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ease
features
design
support

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Description

Segmentation is a technique employed in Print and Broadcast Media that has traditionally relied on manual methods. However, RUZIVO has transformed this practice by introducing automation through Machine Learning, which not only enhances precision but also significantly reduces both time and expenses. The Segmentation process developed by RUZIVO involves identifying the beginning and end markers of radio clips and images intended for Broadcast and Print. In this phase, it is assessed whether the content—be it an image, video, audio clip, or email—qualifies as an advertisement. Once classified as either Ads or non-Ads, various media formats are then cross-referenced with an archive to identify whether they are new or duplicates. This step is crucial, as duplicates are cataloged while new Ads are sorted into categories based on industry, type, and any other client-specific criteria. The automation of the Classification process by RUZIVO has significantly expedited the time it takes for clients to become aware of new advertisements in their chosen categories, resulting in a more streamlined workflow. Consequently, clients can now respond more swiftly to advertising trends and changes in their respective markets.

Description

For tasks such as transcription, data validation, classification, sentiment analysis, and more, UHRS offers comprehensive solutions tailored to your needs. We leverage human intelligence to enhance machine learning models, aiding you in overcoming some of your toughest challenges. Judges can conveniently access UHRS from anywhere at any time with just an internet connection. This streamlined access allows for quick engagement with tasks like video annotation within minutes. With UHRS, managing the classification of thousands of images becomes a straightforward and efficient process. Our platform enables the training of your products and tools through high-quality annotated image data, enhancing capabilities like image detection and boundary recognition. You can efficiently classify images, conduct semantic segmentation, and implement object detection. In addition, we facilitate audio-to-text validation, conversation analysis, and relevance checks. Furthermore, our services extend to sentiment identification for tweets, document classification, and various ad hoc data collection tasks, including information correction, moderation, and conducting surveys. With UHRS, you gain a versatile partner in navigating a wide range of data-related challenges.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Clickworker

Integrations

Clickworker

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Data-Core Systems

Founded

1988

Country

United States

Website

www.ruzivomedia.com

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

prod.uhrs.playmsn.com/uhrs/

Product Features

Marketing Automation

A/B Testing
Analytics / ROI Tracking
Artificial Intelligence
Content / Blogging Platform
Customizable CTAs
Drip Campaigns
Dynamic Content
Landing Pages / Web Forms
Lead Scoring
Sales Intelligence
Search Marketing
Segmentation
Social Marketing
Website Visitor Tracking

Product Features

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Alternatives

Alternatives

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