This document discusses data preparation techniques for machine learning, including data cleaning, feature selection, data transforms, feature engineering, and dimensionality reduction. It provides an overview of each technique and examples of common methods used. The document also outlines a lesson plan that will cover these topics in more detail, with the goal of helping students understand fundamental concepts in data preparation.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
1) The document discusses a study on handwritten digit recognition using machine learning. It reviews various digit recognition methods and analyzes an integrated system that achieved a minimum error rate of 0.32%.
2) The study uses a neural network model to recognize handwritten digits. It trains the model on over 60,000 images from MNIST and custom datasets.
3) Testing involves capturing images using a webcam in real-time, then preprocessing the images and running them through the trained neural network model to predict the digit. The model achieved high accuracy after training on large datasets.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper on handwritten digit recognition using machine learning. The researchers trained a neural network model on over 60,000 images to recognize handwritten digits. The model was trained using two databases - the MNIST database and a self-collected database. It was tested on real-time images captured by a webcam. After training and testing, the integrated system achieved a minimum error rate of 0.32% in recognizing handwritten digits. The document also discusses the image processing techniques used in training and testing the model as well as the neural network architecture.
The document discusses common problems with data for artificial intelligence projects. It explains that real-world data is often messy and requires significant preprocessing before being used to train machine learning models. Some specific problems covered include noisy or undefined data, errors in data definition, capture, measurement, and sampling. The document also notes other considerations like data accessibility, costs, agreements, privacy issues, and reliability.
The document proposes a recruiter recommendation system for undergraduate students to improve college placement processes. It uses machine learning algorithms like logistic regression, random forest, KNN and SVM to analyze previous student data and predict placement probabilities based on marks. This would help students strengthen their skills and recommend eligible companies. The system architecture involves collecting student data like CGPA and technical test scores, training models, and generating recommendations to match students with appropriate recruiters. This automated process aims to make placements more efficient by reducing manual work and better notifying students.
Alumni Management System – Web ApplicationIRJET Journal
This document discusses the development of an alumni management system website using web development tools. It aims to create a responsive website to manage alumni data and interactions between the university and alumni. The proposed system uses HTML, CSS, JavaScript, Python/Django for the front-end and back-end, and SQL for the database. It includes features like registration, login, profile updating for both administrators and alumni. The system is meant to more easily track alumni over time compared to previous manual methods.
Alumni Management System -Web ApplicationMandy Brown
This document discusses the development of an alumni management system website using web development tools. It describes using a database to store alumni information and allow both administrators and alumni to access the data. The proposed system uses HTML, CSS, JavaScript, Python/Django for the front-end and back-end development, and SQL for the database. It includes features like an admin interface to update data, and alumni profiles to view events and job postings. The system aims to more easily manage alumni relationships and data in a centralized, digital manner compared to prior static storage methods.
IRJET - Encoded Polymorphic Aspect of ClusteringIRJET Journal
This document discusses using machine learning techniques for clustering multi-view data. It focuses on an unsupervised learning technique called clustering, which groups similar objects together into clusters while separating dissimilar objects into different clusters. Compared to single-view clustering, multi-view clustering can access more characteristics and structural information hidden in the data by exploiting richer properties to improve clustering performance. It also discusses encoding datasets into binary format for storage, clustering the encoded data, and retrieving desired data through decoding based on user queries. The goal is to efficiently handle large datasets using scalable machine learning algorithms.
A Machine learning based framework for Verification and Validation of Massive...IRJET Journal
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
1) The document discusses a study on handwritten digit recognition using machine learning. It reviews various digit recognition methods and analyzes an integrated system that achieved a minimum error rate of 0.32%.
2) The study uses a neural network model to recognize handwritten digits. It trains the model on over 60,000 images from MNIST and custom datasets.
3) Testing involves capturing images using a webcam in real-time, then preprocessing the images and running them through the trained neural network model to predict the digit. The model achieved high accuracy after training on large datasets.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper on handwritten digit recognition using machine learning. The researchers trained a neural network model on over 60,000 images to recognize handwritten digits. The model was trained using two databases - the MNIST database and a self-collected database. It was tested on real-time images captured by a webcam. After training and testing, the integrated system achieved a minimum error rate of 0.32% in recognizing handwritten digits. The document also discusses the image processing techniques used in training and testing the model as well as the neural network architecture.
The document discusses common problems with data for artificial intelligence projects. It explains that real-world data is often messy and requires significant preprocessing before being used to train machine learning models. Some specific problems covered include noisy or undefined data, errors in data definition, capture, measurement, and sampling. The document also notes other considerations like data accessibility, costs, agreements, privacy issues, and reliability.
The document proposes a recruiter recommendation system for undergraduate students to improve college placement processes. It uses machine learning algorithms like logistic regression, random forest, KNN and SVM to analyze previous student data and predict placement probabilities based on marks. This would help students strengthen their skills and recommend eligible companies. The system architecture involves collecting student data like CGPA and technical test scores, training models, and generating recommendations to match students with appropriate recruiters. This automated process aims to make placements more efficient by reducing manual work and better notifying students.
Alumni Management System – Web ApplicationIRJET Journal
This document discusses the development of an alumni management system website using web development tools. It aims to create a responsive website to manage alumni data and interactions between the university and alumni. The proposed system uses HTML, CSS, JavaScript, Python/Django for the front-end and back-end, and SQL for the database. It includes features like registration, login, profile updating for both administrators and alumni. The system is meant to more easily track alumni over time compared to previous manual methods.
Alumni Management System -Web ApplicationMandy Brown
This document discusses the development of an alumni management system website using web development tools. It describes using a database to store alumni information and allow both administrators and alumni to access the data. The proposed system uses HTML, CSS, JavaScript, Python/Django for the front-end and back-end development, and SQL for the database. It includes features like an admin interface to update data, and alumni profiles to view events and job postings. The system aims to more easily manage alumni relationships and data in a centralized, digital manner compared to prior static storage methods.
IRJET - Encoded Polymorphic Aspect of ClusteringIRJET Journal
This document discusses using machine learning techniques for clustering multi-view data. It focuses on an unsupervised learning technique called clustering, which groups similar objects together into clusters while separating dissimilar objects into different clusters. Compared to single-view clustering, multi-view clustering can access more characteristics and structural information hidden in the data by exploiting richer properties to improve clustering performance. It also discusses encoding datasets into binary format for storage, clustering the encoded data, and retrieving desired data through decoding based on user queries. The goal is to efficiently handle large datasets using scalable machine learning algorithms.
A Machine learning based framework for Verification and Validation of Massive...IRJET Journal
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
The document describes a proposed web-based student assessment data processing system using the CodeIgniter framework. The system aims to address issues with the current semi-computerized assessment process at SMK Negeri 1 Pandeglang, including errors during data entry and a time-consuming report generation process. The proposed system was analyzed using SWOT and other methods. It would feature a teacher interface to enter grades and an admin interface to manage data masters. Diagrams including use case, activity, class, and sequence diagrams were created to design the system's functionality and interactions. The system aims to streamline the assessment process and make it more efficient.
IRJET- Automated CV Classification using Clustering TechniqueIRJET Journal
This document proposes an automated resume classification system using clustering techniques. It aims to help HR departments more efficiently sort through large numbers of resumes by calculating a score for each resume based on skills and assigning resumes to clusters. The system would allow employers to customize job postings and weight desired skills. It would provide candidates a portal to upload resumes and receive scores. K-means clustering would then group resumes, giving HR a classified view. This could reduce the time and effort spent on manual resume sorting while increasing accuracy. The document outlines the system architecture, algorithms and benefits of automating and customizing the resume classification process.
Proposing an Interactive Audit Pipeline for Visual Privacy ResearchChristan Grant
1. The document proposes an interactive audit pipeline for machine learning models that considers fairness, privacy, and ownership issues.
2. It describes a traditional machine learning pipeline and notes that such pipelines do not consider these important issues. Frameworks are recommended for designing new, improved pipelines.
3. An example scenario is presented of building a people counter system. The traditional pipeline is shown to have gaps in considering issues like bias, privacy, and consent. The proposed interactive audit pipeline integrates strategies like fairness and privacy auditing to help address these issues.
Eric Nyberg's Presentation "From Jeopardy! To Cognitive Agents: Effective Learning in the Wild" on Cognitive Systems Institute Group Speaker Series July 9, 2015
CUSTOMER SEGMENTATION IN SHOPPING MALL USING CLUSTERING IN MACHINE LEARNINGIRJET Journal
This document discusses using clustering algorithms in machine learning to segment customers in a shopping mall. It aims to identify groups of customers with similar characteristics like gender, age, spending habits to more effectively market to each group. Specifically, it uses k-means clustering to segment customers and visualize differences in gender and age. It then examines their annual income and proposes that segmentation focuses on improving customer spending scores. The proposed system uses machine learning approaches like k-means clustering which is more accurate and efficient than traditional manual methods for analyzing customer data and finding insights to identify customer segments.
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET Journal
This document compares several machine learning classification algorithms. It first provides background on machine learning and describes common algorithms like linear regression, support vector machines, and decision trees. It then outlines an experimental framework in Python using libraries like Pandas, Scikit-Learn, and Matplotlib. Various classification algorithms are applied to a dataset and their test and train errors are calculated and compared to determine the most accurate algorithm. The proposed algorithm is found to have the lowest test and train errors compared to other algorithms like ridge regression, KNN, Bayesian regression, decision trees, and SVM.
Slides from lecture style tutorial on data quality for ML delivered at SIGKDD 2021.
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models (such as neural architecture search and automated feature selection), there are limited efforts towards improving the data quality.
Assessing the quality of the data across intelligently designed metrics and developing corresponding transformation operations to address the quality gaps helps to reduce the effort of a data scientist for iterative debugging of the ML pipeline to improve model performance. This tutorial highlights the importance of analysing data quality in terms of its value for machine learning applications. Finding the data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue. This tutorial surveys all the important data quality related approaches for structured, unstructured and spatio-temporal domains discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems.
IRJET- Fast Phrase Search for Encrypted Cloud StorageIRJET Journal
This document proposes a technique for fast phrase search on encrypted documents stored in the cloud. It presents a phrase search method based on Bloom filters that is faster than existing solutions, with similar or lower storage and communication costs. The technique uses a series of n-gram filters to support phrase searching functionality. It exhibits a tradeoff between storage size and false positive rate, and can defend against inclusion-relation attacks. The design approach is adaptable based on an application's target false positive rate. The system aims to provide secure and efficient phrase search on encrypted documents outsourced to cloud storage.
Ignou MCA 4th semester mini project report. College admission system. This project is based on real working system of University seat allocation to affiliate colleges. College admission system provide seat allocation process for various UG PG programs for every academic session.
The document discusses topics related to rule-based machine learning, clustering, and association rules. It covers the following key points:
- The learning objectives of the course, which include understanding decision trees, entropy, information gain, clustering, and association rules.
- The course materials, which cover introduction to decision trees, entropy and information gain, clustering, and association rules.
- An introduction to decision trees, including how they represent classification problems using nodes and branches to represent attributes and values. Information theory helps determine the role of attributes in tree construction.
The document discusses engineering minors offered by the School of Computer Science and Engineering. It defines engineering minors as a set of six courses in an engineering discipline that allows students to develop competency in an area outside their major. It provides details on the data science engineering minor, including the courses offered and their descriptions and learning outcomes. The minor aims to provide students with interdisciplinary experience and skills in data analytics, visualization, programming, and big data processing to enhance their career opportunities.
This document discusses predicting loan defaults through machine learning models. It begins by introducing the business problem of banks suffering losses from customer loan defaults. It then describes preprocessing the loan dataset, which includes handling missing data, label encoding categorical variables, and balancing the dataset using SMOTE and SMOTEENN techniques. Logistic regression, decision trees, AdaBoost and random forest algorithms are applied to both the original and balanced datasets. The random forest model on the balanced data using SMOTEENN achieved the best accuracy of 92%. The model is then pickled and integrated into a web application using Flask for users to predict loan defaults.
A Generic Model for Student Data Analytic Web Service (SDAWS)Editor IJCATR
Any university management system accumulates a cartload of data and analytics can be applied on it to gather useful
information to aid the academic decision making process. This paper is a novel attempt to demonstrate the significance of a data
analytic web service in the education domain. This can be integrated with the University Management System or any other application
of the university easily. Analytics as a web service offers much benefits over the traditional analysis methods. The web service can be
hosted on a web server and accessed over the internet or on to the private cloud of the campus. The data from various courses from
different departments can be uploaded and analyzed easily. In this paper we design a web service framework to be used in educational
data mining that provide analysis as a service.
The document discusses artificial intelligence (AI) topics that will be covered in an AI course module. It includes definitions of key AI concepts and terminology to meet the course's learning objectives. The topics that will be covered are: what is AI, the impact of AI on jobs, different approaches to learning AI (conceptual, algorithmic, mathematical, case studies), the machine intelligence continuum (levels from systems that act to systems that relate), expert systems and machine learning, and various AI applications.
This document provides information about the Engineering Minor in Data Science offered by the School of Computer Science and Engineering. It describes what engineering minors are, lists the courses offered in the Data Science minor, and provides brief descriptions and outcomes of each course. The minor consists of six courses spanning four semesters that cover topics like data management, visualization, programming in R, predictive analytics, big data fundamentals, and cluster computing. The document also discusses career opportunities, industrial applications, special requirements, and contacts for additional information about the minor.
This document provides a summary of a project proposal for developing a School Admission Process Management System. It includes sections on project initiation and scheduling, diagrams, project cost estimation, designing the user interface, and testing approaches. The project aims to automate the currently manual school admission process to make it faster and easier to use. It will develop a web-based system using technologies like ASP.NET, SQL Server, and PHP/MySQL. Testing will include white box, black box, unit, integration, and system testing approaches. The document outlines the requirements, feasibility, advantages over the current system, and includes diagrams to depict the system design.
The document describes a student information management system project. It includes sections on the introduction, problem statement, objectives, scope, requirements analysis, feasibility study, system design, implementation, testing, maintenance, and conclusion. The project aims to develop a computerized system to manage student records and information to replace a manual paper-based system. The system will allow administrators to easily search, edit, and find student details and allow students to update their profiles. The requirements analysis and feasibility study ensure the project is technically, operationally, and economically feasible. Overall, the system aims to simplify student information management for organizations.
This document discusses cloning an organization to allow testing and manipulation without affecting the original site. It defines cloning as creating an exact copy that can be used for tasks without risk to the original. Types of clones include the frontend design, backend design, and database. Benefits of cloning for software testing are that it is cost-effective, improves security and product quality, and increases customer satisfaction. The document then discusses various software testing types, reverse engineering, and software development life cycles like waterfall, RAD, spiral, V-model, incremental, agile, iterative, big bang and prototype models. The conclusion is that cloning can help test and learn new features without interrupting the original organization's data and business.
Monitoring Students Using Different Recognition Techniques for Surveilliance ...IRJET Journal
This document discusses using computer vision techniques like convolutional neural networks to monitor students and enforce dress codes in educational institutions. It proposes a system using cameras and image processing to identify whether students are properly dressed according to the dress code. The system would classify images of students as either following or not following the dress code. It also discusses related work on using technologies like biometrics and RFID cards for automated student attendance tracking and implications for security and discipline in schools.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
The document discusses implementing machine learning with Python. It covers loading machine learning data using Python, preparing the data, visualizing it, feature selection, evaluating model performance, and implementing machine learning algorithms and neural networks. Specifically, it demonstrates loading the Pima Indian diabetes dataset, exploring the data distribution and correlations, and performing descriptive statistics to understand the dataset's properties before implementing machine learning techniques.
The document provides an introduction to the topic of deep learning. It outlines the learning objectives, which are to define machine learning techniques like linear regression, rule-based learning, probabilistic learning, and clustering, as well as the basic concepts of deep learning and its implementation in image recognition using convolutional neural networks. The topics to be covered are the history of artificial intelligence and neural networks, visualizing deep learning concepts, essentials of deep learning like perceptrons and backpropagation, and convolutional neural networks in detail.
The document describes a proposed web-based student assessment data processing system using the CodeIgniter framework. The system aims to address issues with the current semi-computerized assessment process at SMK Negeri 1 Pandeglang, including errors during data entry and a time-consuming report generation process. The proposed system was analyzed using SWOT and other methods. It would feature a teacher interface to enter grades and an admin interface to manage data masters. Diagrams including use case, activity, class, and sequence diagrams were created to design the system's functionality and interactions. The system aims to streamline the assessment process and make it more efficient.
IRJET- Automated CV Classification using Clustering TechniqueIRJET Journal
This document proposes an automated resume classification system using clustering techniques. It aims to help HR departments more efficiently sort through large numbers of resumes by calculating a score for each resume based on skills and assigning resumes to clusters. The system would allow employers to customize job postings and weight desired skills. It would provide candidates a portal to upload resumes and receive scores. K-means clustering would then group resumes, giving HR a classified view. This could reduce the time and effort spent on manual resume sorting while increasing accuracy. The document outlines the system architecture, algorithms and benefits of automating and customizing the resume classification process.
Proposing an Interactive Audit Pipeline for Visual Privacy ResearchChristan Grant
1. The document proposes an interactive audit pipeline for machine learning models that considers fairness, privacy, and ownership issues.
2. It describes a traditional machine learning pipeline and notes that such pipelines do not consider these important issues. Frameworks are recommended for designing new, improved pipelines.
3. An example scenario is presented of building a people counter system. The traditional pipeline is shown to have gaps in considering issues like bias, privacy, and consent. The proposed interactive audit pipeline integrates strategies like fairness and privacy auditing to help address these issues.
Eric Nyberg's Presentation "From Jeopardy! To Cognitive Agents: Effective Learning in the Wild" on Cognitive Systems Institute Group Speaker Series July 9, 2015
CUSTOMER SEGMENTATION IN SHOPPING MALL USING CLUSTERING IN MACHINE LEARNINGIRJET Journal
This document discusses using clustering algorithms in machine learning to segment customers in a shopping mall. It aims to identify groups of customers with similar characteristics like gender, age, spending habits to more effectively market to each group. Specifically, it uses k-means clustering to segment customers and visualize differences in gender and age. It then examines their annual income and proposes that segmentation focuses on improving customer spending scores. The proposed system uses machine learning approaches like k-means clustering which is more accurate and efficient than traditional manual methods for analyzing customer data and finding insights to identify customer segments.
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET Journal
This document compares several machine learning classification algorithms. It first provides background on machine learning and describes common algorithms like linear regression, support vector machines, and decision trees. It then outlines an experimental framework in Python using libraries like Pandas, Scikit-Learn, and Matplotlib. Various classification algorithms are applied to a dataset and their test and train errors are calculated and compared to determine the most accurate algorithm. The proposed algorithm is found to have the lowest test and train errors compared to other algorithms like ridge regression, KNN, Bayesian regression, decision trees, and SVM.
Slides from lecture style tutorial on data quality for ML delivered at SIGKDD 2021.
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models (such as neural architecture search and automated feature selection), there are limited efforts towards improving the data quality.
Assessing the quality of the data across intelligently designed metrics and developing corresponding transformation operations to address the quality gaps helps to reduce the effort of a data scientist for iterative debugging of the ML pipeline to improve model performance. This tutorial highlights the importance of analysing data quality in terms of its value for machine learning applications. Finding the data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue. This tutorial surveys all the important data quality related approaches for structured, unstructured and spatio-temporal domains discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems.
IRJET- Fast Phrase Search for Encrypted Cloud StorageIRJET Journal
This document proposes a technique for fast phrase search on encrypted documents stored in the cloud. It presents a phrase search method based on Bloom filters that is faster than existing solutions, with similar or lower storage and communication costs. The technique uses a series of n-gram filters to support phrase searching functionality. It exhibits a tradeoff between storage size and false positive rate, and can defend against inclusion-relation attacks. The design approach is adaptable based on an application's target false positive rate. The system aims to provide secure and efficient phrase search on encrypted documents outsourced to cloud storage.
Ignou MCA 4th semester mini project report. College admission system. This project is based on real working system of University seat allocation to affiliate colleges. College admission system provide seat allocation process for various UG PG programs for every academic session.
The document discusses topics related to rule-based machine learning, clustering, and association rules. It covers the following key points:
- The learning objectives of the course, which include understanding decision trees, entropy, information gain, clustering, and association rules.
- The course materials, which cover introduction to decision trees, entropy and information gain, clustering, and association rules.
- An introduction to decision trees, including how they represent classification problems using nodes and branches to represent attributes and values. Information theory helps determine the role of attributes in tree construction.
The document discusses engineering minors offered by the School of Computer Science and Engineering. It defines engineering minors as a set of six courses in an engineering discipline that allows students to develop competency in an area outside their major. It provides details on the data science engineering minor, including the courses offered and their descriptions and learning outcomes. The minor aims to provide students with interdisciplinary experience and skills in data analytics, visualization, programming, and big data processing to enhance their career opportunities.
This document discusses predicting loan defaults through machine learning models. It begins by introducing the business problem of banks suffering losses from customer loan defaults. It then describes preprocessing the loan dataset, which includes handling missing data, label encoding categorical variables, and balancing the dataset using SMOTE and SMOTEENN techniques. Logistic regression, decision trees, AdaBoost and random forest algorithms are applied to both the original and balanced datasets. The random forest model on the balanced data using SMOTEENN achieved the best accuracy of 92%. The model is then pickled and integrated into a web application using Flask for users to predict loan defaults.
A Generic Model for Student Data Analytic Web Service (SDAWS)Editor IJCATR
Any university management system accumulates a cartload of data and analytics can be applied on it to gather useful
information to aid the academic decision making process. This paper is a novel attempt to demonstrate the significance of a data
analytic web service in the education domain. This can be integrated with the University Management System or any other application
of the university easily. Analytics as a web service offers much benefits over the traditional analysis methods. The web service can be
hosted on a web server and accessed over the internet or on to the private cloud of the campus. The data from various courses from
different departments can be uploaded and analyzed easily. In this paper we design a web service framework to be used in educational
data mining that provide analysis as a service.
The document discusses artificial intelligence (AI) topics that will be covered in an AI course module. It includes definitions of key AI concepts and terminology to meet the course's learning objectives. The topics that will be covered are: what is AI, the impact of AI on jobs, different approaches to learning AI (conceptual, algorithmic, mathematical, case studies), the machine intelligence continuum (levels from systems that act to systems that relate), expert systems and machine learning, and various AI applications.
This document provides information about the Engineering Minor in Data Science offered by the School of Computer Science and Engineering. It describes what engineering minors are, lists the courses offered in the Data Science minor, and provides brief descriptions and outcomes of each course. The minor consists of six courses spanning four semesters that cover topics like data management, visualization, programming in R, predictive analytics, big data fundamentals, and cluster computing. The document also discusses career opportunities, industrial applications, special requirements, and contacts for additional information about the minor.
This document provides a summary of a project proposal for developing a School Admission Process Management System. It includes sections on project initiation and scheduling, diagrams, project cost estimation, designing the user interface, and testing approaches. The project aims to automate the currently manual school admission process to make it faster and easier to use. It will develop a web-based system using technologies like ASP.NET, SQL Server, and PHP/MySQL. Testing will include white box, black box, unit, integration, and system testing approaches. The document outlines the requirements, feasibility, advantages over the current system, and includes diagrams to depict the system design.
The document describes a student information management system project. It includes sections on the introduction, problem statement, objectives, scope, requirements analysis, feasibility study, system design, implementation, testing, maintenance, and conclusion. The project aims to develop a computerized system to manage student records and information to replace a manual paper-based system. The system will allow administrators to easily search, edit, and find student details and allow students to update their profiles. The requirements analysis and feasibility study ensure the project is technically, operationally, and economically feasible. Overall, the system aims to simplify student information management for organizations.
This document discusses cloning an organization to allow testing and manipulation without affecting the original site. It defines cloning as creating an exact copy that can be used for tasks without risk to the original. Types of clones include the frontend design, backend design, and database. Benefits of cloning for software testing are that it is cost-effective, improves security and product quality, and increases customer satisfaction. The document then discusses various software testing types, reverse engineering, and software development life cycles like waterfall, RAD, spiral, V-model, incremental, agile, iterative, big bang and prototype models. The conclusion is that cloning can help test and learn new features without interrupting the original organization's data and business.
Monitoring Students Using Different Recognition Techniques for Surveilliance ...IRJET Journal
This document discusses using computer vision techniques like convolutional neural networks to monitor students and enforce dress codes in educational institutions. It proposes a system using cameras and image processing to identify whether students are properly dressed according to the dress code. The system would classify images of students as either following or not following the dress code. It also discusses related work on using technologies like biometrics and RFID cards for automated student attendance tracking and implications for security and discipline in schools.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
Similar to Modul Topik 4 - Kecerdasan Buatan.pdf (20)
The document discusses implementing machine learning with Python. It covers loading machine learning data using Python, preparing the data, visualizing it, feature selection, evaluating model performance, and implementing machine learning algorithms and neural networks. Specifically, it demonstrates loading the Pima Indian diabetes dataset, exploring the data distribution and correlations, and performing descriptive statistics to understand the dataset's properties before implementing machine learning techniques.
The document provides an introduction to the topic of deep learning. It outlines the learning objectives, which are to define machine learning techniques like linear regression, rule-based learning, probabilistic learning, and clustering, as well as the basic concepts of deep learning and its implementation in image recognition using convolutional neural networks. The topics to be covered are the history of artificial intelligence and neural networks, visualizing deep learning concepts, essentials of deep learning like perceptrons and backpropagation, and convolutional neural networks in detail.
This document discusses topics related to uncertainty problems and probabilistic machine learning. It will cover introducing uncertainty, probability theory for machine learning, Bayesian rules, and applying Bayesian rules. The key concepts that will be learned include understanding basic probability theory such as random variables, probability distributions, and joint, conditional, and marginal probabilities. Learners will also understand how to apply Bayesian reasoning to cases such as predicting gender, COVID-19 infection risk, and medical diagnosis based on symptoms.
The document discusses topics related to linear regression and model selection in machine learning. It will cover simple linear regression, correlation and coefficient of determination, multiple linear regression, and model selection techniques like cross-validation, overfitting, underfitting and regularization. The materials aim to help students understand the basic concepts of linear regression, distinguish between classification and regression, and implement linear regression while avoiding issues like dimensionality and overfitting.
The document discusses topics related to ethics and social impacts of artificial intelligence technology. It covers several learning outcomes, including understanding weaknesses of AI in cases of racial and gender bias due to inaccurate datasets. It also covers various materials, such as racial and gender bias in AI, how AI learns unhealthy stereotypes through word embedding models, explainable AI, deepfakes, and AI cyberattacks. Finally, it discusses ethics in AI development regarding principles of transparency, fairness, safety, accountability, and privacy.
Introduction to Artificial Intelligence - Pengenalan Kecerdasan BuatanSunu Wibirama
Kuliah Pengantar Kecerdasan Buatan oleh Dr. Sunu Wibirama. Kuliah ini dibagi menjadi empat bagian, yakni:
Part 1: Revolusi Industri 4.0 dan Kecerdasan Buatan
Part 2: Sejarah Turing Machine dan Teknologi Kecerdasan Buatan
Part 3: Pengantar Machine Learning
Part 4: Pengantar Deep Neural Network
Instruktur:
Dr. Sunu Wibirama (UGM, Indonesia)
http://sunu.staff.ugm.ac.id
Mengenal Eye Tracking (Introduction to Eye Tracking Research)Sunu Wibirama
Introduction to eye tracking technology, basic working principles behind the technology, and several examples of research and applications of eye tracking. More information can be found here: http://sunu.staff.ugm.ac.id
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
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Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Modul Topik 4 - Kecerdasan Buatan.pdf
1. Topik 4
Konsep Transformasi Data, Ekstraksi Fitur,
dan Seleksi Fitur Dalam Machine Learning
Dr. Sunu Wibirama
Modul Kuliah Kecerdasan Buatan
Kode mata kuliah: UGMx 001001132012
June 13, 2022
2. June 13, 2022
1 Capaian Pembelajaran Mata Kuliah
Topik ini akan memenuhi CPMK 4, yakni mampu mendefinisikan konsep dasar trans-
formasi data dan seleksi fitur (feature selection) untuk machine learning.
Adapun indikator tercapainya CPMK tersebut adalah mampu memahami konsep data
preparation, data cleansing, dan feature selection serta teknik-teknik yang lazim digunakan
dalam machine learning.
2 Cakupan Materi
Cakupan materi dalam topik ini sebagai berikut:
a) Introduction to Data Preparation for Machine Learning: materi ini menjelaskan alasan-
alasan pentingnya melakukan persiapan awal sebelum menggunakan dataset dalam
machine learning. Pada materi ini juga dijelaskan langkah-langkah praktis untuk
mendapatkan data yang akan digunakan pada proses machine learning.
b) Overview of Data Preparation: materi ini menjelaskan teknik-teknik dasar yang akan
digunakan dalam mempersiapkan data, misalnya data cleaning, feature selection, data
transforms, feature engineering, dan dimensionality reduction.
c) Data Cleaning: materi ini menjelaskan konsep-konsep dasar data cleaning, yakni
mengidentifikasi dan mengoreksi kesalahan dalam data. Pada materi ini dijelaskan
konsep untuk mengidentifikasi kolom yang memiliki single value menggunakan pem-
rograman Python. Selain itu, materi ini juga menjelaskan cara-cara mengidentifikasi
outliers dalam data dengan menggunakan metode statistika seperti halnya standard
deviation atau interquartile range.
d) Feature Selection: materi ini menjelaskan teknik-teknik dasar pemilihan fitur. Hal
penting yang perlu diperhatikan dalam proses pemilihan fitur adalah melihat tipe data
pada masukan (input) dan luaran (output) algoritme machine learning. Pada materi
ini juga akan dijelaskan teknik Recursive Feature Elimination (RFE) dan Feature
Importance untuk memilih fitur pada proses machine learning.
e) Data Transforms: materi ini akan menjelaskan teknik-teknik dasar transformasi data,
diantaranya data normalization dan quantile transforms. Data normalization digu-
nakan untuk melakukan normalisasi data pada level individu atau elemen dataset.
Sementara itu, quantile transforms digunakan untuk mengubah distribusi data men-
jadi distribusi normal atau distribusi uniform.
f) Dimensionality Reduction: materi ini akan terbagi menjadi dua bagian, yakni penge-
nalan Principal Component Analysis (PCA) dan implementasi PCA. Pada bagian per-
tama, akan dijelaskan konsep dasar PCA, eigenvalues, dan eigenvector. Pada bagian
kedua, akan dijelaskan langkah-langkah praktis implementasi PCA dan aplikasinya
dengan pemrograman Python.
1