A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/01/practical-image-data-augmentation-methods-for-training-deep-learning-object-detection-models-a-presentation-from-ej-technology-consultants/
Evan Juras, Computer Vision Engineer at EJ Technology Consultants, presents the “Practical Image Data Augmentation Methods for Training Deep Learning Object Detection Models” tutorial at the September 2020 Embedded Vision Summit.
Data augmentation is a method of expanding deep learning training datasets by making various automated modifications to existing images in the dataset. The resulting increased data diversity can enable a more accurate and robust model without the need to manually obtain more images.
In this presentation, Juras explores practical methods of image data augmentation for training object detection models. He also shows how to create an augmented dataset of 50,000 unique images with labeled bounding boxes in a few hours using a short Python script.
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
A framework for adoption of machine learning in industry for software defect ...RAKESH RANA
A framework for adoption of machine learning in industry for software defect prediction
Presented at:
9th International Joint Conference on Software Technologies (ICSOFT-EA), Vienna, Austria
Get full text of publication at:
http://rakeshrana.website/index.php/work/publications/
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. We had the same issue inside Amazon, so we created a Machine Learning engine that Developers can easily use. The same approach is now available in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/01/practical-image-data-augmentation-methods-for-training-deep-learning-object-detection-models-a-presentation-from-ej-technology-consultants/
Evan Juras, Computer Vision Engineer at EJ Technology Consultants, presents the “Practical Image Data Augmentation Methods for Training Deep Learning Object Detection Models” tutorial at the September 2020 Embedded Vision Summit.
Data augmentation is a method of expanding deep learning training datasets by making various automated modifications to existing images in the dataset. The resulting increased data diversity can enable a more accurate and robust model without the need to manually obtain more images.
In this presentation, Juras explores practical methods of image data augmentation for training object detection models. He also shows how to create an augmented dataset of 50,000 unique images with labeled bounding boxes in a few hours using a short Python script.
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
A framework for adoption of machine learning in industry for software defect ...RAKESH RANA
A framework for adoption of machine learning in industry for software defect prediction
Presented at:
9th International Joint Conference on Software Technologies (ICSOFT-EA), Vienna, Austria
Get full text of publication at:
http://rakeshrana.website/index.php/work/publications/
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. We had the same issue inside Amazon, so we created a Machine Learning engine that Developers can easily use. The same approach is now available in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
#ATAGTR2021 Presentation : "Unlocking the Power of Machine Learning in the Mo...Agile Testing Alliance
Interactive Session on "Unlocking the Power of Machine Learning in the Mobile NFT world" by Niruphan Rajendran,Senior Manager Qualitest, Karthikeyan Lakshminarayanan Non-Fucntional Test Consultant Qualitest at #ATAGTR2021.
#ATAGTR2021 was the 6th Edition of Global Testing Retreat.
The video recording of the session is now available on the following link: https://www.youtube.com/watch?v=DIDZjUEnfyw
To know more about #ATAGTR2021, please visit:https://gtr.agiletestingalliance.org/
Dataiku productive application to production - pap is may 2015 Dataiku
Beyond Predictive Analytics : Deploying apps to production and keep them improving
Some smart companies have been putting predictive application in production for decades. Still, either because of lack of sharing or lack of generality, there is still no single and obvious way to put a predictive application in production today.
As a consequence, for most companies, transitioning analytics from development to production is still “the next frontier”.
Behind the single word "production” lays a great number of questions like: what exactly do you put in production: data, model, code all three ? Who is responsible for maintenance and quality check over time : business, tech or both ? How can I make my predictive app continuously improve and check that it delivers the promised business value over time ? What are the best practice for maintenance and updates by the way ? Will my data scientists keep working after first development or should I lay half of them off ? etc…
Let’s make a small analogy with the development of web sites in the 90’s and early 00’s :
Back then, the winners where not necessarily the web sites with an amazing design, but a winner had clearly made the necessary efforts and had a robust way to put their web site reliabily in production
Today, every web developper can enjoy the confort of Heroku, Amazon, Github, docker, Angular, bootstrap … and so we forget. How much time before we get the same confort for the predictive world ?
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes.
Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
My Academic Major Project Movie Recommendation using Artificial Intelligence. We also developed a website named movie engine for the recommendation of movies.
Python Code Camp (Professionals) is a whole day workshop that aims to enable professionals to learn Python Basics and Django.
Visit: http://devcon.ph/events/python-code-camp-professionals-2016
Python Code Camp (Professionals) is a whole day workshop that aims to enable professionals to learn Python Basics and Django.
Visit: http://devcon.ph/events/python-code-camp-professionals-2016
Python Code Camp (Professionals) is a whole day workshop that aims to enable professionals to learn Python Basics and Django.
Visit: http://devcon.ph/events/python-code-camp-professionals-2016
Python Code Camp (Professionals) is a whole day workshop that aims to enable professionals to learn Python Basics and Django.
Visit: http://devcon.ph/events/python-code-camp-professionals-2016
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Who Am I
Software Engineer and Game Developer for 5 years.
Developed Dragon Cubes along with other team members
Developed Bubble Cubes
Currently developing Casino Slots
3. Who Am I
Also currently taking Masters of Science in Computer Science in DLSU.
5. Machine Learning
Field of study that gives the computer the ability to learn and
recognize patterns.
Image R G B Name
A 255 255 255 Animal
B 255 0 0 Animal
C 132 122 230 Plant
D 89 134 200 Plant
Training Dataset Machine Learning
Algorithm Prediction Model/s
Animal? Plant?
6. Supervised Learning
The class to predict is explicitly
given in the dataset
TRAINING
DATASET
Image R G B Name
A 255 255 255 Animal
B 255 0 0 Animal
C 132 122 230 Plant
D 89 134 200 Plant
7. Supervised Learning
Prediction model attempts to predict the missing class value
TRAINING
DATASET
NEW/UNSEEN
DATASET
Image R G B Name
A 255 255 255 Animal
B 255 0 0 Animal
C 132 122 230 Plant
D 89 134 200 Plant
Image R G B Name
E 0 0 255 ??
F 255 0 0 ??
G 0 122 125 ??
H 98 7 2 ??
8. Data Mining
• Extract patterns or provide insights from a
complex dataset.
• Borrows techniques from machine learning and
statistics.
9. Data Mining
• Patterns should be:
• Non-trivial (not normally extracted in an SQL
statement)
• Unknown
• Unexpected
• Potentially useful
• Actionable
10. Why Data Mining?
Drowning in data but starved for knowledge
Unstructured data, or knowledge is deeply buried.
11. Predicting Daily Active Users for Match-3
Mobile Games
Application of supervised learning for Dragon Cubes and Jungle Cubes
17. Dataset Overview
• Two match-3 games
• JNC generating
revenue
• DNC does not
• Both games only differ in
game mechanics.
18. General Methodology of Data Mining*
*How I performed data mining on our data
*For supervised learning. Unsupervised learning use subjective evaluation to determine reliability of model.
Dataset
Feature Selection
Refined
Dataset
Machine Learning
Prediction Models
Model A Model B
Model C Model D
Unseen
Data
Predicted
Result
Accuracy
Measure
19. Features in Dataset
Users are reached via advertising channels
MKTExpenses This is the total amount of marketing expenses, in USD,
spent to advertise the game.
A high marketing expense means more advertising
channels have been used to target more potential
users to install the game.
20. Advertising amount per user per
country*
● Users are reached via advertising channels.
*Market insight taken from Chartboost: http://tinyurl.com/charboost
21. Features in Dataset
How many users discovered our app on a given date?
Install Date Calendar date of installation.
Cohort Size Refers to the total amount of users who have
installed the application on the given install date.
Session Count Refers to the total amount of play sessions on a
given install date.
22. Features in Dataset
How long do players play the game?
AvgSessionSeconds The arithmetic mean of the total amount of time
users spend in the game
MedianSessionSeconds Session length value where half of the sessions are
longer, and half are shorter.
25. Features in Dataset
How many users are engaged?
ActiveUsers This refers to the total amount of unique users who
spent considerable time in the game given a certain
date.
ActiveUsersDay7 This is similar to the ActiveUsers variable but offset
7 days after the install date. This is the variable to
be predicted.
26. Features in Dataset
Screen that triggers the
events
LevelPlayedEvents
LevelSuccessEvents
LevelFailedEvents
30. Correlation Analysis
Measure relationship of two variables.
As X grows, how fast Y grows/declines?
Range (-1.0 to +1.0)
0.0 means no relationship at all.
+1.0 strong positive relationship
-1.0 strong negative relationship
51. Interpretations
Using results from M5Base.
*More details available at: http://www.dlsu.edu.ph/conferences/dlsu-research-congress-proceedings/2016/GRC/GRC-HCT-I-001.pdf
54. Interpretations
JNC and DNC have almost the same total advertising expense. But
DNC did not gain enough daily active users.
Positive correlation summary JNC DAU-Day7 DNC DAU-Day7
MKTExpenses High Low
SessionCount High Low
SessionLength High Low
55. Interpretations
Based from our study, we propose a finding that MKTExpenses gets high
correlation with DAU-Day7 once the game has enough enjoyable content
to keep users engaged.
Ensure high
session
length
Promote
replayability
SessionLength
affects
SessionCount
affects
Satisfies
business
requirements?
Increase
advertising
campaigns
MKTExpenses
affects
DAU
DAU-Day7
influences
56. Conclusion
Did our study correctly predicted the fate of JNC and DNC?
Yes. Jungle Cubes has gained over 1M downloads as
of October 2016 and is still profitable.
Dragon Cubes was pulled out of the market last
September 2016.
57. We thank you Jakob Lykkegaard Pedersen and
Thomas Andreasen for allowing us to use the
dataset for Jungle Cubes and Dragon Cubes. We
would also like to give thanks to Suhana Chooli,
the marketing manager of Playlab Inc., which
provided the details about the marketing
expenses.
Thank you for listening!