Intelligence Density
•Download as PPT, PDF•
0 likes•955 views
Intelligence Density (ID) is a framework that measures the productivity and quality of knowledge work outputs from decision support systems. It assesses four key areas: the quality of the system's model in terms of accuracy, explainability, and response time; the engineering dimensions such as flexibility, scalability, and ease of use; the quality of available resources like tolerance for noise and complexity; and logistical constraints regarding independence from experts and development speed. The document provides examples of how ID could be applied to evaluate different systems based on these criteria.
Report
Share
Report
Share
![Intelligence Density: A measure of organizational intelligence and productivity ,[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Strayer cis 333 week 11 final exam set 1 new
cis 333 11 final exam set 1 new,cis 333 11 final exam set 2 new,cis 333 11 final exam set 3 new,strayer cis 333,cis 333,strayer cis 333 week 11 tutorial,cis 333 week 11 assignment,strayer cis 333 week 11 help
Strayer cis 333 week 11 final exam set 1 new
cis 333 11 final exam set 1 new,cis 333 11 final exam set 2 new,cis 333 11 final exam set 3 new,strayer cis 333,cis 333,strayer cis 333 week 11 tutorial,cis 333 week 11 assignment,strayer cis 333 week 11 help
A predictive system for detection of bankruptcy using machine learning techni...
Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to
ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different
soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy
prediction system to categorize the companies based on extent of risk. The prediction system acts as a
decision support tool for detection of bankruptcy
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
Lecture 23
The document discusses data quality management processes and concepts. It describes establishing a data quality management environment, scoping data quality projects and implementation plans, implementing projects using define, measure, analyze, improve steps, and evaluating methods. It also discusses misconceptions around data quality such as the idea that data can simply be fixed or that it is solely an IT problem. Quality is a company-wide issue requiring business and IT collaboration.
Pitfalls and Countermeasures in Software Quality Measurements and Evaluations
Hironori Washizaki, "Pitfalls and Countermeasures in Software Quality Measurements and Evaluations," 5th International Workshop on Quantitative Approaches to Software Quality (QuASoQ), Keynote, Nanjing, Dec 4, 2017
CIS 333 Life of the Mind/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
CIS 333 Week 2 Lab 1 Performing Reconnaissance and Probing Using Common Tools
Knowledge based systems
This document discusses the development of knowledge-based systems. It describes knowledge-based systems as computer programs that use a knowledge base to reason and solve complex problems. It outlines various methodologies for developing knowledge-based systems, including Weitzel and Kerschberg's KBSDLC methodology which divides the development process into five phases: identification, conceptualization, formalization, implementation, and validation. The document also discusses the roles of knowledge engineers and different representations that can be used such as rules, frames and objects.
Recommended
Strayer cis 333 week 11 final exam set 1 new
cis 333 11 final exam set 1 new,cis 333 11 final exam set 2 new,cis 333 11 final exam set 3 new,strayer cis 333,cis 333,strayer cis 333 week 11 tutorial,cis 333 week 11 assignment,strayer cis 333 week 11 help
Strayer cis 333 week 11 final exam set 1 new
cis 333 11 final exam set 1 new,cis 333 11 final exam set 2 new,cis 333 11 final exam set 3 new,strayer cis 333,cis 333,strayer cis 333 week 11 tutorial,cis 333 week 11 assignment,strayer cis 333 week 11 help
A predictive system for detection of bankruptcy using machine learning techni...
Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to
ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different
soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy
prediction system to categorize the companies based on extent of risk. The prediction system acts as a
decision support tool for detection of bankruptcy
DATA SCIENCE METHODOLOGY FOR CYBERSECURITY PROJECTS
Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
Lecture 23
The document discusses data quality management processes and concepts. It describes establishing a data quality management environment, scoping data quality projects and implementation plans, implementing projects using define, measure, analyze, improve steps, and evaluating methods. It also discusses misconceptions around data quality such as the idea that data can simply be fixed or that it is solely an IT problem. Quality is a company-wide issue requiring business and IT collaboration.
Pitfalls and Countermeasures in Software Quality Measurements and Evaluations
Hironori Washizaki, "Pitfalls and Countermeasures in Software Quality Measurements and Evaluations," 5th International Workshop on Quantitative Approaches to Software Quality (QuASoQ), Keynote, Nanjing, Dec 4, 2017
CIS 333 Life of the Mind/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
CIS 333 Week 2 Lab 1 Performing Reconnaissance and Probing Using Common Tools
Knowledge based systems
This document discusses the development of knowledge-based systems. It describes knowledge-based systems as computer programs that use a knowledge base to reason and solve complex problems. It outlines various methodologies for developing knowledge-based systems, including Weitzel and Kerschberg's KBSDLC methodology which divides the development process into five phases: identification, conceptualization, formalization, implementation, and validation. The document also discusses the roles of knowledge engineers and different representations that can be used such as rules, frames and objects.
CIS 333 Imagine Your Future/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
Data Allocation Strategies for Leakage Detection
This document summarizes data allocation strategies for detecting data leakage when sensitive data is distributed through trusted agents. It proposes injecting "fake but genuine-looking" data along with real data to improve the ability to detect if leakage occurs and identify the agent responsible. Various allocation algorithms are presented, including random selection and maximizing the difference between agents' probabilities of guilt. Empirical results show the average detection metric is improved when fake data is used compared to no fake data. The strategies aim to detect leakage without modifying the original data, unlike traditional watermarking techniques.
CIS 333 Exceptional Education / snaptutorial.com
For more classes visit
www.snaptutorial.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
How to Test Whether Consciousness Can Be Revived From Digital Reflections of ...
The document proposes an experiment to test whether digitally stored mindfiles can be used to regenerate the consciousness of an individual. It outlines a hypothesis and methodology for the experiment. The proposed experiment would involve psychologists interviewing both a "flesh subject" and a "cybersubject" (created using an individual's mindfiles) for 25 hours each over one year. It would then compare the session notes and test for consistency with psychological scales to determine if the cybersubject displays the same consciousness as the individual, with over 80% confidence.
Cis 333 Extraordinary Success/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
CIS 333 Week 2 Lab 1 Performing Reconnaissance and Probing Using
Cis 333 Enhance teaching / snaptutorial.com
For more classes visit
www.snaptutorial.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion
Cis 333 Education Organization / snaptutorial.com
This document outlines the content included in the CIS 333 Entire Course tutorial from SnapTutorial. It provides a week-by-week overview of the topics covered, which include discussions, labs, assignments, case studies and exams on various aspects of information systems security. It also includes 3 sets of final exam questions at the end assessing knowledge of concepts like the CIA triad, risk management, cryptography, malware, network security protocols and standards like HIPAA.
Cmsc411(Pascuappt Report)
This document provides an overview of knowledge-based systems and expert systems, including their development, components, advantages, disadvantages, examples, and historical context. It discusses the prototyping process for developing expert systems, common system components like rules, interfaces, and knowledge bases. Examples are provided of early successful systems like XCON and challenges that led to failures. Neural networks are introduced as a technique to improve system learning over time.
Big data solutions - Big data technology
Big Data Analytics solutions, big data architecture,
The Big Data landscape is dominated by two classes of technology: systems that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored; and systems that provide analytical capabilities for retrospective, complex analysis that may touch most or all of the data. These classes of technology are complementary and frequently deployed together.
For operational Big Data workloads, NoSQL Big Data systems such as document databases have emerged to address a broad set of applications, and other architectures, such as key-value stores, column family stores, and graph databases are optimized for more specific applications. NoSQL technologies, which were developed to address the shortcomings of relational databases in the modern computing environment, are faster and scale much more quickly and inexpensively than relational databases.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
The paper aims at proposing a solution for designing and developing a seamless automation and
integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to
seamlessly handle and scale very large amount of unstructured and structured data from diversified and
heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing
component; 3) the ability to automatically select the most appropriate libraries and tools to compute and
accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high
learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different
application problem domains, with high accuracy, robustness, and scalability. This paper highlights the
research methodologies and research activities that we propose to be conducted by the Big Data
researchers and practitioners in order to develop and support seamless automation and integration of
machine learning capabilities for Big Data analytics.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
BMIS 530Systems Analysis and Redesign Project Phase 1 Assignm
BMIS 530
Systems Analysis and Redesign Project: Phase 1
Assignment Instructions
Overview
The System Analysis and Redesign Project focuses on an original private cloud-based solution for a company you understand well. The purpose of this project is to ensure that you know how to analyze an organization and its’ associated systems. Therefore, the student is the architect. Successful projects will have a brand-new design of a missing feature that is not seen in existing information systems. The project should highlight your advanced skills in information systems, displaying your knowledge of all domains of our field.
A key objective of the project is to analyze and design a new system that can function in a cloud-based and/or a distributed computing architectural environment. This system should replace an existing system at a company or a Church or another organization that you understand well.
The re-designed system will be based upon at least one strong and well-respected information systems and/or informatics framework and several associated standards. The framework and associated standards (e.g. COBIT, NIST) will be used to benchmark the existing system to the new system and determine if improvements were made. A summary spreadsheet should be included in your final project phase that shows how the new system meets improved framework guidelines and sub-standards. Sub-standards that specifically identify how an information system needs to be implemented are mandatory in contrast to subjective designs.
All projects must use an original and new application/software design diagrams and concepts. You must identify a new need that has yet to be programmed/coded. Use of traditional classes/databases/sequence diagrams such as what you see in existing ERPs, CRMs, web forms, etc. will not be given credit. For example, tables or classes like accounts, persons, customers, employees, sales, orders, logins, movies/videos, inventory, hotel reservations, restaurant orders, store fronts, shopping carts, and many more will not count toward points in this class. It is your responsibility to check to make sure what you design is new and original. We want to see what you can design; not what others have already designed. Thus, please do not submit designs of existing information systems.
As you will find, the design must also result in a working system that can be implemented. Designs that do not show proficiency in parallel programming code (e.g. a working object-oriented class implemented in Java, C++, Python, and/or C#) are not very valuable at the more granular stage of design and thus this requirement. Software engineers and/or developers should be able to interpret your diagrams and accurately create the designed system with the level of detail provided.
All phases require screenshots for credit that have details in the screenshot showing your personal computer was used and include an operating system date/time. You must use op ...
MIS Wk-10.ppt
A decision support system (DSS) combines data, models, and software to support decision-making. The document discusses the history and types of DSS, including model-driven DSS which emphasize access and manipulation of models, and data-driven DSS which focus on accessing and analyzing internal and external time-series data. Examples of each type are provided to illustrate how DSS can enhance decision-making in complex, uncertain environments.
INTERNAL Assign no 207( JAIPUR NATIONAL UNI)
This document discusses various topics related to management information systems and decision making. It addresses:
1. The differences between internal and external information used for managerial decision making, and factors analyzed for internal strengths/weaknesses and external opportunities/threats.
2. The support functions provided by decision support systems, including aiding less structured problems, combining models/analytics with data access, and emphasizing ease of use.
3. Applications of artificial intelligence systems such as computer vision, machine learning, neural networks and natural language processing.
4. The acid test ratio for evaluating a firm's ability to meet short-term liabilities.
5. The significance of enterprise resource planning (ERP) systems
TEAM CAPSTONESECTIONS VI & VIIPROJECT MANAGE.docx
TEAM CAPSTONE
SECTIONS VI & VII
PROJECT MANAGEMENT &
RISK MANAGEMENT
IFSM 495: CAPSTONE
November 30, 2014
Taylor Kristich PROJECT MANAGER:
Vinay Vira DOCUMENTATION MANAGER
Willie Williams RESEARCHER
Zariah Hebbron-Totaro COORDINATOR
Zenaida Bishop EDITOR
Table of Contents
VI. PROJECT MANAGEMENT
VII. RISK MANAGEMENT
RISK MATRIX Table 1:
REFERENCES15
VI. PROJECT MANAGEMENT
Trusty Carpet’s will require a series of key personnel to implement critical tasks and fix any issues that may arise during the development of the proposed technology solution. The success of the project will entail that the personnel needed for this task correlate with the use of the spiral model, which is an agile approach similar to the waterfall system development life cycle (SDLC) (Kay, Russell, 2002 pg. 1). This approach will ensure the technology solution is completed as efficiently as possible while allowing modifications to completed stages when needed (Tutor, March 2012). The SDLC used will consist of the following areas: Preliminary Investigation and Project Planning Phase; Requirements Definition: System Design; Systems Development; Systems Installation; and Systems Evaluation and Monitoring. The following personnel will be utilized for Trusty Carpet’s technology solution for the following SDLC phases:
1. Preliminary Investigation and Project Manager (Project Planning Phase): The systems analysts, used by Trusty Carpets, will conduct a preliminary investigation to identify company’s objectives and the scope of the problem. This information will be summarized in a document, the feasibility report, then presented to executives and other stakeholders to determine if incorporating new technology, remaining in status quo, or making slight changes to current business operations will benefit the company. The feasibility report will consist of three components: economic feasibility which evaluates tangible cost and benefits associated with the proposed project; technical feasibility which will identify if the hardware and software needed is available for purchase or can be developed during a reasonable timeframe; and operational feasibility which focuses on stakeholders use, support and operation of the proposed system.
The information obtained from this study will allow the Project Manager, Trusty Carpets and all outsourced partners to review the objectives to identify and study the problems before contributing towards the development of the proposed solutions. This information will originate from customers, employees, suppliers, and by observing Trusty Carpets’ competitors. Once collected, it will be used to determine if the company should maintain the status quo, make slight improvements to existing business operations, or invest in new technology. The chosen option will reduce or eliminate data errors and redundant output, eliminate ...
NMIT-SUPPLY-CHAIN-MANAGEMENT-Lesson2
This document discusses supply chain information systems and decision support. It describes technologies like transportation management systems, warehouse management systems, and global visibility tools that provide supply chain visibility and efficiency. It also covers labor management systems. The benefits of information sharing and collaboration between companies are discussed. Potential resistance to new technologies from employees and vendors is addressed. Key supply chain metrics and performance indicators are defined.
Claims
This document contains confidential information belonging to AAUM. Any disclosure of this confidential information would damage AAUM. AAUM retains ownership of all confidential information contained in this document, regardless of the media. This document contains claim analytics data that AAUM considers confidential.
Machine Learning in Customer Analytics
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Acc 564 Exceptional Education / snaptutorial.com
This document provides details for the ACC 564 Entire Course, including discussion questions, assignments, quizzes and exams for each week. It lists the topics that will be covered each week such as information needs for accounting information systems, attacks on systems, securing data, and fraud prevention. It also provides sample exam questions at the end to demonstrate the type of material covered in the assessments.
Project 3 – Hollywood and IT· Find 10 incidents of Hollywood p.docx
Project 3 – Hollywood and IT
· Find 10 incidents of Hollywood portraying IT security incorrectly
· You can use movies or TV episodes
· Write 2-5 paragraphs for each incident. Use supporting citations for each part.
· What has Hollywood portrayed wrong? Describe the scene and what is being shown. Make sure to state whether it is partially wrong or totally fictitious.
· How would you protect/secure against what they show (answers might include install firewall, load Antivirus etc.)
· Use APA formatting for your sources on everything.
· Make sure to put your name on assignment.
Big Data and Social Media
Colgate Palmolive
Agenda Of socail media use
Buisness intellegence and Social media concenpts
Intellegent organization
Data Anaylysis and Data trustworthiness
Conclusion
Buisness intellegence and Social media concenpts
No-Hassle Documentation
Gain Trusted Followers
Spy on Competition
Learn Customer Demographics
Research and Analyze Events
Advertise More Accurately
Intellegent organization
They consistently use (big) data proactively
They know exactly where they want to go: all-round vision
They continuously discuss business matters: alignment
They talk to each other regarding positive and negative performance
They know their customers through and through
They think and work in an agile way
Data Anaylysis and Data trustworthiness
Data completeness and accuracy
Data credibility
Data consistency
Data processing and algorithms
Data Validity
Conclusion
How Colgate benefit from Big Data and Social Media
Social media increases sales and customers
Big data shows popular trends and popular companies
All around they are both beneficial
Big Data can find trends that can benefit you greatly
Criteria
Title Page:
Name, Contact info, title of Presentation
Slide 1
Adenda : Topic you going to cover in order
Slide 2
Discuss how big data, social media concepts and knowledge to successfully create business intellegence (Support your bullets points with data, analysis, charts)
Slide 3
Describe how big data can be used to build an intelligent organization
Slide 4
Discuss the importance of data source trustworthiness and data analysis
Slide 5
Conclusion
Slide 6
Big Data And Business Intelligence
Business Value With Big Data
For business to survive in a competitive environment, organizational change requires improved governance, sponsorship, processes, and controls, in addition to new skill sets and technology all work in harmony to deliver the benefits of big data. See Fig. 13.2
Data science has taken the business world by storm. Every field of study and area of business has been affected as companies realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de fac to programming language for data science. Its flexibility, power, sophistication, and expressiveness have ma ...
ACC 564 NERD Inspiring Innovation--acc564nerd.com
This document provides details for the ACC 564 entire online course, including discussion questions, assignments, quizzes and exams for each week. It lists the topics that will be covered each week such as information needs for accounting information systems, attacks on systems, fraud detection, and databases. It also includes 50 multiple choice questions that make up the final exam for the course which covers topics like data flow diagrams, internal controls, risk assessment, and auditing.
Cipher_Guide-To-Selecting-the-Right-CI-Software-Solution
This document provides a comprehensive guide for selecting the right competitive intelligence (CI) software solution. It outlines an 11-step process for evaluating solutions, including defining objectives and requirements, identifying stakeholders, assessing technical requirements, data inputs and outputs, workflow, customization options, information access controls, analytics capabilities, seeing solutions demonstrated, and considering pricing. Going through each step is recommended to thoroughly understand needs and find a solution that closely matches existing processes to maximize user adoption and benefits.
More Related Content
What's hot
CIS 333 Imagine Your Future/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
Data Allocation Strategies for Leakage Detection
This document summarizes data allocation strategies for detecting data leakage when sensitive data is distributed through trusted agents. It proposes injecting "fake but genuine-looking" data along with real data to improve the ability to detect if leakage occurs and identify the agent responsible. Various allocation algorithms are presented, including random selection and maximizing the difference between agents' probabilities of guilt. Empirical results show the average detection metric is improved when fake data is used compared to no fake data. The strategies aim to detect leakage without modifying the original data, unlike traditional watermarking techniques.
CIS 333 Exceptional Education / snaptutorial.com
For more classes visit
www.snaptutorial.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
How to Test Whether Consciousness Can Be Revived From Digital Reflections of ...
The document proposes an experiment to test whether digitally stored mindfiles can be used to regenerate the consciousness of an individual. It outlines a hypothesis and methodology for the experiment. The proposed experiment would involve psychologists interviewing both a "flesh subject" and a "cybersubject" (created using an individual's mindfiles) for 25 hours each over one year. It would then compare the session notes and test for consistency with psychological scales to determine if the cybersubject displays the same consciousness as the individual, with over 80% confidence.
Cis 333 Extraordinary Success/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
CIS 333 Week 2 Lab 1 Performing Reconnaissance and Probing Using
Cis 333 Enhance teaching / snaptutorial.com
For more classes visit
www.snaptutorial.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion
Cis 333 Education Organization / snaptutorial.com
This document outlines the content included in the CIS 333 Entire Course tutorial from SnapTutorial. It provides a week-by-week overview of the topics covered, which include discussions, labs, assignments, case studies and exams on various aspects of information systems security. It also includes 3 sets of final exam questions at the end assessing knowledge of concepts like the CIA triad, risk management, cryptography, malware, network security protocols and standards like HIPAA.
What's hot (7)
How to Test Whether Consciousness Can Be Revived From Digital Reflections of ...
How to Test Whether Consciousness Can Be Revived From Digital Reflections of ...
Similar to Intelligence Density
Cmsc411(Pascuappt Report)
This document provides an overview of knowledge-based systems and expert systems, including their development, components, advantages, disadvantages, examples, and historical context. It discusses the prototyping process for developing expert systems, common system components like rules, interfaces, and knowledge bases. Examples are provided of early successful systems like XCON and challenges that led to failures. Neural networks are introduced as a technique to improve system learning over time.
Big data solutions - Big data technology
Big Data Analytics solutions, big data architecture,
The Big Data landscape is dominated by two classes of technology: systems that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored; and systems that provide analytical capabilities for retrospective, complex analysis that may touch most or all of the data. These classes of technology are complementary and frequently deployed together.
For operational Big Data workloads, NoSQL Big Data systems such as document databases have emerged to address a broad set of applications, and other architectures, such as key-value stores, column family stores, and graph databases are optimized for more specific applications. NoSQL technologies, which were developed to address the shortcomings of relational databases in the modern computing environment, are faster and scale much more quickly and inexpensively than relational databases.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
The paper aims at proposing a solution for designing and developing a seamless automation and
integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to
seamlessly handle and scale very large amount of unstructured and structured data from diversified and
heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing
component; 3) the ability to automatically select the most appropriate libraries and tools to compute and
accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high
learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different
application problem domains, with high accuracy, robustness, and scalability. This paper highlights the
research methodologies and research activities that we propose to be conducted by the Big Data
researchers and practitioners in order to develop and support seamless automation and integration of
machine learning capabilities for Big Data analytics.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
BMIS 530Systems Analysis and Redesign Project Phase 1 Assignm
BMIS 530
Systems Analysis and Redesign Project: Phase 1
Assignment Instructions
Overview
The System Analysis and Redesign Project focuses on an original private cloud-based solution for a company you understand well. The purpose of this project is to ensure that you know how to analyze an organization and its’ associated systems. Therefore, the student is the architect. Successful projects will have a brand-new design of a missing feature that is not seen in existing information systems. The project should highlight your advanced skills in information systems, displaying your knowledge of all domains of our field.
A key objective of the project is to analyze and design a new system that can function in a cloud-based and/or a distributed computing architectural environment. This system should replace an existing system at a company or a Church or another organization that you understand well.
The re-designed system will be based upon at least one strong and well-respected information systems and/or informatics framework and several associated standards. The framework and associated standards (e.g. COBIT, NIST) will be used to benchmark the existing system to the new system and determine if improvements were made. A summary spreadsheet should be included in your final project phase that shows how the new system meets improved framework guidelines and sub-standards. Sub-standards that specifically identify how an information system needs to be implemented are mandatory in contrast to subjective designs.
All projects must use an original and new application/software design diagrams and concepts. You must identify a new need that has yet to be programmed/coded. Use of traditional classes/databases/sequence diagrams such as what you see in existing ERPs, CRMs, web forms, etc. will not be given credit. For example, tables or classes like accounts, persons, customers, employees, sales, orders, logins, movies/videos, inventory, hotel reservations, restaurant orders, store fronts, shopping carts, and many more will not count toward points in this class. It is your responsibility to check to make sure what you design is new and original. We want to see what you can design; not what others have already designed. Thus, please do not submit designs of existing information systems.
As you will find, the design must also result in a working system that can be implemented. Designs that do not show proficiency in parallel programming code (e.g. a working object-oriented class implemented in Java, C++, Python, and/or C#) are not very valuable at the more granular stage of design and thus this requirement. Software engineers and/or developers should be able to interpret your diagrams and accurately create the designed system with the level of detail provided.
All phases require screenshots for credit that have details in the screenshot showing your personal computer was used and include an operating system date/time. You must use op ...
MIS Wk-10.ppt
A decision support system (DSS) combines data, models, and software to support decision-making. The document discusses the history and types of DSS, including model-driven DSS which emphasize access and manipulation of models, and data-driven DSS which focus on accessing and analyzing internal and external time-series data. Examples of each type are provided to illustrate how DSS can enhance decision-making in complex, uncertain environments.
INTERNAL Assign no 207( JAIPUR NATIONAL UNI)
This document discusses various topics related to management information systems and decision making. It addresses:
1. The differences between internal and external information used for managerial decision making, and factors analyzed for internal strengths/weaknesses and external opportunities/threats.
2. The support functions provided by decision support systems, including aiding less structured problems, combining models/analytics with data access, and emphasizing ease of use.
3. Applications of artificial intelligence systems such as computer vision, machine learning, neural networks and natural language processing.
4. The acid test ratio for evaluating a firm's ability to meet short-term liabilities.
5. The significance of enterprise resource planning (ERP) systems
TEAM CAPSTONESECTIONS VI & VIIPROJECT MANAGE.docx
TEAM CAPSTONE
SECTIONS VI & VII
PROJECT MANAGEMENT &
RISK MANAGEMENT
IFSM 495: CAPSTONE
November 30, 2014
Taylor Kristich PROJECT MANAGER:
Vinay Vira DOCUMENTATION MANAGER
Willie Williams RESEARCHER
Zariah Hebbron-Totaro COORDINATOR
Zenaida Bishop EDITOR
Table of Contents
VI. PROJECT MANAGEMENT
VII. RISK MANAGEMENT
RISK MATRIX Table 1:
REFERENCES15
VI. PROJECT MANAGEMENT
Trusty Carpet’s will require a series of key personnel to implement critical tasks and fix any issues that may arise during the development of the proposed technology solution. The success of the project will entail that the personnel needed for this task correlate with the use of the spiral model, which is an agile approach similar to the waterfall system development life cycle (SDLC) (Kay, Russell, 2002 pg. 1). This approach will ensure the technology solution is completed as efficiently as possible while allowing modifications to completed stages when needed (Tutor, March 2012). The SDLC used will consist of the following areas: Preliminary Investigation and Project Planning Phase; Requirements Definition: System Design; Systems Development; Systems Installation; and Systems Evaluation and Monitoring. The following personnel will be utilized for Trusty Carpet’s technology solution for the following SDLC phases:
1. Preliminary Investigation and Project Manager (Project Planning Phase): The systems analysts, used by Trusty Carpets, will conduct a preliminary investigation to identify company’s objectives and the scope of the problem. This information will be summarized in a document, the feasibility report, then presented to executives and other stakeholders to determine if incorporating new technology, remaining in status quo, or making slight changes to current business operations will benefit the company. The feasibility report will consist of three components: economic feasibility which evaluates tangible cost and benefits associated with the proposed project; technical feasibility which will identify if the hardware and software needed is available for purchase or can be developed during a reasonable timeframe; and operational feasibility which focuses on stakeholders use, support and operation of the proposed system.
The information obtained from this study will allow the Project Manager, Trusty Carpets and all outsourced partners to review the objectives to identify and study the problems before contributing towards the development of the proposed solutions. This information will originate from customers, employees, suppliers, and by observing Trusty Carpets’ competitors. Once collected, it will be used to determine if the company should maintain the status quo, make slight improvements to existing business operations, or invest in new technology. The chosen option will reduce or eliminate data errors and redundant output, eliminate ...
NMIT-SUPPLY-CHAIN-MANAGEMENT-Lesson2
This document discusses supply chain information systems and decision support. It describes technologies like transportation management systems, warehouse management systems, and global visibility tools that provide supply chain visibility and efficiency. It also covers labor management systems. The benefits of information sharing and collaboration between companies are discussed. Potential resistance to new technologies from employees and vendors is addressed. Key supply chain metrics and performance indicators are defined.
Claims
This document contains confidential information belonging to AAUM. Any disclosure of this confidential information would damage AAUM. AAUM retains ownership of all confidential information contained in this document, regardless of the media. This document contains claim analytics data that AAUM considers confidential.
Machine Learning in Customer Analytics
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Acc 564 Exceptional Education / snaptutorial.com
This document provides details for the ACC 564 Entire Course, including discussion questions, assignments, quizzes and exams for each week. It lists the topics that will be covered each week such as information needs for accounting information systems, attacks on systems, securing data, and fraud prevention. It also provides sample exam questions at the end to demonstrate the type of material covered in the assessments.
Project 3 – Hollywood and IT· Find 10 incidents of Hollywood p.docx
Project 3 – Hollywood and IT
· Find 10 incidents of Hollywood portraying IT security incorrectly
· You can use movies or TV episodes
· Write 2-5 paragraphs for each incident. Use supporting citations for each part.
· What has Hollywood portrayed wrong? Describe the scene and what is being shown. Make sure to state whether it is partially wrong or totally fictitious.
· How would you protect/secure against what they show (answers might include install firewall, load Antivirus etc.)
· Use APA formatting for your sources on everything.
· Make sure to put your name on assignment.
Big Data and Social Media
Colgate Palmolive
Agenda Of socail media use
Buisness intellegence and Social media concenpts
Intellegent organization
Data Anaylysis and Data trustworthiness
Conclusion
Buisness intellegence and Social media concenpts
No-Hassle Documentation
Gain Trusted Followers
Spy on Competition
Learn Customer Demographics
Research and Analyze Events
Advertise More Accurately
Intellegent organization
They consistently use (big) data proactively
They know exactly where they want to go: all-round vision
They continuously discuss business matters: alignment
They talk to each other regarding positive and negative performance
They know their customers through and through
They think and work in an agile way
Data Anaylysis and Data trustworthiness
Data completeness and accuracy
Data credibility
Data consistency
Data processing and algorithms
Data Validity
Conclusion
How Colgate benefit from Big Data and Social Media
Social media increases sales and customers
Big data shows popular trends and popular companies
All around they are both beneficial
Big Data can find trends that can benefit you greatly
Criteria
Title Page:
Name, Contact info, title of Presentation
Slide 1
Adenda : Topic you going to cover in order
Slide 2
Discuss how big data, social media concepts and knowledge to successfully create business intellegence (Support your bullets points with data, analysis, charts)
Slide 3
Describe how big data can be used to build an intelligent organization
Slide 4
Discuss the importance of data source trustworthiness and data analysis
Slide 5
Conclusion
Slide 6
Big Data And Business Intelligence
Business Value With Big Data
For business to survive in a competitive environment, organizational change requires improved governance, sponsorship, processes, and controls, in addition to new skill sets and technology all work in harmony to deliver the benefits of big data. See Fig. 13.2
Data science has taken the business world by storm. Every field of study and area of business has been affected as companies realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de fac to programming language for data science. Its flexibility, power, sophistication, and expressiveness have ma ...
ACC 564 NERD Inspiring Innovation--acc564nerd.com
This document provides details for the ACC 564 entire online course, including discussion questions, assignments, quizzes and exams for each week. It lists the topics that will be covered each week such as information needs for accounting information systems, attacks on systems, fraud detection, and databases. It also includes 50 multiple choice questions that make up the final exam for the course which covers topics like data flow diagrams, internal controls, risk assessment, and auditing.
Cipher_Guide-To-Selecting-the-Right-CI-Software-Solution
This document provides a comprehensive guide for selecting the right competitive intelligence (CI) software solution. It outlines an 11-step process for evaluating solutions, including defining objectives and requirements, identifying stakeholders, assessing technical requirements, data inputs and outputs, workflow, customization options, information access controls, analytics capabilities, seeing solutions demonstrated, and considering pricing. Going through each step is recommended to thoroughly understand needs and find a solution that closely matches existing processes to maximize user adoption and benefits.
Entity resolution for hierarchical data using attributes value comparison ove...
This document summarizes an article from the International Journal of Computer Engineering and Technology. The article discusses entity resolution for hierarchical data using attribute value comparison over distributed databases. It introduces the challenges of entity resolution for large datasets and proposes a system that uses parsing, sorting, and matching of record attributes like name, address, date of birth, and phone number to identify duplicate records in a distributed database in a limited amount of time with less memory usage. The system divides entity resolution into three modules - name parsing, address parsing, and a confirmation module to improve accuracy while reducing processing time and memory requirements.
Value Stories - 3rd issue - April 2019
PLAY TO WIN
In Business, As In Chess, Forethought Wins
Showcasing exemplary stories of success where channel partners have gone to great lengths to implement innovative solutions. Acclaiming those partners who have risen to the challenges of the digital era and transformed their business to a solutions offering. Inspiring channel businesses to become value-added providers and trusted allies to their customers. Stories that made a Difference.
Applying Auto-Data Classification Techniques for Large Data Sets
In the current data security landscape, large volumes of data are being created across the enterprise. Manual techniques to inventory and classify data makes it a tedious and expensive activity. To create a time and cost effective implementation of security and access controls, it becomes key to automate the data classification process.
(Source: RSA USA 2016-San Francisco)
Cst 610 Your world/newtonhelp.com
This document outlines 6 projects for a cybersecurity course (CST 610). Project 1 involves assessing an organization's information systems infrastructure and identity management. Project 2 involves evaluating operating system vulnerabilities in Windows and Linux. Project 3 involves assessing vulnerabilities and risks after a security breach at the Office of Personnel Management. Project 4 involves threat analysis and exploitation. Project 5 involves cryptography. Project 6 involves digital forensics analysis. Each project provides details on required deliverables and evaluation criteria.
Cst 610 Motivated Minds/newtonhelp.com
For more course tutorials visit
www.newtonhelp.com
CST 610 Project 1 Information Systems and Identity Management
CST 610 Project 2 Operating Systems Vulnerabilities (Windows and Linux)
CST 610 Project 3 Assessing Information System Vulnerabilities and Risk
Similar to Intelligence Density (20)
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...
BMIS 530Systems Analysis and Redesign Project Phase 1 Assignm
BMIS 530Systems Analysis and Redesign Project Phase 1 Assignm
Project 3 – Hollywood and IT· Find 10 incidents of Hollywood p.docx
Project 3 – Hollywood and IT· Find 10 incidents of Hollywood p.docx
Cipher_Guide-To-Selecting-the-Right-CI-Software-Solution
Cipher_Guide-To-Selecting-the-Right-CI-Software-Solution
Entity resolution for hierarchical data using attributes value comparison ove...
Entity resolution for hierarchical data using attributes value comparison ove...
Applying Auto-Data Classification Techniques for Large Data Sets
Applying Auto-Data Classification Techniques for Large Data Sets
Recently uploaded
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Fueling AI with Great Data with Airbyte Webinar
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.
Skybuffer SAM4U tool for SAP license adoption
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.
Azure API Management to expose backend services securely
How to use Azure API Management to expose backend service securely
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Recently uploaded (20)
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Intelligence Density
- 17. Intelligence Density: Case Examples