Easing Overload in Video Tracking Applications

Easing Server Overload in Video Tracking Applications

Video tracking applications are becoming commonplace with a growing number of use cases such as perimeter surveillance, asset tracking, medical imaging, and traffic violation monitoring. One of the problems that a developer must tackle while building a video tracking application is server overload.

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Automated Data Extraction from Medical Records

Automated Data Extraction from Lab Reports

Regardless of the many technological leaps made over the past decade, firms in the healthcare, insurance, and finance sectors still deal with a staggering amount of paperwork. Because of the lack of unified data platforms, it is still a common practice to use paper documents when there is a need for ad hoc data transfer between organizations. This is especially true in the case of insurance companies in India, which require you to attach medical records from hospitals with the claim request forms.

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Apple Business Manager—The New Path for Distributing iOS Apps In-House

Apple Business Manager—The New Path for Distributing iOS Apps In-House

In 2020, when a surging pandemic and safety protocols shuttered many offices, we were among the IT firms to switch entirely to remote work. We adapted to its rhythms almost instantly, logging into work like clockwork, collabing over Zoom and Meet, and diligently meeting deadlines and release dates. Access to internal systems and data was something to be ironed out. An in-house mobile application became the need of the hour. Building the iOS app was no sweat but the distribution path wasn’t so clear-cut at first. 

I’ll soon explain how we sorted this out. 

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/home/raji/Downloads/Public Key Info Hash SSL Pinning in Swift Using TrustKit

Public Key Info Hash SSL Pinning in Swift Using TrustKit

This post is for anyone who’s trying to implement Public Key Info Hash SSL Pinning in iOS using TrustKit. The process is very straightforward except when you goof up by missing a tiny detail. A lot of documentation is already available on this topic. I’m just bringing the whole process under one roof.

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Automating Insurance Claims Adjudication Process

Automating Insurance Claim Adjudication

Claim adjudication, the process of determining the financial liability of a claim by the insurance company, is quite complex and time-consuming. Adjudication can be quick if the received claim is clear to the dot, in the sense that all the information is accurate and the claim is within the limits of the policy. But, as with all things in life, this is never the case. 

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PySpark CLI—An Efficient Way to Manage Your PySpark Projects

PySpark CLI—An Efficient Way to Manage Your PySpark Projects

In the world of big data analytics, PySpark, the Python API for Apache Spark, has a lot of traction because of its rapid development possibilities. Apart from Python, it provides high-level APIs in Java, Scala, and R. Despite the simplicity of the Python interface, creating a new PySpark project involves the execution of long commands. Take for example the command to create a new project:

$SPARK_HOME/bin/spark-submit \ --master local[*] \ --packages 'com.somesparkjar.dependency:1.0.0' \ --py-files packages.zip \ --files configs/etl_config.json \ jobs/etl_job.py

It is NOT the most convenient or intuitive method to create a simple file structure.

So is there an easy way to get started with PySpark?

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Ease the Full-Stack Pressure with Laravel Livewire

Ease the “Full-Stack Pressure” with Laravel Livewire

In the contemporary web development world, being a full-stack developer is the new norm. You may be a backend or front end developer, but you are expected to know technologies outside your field of specialization. Talk about full-stack pressure! I was a PHP developer when I felt this pressure for the first time. There is no question that JavaScript is a great language. But, for me, who was more into PHP or server-side, it was a bit overwhelming. That scene has changed with the arrival of Laravel Livewire.

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Comparative Analysis of ML Models for Fraud Detection

Comparative Analysis of ML Models for Fraud Detection

A large variety of fraud patterns combined with insufficient data on fraud makes insurance fraud detection a very challenging problem. Many algorithms are available today to classify fraudulent and genuine claims. To understand the various classification algorithms applied in fraud detection, I did a comparison using vehicle insurance claims data. 

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