This document describes a project that uses the K-nearest neighbors (KNN) algorithm to classify job applicants at Western Kentucky University into different groups of jobs based on their profiles. The project analyzes existing employee data and then classifies new applicants based on their similarities to existing employees. The document outlines the motivation, dataset, approach, future work, results and conclusion of the project. It aims to simplify the job application process for applicants by recommending suitable jobs based on their qualifications.
CompSci: 221 Winter 2017 Search Engine for UCISoham Kulkarni
▪ Implemented text processing for web pages to extract tokens, n- grams and anagrams in Python
▪ Designed a spider to crawl ics.uci.edu domain and accumulate crawled pages into a MySQL database
▪ Constructed an indexer for crawled pages and a page ranking mechanism based on Page Rank and collection frequency
Explore in-depth insights into the intricate world of bank loan approval with this compelling data analysis project presented by Boston Institute of Analytics. Our talented students delve into the complexities of loan approval processes, leveraging advanced data analysis techniques to uncover patterns, trends, and factors influencing loan decisions. From evaluating credit scores and income levels to analyzing loan terms and default rates, this project offers a comprehensive examination of the key metrics and variables impacting bank loan approval. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on bank loan approval dynamics. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
Details
For September, DataScience Sg is starting a new series specially for the undergrads. The series aims to showcase undergrads and fresh grads project work.
The series is meant to encourage youths in joining the data science & artificial intelligence career. And for the employers to come in and recruit talents for your companies.
In this inaugural meetup for the series, we have the following youths to share about their work and project and how their projects helped them in their current career.
DSSG strongly encourage current undergrads and fresh grads to join us in this series. Its still open to the general community!
Details:
Ivan is currently a Data Scientist at Tech In Asia (TIA), with experience in developing recommender systems, customer churn prediction, network analysis and driving BI solutions through data visualization and analytics. He graduated with a Bachelor of Science (Informations Systems) and Major in Marketing Analytics from SMU in 2018.
Ivan will be sharing about his Final Year Project when he was an undergrad at SMU — KDDLabs, a web-based data mining application while explaining the team’s motivations, challenges and key takeaways. In addition, he will also be talking about his first data product at TIA, developing recommender systems to help better connect jobseekers with employers and vice versa.
LinkedIn: https://www.linkedin.com/in/yongsiang/
FYP: http://smu.sg/kddlabs
CompSci: 221 Winter 2017 Search Engine for UCISoham Kulkarni
▪ Implemented text processing for web pages to extract tokens, n- grams and anagrams in Python
▪ Designed a spider to crawl ics.uci.edu domain and accumulate crawled pages into a MySQL database
▪ Constructed an indexer for crawled pages and a page ranking mechanism based on Page Rank and collection frequency
Explore in-depth insights into the intricate world of bank loan approval with this compelling data analysis project presented by Boston Institute of Analytics. Our talented students delve into the complexities of loan approval processes, leveraging advanced data analysis techniques to uncover patterns, trends, and factors influencing loan decisions. From evaluating credit scores and income levels to analyzing loan terms and default rates, this project offers a comprehensive examination of the key metrics and variables impacting bank loan approval. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on bank loan approval dynamics. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
Details
For September, DataScience Sg is starting a new series specially for the undergrads. The series aims to showcase undergrads and fresh grads project work.
The series is meant to encourage youths in joining the data science & artificial intelligence career. And for the employers to come in and recruit talents for your companies.
In this inaugural meetup for the series, we have the following youths to share about their work and project and how their projects helped them in their current career.
DSSG strongly encourage current undergrads and fresh grads to join us in this series. Its still open to the general community!
Details:
Ivan is currently a Data Scientist at Tech In Asia (TIA), with experience in developing recommender systems, customer churn prediction, network analysis and driving BI solutions through data visualization and analytics. He graduated with a Bachelor of Science (Informations Systems) and Major in Marketing Analytics from SMU in 2018.
Ivan will be sharing about his Final Year Project when he was an undergrad at SMU — KDDLabs, a web-based data mining application while explaining the team’s motivations, challenges and key takeaways. In addition, he will also be talking about his first data product at TIA, developing recommender systems to help better connect jobseekers with employers and vice versa.
LinkedIn: https://www.linkedin.com/in/yongsiang/
FYP: http://smu.sg/kddlabs
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/
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Search and Society: Reimagining Information Access for Radical Futures
Dm project report
1. Project Report
Version 1.1
May 6, 2010
WKU Job Applicant’s Profile Evaluator using KNN
Vijayeandra Parthepan
Mohnish Thallavajhula
Professor: Dr. Huanjing Wang
Submitted in partial fulfillment
Of the requirements of
CS565 Data Mining
Western Kentucky University
3. Project Report
12/05/11
1.0. INTRODUCTION:
K Nearest Neighbor (KNN) is the supervised data mining pattern recognition algorithm. It
classifies objects based on closest training exam-nearest neighbor algorithm. It is amongst the simplest of
all machine learning algorithms. An object is classified by a majority vote of its neighbors. K is small
positive integer and it is usually previously set.
WKU job applicant’s profile evaluator using KNN analyzes the status of the current job applicant
based on the applicant’s details and classifies the applicant to the group of jobs that the applicant can apply.
2.0. MOTIVATION:
The potential employee’s who wish to find some jobs in the university are not sure which jobs
they are most likely to get and hence they may end up applying to jobs which may not suit their profile. So,
in order to make their job search more accurate, we are going to compare their profile with already existing
employee’s and provides them the job suggestions. We are going to analyze the status of WKU employees
using KNN. The KNN algorithm classifies the new employee to a particular class based on the existing
records. The k – “nearest” details of the existing job assignments will be considered and the job applicant
will be classified into which group the applicant belongs to.
3.0. DATASET DESCRIPTION:
Training data is the existing assignments of the jobs.
Sample Training Data:
A G 3.0 CS 2
B UG 2.5 ANY 3
C G 3.0 MPH 5
Test data is the details of the Job Applicant.
Sample Test Data:
G 3.5 CS 5
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4. Project Report
12/05/11
Test Data Description:
Training data has:
Class Name in 1st column
Qualification in 2nd column
GPA in 3rd column
Department in 4th column
Years of experience in 5th column
Training Data Description:
Qualification in 1st column
GPA in 2nd column
Department in 3rd column
Years of experience in 4th column
4.0. APPROACHES:
After calculating the group to which the Job Applicant belongs to, the list of jobs that the Job
applicant can apply is displayed.
The algorithm of the k-nearest neighbor that we apply in our project is as follows,
1. Calculate the “distance” from the test record to the training records.
2. Find the “k - nearest” training records.
3. Check the majority class from the k – nearest training records.
4. The class label for the training record is predicted as the class with the majority votes/weight
among the k – nearest training.
We are classifying the job applicants based on their details into different classes of jobs.
Group A: {Graduate Assistant, Research Assistant}
Group B: {Lab Assistant, Desk Clerk, Night Clerk}
Group C: {Shuttle driver, Receptionist}
The application has been developed using C# .NET.
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5. Project Report
12/05/11
5.0. FUTURE WORK:
Convert the Windows implementation into Web Application.
Provide direct application process to the jobs by taking the applicant’s details.
6.0. RESULTS:
Screen shot of the help menu:
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6. Project Report
12/05/11
Screen shot of the main menu:
7.0. CONCLUSION:
By implementing k – NN, the applicant is classified into a particular group of jobs. Thus, the job
application process is simplified. Since we have implemented k – NN, the implementation is much simpler than
it’s counter parts i.e. Decision Trees, Naïve Bayes, Support Vector Machines.
8.0. REFERRENCES:
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
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