Mediaboss Marketing

Exeter Engineering, an American manufacturer of electronic sorting, washing, receiving, and related machinery for the fresh produce industry, has installed an upgraded version of its proprietary ID Technology system that uses artificial intelligence (AI) to automatically grade and sort bell peppers.

The system uses an integrated suite of cameras, lighting, and software to size and grade bell peppers for color, size, weight and even shape defects, all using AI to implement operator-set grading choices. It transmits live images of every bell pepper passing through the sorter, across all lanes, to operators, allowing them to adjust on the fly or see the effects of adjustments they are considering.

"AI-powered grading gives packers some gains in speed and accuracy, especially compared to older systems," said Matt Lonczynski, sales engineer at Exeter Engineering. "We are already seeing accuracy rates of 90 percent."

The bell pepper capability is one part of the upgrades made to ID Technology in its second major release: version 2.0. Hardware improvements in version 2.0 included a new camera hood and upgraded lighting. "The new lighting was crucial for getting imagery of sufficient quality to successfully size and grade bell peppers," said Boomer Batchman, president of Exeter Engineering. "With better imagery in both visible and infrared light spectrums, the AI gives more accurate results."

Oculus 1300 compact 3-drop sorter and an example of ID Technology 2.0 for potatoes.

PotatoesThe ID Technology system has been working on potatoes for over a year, with accuracy rates of 95 percent on some customers' packing lines. The new version 2.0 hardware, combined with a software upgrade, now allows potato packers the option to use the AI to differentiate between three or more grades. They can also use the new "severity classification" feature, which allows operators to adjust for the severity of a variety of defects to move products from one category to another.

"With 'Severity classification,' ID Technology is coming out with another unique feature," said Lonczynski. "It allows the sort of granular control that used to come only after an operator had years of experience. Now it's automatic."

Exeter Engineering is currently testing a new version of ID Technology's software suite that will add the ability to sort sweet potatoes, and it hopes to have that capability by Fall 2022.

"By upgrading to an AI-powered system, packing houses can save both time and money," said Batchman. "ID Technology provides reliability and consistency quicker and more efficiently than older systems, which relied more on the skill and experience of the operator. Our potato clients are looking at returns on their investment in a year, a year-and-a-half. We think there will be a similar result for bell peppers, cucumbers, and other produce types we have in the pipeline."

This screen shows the accuracy of various defects. It's just one screen of the ID Technology 2.0 control interface.

For more information: Boomer BatchmanExeter Engineering Tel: +1 (559) 592-3161bbatchman@exetereng.com

Matt LonczynskiExeter Engineering Tel: +1 (559) 592-3161mlonczynski@exetereng.comhttps://www.exeterengineering.com/

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The use of Artificial Intelligence to automatically sort bell peppers, potatoes and more - hortidaily.com

If youve heard the term machine learning, but arent quite sure what it means, then youve come to the right place. Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being specifically programmed to do so. Basically, machine learning (MI) and artificial intelligence (AI) are helping businesses by improving customer service, reducing errors, managing automation and much more. Why do you need to know all of this? Well, for all of you out there looking to boost your income and career opportunities, you should consider this handy bundle that will give you the basics in machine learning.

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BELLEVUE, Wash. and WATKINSVILLE, Ga., Sept. 13, 2022 (GLOBE NEWSWIRE) -- AI Dynamics and PETTIGREW Medical announced today the formation of a joint venture, with the working name mAIcode, designed to accelerate the automation of medical record coding by applying advanced machine learning. AI Dynamics is an organization founded on the belief that everyone should have access to the power of artificial intelligence (AI) to change the world. PETTIGREW Medical is a pioneer in providing revenue cycle management services and has expanded into a diversified and accredited industry leader on a global scale.

The joint venture with AI Dynamics will enable us to create an automated coding solution that provides an order of magnitude improvement in productivity, efficiency and accuracy, while also reducing costs, said David Young, president and chief financial officer, PETTIGREW Medical. Once the joint venture is fully operational, we look forward to serving a larger percentage of the $18 billion annual medical coding market, which is growing at a compounded annual growth rate (CAGR) of eight percent.

Today, the medical coding market is highly complex, with more than 68,000 diagnostic codes and over 10,000 Current Procedural Terminology (CPT) codes. Current coding approaches are expensive, with the median salary of a medical coder in the U.S being more than $50,000. The typical coder can code at most a few hundred medical records a day, with each record costing between $2 to $20 to code. The volume of content to code is enormous and growing, meaning costs will grow as well.

Unlike other AI coding companies that are focused primarily on the cloud, we have developed mAIcode to be equally efficient for customers that want to manage their data on premise or in a more secure environment. We are also orienting the solution to audit-level accuracy, backed by the NeoPulse Platform, said Rajeev Dutt, founder and CEO of AI Dynamics. Our solution relies on multiple deep learning models built on the NeoPulse Platform, that provides the joint venture with the unique ability to continuously improve its own medical coding capabilities based on experience the AI solution is learning continuously.

The solution is built on a SaaS model that can be run in the cloud or at the customers location. At the core of the solution is AI Dynamics NeoPulse Framework, which will enable customers to manage their entire AI workflow and infrastructure from one place. NeoPulse enables lower cost and faster design and deployment of AI solutions. Customers can adopt the solution to their own medical chart formats. It also features clear explainability, which is necessary for audits, increasing confidence in decisions, and providing peace of mind to clients and auditors. It incorporates federated learning data privacy technology; data never leaves the data owners firewall but the solution enables data users to generate insights from the data, ensuring all parties remain in compliance with HIPAA, U.S state data privacy regulations and data residency requirements. As mAIcode learns from customer use, it will quickly outperform manual solutions.

About AI Dynamics:AI Dynamics aims to make artificial intelligence (AI) accessible to organizations of all sizes. The company's NeoPulse Framework is an intuitive development and management platform for AI, which enables companies to develop and implement deep neural networks and other machine learning models that can improve key performance metrics. The company's team brings decades of experience in the fields of machine learning and artificial intelligence from leading companies and research organizations. For more information, please visit aidynamics.com.

About Pettigrew Medical:PETTIGREW Medical specializes in billing, coding, accounts receivable management and contact center solutions for healthcare billing companies, hospitals, private practices and insurers with large central business office operations. Since 1989, PETTIGREW has provided superior, aggressive, and compliant services to our clients. PETTIGREW is continuously seeking ways to make the experience of running a facility or group easier on owners and medical directors, and of making their practice's information easily accessible. For more information, please visit pettigrewmedical.com.

Media Contact:Madi Oliv / Valeria CarrilloUPRAISE Marketing + PR for AI Dynamics and PETTIGREW Medicalaidynamics@upraisepr.com

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AI Dynamics and PETTIGREW Medical Announce Joint Venture that Applies Advanced Machine Learning to Accelerate Automation of Medical Record Coding -...

Looking for a fun introduction to AI with a sense of humor? Look no further than Making Friends with machine learning (MFML), a lovable free YouTube course designed with everyone in mind. Yes,everyone. If youre reading this, the course is for you!

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Everything Youve Ever Wanted to Know About Machine Learning - KDnuggets

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. The American insurance industry is one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology like machine learning, insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen.

Insurance data is vast and complex, composed of many individuals with many instances and many factors used in determining the claims. Moreover, the type of insurance increases the complexity of data ingestion and processing. Life insurance is different from automobile insurance, health insurance is different from property insurance, and so forth. While some of the processes are similar, the data can vary greatly.

As a result, insurance enterprises must prioritize digital initiatives to handle huge volumes of data and support vital business objectives. In the insurance industry, advanced technologies are critical for improving operational efficiency, providing excellent customer service, and, ultimately, increasing the bottom line.

ML can handle the size and complexity of insurance data. It can be implemented in multiple aspects of the insurance practice, and facilitates improvements in customer experiences, claims processing, risk management, and other general operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud, which plagues the entire industry. It is a big development in fraud detection and insurance organizations must add it to their fraud prevention toolkit.

In this post, we lay out how insurance companies are using ML to improve their insurance processes and flag insurance fraud before it affects their bottom lines. Read on to see how ML can fit within your insurance organization.

ML is a technology under the AI umbrella. ML is designed to analyze data so computers can make predictions and decisions based on the identification of patterns and historical data. All of this is without being explicitly programmed and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and are constantly learning. Ultimately, AI/ML will handle menial tasks and free human agents to perform more complex requests and analyses.

There are several use cases for ML within an insurance organization regardless of insurance type. Below are some top areas for ML application in the insurance industry:

For insurers and salespeople, ML can identify leads using valuable insights from data. ML can even personalize recommendations according to the buyer's previous actions and history, which enables salespeople to have more effective conversations with buyers.

For a majority of customers, insurance can seem daunting, complex, and unclear. It's important for insurance companies to assist their customers at every stage of the process in order to increase customer acquisition and retention. ML via chatbots on messaging apps can be very helpful in guiding users through claims processing and answering basic frequently asked questions. These chatbots use neural networks, which can be developed to comprehend and answer most customer inquiries via chat, email, or even phone calls. Additionally, ML can take data and determine the risk of customers. This information can be used to recommend the best offer that has the highest likelihood of retaining a customer.

ML utilizes data and algorithms to instantly detect potentially abnormal or unexpected activity, making ML a crucial tool in loss prediction and risk management. This is vital for usage-based insurance devices, which determine auto insurance rates based on specific driving behaviors and patterns.

Unfortunately, fraud is rampant in the insurance industry. Property and casualty insurance alone loses about $30 billion to fraud every year, and fraud occurs in nearly 10% of all P&C losses. ML can mitigate this issue by identifying potential claim situations early in the process. Flagging early allows insurers to investigate and correctly identify a fraudulent claim.

Claims processing is notoriously arduous and time-consuming. ML technology is a tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker/adjuster.

Fraud is one of the biggest problems for the insurance industry, so let's return to the fraud detection stage in the insurance lifecycle and detail the benefits of ML for this common issue. Considering the insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums each year, there are huge opportunities and incentives for insurance fraud to occur.

Insurance fraud is an issue that has worsened since the COVID-19 pandemic began. Some industry professionals believe that the number of claims with some element of fraud has almost doubled since the pandemic.

Below are the various stages in which insurance fraud can occur during the insurance lifecycle:

Based on the amount of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection toolkits. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. The ML approaches and algorithms that facilitate fraud detection are the following:

ML is instrumental in fraud prevention and detection. It allows companies to identify claims suspected of fraud quickly and accurately, process data efficiently, and avoid wasting valuable human resources.

Implementing digital technologies, like ML, is vital for insurance businesses to handle their data and analytics. It allows insurance companies to increase operational efficiency and mitigate the top-of-mind risk of insurance fraud.

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5 use cases for machine learning in the insurance industry - Digital Insurance