This document summarizes an analysis of traffic violation data from 2017. It describes the dataset, data preprocessing steps including extraction, classification, oversampling, and imputation. Descriptive analysis examines relationships between variables like seat belt usage, gender, licenses and accidents. Modeling techniques applied include decision trees, logistic regression, neural networks, and auto neural networks. Models using only significant variables like description, time, violation type and day achieved similar accuracy. The analysis found accidents are more common for men than women and many drivers in accidents lacked proper licenses.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
During this webinar you will learn:
How new advanced fraud detection models, including clustering, data/text mining, machine learning and network analysis can detect more suspicious transactions and behaviours
How workflow decision learning will make your system smarter by learning based on previous decisions and interactions
How batch file attachments can be used to attach invoices, receipts and other documentation to alerts for proper record keeping during investigations
Our new search feature that allows organizations to search alerts, work items, cases, regulatory reports, comments and attachments, as well as data from outside sources, to look for potential risks (for example, searching Export Control Lists to screen for export controlled goods)
How Concur users can now open original images of receipts directly in CaseWare Monitor, making investigations easier
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
Many customers often switch or unsubscribe (churn) from their telecom providers for a variety of reasons. These could range from unsatisfactory service, better pricing from competitors, customers moving to different cities etc. Therefore, telecom companies are interested in analyzing the patterns for customers who churn from their services and use the resultant analysis to determine in the future which customers are more likely to unsubscribe from their services. One such company is Telco Systems. Telco Systems is interested in identifying the precise patterns for their churning customers and have provided the customer data for this project.
Machine Learning in Autonomous Data WarehouseSandesh Rao
Machine Learning in Autonomous Data Warehouse: One can use Oracle Autonomous Data Warehouse for machine learning. There are several ways to do this. This presentation explores these different but related options for performing machine learning. Each of these options enables people with different backgrounds to engage with building machine learning solutions on their data. At the end of the session, you will know which option will work best for you
This is from the Bay area Cloud Computing event https://www.meetup.com/All-Things-Cloud-Computing-Bay-Area/events/271017950/
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
During this webinar you will learn:
How new advanced fraud detection models, including clustering, data/text mining, machine learning and network analysis can detect more suspicious transactions and behaviours
How workflow decision learning will make your system smarter by learning based on previous decisions and interactions
How batch file attachments can be used to attach invoices, receipts and other documentation to alerts for proper record keeping during investigations
Our new search feature that allows organizations to search alerts, work items, cases, regulatory reports, comments and attachments, as well as data from outside sources, to look for potential risks (for example, searching Export Control Lists to screen for export controlled goods)
How Concur users can now open original images of receipts directly in CaseWare Monitor, making investigations easier
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
Many customers often switch or unsubscribe (churn) from their telecom providers for a variety of reasons. These could range from unsatisfactory service, better pricing from competitors, customers moving to different cities etc. Therefore, telecom companies are interested in analyzing the patterns for customers who churn from their services and use the resultant analysis to determine in the future which customers are more likely to unsubscribe from their services. One such company is Telco Systems. Telco Systems is interested in identifying the precise patterns for their churning customers and have provided the customer data for this project.
Machine Learning in Autonomous Data WarehouseSandesh Rao
Machine Learning in Autonomous Data Warehouse: One can use Oracle Autonomous Data Warehouse for machine learning. There are several ways to do this. This presentation explores these different but related options for performing machine learning. Each of these options enables people with different backgrounds to engage with building machine learning solutions on their data. At the end of the session, you will know which option will work best for you
This is from the Bay area Cloud Computing event https://www.meetup.com/All-Things-Cloud-Computing-Bay-Area/events/271017950/
Predicting Cab Booking Cancellations- Data Mining Projectraj
The project report is on a project where we 'predict whether a cab booking cancellation will get classified properly'. The dataset is about a cab company based in Bangalore. The name of the cab company is YourCabs.com. The data set was taken from Kaggle.com. The topic deals with the cost, the company incurs in terms of misclassifying the cab cancellations as not cancelled. Thus, we understand that this classification task takes into consideration the misclassification costs. We need to obtain the lowest average cost of booking. Our analysis is also a case where one class is more important than the other i.e., one misclassification error is important than the other.
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
If you are a university student seeking assistance with your assignment reach Treat
Assignment to help Australia . It will help you gain good grades and improve your academic
performance.
Anomaly Detection - Real World Scenarios, Approaches and Live ImplementationImpetus Technologies
Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.
However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.
Join our speakers Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Impetus Technologies and Saurabh Dutta, Technical Product Manager - StreamAnalytix, in a discussion on:
Importance of anomaly detection in enterprise data, types of anomalies, and challenges
Prominent real-time application areas
Approaches, techniques and algorithms for anomaly detection
Sample use-case implementation on the StreamAnalytix platform
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
Introduction to Machine Learning and Data Science using the Autonomous databa...Sandesh Rao
This session will focus on basics of what Machine Learning is, different types of Machine Learning and Neural Networks, supervised and unsupervised machine learning, AutoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use many examples from the ADW Oracle Autonomous Database offering, Oracle Machine Learning library to make this a session with lots of code examples in addition to the theory of Machine Learning and you will walk out having a definitive path to being a data scientist
Identifying and classifying unknown Network Disruptionjagan477830
Since the evolution of modern technology and with the drastic increase in the scale of network communication more and more network disruptions in traffic and private protocols have been taking place. Identifying and classifying the unknown network disruptions can provide support and even help to maintain the backup systems.
[Amended upload]
Presented by PhD student Segun Aluko at UTSG2014.
www.its.leeds.ac.uk/people/s.aluko
www.utsg.net/web/uploads/UTSG%202014%20Newcastle%20Programme.pdf
The data set used in this project is available in the Kaggle and contains nineteen columns (independent variables) that indicate the characteristics of the clients of a fictional telecommunications corporation. The Churn column (response variable) indicates whether the customer departed within the last month or not. The class No includes the clients that did not leave the company last month, while the class YES contains the clients that decided to terminate their relations with the company. The objective of the analysis is to obtain the relation between the customer’s characteristics and the churn.
#Interactive Session by Vivek Patle and Jahnavi Umarji, "Empowering Functiona...Agile Testing Alliance
#Interactive Session by Vivek Patle and Jahnavi Umarji, "Empowering Functional Testing with Support Vector Machines: An Experimental Journey" at #ATAGTR2023.
#ATAGTR2023 was the 8th Edition of Global Testing Retreat.
To know more about #ATAGTR2023, please visit: https://gtr.agiletestingalliance.org/
Network and IT Ops Series: Build Production Solutions Neo4j
Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database.
For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.
Impact of Asymmetry of Internet Traffic for Heuristic Based ClassificationCSCJournals
Accurate traffic classification is necessary for many administrative networking tasks like security monitoring, providing Quality of Service and network design or planning. In this paper we illustrate the accuracy of 18 different machine learning algorithms with different statistical parameter combinations. Additionally, we divide the statistical parameters into upstream and downstream to observe the influence of the protocol inherent differences of client and server behaviour for traffic classification. Our results show that this differentiation can increase the protocol detection rate and decrement the processing time.
Predicting Cab Booking Cancellations- Data Mining Projectraj
The project report is on a project where we 'predict whether a cab booking cancellation will get classified properly'. The dataset is about a cab company based in Bangalore. The name of the cab company is YourCabs.com. The data set was taken from Kaggle.com. The topic deals with the cost, the company incurs in terms of misclassifying the cab cancellations as not cancelled. Thus, we understand that this classification task takes into consideration the misclassification costs. We need to obtain the lowest average cost of booking. Our analysis is also a case where one class is more important than the other i.e., one misclassification error is important than the other.
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
If you are a university student seeking assistance with your assignment reach Treat
Assignment to help Australia . It will help you gain good grades and improve your academic
performance.
Anomaly Detection - Real World Scenarios, Approaches and Live ImplementationImpetus Technologies
Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.
However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.
Join our speakers Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Impetus Technologies and Saurabh Dutta, Technical Product Manager - StreamAnalytix, in a discussion on:
Importance of anomaly detection in enterprise data, types of anomalies, and challenges
Prominent real-time application areas
Approaches, techniques and algorithms for anomaly detection
Sample use-case implementation on the StreamAnalytix platform
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
Introduction to Machine Learning and Data Science using the Autonomous databa...Sandesh Rao
This session will focus on basics of what Machine Learning is, different types of Machine Learning and Neural Networks, supervised and unsupervised machine learning, AutoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use many examples from the ADW Oracle Autonomous Database offering, Oracle Machine Learning library to make this a session with lots of code examples in addition to the theory of Machine Learning and you will walk out having a definitive path to being a data scientist
Identifying and classifying unknown Network Disruptionjagan477830
Since the evolution of modern technology and with the drastic increase in the scale of network communication more and more network disruptions in traffic and private protocols have been taking place. Identifying and classifying the unknown network disruptions can provide support and even help to maintain the backup systems.
[Amended upload]
Presented by PhD student Segun Aluko at UTSG2014.
www.its.leeds.ac.uk/people/s.aluko
www.utsg.net/web/uploads/UTSG%202014%20Newcastle%20Programme.pdf
The data set used in this project is available in the Kaggle and contains nineteen columns (independent variables) that indicate the characteristics of the clients of a fictional telecommunications corporation. The Churn column (response variable) indicates whether the customer departed within the last month or not. The class No includes the clients that did not leave the company last month, while the class YES contains the clients that decided to terminate their relations with the company. The objective of the analysis is to obtain the relation between the customer’s characteristics and the churn.
#Interactive Session by Vivek Patle and Jahnavi Umarji, "Empowering Functiona...Agile Testing Alliance
#Interactive Session by Vivek Patle and Jahnavi Umarji, "Empowering Functional Testing with Support Vector Machines: An Experimental Journey" at #ATAGTR2023.
#ATAGTR2023 was the 8th Edition of Global Testing Retreat.
To know more about #ATAGTR2023, please visit: https://gtr.agiletestingalliance.org/
Network and IT Ops Series: Build Production Solutions Neo4j
Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database.
For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.
Impact of Asymmetry of Internet Traffic for Heuristic Based ClassificationCSCJournals
Accurate traffic classification is necessary for many administrative networking tasks like security monitoring, providing Quality of Service and network design or planning. In this paper we illustrate the accuracy of 18 different machine learning algorithms with different statistical parameter combinations. Additionally, we divide the statistical parameters into upstream and downstream to observe the influence of the protocol inherent differences of client and server behaviour for traffic classification. Our results show that this differentiation can increase the protocol detection rate and decrement the processing time.
Similar to Traffic violations Data Analysis Using SAS (20)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
3. About
The
Dataset
• Traffic Violation Data 2017
Source: Data.gov
• Groups of Attributes
1. Location
2. Causes of Violation
3. Vehicle Information
4. Driver’s Information
5. Consequences of Violation
• Type of Attributes
4. Huge Data
Excess of categories in a column
Imbalanced categories inTargetVariable
MissingValues
9. Descriptive
Analysis
Seat Belt as a cause of Accident
Gender involved in anAccident
Relation between Commercial License andAccident
Day-wise distribution ofTrafficViolation cases
Different types ofViolations
Vehicle types involved inTrafficViolation
14. Data Modeling
using all input
variables
DecisionTree
Simple and Easy to use
Suitable for binary target variable
ImportantVariables
Type of violation
Personal Injury
Property Damage
Charge
Description
15. Data Modeling
using all input
variables
Logistic Regression
Recommended for binary target variables
Uses Maximum Likelihood to estimate the model
parameters
ImportantVariables
Day ofWeek
Hour of Day
Personal Injury
Property Damage
ViolationType
Description
16. Data Modeling
using all input
variables
Neural Network
• It is supervised machine learning algorithm
• Data partitioned into
Train – 70%
Validation – 15%
Test – 15%
Train Validation Test
0.0394 0.5042 0.4938
17. Data Modeling
using all input
variables
Auto Neural Network
• More Flexible than Neural Network
• We can specify number of Hidden
Units
Number of
Hidden Units
Misclassification
Rate - Train
Misclassification
Rate - Validate
Misclassification
Rate - Test
1 0.0 0.50 0.48
2 0.49 0.5 0.5021
3 0.46 0.57 0.56
23. Results &
Implications
1. Accidents can happen on any day
2. Men are more involved in an accident and not women
3. The Majority drivers involved in accidents were not having the
official driving license
4. Can be useful for driving license department to show the people
the importance of proper training and seat belts
