This material contains an understanding of algorithm concept that comprises of historical, terminology, characteristics, example, and their correlation with data structure
RapidMiner is an environment for machine learning and data mining processes that follows a modular operator concept. It introduces transparent data handling and process modeling to ease configuration for end users. Additionally, its clear interfaces and scripting language based on XML make it an integrated developer environment for data mining and machine learning. To get started with RapidMiner, users download the file for their system from the website, install it by accepting the license agreement and specifying the installation directory, then launch it by double clicking the desktop icon.
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Dr. Dipali Meher's document discusses data preprocessing techniques. It covers the need for data preprocessing to clean and transform raw data. Specific techniques discussed include data cleaning, integration, transformation, and reduction. Data cleaning involves handling missing values and noisy data. Data integration combines data from multiple sources. Data transformation techniques include smoothing, aggregation, discretization, and normalization. Data reduction techniques include attribute selection, cube aggregation, and dimensionality reduction.
Pattern recognition is the process of assigning patterns to categories or classes. It involves extracting features from patterns using measurements or observations. These features are represented as vectors in a feature space. Pattern recognition systems use classification algorithms like statistical, syntactic, or neural network approaches to assign patterns to prespecified categories based on their features. The goal is to develop machines that can perceive and recognize patterns like humans.
This presentation is based on ranking of web pages, mainly it consist of PageRank algorithm and HITS algorithm. It gives brief knowledge of how to calculate page rank by looking at the links between the pages. It tells you about different techniques of search engine optimization.
Business process modeling and analysis for data warehouse designSlava Kokaev
The document discusses business process modeling and analysis for data warehouse design. It provides an overview of key concepts like business intelligence, business processes, dimensional modeling and ETL. The document presents examples of modeling dimensions, hierarchies and fact tables to design a dimensional schema for a reseller sales scenario. It also shows examples of identifying business processes and mapping them to the dimensional model for analysis in a data warehouse.
A Simple Introduction to Neural Information RetrievalBhaskar Mitra
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. In this lecture, we will cover some of the fundamentals of neural representation learning for text retrieval. We will also discuss some of the recent advances in the applications of deep neural architectures to retrieval tasks.
(These slides were presented at a lecture as part of the Information Retrieval and Data Mining course taught at UCL.)
RapidMiner is an environment for machine learning and data mining processes that follows a modular operator concept. It introduces transparent data handling and process modeling to ease configuration for end users. Additionally, its clear interfaces and scripting language based on XML make it an integrated developer environment for data mining and machine learning. To get started with RapidMiner, users download the file for their system from the website, install it by accepting the license agreement and specifying the installation directory, then launch it by double clicking the desktop icon.
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Dr. Dipali Meher's document discusses data preprocessing techniques. It covers the need for data preprocessing to clean and transform raw data. Specific techniques discussed include data cleaning, integration, transformation, and reduction. Data cleaning involves handling missing values and noisy data. Data integration combines data from multiple sources. Data transformation techniques include smoothing, aggregation, discretization, and normalization. Data reduction techniques include attribute selection, cube aggregation, and dimensionality reduction.
Pattern recognition is the process of assigning patterns to categories or classes. It involves extracting features from patterns using measurements or observations. These features are represented as vectors in a feature space. Pattern recognition systems use classification algorithms like statistical, syntactic, or neural network approaches to assign patterns to prespecified categories based on their features. The goal is to develop machines that can perceive and recognize patterns like humans.
This presentation is based on ranking of web pages, mainly it consist of PageRank algorithm and HITS algorithm. It gives brief knowledge of how to calculate page rank by looking at the links between the pages. It tells you about different techniques of search engine optimization.
Business process modeling and analysis for data warehouse designSlava Kokaev
The document discusses business process modeling and analysis for data warehouse design. It provides an overview of key concepts like business intelligence, business processes, dimensional modeling and ETL. The document presents examples of modeling dimensions, hierarchies and fact tables to design a dimensional schema for a reseller sales scenario. It also shows examples of identifying business processes and mapping them to the dimensional model for analysis in a data warehouse.
A Simple Introduction to Neural Information RetrievalBhaskar Mitra
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. In this lecture, we will cover some of the fundamentals of neural representation learning for text retrieval. We will also discuss some of the recent advances in the applications of deep neural architectures to retrieval tasks.
(These slides were presented at a lecture as part of the Information Retrieval and Data Mining course taught at UCL.)
This document provides an overview and agenda for an introductory web programming course. It discusses the basic web architecture involving client-side and server-side communication. It also introduces some key HTML tags for adding common elements like text, links, images and lists. Finally, it gives an introduction to CSS for applying styles to HTML elements through selectors, properties and other concepts. The goal is to lay the foundation for building web pages with basic HTML and CSS.
Introduction to text classification using naive bayesDhwaj Raj
This document provides an overview of text classification and the Naive Bayes classification method. It defines text classification as assigning categories, topics or genres to documents. It describes classification methods like hand-coded rules and supervised machine learning. It explains the bag-of-words representation and how Naive Bayes classification works by calculating the probability of a document belonging to a class using Bayes' rule and independence assumptions. It discusses parameter estimation and how to build a multinomial Naive Bayes classifier for text classification tasks.
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
This document provides instructions and examples for various HTML and SQL concepts taught in an introductory database lab course. It covers HTML topics like basic page structure, headings, paragraphs, links, images and tables. It also covers SQL topics like creating and managing tables, queries, functions, and connecting databases with PHP. The document is organized into 12 labs that progressively build skills with HTML, PHP, SQL, MySQL and PL/SQL.
This document discusses different types of concept hierarchies that can be used in data warehousing including schema hierarchies, set grouping hierarchies, and operation-derived hierarchies. It also discusses techniques for data discretization such as binning methods and concept hierarchies that reduce data by replacing low-level concepts with higher-level concepts. Finally, it briefly mentions histogram analysis, clustering analysis, and different ways concept hierarchies can be specified including explicitly by users or generated automatically through analysis.
This document discusses how FastAPI can be used to create web APIs for machine learning models. FastAPI allows ML developers to easily share models with colleagues by making them available as web APIs. It provides auto-generated documentation and supports features like validation, authentication, and file uploads that are useful for building ML APIs. FastAPI offers high performance and is easy to code, making it well-suited for both prototyping and production ML APIs.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
- Bayesian networks can model conditional independencies between variables based on the network structure. Each variable is conditionally independent of its non-descendants given its parents.
- The d-separation algorithm allows determining if two variables are conditionally independent given some evidence by checking if all paths between them are "blocked".
- For trees/forests where each node has at most one parent, inference can be done efficiently in linear time by decomposing probabilities and passing messages between nodes.
This document provides an overview of classification techniques. It defines classification as assigning records to predefined classes based on their attribute values. The key steps are building a classification model from training data and then using the model to classify new, unseen records. Decision trees are discussed as a popular classification method that uses a tree structure with internal nodes for attributes and leaf nodes for classes. The document covers decision tree induction, handling overfitting, and performance evaluation methods like holdout validation and cross-validation.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
This document introduces Flask, a Python framework for building web applications. It explains that Flask uses Python decorators to define routes for the web server. Before writing a Flask application, the reader is instructed to install Python, pip, virtualenv, and Flask within a new project directory. The basics of writing a Flask application are then covered, including running the application and defining routes to return responses. The document ends with quiz questions and resources for learning more about Flask.
The document discusses Kettle, an open source ETL tool from Pentaho. It provides an introduction to the ETL process and describes Kettle's major components: Spoon for designing transformations and jobs, Pan for executing transformations, and Kitchen for executing jobs. Transformations in Kettle perform tasks like data filtering, field manipulation, lookups and more. Jobs are used to call and sequence multiple transformations. The document also covers recent Kettle releases and how it can help address challenges in data integration projects.
This document discusses exploratory data analysis (EDA) and its application to analyzing computer networking data. EDA involves graphically summarizing data to uncover patterns, relationships, and structure without formal hypothesis testing. The document outlines the EDA process, including identifying key metrics and factors to explore. It provides examples of EDA graphs that could be used to analyze simulated WiFi data, examining how various factors like vendor, user type, and distance affect network performance metrics. The goal of EDA is to gain insights, detect anomalies, and inform modeling before running extensive simulations or experiments.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
This document discusses connecting Oracle Analytics Cloud (OAC) Essbase data to Microsoft Power BI. It provides an overview of Power BI and OAC, describes various methods for connecting the two including using a REST API and exporting data to Excel or CSV files, and demonstrates some visualization capabilities in Power BI including trends over time. Key lessons learned are that data can be accessed across tools through various connections, analytics concepts are often similar between tools, and while partnerships exist between Microsoft and Oracle, integration between specific products like Power BI and OAC is still limited.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and make predictions about future trends. Neural networks are also effective for stock prediction due to their ability to handle uncertain and changing data. The document examines different data mining methods like statistical analysis, neural networks, clustering and fuzzy sets. It suggests that combining data mining and neural networks could improve the reliability of stock market predictions by uncovering the nonlinear patterns in stock price data.
This document provides an overview and agenda for an introductory web programming course. It discusses the basic web architecture involving client-side and server-side communication. It also introduces some key HTML tags for adding common elements like text, links, images and lists. Finally, it gives an introduction to CSS for applying styles to HTML elements through selectors, properties and other concepts. The goal is to lay the foundation for building web pages with basic HTML and CSS.
Introduction to text classification using naive bayesDhwaj Raj
This document provides an overview of text classification and the Naive Bayes classification method. It defines text classification as assigning categories, topics or genres to documents. It describes classification methods like hand-coded rules and supervised machine learning. It explains the bag-of-words representation and how Naive Bayes classification works by calculating the probability of a document belonging to a class using Bayes' rule and independence assumptions. It discusses parameter estimation and how to build a multinomial Naive Bayes classifier for text classification tasks.
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
This document provides instructions and examples for various HTML and SQL concepts taught in an introductory database lab course. It covers HTML topics like basic page structure, headings, paragraphs, links, images and tables. It also covers SQL topics like creating and managing tables, queries, functions, and connecting databases with PHP. The document is organized into 12 labs that progressively build skills with HTML, PHP, SQL, MySQL and PL/SQL.
This document discusses different types of concept hierarchies that can be used in data warehousing including schema hierarchies, set grouping hierarchies, and operation-derived hierarchies. It also discusses techniques for data discretization such as binning methods and concept hierarchies that reduce data by replacing low-level concepts with higher-level concepts. Finally, it briefly mentions histogram analysis, clustering analysis, and different ways concept hierarchies can be specified including explicitly by users or generated automatically through analysis.
This document discusses how FastAPI can be used to create web APIs for machine learning models. FastAPI allows ML developers to easily share models with colleagues by making them available as web APIs. It provides auto-generated documentation and supports features like validation, authentication, and file uploads that are useful for building ML APIs. FastAPI offers high performance and is easy to code, making it well-suited for both prototyping and production ML APIs.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
- Bayesian networks can model conditional independencies between variables based on the network structure. Each variable is conditionally independent of its non-descendants given its parents.
- The d-separation algorithm allows determining if two variables are conditionally independent given some evidence by checking if all paths between them are "blocked".
- For trees/forests where each node has at most one parent, inference can be done efficiently in linear time by decomposing probabilities and passing messages between nodes.
This document provides an overview of classification techniques. It defines classification as assigning records to predefined classes based on their attribute values. The key steps are building a classification model from training data and then using the model to classify new, unseen records. Decision trees are discussed as a popular classification method that uses a tree structure with internal nodes for attributes and leaf nodes for classes. The document covers decision tree induction, handling overfitting, and performance evaluation methods like holdout validation and cross-validation.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
This document introduces Flask, a Python framework for building web applications. It explains that Flask uses Python decorators to define routes for the web server. Before writing a Flask application, the reader is instructed to install Python, pip, virtualenv, and Flask within a new project directory. The basics of writing a Flask application are then covered, including running the application and defining routes to return responses. The document ends with quiz questions and resources for learning more about Flask.
The document discusses Kettle, an open source ETL tool from Pentaho. It provides an introduction to the ETL process and describes Kettle's major components: Spoon for designing transformations and jobs, Pan for executing transformations, and Kitchen for executing jobs. Transformations in Kettle perform tasks like data filtering, field manipulation, lookups and more. Jobs are used to call and sequence multiple transformations. The document also covers recent Kettle releases and how it can help address challenges in data integration projects.
This document discusses exploratory data analysis (EDA) and its application to analyzing computer networking data. EDA involves graphically summarizing data to uncover patterns, relationships, and structure without formal hypothesis testing. The document outlines the EDA process, including identifying key metrics and factors to explore. It provides examples of EDA graphs that could be used to analyze simulated WiFi data, examining how various factors like vendor, user type, and distance affect network performance metrics. The goal of EDA is to gain insights, detect anomalies, and inform modeling before running extensive simulations or experiments.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
This document discusses connecting Oracle Analytics Cloud (OAC) Essbase data to Microsoft Power BI. It provides an overview of Power BI and OAC, describes various methods for connecting the two including using a REST API and exporting data to Excel or CSV files, and demonstrates some visualization capabilities in Power BI including trends over time. Key lessons learned are that data can be accessed across tools through various connections, analytics concepts are often similar between tools, and while partnerships exist between Microsoft and Oracle, integration between specific products like Power BI and OAC is still limited.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and make predictions about future trends. Neural networks are also effective for stock prediction due to their ability to handle uncertain and changing data. The document examines different data mining methods like statistical analysis, neural networks, clustering and fuzzy sets. It suggests that combining data mining and neural networks could improve the reliability of stock market predictions by uncovering the nonlinear patterns in stock price data.
This document reviews the use of data mining and neural network techniques for stock market prediction. It discusses how data mining can extract hidden patterns from large datasets and neural networks can handle nonlinear and uncertain financial data. Specifically, it examines how a combination of data mining and neural networks may improve the reliability of stock predictions by leveraging their complementary strengths. The document also provides an overview of common data mining and neural network methods used for this purpose, such as statistical data mining, neural network-based data processing, clustering, and fuzzy logic. It reviews several previous studies that found neural networks and other nonlinear techniques often outperform traditional statistical models at predicting stock prices and indices.
This slide describe all the necessary topic on Data-Mining. Even this covered all the important Questions on Data Mining in Graduation Level. Basically it covers the actual 2 and 4 marks questions along with the answers that you will need after.
This document compares hierarchical and non-hierarchical clustering algorithms. It summarizes four clustering algorithms: K-Means, K-Medoids, Farthest First Clustering (hierarchical algorithms), and DBSCAN (non-hierarchical algorithm). It describes the methodology of each algorithm and provides pseudocode. It also describes the datasets used to evaluate the performance of the algorithms and the evaluation metrics. The goal is to compare the performance of the clustering methods on different datasets.
Data Structures and Algorithm - Module 1.pptxEllenGrace9
This document provides an introduction to data structures and algorithms from instructor Ellen Grace Porras. It defines data structures as ways of organizing data to allow for efficient operations. Linear data structures like arrays, stacks, and queues arrange elements sequentially, while non-linear structures like trees and graphs have hierarchical relationships. The document discusses characteristics of good data structures and algorithms, provides examples of common algorithms, and distinguishes between linear and non-linear data structures. It aims to explain the fundamentals of data structures and algorithms.
In this review paper, we will discuss about data structure & its algorithms and their complexity. First we begin with the
introduction of data structure and the algorithm and then we discuss the relationship be tween them. Then we will discuss about
complexities of the algorithm in data structure. A data structure is a specialized format for organizing and storing data. An algorithm
is a step by step method of solving a problem. It is commonly used for data processing, calculation and other related computer and
mathematical operations. To write any program we have to select proper algorithm and the data structure. If we choose improper
data structure, algorithm cannot work effectively. Similarly, if we choose improper algorithm we cannot utilize the data structure
effectively. Thus there is a strong relationship between data structure and algorithm. The complexity of an algorithm is a me of the time and/or space required by an algorithm for an input of a given size (n).
IRJET- Semantics based Document ClusteringIRJET Journal
This document describes a proposed ontology-based document clustering system. The system uses a two-step clustering algorithm that first applies K-means partitioning clustering followed by hierarchical agglomerative clustering. Ontology is introduced through a weighting scheme that integrates traditional TF-IDF word weights with weights of semantic relations between words from the ontology. The goal is to produce document clusters that are semantically meaningful by accounting for relationships between words, rather than just word co-occurrence. An overview of the system architecture and modules is provided, along with descriptions of preprocessing, concept weighting, clustering approaches, and initial implementation results.
Classifier Model using Artificial Neural NetworkAI Publications
This document summarizes a research paper that investigates using supervised instance selection (SIS) as a preprocessing step to improve the performance of artificial neural networks (ANNs) for classification tasks. SIS aims to select a subset of examples from the original dataset to enhance the accuracy of future classifications. The goal of applying SIS before ANNs is to provide a cleaner input dataset that handles noisy or redundant data better. The paper presents the architecture of feedforward neural networks and the backpropagation algorithm for training networks. It also discusses using mutual information-based feature selection as part of the SIS preprocessing approach.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
This document discusses object-oriented programming (OOP) and structured programming paradigms. It defines programming paradigms as fundamental styles of computer programming that classify languages. OOP focuses on modeling real-world entities and their relationships using objects that encapsulate both data and methods. In contrast, structured programming emphasizes functions and procedures that operate sequentially on passive data. The document also notes that OOP enables dividing complex systems into manageable modules and that its use of objects requires more memory but provides increased security compared to structured programming.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Multidimensional schema for agricultural data warehouseeSAT Journals
Abstract Agriculture is one of the important issues for a nation’s economy and it required technical breakthroughs in this century. With today’s computerized world, the agricultural data processing is an increasing need for formers and decision makers. Agricultural data is diversified, complex and non-standard. Developing a data warehouse for agricultural is a key challenge for researchers. The objective of this work is to design a data warehouse about crops and their requirements. The proposed data warehouse may be extended to Decision Support System (DSS) combine with data mining techniques. We proposed a multidimensional data warehouse for agriculture that provides solutions for farmers and gives response of their ad-hoc quires. This multidimensional schema further promotes star schema and snowflake schema that are commonly used to design data warehouses. In our manuscript normalization is applied to store the data in to star schema and duplicate values are removed, so that space and time complexities could be minimized. Index Terms: Agriculture, Data Warehouse, Multidimensional Schema, Dimensional Modeling, OLAP
Abdul Ahad Abro presented on data science, predictive analytics, machine learning algorithms, regression, classification, Microsoft Azure Machine Learning Studio, and academic publications. The presentation introduced key concepts in data science including machine learning, predictive analytics, regression, classification, and algorithms. It demonstrated regression analysis using Microsoft Azure Machine Learning Studio and Microsoft Excel. The methodology section described using a dataset from Azure for classification and linear regression in both Azure and Excel to compare results.
Recommendation system using unsupervised machine learning algorithm & associjerd
This document discusses using a combination of unsupervised machine learning algorithms, including Farthest First clustering and the Apriori association rule algorithm, for a course recommendation system. It presents an approach that clusters student data from a learning management system (LMS) like Moodle without needing to preprocess the data. Then, association rules are generated to find the best combinations of courses based on the student clusters. The combined approach is tested on sample LMS data to demonstrate its ability to recommend courses without requiring data preparation steps compared to using only the Apriori algorithm.
This document provides an introduction to data structures and algorithms. It defines key concepts like data structures, algorithms, complexity analysis and asymptotic notations. It discusses different types of data structures like linear and non-linear as well as common operations. It also explains algorithm development, best case, worst case and average case analysis, and commonly used notations like Big-O, Omega, Theta and Little-o to analyze asymptotic time and space complexities of algorithms.
Machine learning techniques to improve data management and data quality - presentation by Tobias Pentek and Martin Fadler from the Competence Center Corporate Data Quality. This presentation was presented during the Marcus Evans Event in Amsterdam 08.02.2019
Knowledge Management in the AI Driven Scintific SystemSubhasis Dasgupta
In this dynamic talk, we'll explore the transformative role of AI in scientific knowledge management. We'll delve into how AI revolutionizes data organization, analysis, and hypothesis testing, enhancing efficiency and discovery. Highlighting the seamless integration with existing research processes, we'll address the training and ethical considerations of AI adoption. Through real-world examples, we'll demonstrate AI's impact on scientific breakthroughs, emphasizing the shift towards more collaborative and innovative research landscapes. This presentation aims to inspire the scientific community to embrace AI, leveraging its potential to redefine the boundaries of knowledge and innovation.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep
learning technique is to improve the implementation rate based upon the capability of the society. The
main objective of the proposed system is to apply the deep learning using data driven approach for
controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.
Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.Through this system, huge volume of data’s that are generated by the system will also get control.
Similar to Algorithm and Data Structure - Concept of Algorithm and Data Structure (20)
Material for this slide includes:
1. Description of firebase and reason to use it
2. What are the benefits of firebase?
3. Major features of firebase
4. Description of firebase cloud storage and their benefits
5. Description of firebase realtime database and their benefits
6. Description of firebase authentication and their benefits
7. Description of firebase analytics and their benefits
8. How to Setup Firebase?
Mobile Programming - 9 Profile UI, Navigation Basic and Splash ScreenAndiNurkholis1
Material for this slide includes:
1. Description of profile UI and their examples
2. Tips on how to build profile UI
3. Description of navigation and their examples
4. Tips on how to build navigation
5. Description and how splash screen works
Mobile Programming - 8 Progress Bar, Draggable Music Knob, TimerAndiNurkholis1
Material for this slide includes:
1. Description of progress bar and their types
2. Description of draggable music knob and their examples
3. Description of timer and and their examples
Mobile Programming - 7 Side Effects, Effect Handlers, and Simple AnimationsAndiNurkholis1
Material for this slide includes:
1. Description of effect handlers and their types
2. Description of side effects and their examples
3. Description of animations and their APIs in Jetpack Compose
Mobile Programming - 6 Textfields, Button, Showing Snackbars and ListsAndiNurkholis1
Material for this slide includes:
1. Jetpack compose UI element
2. Textfield in jetpack compose (simple, outlined, rounded corner, password)
3. Button in jetpack compose (simple, round, outlined, background color)
4. Snackbar in jetpack compose (simple and custom)
5. Description of list and examples
Mobile Programming - 4 Modifiers and Image CardAndiNurkholis1
Material for this slide includes:
1. Description of modifiers and examples
2. Built-in modifiers
3. Description of image card and examples
4. Styling the card
Mobile Programming - 3 Rows, Column and Basic SizingAndiNurkholis1
Material for this slide includes:
1. Compose Layout Basics
2. Jetpack Compose Layout Structure
3. Composable Function
4. Column Layout
5. Row Layout
6. Box Layout
7. Children Position
Material for this slide includes:
1. Android Jetpack
2. Advantage of Jetpack
3. Jetpack Compose for UI
4. Why is Compose Getting So Popular?
5. Composable Function
Algoritma dan Struktur Data (Python) - Struktur I/OAndiNurkholis1
Struktur input/output dan runtunan adalah konsep dasar dalam pemrograman yang mencakup proses memasukkan dan mengeluarkan data, serta alur kerja program dari atas ke bawah mulai dari input, proses, hingga output. Variabel, tipe data, operator, dan komentar merupakan komponen penting lainnya yang mendukung struktur tersebut.
Algoritma dan Struktur Data (Python) - Notasi AlgoritmikAndiNurkholis1
Notasi algoritmik adalah media untuk mendokumentasikan algoritma menjadi bentuk yang dapat dimengerti secara universal dengan menggunakan simbol-simbol dan aturan-aturan tertentu seperti notasi deskriptif, flowchart, dan pseudo-code."
Algoritma dan Struktur Data (Python) - Pengantar AlgoritmaAndiNurkholis1
Mahasiswa memahami prinsip kerja program dan mampu menggambarkan logika jalannya program dalam bentuk algoritma dan diagram alir. Penilaian mata kuliah ini didasarkan pada quiz, tugas, ujian tengah semester, dan ujian akhir semester dengan bobot tertentu.
Algorithm and Data Structure - Binary SearchAndiNurkholis1
This material aims to enable students to:
1) Understanding searching algorithm concept
2) Understanding characteristic of binary search
3) Understanding steps of binary search
4) Knowing of advantage and disadvantage of binary search
Algorithm and Data Structure - Linear SearchAndiNurkholis1
This material aims to enable students to:
1) Understanding searching algorithm concept
2) Understanding characteristic of linear search
3) Understanding steps of linear search
4) Knowing of advantage and disadvantage of linear search
This material aims to enable students to:
1) Understanding queue concept
2) Understanding enqueue, dequeue, front, rear operation in a queue
3) Understanding working of queue
4) Knowing of queue application
This material aims to enable students to:
1) Understanding stack concept
2) Understanding push, pop, peek or top, isEmpty, isFull operation in a stack
3) Understanding working of stack
4) Knowing of stack application
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
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!
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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).
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
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.
Algorithm and Data Structure - Concept of Algorithm and Data Structure
1. Algorithm and
Data Structure
Andi Nurkholis, S.Kom, M.Kom
Study Program of Informatics
Faculty of Engineering and Computer Science
SY. 2020-2021
February 15, 2021
3. 3
What is Algorithm?
Algorithm is a sequence of steps to solve problems
that are arranged systematically and structured as
outlined in standard / universal notation
4. 4
What is Data Structure?
Data Structure is a way of collecting and organizing data in such a
way that we can perform operations on these data in an effective
way. Data Structures is about rendering data elements in terms of
some relationship, for better organization and storage.
5. 5
Correlation of Algorithm
and Data Structure
Data structure plays an important role in data organization for the optimal
algorithm implementation process
6. 6
History of Algorithm
Algorism Algorithm
Algoritma
Algoritme
Abu Ja’far Muhammad
Ibnu Musa Al-Khuwarismi
The Book of Al Jabar wal
Muqabala
9. 9
Example of
Algorithm
• Ambil kantong kentang dari rak
• Ambil panci dari almari
• depend on baju
• berwarna muda : pakai celemek
• tidak berwarna muda : -
• while jumlah kentang terkupas belum
cukup do
• depend on kantong kentang
• ada isinya : kupas 1 kentang
• tidak ada isinya : (1) ambil
kantong kentang lain dari rak, (2)
kupas 1 kentang
11. Thank You, Next …
Array and Struct
February 15, 2021
Andi Nurkholis, S.Kom, M.Kom
Study Program of Informatics
Faculty of Engineering and Computer Science
SY. 2020-2021