This document provides an introduction to using the STELLA software. It walks through rendering a mental model of deer population dynamics as an example. The key steps covered are:
1. Rendering the initial stock of deer population and adding inflows and outflows like births and deaths.
2. Adding converters and connectors to complete the logic and show dependencies between variables.
3. Taking a brief look at the underlying equations generated by STELLA to set up the model for simulation.
4. Preparing to enter numerical values into the model to ready it for running simulations.
The tutorial provides a progressive set of screenshots to guide the user through each step of mapping out and building the initial deer
Everything You Need to Know About Computer VisionKavika Roy
https://www.datatobiz.com/blog/computer-vision-guide/
To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision.
There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos.
Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans.
One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.
Human Emotion Recognition using Machine Learningijtsrd
It is quite interesting to recognize the human emotions in the field of machine learning. Using a person's facial expression one can know his emotions or what the person wants to express. But at the same time it's not easy to recognize one's emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25217.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/25217/human-emotion-recognition-using-machine-learning/prof-mrs-dhanamma-jagli
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
FORMALIZATION & DATA ABSTRACTION DURING USE CASE MODELING IN OBJECT ORIENTED ...cscpconf
In object oriented analysis and design, use cases represent the things of value that the system performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves around ‘what’ only we are not able to extract the data. So in order to incorporate data in use cases one must feel the need of data at the initial stages of the development. We have developed the technique to integrate data in to the uses cases. This paper is regarding our investigations to take care of data during early stages of the software development. The collected abstraction of data helps in the analysis and then assist in forming the attributes of the candidate classes. This makes sure that we will not miss any attribute that is required in the abstracted behavior using use cases. Formalization adds to the accuracy of the data abstraction. We have investigated object constraint language to perform better data abstraction during analysis & design in unified paradigm. In this paper we have presented our research regarding early stage data abstraction and its formalization.
Formalization & data abstraction during use case modeling in object oriented ...csandit
In object oriented analysis and design, use cases represent the things of value that the system
performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral
abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users
in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data
abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to
account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves
around ‘what’ only we are not able to extract the data. So in order to incorporate data in use
cases one must feel the need of data at the initial stages of the development. We have developed
the technique to integrate data in to the uses cases. This paper is regarding our investigations
to take care of data during early stages of the software development. The collected abstraction
of data helps in the analysis and then assist in forming the attributes of the candidate classes.
This makes sure that we will not miss any attribute that is required in the abstracted behavior
using use cases. Formalization adds to the accuracy of the data abstraction. We have
investigated object constraint language to perform better data abstraction during analysis &
design in unified paradigm. In this paper we have presented our research regarding early stage
data abstraction and its formalization.
How to make data-driven interactive PowerPoint presentations for operationsGramener
Interactive data-driven presentations are a new way of presenting data. They allow the presenter and the audience to engage actively and drill into the data within PowerPoint.
Author: S. Anand - CEO, Gramener
Check out the full webinar on the topic: https://info.gramener.com/interactive-powerpoint-for-operations
This is a presentation I delivered at Enterprise Data World 2018 to make the case for developing intelligent systems using a hybrid or blended approach combining statistical-based machine learning with knowledge-based approaches that involve ontologies, taxonomies or knowledge graphs.
Everything You Need to Know About Computer VisionKavika Roy
https://www.datatobiz.com/blog/computer-vision-guide/
To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision.
There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos.
Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans.
One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.
Human Emotion Recognition using Machine Learningijtsrd
It is quite interesting to recognize the human emotions in the field of machine learning. Using a person's facial expression one can know his emotions or what the person wants to express. But at the same time it's not easy to recognize one's emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25217.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/25217/human-emotion-recognition-using-machine-learning/prof-mrs-dhanamma-jagli
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
FORMALIZATION & DATA ABSTRACTION DURING USE CASE MODELING IN OBJECT ORIENTED ...cscpconf
In object oriented analysis and design, use cases represent the things of value that the system performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves around ‘what’ only we are not able to extract the data. So in order to incorporate data in use cases one must feel the need of data at the initial stages of the development. We have developed the technique to integrate data in to the uses cases. This paper is regarding our investigations to take care of data during early stages of the software development. The collected abstraction of data helps in the analysis and then assist in forming the attributes of the candidate classes. This makes sure that we will not miss any attribute that is required in the abstracted behavior using use cases. Formalization adds to the accuracy of the data abstraction. We have investigated object constraint language to perform better data abstraction during analysis & design in unified paradigm. In this paper we have presented our research regarding early stage data abstraction and its formalization.
Formalization & data abstraction during use case modeling in object oriented ...csandit
In object oriented analysis and design, use cases represent the things of value that the system
performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral
abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users
in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data
abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to
account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves
around ‘what’ only we are not able to extract the data. So in order to incorporate data in use
cases one must feel the need of data at the initial stages of the development. We have developed
the technique to integrate data in to the uses cases. This paper is regarding our investigations
to take care of data during early stages of the software development. The collected abstraction
of data helps in the analysis and then assist in forming the attributes of the candidate classes.
This makes sure that we will not miss any attribute that is required in the abstracted behavior
using use cases. Formalization adds to the accuracy of the data abstraction. We have
investigated object constraint language to perform better data abstraction during analysis &
design in unified paradigm. In this paper we have presented our research regarding early stage
data abstraction and its formalization.
How to make data-driven interactive PowerPoint presentations for operationsGramener
Interactive data-driven presentations are a new way of presenting data. They allow the presenter and the audience to engage actively and drill into the data within PowerPoint.
Author: S. Anand - CEO, Gramener
Check out the full webinar on the topic: https://info.gramener.com/interactive-powerpoint-for-operations
This is a presentation I delivered at Enterprise Data World 2018 to make the case for developing intelligent systems using a hybrid or blended approach combining statistical-based machine learning with knowledge-based approaches that involve ontologies, taxonomies or knowledge graphs.