Telecom Fraud Detection

•

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A telecom company named as Bad Idea is expecting for fraudsters. They designed a weird rate plan called Praxis plan where only four calls are allowed during a day. Bad Idea has their call logs spanning over one and half months.We are using the Naive Bayesian Classification rule to predict the fraudsters for telecom company.

Technology

Copyright © Wondershare Software

www.wondershare.com

• Introduction
• Problem Understanding
• Data Understanding
• Course of Action

Copyright © Wondershare Software

• A telecom company named as Bad
Idea is expecting for fraudsters.
• They designed a weird rate plan
called Praxis plan where only four
calls are allowed during a day.
• Bad Idea has their call logs
spanning over one and half
months.

Copyright © Wondershare Software

• Two datasets are given:


Blacklist subscribers call log



Audited call log

• No of rows: 138
• Call timing:
•

Morning- 9AM to Noon

•

Afternoon- Noon to 4PM

•

Evening- 4PM to 9PM

•

Night- 9PM to Midnight

•

Callers: Virginia, Sally, Vince

•

Tool Used: Rapidminer & R
Copyright © Wondershare Software

•

A gang of fraudsters consist of three
people: Sally, Virginia & Vince targeted
the company.

•

There subscriptions were terminated.

•

The audit is done every 5 days to keep on
track for the fraudsters.

•

They reviewed the list of subscribers who
have made calls to the same people & in
the same time frame as those fraudsters.

Copyright © Wondershare Software

•

Algorithm: Naïve Bayesian Classifier

•

Import the data using read csv

Copyright © Wondershare Software

•

Use Split validation, it splits up the example set into a training and test
set and evaluates the model. 70% of data is used as training sample &
rest 30% is development set.

•

The first inner operator accept an Training Set while the second accept an
Test Set and the output of the first (which is in most cases a Model) and
produce a Performance Vector. Here in the first inner Naïve Bayes is used.

Copyright © Wondershare Software

•

The audit data set is imported using Read csv.

•

“Select Attributes” is applied to remove the unwanted attributes.

Copyright © Wondershare Software

•

“Apply Model” operator is used to apply the model to the training
data. The information is used to predict the value of possibly
unknown label.

•

All needed parameters are stored within the model object.

Copyright © Wondershare Software

•

Output with the probabilities of callers.

Copyright © Wondershare Software

Import the data
By using the Sampling
method divided the
dataset into train test
data

Develop the
model
Applied the model
on Test data

Applied the model
on the Audit log
set

Copyright © Wondershare Software

Converted the
predictability into
percentage

•

Import the data and convert the data into the train and test
sample:

Copyright © Wondershare Software

•

All the data which contain in the train dataset:
Dda

Copyright © Wondershare Software

•

Develop the Model using the train data set:

Copyright © Wondershare Software

•

Probability of the Caller and Predict on the test Data:

Copyright © Wondershare Software

•

Probability as per the test data and applied on the Audit Log set data and
converted in to percentage :

Copyright © Wondershare Software

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The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

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Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

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Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

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Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

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Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

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Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

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Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

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Telecom Fraud Detection

3. • A telecom company named as Bad Idea is expecting for fraudsters. • They designed a weird rate plan called Praxis plan where only four calls are allowed during a day. • Bad Idea has their call logs spanning over one and half months. Copyright © Wondershare Software

4. • Two datasets are given:  Blacklist subscribers call log  Audited call log • No of rows: 138 • Call timing: • Morning- 9AM to Noon • Afternoon- Noon to 4PM • Evening- 4PM to 9PM • Night- 9PM to Midnight • Callers: Virginia, Sally, Vince • Tool Used: Rapidminer & R Copyright © Wondershare Software

5. • A gang of fraudsters consist of three people: Sally, Virginia & Vince targeted the company. • There subscriptions were terminated. • The audit is done every 5 days to keep on track for the fraudsters. • They reviewed the list of subscribers who have made calls to the same people & in the same time frame as those fraudsters. Copyright © Wondershare Software

7. • Use Split validation, it splits up the example set into a training and test set and evaluates the model. 70% of data is used as training sample & rest 30% is development set. • The first inner operator accept an Training Set while the second accept an Test Set and the output of the first (which is in most cases a Model) and produce a Performance Vector. Here in the first inner Naïve Bayes is used. Copyright © Wondershare Software

9. • “Apply Model” operator is used to apply the model to the training data. The information is used to predict the value of possibly unknown label. • All needed parameters are stored within the model object. Copyright © Wondershare Software

11. Import the data By using the Sampling method divided the dataset into train test data Develop the model Applied the model on Test data Applied the model on the Audit log set Copyright © Wondershare Software Converted the predictability into percentage

17. ID Morning 491 Robert 61 Quentin 703 Quentin 996 Quentin 173 Robert 575 Kelly 365 Larry 967 Mark 650 Nancy 165 Olga 557 Robert 808 Quentin 936 Robert 836 Kelly 976 Robert Company Logo AfternoonEvening John David George David George David John Emily George Frank John David John David George Frank Harry Frank Harry Frank John Frank George David George David Harry Frank George Frank Night Alex Alex Beth Alex Alex Clark Clark Clark Clark Clark Clark Beth Alex Clark Alex Customer Customer X Customer X Customer X Customer X Customer X Customer Y Customer Y Customer Y Customer Y Customer Y Customer Z Customer Z Customer Z Customer Z Customer Z Probable Fraudster Vince Sally Sally Vince Vince Vince Vince Virginia Virginia Virginia Virginia Sally Vince Virginia Vince Copyright © Wondershare Software Probability 0.96 0.94 0.81 0.50 0.57 0.72 0.66 0.87 0.98 0.81 0.70 0.81 0.94 0.62 0.57

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