictions are from the correct values on average, while variance captures differences between predictions on different training data. The ideal model has low bias and low variance, accurately fitting training data while generalizing to new examples.
The document provides an overview of steganography, including its definition, history, techniques, applications, and future scope. It discusses different types of steganography such as text, image, and audio steganography. For image steganography, it describes techniques such as LSB insertion and compares image and transform domain methods. It also provides examples of steganography tools and their usage for confidential communication and data protection.
This document provides an introduction to naive Bayesian classification. It begins with an agenda that outlines key topics such as introduction, solved examples, advantages, and disadvantages. The introduction section defines naive Bayesian classification and provides the Bayes' theorem formula. Four examples are then shown applying naive Bayesian classification to classification problems involving predicting whether someone buys a computer, has the flu, has an item stolen, or plays golf. Each example calculates the probabilities and classifies a data sample. The document concludes by listing advantages such as being simple, scalable, and able to handle different data types, and disadvantages such as the independence assumption and data scarcity issues. References are also provided.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Raffael Marty gave a presentation on big data visualization. He discussed using visualization to discover patterns in large datasets and presenting security information on dashboards. Effective dashboards provide context, highlight important comparisons and metrics, and use aesthetically pleasing designs. Integration with security information management systems requires parsing and formatting data and providing interfaces for querying and analysis. Marty is working on tools for big data analytics, custom visualization workflows, and hunting for anomalies. He invited attendees to join an online community for discussing security visualization.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
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.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
The document discusses various evaluation metrics that can be used for binary classification and click prediction, including AUC, RIG, LogLoss, precision, recall, and F1. It notes that AUC ignores predicted probabilities and considers type 1 and type 2 errors equally. RIG is bad for comparing models with different data distributions but can be used to compare multiple models trained on the same data. The document also provides a reference for more information on offline and online predictive model performance evaluations.
A birthday attack is a type of cryptographic attack that exploits the mathematics behind the birthday problem in probability theory. This attack can be used to abuse communication between two or more parties. The attack depends on the higher likelihood of collisions found between random attack attempts and a fixed degree of permutations
Machine learning models involve a bias-variance tradeoff, where increased model complexity can lead to overfitting training data (high variance) or underfitting (high bias). Bias measures how far model predictions are from the correct values on average, while variance captures differences between predictions on different training data. The ideal model has low bias and low variance, accurately fitting training data while generalizing to new examples.
The document provides an overview of steganography, including its definition, history, techniques, applications, and future scope. It discusses different types of steganography such as text, image, and audio steganography. For image steganography, it describes techniques such as LSB insertion and compares image and transform domain methods. It also provides examples of steganography tools and their usage for confidential communication and data protection.
This document provides an introduction to naive Bayesian classification. It begins with an agenda that outlines key topics such as introduction, solved examples, advantages, and disadvantages. The introduction section defines naive Bayesian classification and provides the Bayes' theorem formula. Four examples are then shown applying naive Bayesian classification to classification problems involving predicting whether someone buys a computer, has the flu, has an item stolen, or plays golf. Each example calculates the probabilities and classifies a data sample. The document concludes by listing advantages such as being simple, scalable, and able to handle different data types, and disadvantages such as the independence assumption and data scarcity issues. References are also provided.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
The document is an agenda for a seminar on machine learning techniques and tools. It will cover an introduction to machine learning, common techniques like classification, clustering and regression. It will also discuss tools for machine learning like Apache Mahout, Weka, Spark MLLib and R. Finally, it will include a hands-on demonstration of machine learning algorithms and discuss benefits of using machine learning.
This slide provides an overview of some of the core concepts related to building machine learning models. Machine learning is a branch of computer science that aims to make computers learn from data without being explicitly programmed. Learning problems can be classified into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves learning a function that maps inputs to outputs, given a set of labeled examples. Unsupervised learning involves finding patterns or structure in unlabeled data. Reinforcement learning involves learning how to act or behave in an environment, given feedback or rewards from the environment.
Other important concepts related to machine learning include generalization, overfitting, representation, features, models, evaluation, optimization, bias-variance tradeoff, and Occam's razor. Generalization refers to the ability of a machine learning model to perform well on new or unseen data, not just on the training data. Overfitting occurs when a model fits the training data too closely, resulting in poor generalization. Representation refers to the way of encoding or describing the input and output data for a machine learning problem. Features are the attributes or characteristics of the input data that are used for learning. Models are the mathematical or computational structures that represent or approximate the function that maps inputs to outputs. Evaluation involves measuring the performance or accuracy of a machine learning model on a given data set. Optimization involves finding the best or optimal parameters or settings for a machine learning model that minimize the error or maximize the accuracy on the training data. Bias-variance tradeoff refers to the balance between model complexity and generalization ability. Occam's razor is a principle that favors simpler explanations or models when competing hypotheses explain the data equally well.
Understanding these core concepts is crucial for anyone who wants to learn and apply machine learning in practice. This slide provides a concise summary of these concepts and can serve as a useful reference for beginners and experts alike.
1) Relational databases try to maintain strong consistency by avoiding inconsistencies, while NoSQL databases accept some inconsistencies due to the CAP theorem and eventual consistency.
2) Consistency in databases refers to only allowing valid data transactions according to the defined rules to prevent violations. NoSQL databases sacrifice some consistency for availability and partition tolerance.
3) Eventual consistency means replicas may show temporary inconsistencies but will eventually converge to the same state with further updates. This can cause problems for applications that require strong consistency.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
This document provides an overview of a university course on Cryptography and Network Security. It begins with the course syllabus, which outlines topics like security concepts, cryptography concepts and techniques, and types of security attacks. It then discusses key security concepts such as security services, security mechanisms, security attacks, and models for network and access security. It provides examples of security services like authentication, access control, and data confidentiality. It also describes security mechanisms and different classes of security attacks. The document concludes by listing reference books, online videos, related courses, tutorials, and sample multiple choice and problems related to cryptography and network security.
CYBERSPACE
CYBER POWER
INTERNET
CYBER-ATTACK
INDIA AND PAKISTAN CYBER ATTACKS
TYPES OF CYBER CRIME
Hypotheses in the Research
Literature on the Influence of Mass Media on Criminal Behavior
Technology-Related Risk Factors for Criminal Behavior
COPYCAT CRIME:
CYBER SECURITY
SAFETY TIPS FOR CYBER CRIME
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Definition of classification
Basic principles of classification
Typical
How Does Classification Works?
Difference between Classification & Prediction.
Machine learning techniques
Decision Trees
k-Nearest Neighbors
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
This document provides an overview of exploratory data analysis (EDA). It discusses the key stages of EDA including data requirements, collection, processing, cleaning, exploration, modeling, products, and communication. The stages involve examining available data to discover patterns and relationships. EDA is the first step in data mining projects to understand data without assumptions. The document also outlines the problem definition, data preparation, analysis, and result development and representation steps of EDA. Finally, it discusses different types of data like numeric, categorical, and the importance of understanding data types for analysis.
The document defines data mining as extracting useful information from large datasets. It discusses two main types of data mining tasks: descriptive tasks like frequent pattern mining and classification/prediction tasks like decision trees. Several data mining techniques are covered, including association, classification, clustering, prediction, sequential patterns, and decision trees. Real-world applications of data mining are also outlined, such as market basket analysis, fraud detection, healthcare, education, and CRM.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
This document provides an overview of probabilistic graphical models. It discusses two types of probabilistic graphical models - Bayesian networks and Markov networks. Bayesian networks use directed graphs to represent conditional independence relationships between random variables. Markov networks use undirected graphs for the same purpose. The document outlines topics like representation, examples including naive Bayes classifiers and the Ising model, and inference and learning algorithms for probabilistic graphical models.
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases).
This Machine Learning presentation will cover the following topics:
1. Life without Machine Learning
2. Life with Machine Learning
3. What is Machine Learning
4. Machine Learning Process
5. Types of Machine Learning
6. Supervised Vs Unsupervised
7. The right Machine Learning solutions
8. Machine Learning Algorithms
9. Use case - Predicting the price of a house using Linear Regression
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
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.
- - - - - - -
Who should take this Machine Learning Training Course?
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
- - - - - -
This document discusses the roles of data science and data scientists. It states that data science involves specialized skills in statistics, mathematics, programming, and computer science. A data scientist explores different data sources to discover hidden insights that can provide competitive advantages or address business problems. They are inquisitive individuals who can analyze data from multiple angles and recommend ways to apply findings to business challenges.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
IRJET - Machine Learning for Diagnosis of DiabetesIRJET Journal
This document describes a study that uses machine learning models to predict whether a person has diabetes based on patient data. The researchers created several classification models using algorithms like logistic regression and support vector machines on a diabetes dataset. The models with the highest accuracy at predicting diabetes were random forest and gradient boosting. An Android app was also developed to input patient data, run the predictions from the trained models, and display the results to help diagnose diabetes. The goal is to help reduce diabetes rates and healthcare costs by improving diagnosis.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
The document is an agenda for a seminar on machine learning techniques and tools. It will cover an introduction to machine learning, common techniques like classification, clustering and regression. It will also discuss tools for machine learning like Apache Mahout, Weka, Spark MLLib and R. Finally, it will include a hands-on demonstration of machine learning algorithms and discuss benefits of using machine learning.
This slide provides an overview of some of the core concepts related to building machine learning models. Machine learning is a branch of computer science that aims to make computers learn from data without being explicitly programmed. Learning problems can be classified into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves learning a function that maps inputs to outputs, given a set of labeled examples. Unsupervised learning involves finding patterns or structure in unlabeled data. Reinforcement learning involves learning how to act or behave in an environment, given feedback or rewards from the environment.
Other important concepts related to machine learning include generalization, overfitting, representation, features, models, evaluation, optimization, bias-variance tradeoff, and Occam's razor. Generalization refers to the ability of a machine learning model to perform well on new or unseen data, not just on the training data. Overfitting occurs when a model fits the training data too closely, resulting in poor generalization. Representation refers to the way of encoding or describing the input and output data for a machine learning problem. Features are the attributes or characteristics of the input data that are used for learning. Models are the mathematical or computational structures that represent or approximate the function that maps inputs to outputs. Evaluation involves measuring the performance or accuracy of a machine learning model on a given data set. Optimization involves finding the best or optimal parameters or settings for a machine learning model that minimize the error or maximize the accuracy on the training data. Bias-variance tradeoff refers to the balance between model complexity and generalization ability. Occam's razor is a principle that favors simpler explanations or models when competing hypotheses explain the data equally well.
Understanding these core concepts is crucial for anyone who wants to learn and apply machine learning in practice. This slide provides a concise summary of these concepts and can serve as a useful reference for beginners and experts alike.
1) Relational databases try to maintain strong consistency by avoiding inconsistencies, while NoSQL databases accept some inconsistencies due to the CAP theorem and eventual consistency.
2) Consistency in databases refers to only allowing valid data transactions according to the defined rules to prevent violations. NoSQL databases sacrifice some consistency for availability and partition tolerance.
3) Eventual consistency means replicas may show temporary inconsistencies but will eventually converge to the same state with further updates. This can cause problems for applications that require strong consistency.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
This document provides an overview of a university course on Cryptography and Network Security. It begins with the course syllabus, which outlines topics like security concepts, cryptography concepts and techniques, and types of security attacks. It then discusses key security concepts such as security services, security mechanisms, security attacks, and models for network and access security. It provides examples of security services like authentication, access control, and data confidentiality. It also describes security mechanisms and different classes of security attacks. The document concludes by listing reference books, online videos, related courses, tutorials, and sample multiple choice and problems related to cryptography and network security.
CYBERSPACE
CYBER POWER
INTERNET
CYBER-ATTACK
INDIA AND PAKISTAN CYBER ATTACKS
TYPES OF CYBER CRIME
Hypotheses in the Research
Literature on the Influence of Mass Media on Criminal Behavior
Technology-Related Risk Factors for Criminal Behavior
COPYCAT CRIME:
CYBER SECURITY
SAFETY TIPS FOR CYBER CRIME
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Definition of classification
Basic principles of classification
Typical
How Does Classification Works?
Difference between Classification & Prediction.
Machine learning techniques
Decision Trees
k-Nearest Neighbors
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
This document provides an overview of exploratory data analysis (EDA). It discusses the key stages of EDA including data requirements, collection, processing, cleaning, exploration, modeling, products, and communication. The stages involve examining available data to discover patterns and relationships. EDA is the first step in data mining projects to understand data without assumptions. The document also outlines the problem definition, data preparation, analysis, and result development and representation steps of EDA. Finally, it discusses different types of data like numeric, categorical, and the importance of understanding data types for analysis.
The document defines data mining as extracting useful information from large datasets. It discusses two main types of data mining tasks: descriptive tasks like frequent pattern mining and classification/prediction tasks like decision trees. Several data mining techniques are covered, including association, classification, clustering, prediction, sequential patterns, and decision trees. Real-world applications of data mining are also outlined, such as market basket analysis, fraud detection, healthcare, education, and CRM.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
This document provides an overview of probabilistic graphical models. It discusses two types of probabilistic graphical models - Bayesian networks and Markov networks. Bayesian networks use directed graphs to represent conditional independence relationships between random variables. Markov networks use undirected graphs for the same purpose. The document outlines topics like representation, examples including naive Bayes classifiers and the Ising model, and inference and learning algorithms for probabilistic graphical models.
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases).
This Machine Learning presentation will cover the following topics:
1. Life without Machine Learning
2. Life with Machine Learning
3. What is Machine Learning
4. Machine Learning Process
5. Types of Machine Learning
6. Supervised Vs Unsupervised
7. The right Machine Learning solutions
8. Machine Learning Algorithms
9. Use case - Predicting the price of a house using Linear Regression
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
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.
- - - - - - -
Who should take this Machine Learning Training Course?
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
- - - - - -
This document discusses the roles of data science and data scientists. It states that data science involves specialized skills in statistics, mathematics, programming, and computer science. A data scientist explores different data sources to discover hidden insights that can provide competitive advantages or address business problems. They are inquisitive individuals who can analyze data from multiple angles and recommend ways to apply findings to business challenges.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
IRJET - Machine Learning for Diagnosis of DiabetesIRJET Journal
This document describes a study that uses machine learning models to predict whether a person has diabetes based on patient data. The researchers created several classification models using algorithms like logistic regression and support vector machines on a diabetes dataset. The models with the highest accuracy at predicting diabetes were random forest and gradient boosting. An Android app was also developed to input patient data, run the predictions from the trained models, and display the results to help diagnose diabetes. The goal is to help reduce diabetes rates and healthcare costs by improving diagnosis.
Multi Disease Detection using Deep LearningIRJET Journal
1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
IRJET- GDPS - General Disease Prediction SystemIRJET Journal
The document describes a General Disease Prediction System (GDPS) that uses machine learning and data mining techniques to predict diseases based on patient symptoms.
The GDPS first collects patient data, preprocesses it, and extracts relevant features. It then implements the ID3 decision tree algorithm to generate a predictive model and classify diseases. As an admin, one can train the model using sample data. As a user, one can enter symptoms and the trained model will predict the likely disease and recommend precautions.
The GDPS was tested on a dataset of 120 patients and achieved 86.67% accuracy in disease prediction. The system currently covers common diseases but future work involves expanding it to predict more serious or fatal diseases like various cancers
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
Utilizing Machine Learning, Detect Chronic Kidney Disease and Suggest A Healt...IRJET Journal
The document discusses using machine learning models to detect chronic kidney disease and recommend a healthy diet. It evaluates four machine learning algorithms - decision tree, K-nearest neighbor, random forest, and support vector machine - to predict chronic kidney disease based on patient data. The random forest algorithm achieved the highest accuracy of 100% compared to 96.88% for support vector machine, 81.25% for K-nearest neighbor, and 100% for decision tree. The proposed system uses feature selection and the patient's blood potassium level to recommend an appropriate diet to help patients and clinicians.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
IRJET- Predicting Heart Disease using Machine Learning AlgorithmIRJET Journal
1) The document discusses using machine learning algorithms like Naive Bayes and Decision Trees to predict heart disease using a dataset from Kaggle.
2) It describes preprocessing the dataset, training models, and evaluating accuracy. Decision Trees were found to more accurately predict heart disease than Naive Bayes.
3) The models use 13 attributes like age, sex, cholesterol levels, and examine classification performance to identify individuals at risk of heart disease.
Health Care Application using Machine Learning and Deep LearningIRJET Journal
This document presents a study on using machine learning and deep learning techniques for healthcare applications like disease prediction. It discusses algorithms like logistic regression, decision trees, random forests, SVMs and deep learning models like VGG16 applied on various disease datasets. For diabetes, heart and liver diseases, ML algorithms were used while CNN models were used for malaria and pneumonia image datasets. Random forest achieved the highest accuracy of 84.81% for diabetes prediction, SVM had 81.57% accuracy for heart disease and random forest was best at 83.33% for liver disease. The VGG16 model attained accuracies of 94.29% and 95.48% for malaria and pneumonia respectively. The study aims to develop an intelligent healthcare application for predicting different
Heart Disease Prediction using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to predict heart disease based on patient attributes. It begins with an introduction describing heart disease as a major cause of death and the importance of early detection. It then discusses using machine learning techniques like logistic regression, backward elimination, and recursive feature elimination on a publicly available heart disease dataset to classify patients and evaluate the results. The goal is to help identify patients at high risk of heart disease so lifestyle changes can be made to reduce complications. Various machine learning algorithms are tested and evaluated to determine the best approach for heart disease prediction.
DISEASE PREDICTION SYSTEM USING SYMPTOMSIRJET Journal
This document describes a disease prediction system that uses machine learning techniques like naive bayes classification to predict diseases based on patient symptoms. The system collects medical data from various sources to build a dataset with 5000 rows and 133 columns. It preprocesses the data and builds a model using naive bayes classification that is able to accurately predict diseases from the test data with 100% accuracy based on a confusion matrix. The system architecture allows patients to input symptoms and receives a predicted disease and confidence score. It then refers the patient to doctors specialized in the predicted disease to enable online consultation.
Predicting disease at an early stage becomes critical, and the most difficult challenge is to predict it correctly along with the sickness. The prediction happens based on the symptoms of an individual. The model presented can work like a digital doctor for disease prediction, which helps to timely diagnose the disease and can be efficient for the person to take immediate measures. The model is much more accurate in the prediction of potential ailments. The work was tested with four machine learning algorithms and got the best accuracy with Random Forest.
LIFE EXPECTANCY PREDICTION FOR POST THORACIC SURGERYIRJET Journal
This document discusses using machine learning algorithms to predict life expectancy after thoracic surgery. Researchers used attribute ranking and selection methods to identify the most important attributes from a dataset of patient health records. They evaluated algorithms like logistic regression and random forest on the reduced dataset. Logistic regression achieved the highest prediction accuracy of 85%. The goal was to more accurately predict mortality risk based on a patient's underlying health issues and attributes related to lung cancer.
IRJET - Breast Cancer Prediction using Supervised Machine Learning Algorithms...IRJET Journal
This document describes a study that uses supervised machine learning algorithms to predict breast cancer. Three algorithms - decision tree, logistic regression, and random forest - are applied to preprocessed breast cancer data. The random forest model achieved the best accuracy at 98.6% for predicting whether a tumor was benign or malignant. The study aims to develop an early prediction system for breast cancer using machine learning techniques.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
A SURVEY ON BLOOD DISEASE DETECTION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper that evaluates different machine learning algorithms for detecting blood diseases from laboratory test results. It first introduces the objective to classify and predict diseases like anemia and leukemia. It then evaluates three algorithms: Gaussian, Random Forest, and Support Vector Classification (SVC). SVC achieved the highest accuracy of 98% for anemia detection. The models are deployed using Streamlit so users can access them online or offline. Benefits include low hardware requirements and mobile access. Future work will add more disease predictions and integrate nutritional guidance.
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
IRJET- A Survey on Prediction of Heart Disease Presence using Data Mining and...IRJET Journal
This document summarizes a survey on using data mining and machine learning techniques to predict heart disease. It begins with an abstract stating that heart disease is a leading cause of death worldwide and that earlier detection through technology could help reduce serious cases. The document then provides an overview of data mining, machine learning, and classification algorithms that have been used by researchers to predict heart disease. It describes a commonly used heart disease dataset and discusses evaluation measures for comparing classification algorithms to identify the best model for predicting heart disease.
Similar to MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DISORDER (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
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.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
2. 68 Computer Science & Information Technology (CS & IT)
means that thyroids are very active in Hyperthyroidism. In contrast, thyroids are not active in
Hypothyroidism [1] [2].
A decision tree is one of the very effective machine learning classifier where the algorithm makes
a tree structure, where every non-leaf node denotes a test on an attribute, each branch performs an
outcome of the test and each leaf node holds a class label. Decision tree is very simple and
effective classifier so this algorithm can be used in several application areas such as medicine,
financial analysis, astronomy and molecular biology for classification [3] [4]. We are going to
use decision tree to identify and predict thyroid diseases whether new patient data (symptom or
features) show thyroid disorder or normal.
This study developed a tool to be used for diagnosing thyroid diseases. We named our tool as a
MLTDD (machine learning tool for thyroid disease diagnosis). MLTDD could predict 99.81%
accuracy thyroid diseases with sample dataset.
The paper is organized as follows: The Section 2 describes the thyroid dataset and introduces
decision tree techniques. The obtained experimental results in application are given in Section 3.
Discussions and comparisons with previous work will be found in Section 4. Finally, Section 5
presents the conclusions.
2. METHODOLOGY USED
This section explains about the algorithm, language and software used for this work. The data set
used for experimental purpose is downloaded from university of California of Irvin (UCI)
repository site (web source http://www.archive.ics.uci.edu/ml/datasets.html). Details of data set is
given in Section 2.1.
An implementation process of thyroid gland diagnosis process is depicted in Figure 1.
Figure 1: Implementation of MLTDD Process
2.1 Description of data-set
Thyroid dataset for this work had been collected from UCI machine learning repository. It
consists of 7200 instances and 3 classes, 3772 are training instances, 3428 testing are instances
3. Computer Science & Information Technology (CS & IT) 69
and 21 attributes as shown in Table 1 [5]. The task is to detect is a given patient has a normal
condition (1) or suffers from hyperthyroidism (2) or hypothyroidism (3).
This section describes the main characteristics of the thyroid data set and its attributes: Each
measurement vector consists of 21 values – 15 binary and 6 are continuous. Three classes are
assigned to each of the measurement vectors, which correspond with hyper-rthyroidism, hypo-
rthyroidism and normal function of the thyroid gland (Table 2).
Table 1. General information
Table 2. Attribute description.
4. 70 Computer Science & Information Technology (CS & IT)
2.2 Decision Tree classification method
We used decision tree classification for our thyroid diseases diagnosis tool. Decision tree takes
training datasets and construct an internal tree structure. The non-leaf node will have a question
or a condition to check based on feature of the data. The leaf nodes of the tree have the labels.
The algorithm of constructing decision tree is a divide-and-conquer algorithm. The root of the
tree asks one of the feature questions to splits datasets into multiple branches based on the answer
of the parent question. If all the subsets of data in the branch have the same label, that branch will
stop. It will not grow from that branch any further. Otherwise, the constructor algorithm can split
the data further with new conditions from other features of the data sets. The algorithm will
recursively try to create new branches of the tree based on the features (or attributes) of datasets.
Basically algorithm try to understand which features effect the decision for labeling.
When we have a record of a new patient, we use the decision tree as if it is a flow-charts. We try
to go the labeled leaf node by asking the same questions of the tree until we reach the leaf node.
We then classify the new patient the same as the leaf node label which we reach from the root of
the decision tree. There are huge number of research how to select which attributes is best to
select for the root and non-leaf nodes. Basically, the constructer will choose the attributes to have
maximum information gains [6].
2.3 MLTDD: Intelligent Thyroid Gland Disease Prediction System
We designed this application to be used by doctors, who are not expert computer users. So our
design goal was to develop the user interface of the thyroid diseases application as user friendly
as possible. The application has been implemented in Eclipse Python (PyDev plugged in eclipse
JUNO Version: 4.2.2) coded in python 2.7. The GUI has been designed in Qt designer version
5.5.1 which is graphical user interface for Qt application. MLTDD runs on windows
environment (Fig. 2).
Fig. 2. Screen of prediction test of MLTDD app.
5. Compute
Ann dataset has been used to train the decision tree algorithm and create a model then predict a
diagnosis for a new patient whose data will
pass them to a python program which uses the model created to classify the patient according to
its data as a hypothyroidism or hyperthyroidism or a normal diagnosis, then by passing the
diagnosis to the program which dealing with the GUI so that it can be printed out to the user.
3. RESULTS
For preprocessing, we applied “PCA” for feature selection but we noticed that the accuracy
decreased from 99.644 to 97.533, we also applied another preprocessing me
subset” but we got the same result. For the purpose of decreasing the number of
able to design an acceptable GUI, since the dataset has 21 features "information gain attribute
evaluation” has been applied as a feature selection
which helps to eliminate the ten least important attributes and keep the other 11
11 features we got a 99.70 % accuracy and it doesn't decrease so much, since the best accuracy
we got with the all 21 features is 99.82% with 23% testing dataset as shown at Table 3. So we
decided to eliminate those 10 feature to make my "GUI" more acceptable and easier
Table 3, there are different accuracy values with different splitting of dataset comparin
and after applying the feature selection method and eliminating the less important features, in
Figure 3 the graph shows this comparison as well
Table 3: Specifies the values of Accuracy before and after eliminating 10 features.
Percentage split for
testing dataset
10 %
15 %
20 %
23 %
30 %
32 %
33 %
Figure 3: Accuracy comparison graph for the splitting percentage
Computer Science & Information Technology (CS & IT)
Ann dataset has been used to train the decision tree algorithm and create a model then predict a
diagnosis for a new patient whose data will be entered by the user. Taking the data from GUI and
pass them to a python program which uses the model created to classify the patient according to
its data as a hypothyroidism or hyperthyroidism or a normal diagnosis, then by passing the
program which dealing with the GUI so that it can be printed out to the user.
For preprocessing, we applied “PCA” for feature selection but we noticed that the accuracy
decreased from 99.644 to 97.533, we also applied another preprocessing method “ROUNDUM
subset” but we got the same result. For the purpose of decreasing the number of features to be
able to design an acceptable GUI, since the dataset has 21 features "information gain attribute
evaluation” has been applied as a feature selection method. A good ranking has been obtained
which helps to eliminate the ten least important attributes and keep the other 11 attributes. With
11 features we got a 99.70 % accuracy and it doesn't decrease so much, since the best accuracy
21 features is 99.82% with 23% testing dataset as shown at Table 3. So we
feature to make my "GUI" more acceptable and easier
Table 3, there are different accuracy values with different splitting of dataset comparin
and after applying the feature selection method and eliminating the less important features, in
Figure 3 the graph shows this comparison as well.
Table 3: Specifies the values of Accuracy before and after eliminating 10 features.
split for
testing dataset
Accuracy (%)
With 21 features
Accuracy (%)
With 11 features
99.58 % 99.17 %
99.72 % 99.35 %
99.79 % 99.51 %
99.82 % 99.58 %
99.68 % 99.68 %
99.65 % 99.70 %
99.62 % 99.66 %
Figure 3: Accuracy comparison graph for the splitting percentage
71
Ann dataset has been used to train the decision tree algorithm and create a model then predict a
be entered by the user. Taking the data from GUI and
pass them to a python program which uses the model created to classify the patient according to
its data as a hypothyroidism or hyperthyroidism or a normal diagnosis, then by passing the
program which dealing with the GUI so that it can be printed out to the user.
For preprocessing, we applied “PCA” for feature selection but we noticed that the accuracy
“ROUNDUM
features to be
able to design an acceptable GUI, since the dataset has 21 features "information gain attribute
method. A good ranking has been obtained
attributes. With
11 features we got a 99.70 % accuracy and it doesn't decrease so much, since the best accuracy
21 features is 99.82% with 23% testing dataset as shown at Table 3. So we
feature to make my "GUI" more acceptable and easier to use. In
Table 3, there are different accuracy values with different splitting of dataset comparing before
and after applying the feature selection method and eliminating the less important features, in
Table 3: Specifies the values of Accuracy before and after eliminating 10 features.
6. 72 Computer Science & Information Technology (CS & IT)
4. RELATED WORK
Many methods and algorithms have been utilized in the past in medical disease classification. A
skimpy abstract of them as follows:
Anupam Skukla et al. [8] suggested the diagnosis of thyroid disorders with Artificial Neural
Networks (ANNs). Three ANN algorithms were; the Radial Basis Function (RBFN), the Back
propagation algorithm (BPA), and the Learning Vector Quantization (LVQ) Networks have been
utilized for the diagnosis.
Lale Ozyilmaz et al. [9] concentrated on suitable interpretation of the thyroid data. Several neural
network methods such as fast back-propagation (FBP), Radial Basis Function (RBF), adaptive
Conic Section Function Neural Network (CSFNN), and Multi-Layer Perceptron (MLP) with
back-propagation (BP) have been utilized and compared for the diagnosis of thyroid disease.
Fatemeh Saiti et al. [10] proposed two algorithms, which are Support Vector Machines and
Probabilistic Neural Network considering separating and classification the Hypothyroidism and
hyperthyroidism diseases, which plays a vital role for thyroid diagnosis. These methods depend
on robust classification algorithms, in order to deal with irrelevant and redundant features.
Carlos Ordonez et al. [11] compared decision tree rules with association rules. Association rules
research for hidden patterns turning them out to be suitable for discovering predictive rules
including subsets of the medical data set.
A.S.Varde et al. [12] has developed the clinical laboratory expert system in 1991 for the
diagnosis of thyroid disorder. The system had considered clinical findings and the results of
applicable laboratory tests along with the patient’s medical history. The system had been execute
using VP-Expert, version 2.02 that is commercially available software.
Palanichamy Jaganathan et al. [13] developed F-score feature selection method that used to
nominate the most relevant features for classification of thyroid dataset. The result shows that
their new feature selection method applied to this dataset has generated better classification
accuracy than GDA-WSVM combination, with an amelioration of 1.63%; of accuracy for
improved F-score-MLP combination (93.49%).
5. CONCLUSIONS
In recent years, machine learning is becoming very important computer science fields. It is
slowly changing many industry including health industry. We collect many data and we have
very fast computational power. Together with very effective algorithms, we can develop tools to
help human to do their job better. In this study we develop a tool to help doctor for the disease
diagnosis. We do not expect our tool to be used by a doctor as it is, but these kind of tool can be
used by medical student to be a learning tool.
Thyroid disease can be tricky to diagnose because symptoms are easily confused with other
illness condition. When thyroid disease is caught early, treatment can be given patients very
effectively. In this study, diagnosing thyroid disease is aimed with a machine learning tool called
7. Computer Science & Information Technology (CS & IT) 73
as MLTDD (machine learning tool for thyroid disease diagnosis). MLTDD could form a
foresight diagnosis with 99.7% accuracy for thyroid diseases in the datasets we used.
In our opinion, we can develop some more diseases diagnosis tool to help medical students to
learn the disease characteristics for testing purposes. We do not believe these tools will replace
the doctor yet. But they can be a helper to doctor if we have further improvement at technology.
However, students who is studying endocrinology for thyroid diseases can use this tool for testing
their knowledge by comparing their predictions with MLTDD. We have to collect more data to
training these algorithms. The more data means we need to have high performing computational
units. We believe that when we put big data, high speed computational power and advance
machine learning algorithm, we can have very effective diagnostic tools.
REFERENCES
[1] Dr. Sahni BS, Thyroid Disorders .Online Available:
http://www.homoeopathyclinic.com/articles/diseases/tyro id.pdf
[2] Thyroid: https://en.wikipedia.org/wiki/Thyroid
[3] Jiawei Han , Micheline Kamber, Data Mining Concepts and Techniques. Published by Elsevier 2006.
[4] V. Podgorelec, P. Kokol, B. Stiglic, and I. Rozman, Decision trees: An overview and their use in
medicine. In Proceedings of Journal Medical System 2002.
[5] http://archive.ics.uci.edu/ml/machine-learning-databases/ thyroid-disease/
[6] H.S.Hota, Diagnosis of Breast Cancer Using Intelligent Techniques, International Journal of
Emerging Science and Engineering (IJESE), January 2013 .
[7] Jiawei Han , Micheline Kamber, Data Mining Concepts and Techniques. Published by Elsevier 2006.
[8] Anupam Shukla, Prabhdeep Kaur, Ritu Tiwari and R.R. Janghel, Diagnosis of Thyroid disease using
Artificial Neural Network. In Proceedings of IEEE IACC 2009.
[9] Lale Ozyilmaz , Tulay Yildirim, Diagnosis of Thyroid disease using Artificial Neural Network
Methods. In Proceedings of ICONIP 2002.
[10] Fatemeh Saiti and Mahdi Aliyari, Thyroid Disease Diagnosis based on Genetic algorithms using PNN
and SVM. In Proceedings of IEEE 2009.
[11] Carlos Ordonez University of Houston, Houston, TX, “Comparing association rules and decision
trees for disease prediction”. In Proceedings of Int. Conf. Inf. Knowl. Manage., 2006.
[12] A.S.Varde,K.L.Massey and H.C.Wood, “A Clinical Laboratory Expert System for the Diagnosis of
Thyroid Disfunction”, Proceeding of Annual International Conference of the IEEE Engineering in
Medicine and Biology Society.
[13] Palanichamy Jaganathan and Nallamuthu Rajkumar, ” An expert system for optimizing thyroid
disease diagnosis”. In Proceedings of International Journal of Computational Science and
Engineering,2012.