This document describes how to calculate descriptive statistics using SPSS. It discusses entering data into SPSS, calculating frequencies, means, medians, modes, standard deviations and other measures. It provides three methods for computing descriptive statistics in SPSS: frequencies analysis, descriptives analysis, and explore analysis. Finally, it demonstrates how to create graphs like histograms, bar charts and pie charts to represent the data visually. The overall purpose is to introduce the key concepts and applications of descriptive statistics using the SPSS software package.
The document discusses 2-3 and 2-3-4 trees, which are self-balancing search trees where internal nodes can have 2, 3, or 4 child nodes. 2-3 and 2-3-4 trees stay balanced during insertions and deletions by splitting or merging nodes. Key advantages are that they require fewer rebalancing operations than regular binary search trees and can perform insertions and deletions in logarithmic time. The document provides examples and explanations of how nodes are split or merged during insertions and deletions to maintain balance in 2-3 and 2-3-4 trees.
This document introduces doubly linked lists. A doubly linked list allows navigation in both directions by including a prev pointer in each node in addition to the next pointer found in singly linked lists. Each node contains data, a next pointer, and a prev pointer. The list also contains pointers to the first and last nodes. Basic operations on doubly linked lists include insertion and deletion at the beginning or end of the list as well as insertion or deletion after a specified node.
Nescafe began in 1929 when Nestle developed an instant coffee product to preserve surplus coffee beans. It aimed to create a delicious cup of coffee simply by adding water. There are now over 5,000 Nescafe products worldwide, with the brand available in over 180 countries and consuming 5,500 cups every second, accounting for one-fifth of the world's coffee.
Expectation Maximization and Gaussian Mixture Modelspetitegeek
Here are some other potential applications of EM:
- EM can be used for parameter estimation in hidden Markov models (HMMs). The hidden states are the latent variables estimated using EM.
- EM can be used for topic modeling using latent Dirichlet allocation (LDA). The topics are the latent variables estimated from documents.
- As mentioned in the document, EM can also be used for Gaussian mixture models (GMMs) for clustering and density estimation. The cluster assignments are latent.
- EM can be used for missing data problems, where the missing values are treated as latent variables estimated each iteration.
- Bayesian networks and directed graphical models more generally can also be estimated using EM by treating the conditional probabilities as latent
This document provides instructions for performing various statistical analyses and data management tasks in SPSS, including sorting data, selecting cases, splitting files, merging files, visual binning, frequencies analysis, descriptive statistics, cross tabulation and chi-square tests, independent samples t-tests, and one-way ANOVA. The document is authored by trainers from the Department of Applied Statistics at the University of Rwanda and dated December 6, 2014.
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It introduces the main interfaces for working with data in SPSS, including the data view, variable view, output view, draft view, and syntax view. It also provides instructions for installing sample data files and demonstrates how to generate a basic cross-tabulation output of employment by gender using the automated features.
Link list presentation slide(Daffodil international university)shah alom
The document summarizes information about linked lists, including:
- Linked lists are linear collections of data elements called nodes connected by pointers.
- There are single linked lists, double linked lists, and circular linked lists.
- Traversing a single linked list involves iterating through each node using the next pointer.
- Insertion can occur at the beginning, end, or middle of a list by creating a new node and adjusting pointers.
- Deletion involves removing a node by adjusting pointers of the previous and next nodes.
- A basic node implementation uses a struct with a data field and next pointer.
This document describes how to calculate descriptive statistics using SPSS. It discusses entering data into SPSS, calculating frequencies, means, medians, modes, standard deviations and other measures. It provides three methods for computing descriptive statistics in SPSS: frequencies analysis, descriptives analysis, and explore analysis. Finally, it demonstrates how to create graphs like histograms, bar charts and pie charts to represent the data visually. The overall purpose is to introduce the key concepts and applications of descriptive statistics using the SPSS software package.
The document discusses 2-3 and 2-3-4 trees, which are self-balancing search trees where internal nodes can have 2, 3, or 4 child nodes. 2-3 and 2-3-4 trees stay balanced during insertions and deletions by splitting or merging nodes. Key advantages are that they require fewer rebalancing operations than regular binary search trees and can perform insertions and deletions in logarithmic time. The document provides examples and explanations of how nodes are split or merged during insertions and deletions to maintain balance in 2-3 and 2-3-4 trees.
This document introduces doubly linked lists. A doubly linked list allows navigation in both directions by including a prev pointer in each node in addition to the next pointer found in singly linked lists. Each node contains data, a next pointer, and a prev pointer. The list also contains pointers to the first and last nodes. Basic operations on doubly linked lists include insertion and deletion at the beginning or end of the list as well as insertion or deletion after a specified node.
Nescafe began in 1929 when Nestle developed an instant coffee product to preserve surplus coffee beans. It aimed to create a delicious cup of coffee simply by adding water. There are now over 5,000 Nescafe products worldwide, with the brand available in over 180 countries and consuming 5,500 cups every second, accounting for one-fifth of the world's coffee.
Expectation Maximization and Gaussian Mixture Modelspetitegeek
Here are some other potential applications of EM:
- EM can be used for parameter estimation in hidden Markov models (HMMs). The hidden states are the latent variables estimated using EM.
- EM can be used for topic modeling using latent Dirichlet allocation (LDA). The topics are the latent variables estimated from documents.
- As mentioned in the document, EM can also be used for Gaussian mixture models (GMMs) for clustering and density estimation. The cluster assignments are latent.
- EM can be used for missing data problems, where the missing values are treated as latent variables estimated each iteration.
- Bayesian networks and directed graphical models more generally can also be estimated using EM by treating the conditional probabilities as latent
This document provides instructions for performing various statistical analyses and data management tasks in SPSS, including sorting data, selecting cases, splitting files, merging files, visual binning, frequencies analysis, descriptive statistics, cross tabulation and chi-square tests, independent samples t-tests, and one-way ANOVA. The document is authored by trainers from the Department of Applied Statistics at the University of Rwanda and dated December 6, 2014.
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It introduces the main interfaces for working with data in SPSS, including the data view, variable view, output view, draft view, and syntax view. It also provides instructions for installing sample data files and demonstrates how to generate a basic cross-tabulation output of employment by gender using the automated features.
Link list presentation slide(Daffodil international university)shah alom
The document summarizes information about linked lists, including:
- Linked lists are linear collections of data elements called nodes connected by pointers.
- There are single linked lists, double linked lists, and circular linked lists.
- Traversing a single linked list involves iterating through each node using the next pointer.
- Insertion can occur at the beginning, end, or middle of a list by creating a new node and adjusting pointers.
- Deletion involves removing a node by adjusting pointers of the previous and next nodes.
- A basic node implementation uses a struct with a data field and next pointer.
This portfolio contains examples of the author's work with Microsoft's SQL Server 2008 Business Intelligence stack. It includes projects with Transact SQL, SQL Server Integration Services, SQL Server Analysis Services, SQL Server Reporting Services, and PerformancePoint Server. The projects were completed as part of a 12-week hands-on Master's program and involved building databases, an OLAP cube, reports and dashboards using real-world business scenarios and data from various sources like Excel, XML and CSV files.
This portfolio document outlines a Business Intelligence project that involves extracting data from various sources into a SQL Server database, building an Analysis Services cube with dimensions and measures to analyze company data, creating Reporting Services reports on the data, and developing PerformancePoint dashboards and Excel Services reports to visualize key metrics. The project transfers raw data into a data warehouse, performs analysis with SSAS, generates reports with SSRS, and builds dashboards with PPS and Excel Services to provide business intelligence insights. Samples and screenshots are provided of the ETL processes, cube design, MDX queries, reports, and dashboards created in the project.
The document summarizes the development of business intelligence reports for a project. It involved creating dashboards using Performance Point Server (PPS) and publishing them to SharePoint. SQL Server Reporting Services (SSRS) reports were also created and published. Excel reports were integrated into PPS dashboards. Data connections, filters, and scheduling were established to provide automated daily generation and viewing of reports.
This presentations shows how to create a time/date dimension for PowerPivot from the date data in your fact table. I also shows the DAX functions that you can use to add columns to the fact table or a separate dimension table.
This document contains a portfolio of business intelligence projects completed by Hong-Bing Li using Microsoft's BI product stack. It includes examples of SQL Server Integration Services (SSIS) packages to perform ETL, SQL programming, SQL Server Reporting Services (SSRS) reports including dashboards, SQL Server Analysis Services (SSAS) cubes, and MDX queries. The portfolio demonstrates skills in data integration, reporting, analytics, and dashboard development with a focus on Microsoft tools.
Nitin\'s Business Intelligence Portfolionpatel2362
The document provides samples of work from a Business Intelligence portfolio including T-SQL queries, MDX queries, SSIS packages, SSAS cube design, SSRS reports, and Excel Services reports with KPIs. It includes descriptions and screenshots of projects completed involving data integration, analysis, and reporting for a simulated construction company using SQL Server 2005 and Microsoft BI technologies.
Online Statistics Gathering for Bulk Loads - the official name of the feature - was introduced in Oracle 12.1. The idea is to gather optimizer statistics "on the fly" for direct path loads. Sounds good for ETL? In certain scenarios it makes sense but even then there are many points to consider so that it becomes a reliable part of your ETL processes. When exactly will it be working and when not? Do you prevent it yourself? Documented, undocumented cases, known bugs. Which statistics are gathered and which are not? What has to be considered with partitioned tables? Interval partitioning - special case?
This document provides a summary of the author's skills and experience in business intelligence development. It outlines various demos including: data integration using SQL Server Integration Services; SQL programming techniques; analytical reporting using SQL Server Reporting Services; dashboards and key performance indicators using SharePoint; multi-dimensional cubes and partitions using SQL Server Analysis Services; and MDX coding. For each area, 1-3 sentences describe example projects or techniques the author has implemented, such as ETL packages to load data or dual axis charts for data analysis.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
The document summarizes Arthur Chan's business intelligence portfolio from his master's program. It includes samples and descriptions of projects involving extracting, transforming, and loading data with SQL Server Integration Services, modeling data with SQL Server Analysis Services, creating reports with SQL Server Reporting Services, and developing dashboards and scorecards with PerformancePoint and SharePoint. The portfolio aims to demonstrate Arthur's skills in core business intelligence technologies like SSIS, SSAS, SSRS, and Microsoft Office products for performance management and business analytics.
This document contains examples of business intelligence projects using Microsoft's BI product stack, including SQL Server Integration Services, SQL Server Analysis Services, SQL Server Reporting Services, Performance Point Services, SharePoint Server, and MDX programming. It describes the development of ETL packages in SSIS to load data into a data warehouse, the creation of an analysis cube in SSAS, reports built in SSRS including one with cascading parameters, dashboards and scorecards developed in PPS, an employee report deployed to SharePoint, and sample MDX queries. The portfolio aims to demonstrate skills and experience working with Microsoft's complete BI platform and solutions.
This document provides guidelines for tuning SQL statements to improve response time. It discusses reviewing table and column statistics, execution plans, and restructuring SQL statements and indexes. Specific techniques covered include gathering statistics, reviewing access paths like index scans and joins, and using SQL profiles to lock optimized plans.
How to Integrate OBIEE and Essbase / EPM Suite (OOW 2012)Mark Rittman
Oracle plans to integrate Oracle Essbase and the EPM product suite with Oracle Business Intelligence Enterprise Edition and Oracle Fusion Middleware. So with the latest release of Oracle Business Intelligence Enterprise Edition, 11.1.1.6, how do you connect Oracle Business Intelligence Enterprise Edition to your Oracle Essbase databases and how well does it handle Oracle Essbase features such as scenario and account dimensions, changing outlines, and unbalanced/parent-child hierarchies? How well do Oracle Business Intelligence Enterprise Edition’s ad hoc reporting tools handle Oracle Essbase hierarchies and member selections in the 11.1.1.6 release? Can we still embed Oracle Business Intelligence Enterprise Edition dashboards in Oracle Workspaces? Learn the answers in this session.
This portfolio contains examples of the author's work with SQL Server Business Intelligence tools. It includes projects developed using SQL Server Integration Services (SSIS) for Extract, Transform and Load (ETL) processes, SQL Server Analysis Services (SSAS) for developing an OLAP cube, SQL Server Reporting Services (SSRS) for building reports and dashboards, and Performance Point Server (PPS) for scorecards, charts and analytics. It also includes examples of using SharePoint Server and writing MDX queries for OLAP cubes.
This document outlines the design of a business intelligence portfolio for All Works Construction Company including a data warehouse, ETL processes, OLAP cube, and reports. The data warehouse will use a snowflake schema design. The ETL processes will load dimension and fact data into a staging area. An OLAP cube will be created using SQL Server Analysis Services with a MOLAP storage mode and partitioning strategy. Reports will be created in SQL Server Reporting Services, Excel Services, and Performance Point and published to SharePoint.
The document summarizes several Microsoft Business Intelligence projects including an SSIS ETL project to extract, transform and load data from multiple sources into a staging database, an SSAS OLAP cube project to build a cube for analysis using the staging data, and an SSRS reporting project to create reports from the cube data and deploy them to SharePoint. It also describes a final team project that leveraged all the BI tools to create a real-world solution for a client, including designing databases, building an ETL process, creating an OLAP cube with calculations and KPIs, and generating reports in various tools deployed to SharePoint.
This document is a report submitted by a group of students for their income tax calculator project. It includes sections on the purpose of the project, basic income tax fundamentals, a description of the application including screenshots, and the source code for building the GUI application in Python using Tkinter. The application allows users to calculate their income tax liability under the old and new tax regimes and determine which provides greater savings.
LPU Summer Training Project Viva PPT - Modern Big Data Analysis with SQL Spec...Qazi Maaz Arshad
The document summarizes a Modern Big Data Analysis with SQL Specialization course completed as part of a summer training. It discusses the course content and timeline, a project to analyze movie data using SQL and MySQL Workbench, and key learnings. The project involved creating a database from multiple data sets and using SQL queries to calculate results. An entity relationship diagram and screenshots demonstrate the project design and outputs.
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This portfolio contains examples of the author's work with Microsoft's SQL Server 2008 Business Intelligence stack. It includes projects with Transact SQL, SQL Server Integration Services, SQL Server Analysis Services, SQL Server Reporting Services, and PerformancePoint Server. The projects were completed as part of a 12-week hands-on Master's program and involved building databases, an OLAP cube, reports and dashboards using real-world business scenarios and data from various sources like Excel, XML and CSV files.
This portfolio document outlines a Business Intelligence project that involves extracting data from various sources into a SQL Server database, building an Analysis Services cube with dimensions and measures to analyze company data, creating Reporting Services reports on the data, and developing PerformancePoint dashboards and Excel Services reports to visualize key metrics. The project transfers raw data into a data warehouse, performs analysis with SSAS, generates reports with SSRS, and builds dashboards with PPS and Excel Services to provide business intelligence insights. Samples and screenshots are provided of the ETL processes, cube design, MDX queries, reports, and dashboards created in the project.
The document summarizes the development of business intelligence reports for a project. It involved creating dashboards using Performance Point Server (PPS) and publishing them to SharePoint. SQL Server Reporting Services (SSRS) reports were also created and published. Excel reports were integrated into PPS dashboards. Data connections, filters, and scheduling were established to provide automated daily generation and viewing of reports.
This presentations shows how to create a time/date dimension for PowerPivot from the date data in your fact table. I also shows the DAX functions that you can use to add columns to the fact table or a separate dimension table.
This document contains a portfolio of business intelligence projects completed by Hong-Bing Li using Microsoft's BI product stack. It includes examples of SQL Server Integration Services (SSIS) packages to perform ETL, SQL programming, SQL Server Reporting Services (SSRS) reports including dashboards, SQL Server Analysis Services (SSAS) cubes, and MDX queries. The portfolio demonstrates skills in data integration, reporting, analytics, and dashboard development with a focus on Microsoft tools.
Nitin\'s Business Intelligence Portfolionpatel2362
The document provides samples of work from a Business Intelligence portfolio including T-SQL queries, MDX queries, SSIS packages, SSAS cube design, SSRS reports, and Excel Services reports with KPIs. It includes descriptions and screenshots of projects completed involving data integration, analysis, and reporting for a simulated construction company using SQL Server 2005 and Microsoft BI technologies.
Online Statistics Gathering for Bulk Loads - the official name of the feature - was introduced in Oracle 12.1. The idea is to gather optimizer statistics "on the fly" for direct path loads. Sounds good for ETL? In certain scenarios it makes sense but even then there are many points to consider so that it becomes a reliable part of your ETL processes. When exactly will it be working and when not? Do you prevent it yourself? Documented, undocumented cases, known bugs. Which statistics are gathered and which are not? What has to be considered with partitioned tables? Interval partitioning - special case?
This document provides a summary of the author's skills and experience in business intelligence development. It outlines various demos including: data integration using SQL Server Integration Services; SQL programming techniques; analytical reporting using SQL Server Reporting Services; dashboards and key performance indicators using SharePoint; multi-dimensional cubes and partitions using SQL Server Analysis Services; and MDX coding. For each area, 1-3 sentences describe example projects or techniques the author has implemented, such as ETL packages to load data or dual axis charts for data analysis.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
The document summarizes Arthur Chan's business intelligence portfolio from his master's program. It includes samples and descriptions of projects involving extracting, transforming, and loading data with SQL Server Integration Services, modeling data with SQL Server Analysis Services, creating reports with SQL Server Reporting Services, and developing dashboards and scorecards with PerformancePoint and SharePoint. The portfolio aims to demonstrate Arthur's skills in core business intelligence technologies like SSIS, SSAS, SSRS, and Microsoft Office products for performance management and business analytics.
This document contains examples of business intelligence projects using Microsoft's BI product stack, including SQL Server Integration Services, SQL Server Analysis Services, SQL Server Reporting Services, Performance Point Services, SharePoint Server, and MDX programming. It describes the development of ETL packages in SSIS to load data into a data warehouse, the creation of an analysis cube in SSAS, reports built in SSRS including one with cascading parameters, dashboards and scorecards developed in PPS, an employee report deployed to SharePoint, and sample MDX queries. The portfolio aims to demonstrate skills and experience working with Microsoft's complete BI platform and solutions.
This document provides guidelines for tuning SQL statements to improve response time. It discusses reviewing table and column statistics, execution plans, and restructuring SQL statements and indexes. Specific techniques covered include gathering statistics, reviewing access paths like index scans and joins, and using SQL profiles to lock optimized plans.
How to Integrate OBIEE and Essbase / EPM Suite (OOW 2012)Mark Rittman
Oracle plans to integrate Oracle Essbase and the EPM product suite with Oracle Business Intelligence Enterprise Edition and Oracle Fusion Middleware. So with the latest release of Oracle Business Intelligence Enterprise Edition, 11.1.1.6, how do you connect Oracle Business Intelligence Enterprise Edition to your Oracle Essbase databases and how well does it handle Oracle Essbase features such as scenario and account dimensions, changing outlines, and unbalanced/parent-child hierarchies? How well do Oracle Business Intelligence Enterprise Edition’s ad hoc reporting tools handle Oracle Essbase hierarchies and member selections in the 11.1.1.6 release? Can we still embed Oracle Business Intelligence Enterprise Edition dashboards in Oracle Workspaces? Learn the answers in this session.
This portfolio contains examples of the author's work with SQL Server Business Intelligence tools. It includes projects developed using SQL Server Integration Services (SSIS) for Extract, Transform and Load (ETL) processes, SQL Server Analysis Services (SSAS) for developing an OLAP cube, SQL Server Reporting Services (SSRS) for building reports and dashboards, and Performance Point Server (PPS) for scorecards, charts and analytics. It also includes examples of using SharePoint Server and writing MDX queries for OLAP cubes.
This document outlines the design of a business intelligence portfolio for All Works Construction Company including a data warehouse, ETL processes, OLAP cube, and reports. The data warehouse will use a snowflake schema design. The ETL processes will load dimension and fact data into a staging area. An OLAP cube will be created using SQL Server Analysis Services with a MOLAP storage mode and partitioning strategy. Reports will be created in SQL Server Reporting Services, Excel Services, and Performance Point and published to SharePoint.
The document summarizes several Microsoft Business Intelligence projects including an SSIS ETL project to extract, transform and load data from multiple sources into a staging database, an SSAS OLAP cube project to build a cube for analysis using the staging data, and an SSRS reporting project to create reports from the cube data and deploy them to SharePoint. It also describes a final team project that leveraged all the BI tools to create a real-world solution for a client, including designing databases, building an ETL process, creating an OLAP cube with calculations and KPIs, and generating reports in various tools deployed to SharePoint.
This document is a report submitted by a group of students for their income tax calculator project. It includes sections on the purpose of the project, basic income tax fundamentals, a description of the application including screenshots, and the source code for building the GUI application in Python using Tkinter. The application allows users to calculate their income tax liability under the old and new tax regimes and determine which provides greater savings.
LPU Summer Training Project Viva PPT - Modern Big Data Analysis with SQL Spec...Qazi Maaz Arshad
The document summarizes a Modern Big Data Analysis with SQL Specialization course completed as part of a summer training. It discusses the course content and timeline, a project to analyze movie data using SQL and MySQL Workbench, and key learnings. The project involved creating a database from multiple data sets and using SQL queries to calculate results. An entity relationship diagram and screenshots demonstrate the project design and outputs.
Municipal Solid Waste Management in Developing CountriesQazi Maaz Arshad
This document discusses municipal solid waste management in developing countries. It begins by defining waste and providing classifications of waste based on source and type. It then outlines the key steps in municipal solid waste management systems, including waste generation, storage, collection, transport, processing, recovery, and disposal. Several factors that affect municipal solid waste management are also discussed. The document then provides an overview of the current scenario of municipal solid waste management in India, challenges faced, key stakeholders, and policies and initiatives implemented by the Indian government. It concludes by comparing municipal solid waste management approaches between developed, developing, and least developed countries.
This document outlines topics to be covered in ultrasonic testing including basic principles of sound generation, test techniques, inspection applications, equipment, instrumentation, and ultrasonic flaw detection which will be discussed to understand non-destructive testing using ultrasound.
Open data science is an approach to data science that promotes transparency, reproducibility and sharing of data and results. It encourages making data, code, and analyses openly available so others can verify and build upon the work. The goal is to advance science and research through open collaboration.
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Making meditation a part of a daily routine, even if just 10-15 minutes per day, can offer improvements to mood, focus, and overall well-being over time.
The workshop focused on gesture robotics and how to program robots to understand human gestures. Participants learned how to use motion sensors and machine learning algorithms to teach robots new gestures through physical demonstrations. By the end of the workshop, participants had built gesture-controlled robots that could respond to hand waves, finger snaps, and other motions.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against developing mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
Qazi Maaz Arshad is a Pakistani national who was born in Lahore, Pakistan in 1990. He studied at the University of Management and Technology where he earned a bachelor's degree in computer science. After graduating in 2012, he worked as a software engineer for Anthropic, an AI safety startup based in San Francisco.
This document is a project report submitted by Qazi Maaz Arshad to Lovely Professional University in partial fulfillment of the requirements for a Bachelor of Technology degree in Computer Science and Engineering. The report covers Municipal Solid Waste Management in Developing Countries, based on a course taken through Coursera provided by EPFL. The report includes an introduction to solid waste classification, an overview of municipal solid waste management systems and factors affecting them, the current scenario of waste management in India and other developing countries, and the future outlook for solid waste management globally and in India.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
3. INTRODUCTION
EXCEL DASHBOARD
The Excel Dashboard is used to display
overviews of large data tracks. Excel
Dashboards use dashboard elements like
tables and charts to show the overviews.
4. MY DATASET
120 YEARS OF OLYMPICS
HISTORY
The data set used contains information
regarding all the previous Winter and
Summer Olympics.
8. ETL PROCESS
EXTRACT, TRANSFORM, LOAD
EXTRACTING DATA
Downloading Data, Importing Data
TRANSFORMING DATA
Merging Data Sets, Removing
Unwanted Values, Columns
LOADING DATA
Saving Clean Data
9. DASHBOARD
OBJECTIVES
The main aim of the project is to
analyze the Olympics data set to
deduce all the Olympics statistics
which includes the records, facts,
and trends of all the Summer and
Winter Olympics since 1896 with
respect to participants, nations,
and games in various aspects.
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MAIN OBJECTIVES
FINDING TRENDS AND FIGURES
BY GENDER
Medal Victory, Sports
Participation in terms of
sex.
BY NATIONS
Best Performing Countries,
Year Wise, Male vs
Female, in specific Sports.
BY AGE
Player Participation,
Victory, Male/Female
Ratio with respect to age.
11. This dashboard helps in finding
the required results from the
Olympics data and eases the
decision-making process by
showing the vital parts of the
data.
THE
DASHBOARD
OLYMPICS STATISTICS
15. S W
O T
STRENGTHS
Excellent UI
Easy Navigation
Abstract View
Detailed Analysis
Easy to Understand
WEAKNESSES
Tasks not Achieved due
to (Limited Excel
Features)
OPPORTUNITIES
Work on Weaknesses
Make Dashboard Fit
Screen
THREATS
Lack of Security.
Slow
Occupies large space
16. TOOLS & FEATURES OF EXCEL
MOST USED
Pivot Table Pivot Chart Links
Images &
Icons
VBA