Best AWS Marketplace Software - Page 107

MintJams Content Repository 6 on Cloud

MintJams Content Repository 6 on Cloud is a comprehensive content management system designed to streamline the sharing and management of various content types, including documents, photos, and videos. It offers a hierarchical content store that supports both structured and unstructured content, featuring capabilities such as full-text search, versioning, transactions, and observation. For developers, the embedded Apache Felix OSGi framework and console provide a dynamic runtime environment, allowing code and content bundles to be loaded, unloaded, and reconfigured at runtime. Key Features and Functionality: - Drag-and-Drop Upload: Simplifies the process of uploading files, enhancing user efficiency. - Web Console: Delivers a consistent and positive user experience across all devices. - Script Engine: Supports ECMAScript and Groovy scripts, enabling the creation of dynamic websites. Primary Value and User Solutions: MintJams Content Repository 6 on Cloud addresses the complexities of content management by providing a robust and scalable platform that facilitates efficient content sharing and organization. Its support for both structured and unstructured content, combined with advanced features like full-text search and versioning, ensures that users can manage their digital assets effectively. The inclusion of a dynamic runtime environment empowers developers to adapt and extend functionalities as needed, making it a versatile solution for various content management needs.

Missing Charges

Identifies missing charges in a claim. The model will take all the charges on the medical claim and predict the probability that certain codes are missing. By using the input data of medical claims from an insurance company (i.e. common medical codes HCPCS, CPT, ICD10, REV, LOC), a score of every possible charge code is generated. High scores mean the charge should be on the claim. To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data. Our machine learning models return actual Output Data and are available through a private offer.

Mobile Compliance Recording

Mobile Customer Churn Prediction

Customer churn refers to the loss of existing clients or customers. This solution identifies mobile network subscribers who are more likely to change their operator. During the training stage, the solution automatically conducts feature interaction on the training data and selects a subset of features based on feature importance. It then trains multiple models and identifies the best performing model. This model is then selected for prediction on new data.

MobileNet V1 0.25 224

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

MobileNet V1 0.50 128

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

MobileNet V1 0.75 224

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

MobileNet V1 1.00 160

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

MobileNet V2

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

MobileNet V2 1.40 224

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Mobile Phone Review Sentiment Analyzer

The solution analyzes reviews of mobile phones and accessories and classifies them into positive and negative sentiments. It uses text analysis, natural language processing, machine learning techniques to predict the sentiment classes. It can be used to analyze product feedback from customers by predicting the sentiments of reviews.

Modelfactory.io

ModelFactory.io enables data-driven companies to reach new productivity and optimization levels. It is an intelligent automation system that allows total control and organization to produce, maintain, score and deploy multiple Python machine learning models quickly and simply.

MoinMoin - Wiki Engine powered by TurnKey GNU/Linux (HVM)

MoinMoin is a user-friendly and extensible wiki engine implemented in Python, designed to facilitate collaborative content creation and management. Unlike traditional database-driven wikis, MoinMoin utilizes a flat-file storage system, allowing for straightforward content manipulation directly on the server. This architecture simplifies tasks such as revision management and recovery from spam attacks. The TurnKey GNU/Linux MoinMoin appliance offers a pre-configured, secure, and easy-to-maintain deployment of MoinMoin, streamlining the setup process for users. Key Features and Functionality: - Flat-File Storage System: MoinMoin stores data in flat files and folders, eliminating the need for a database and enabling direct server-side content editing. - Pre-Configured Deployment: The TurnKey appliance comes with MoinMoin installed from package management, with wiki data maintained in `/var/www/moin/mywiki`. - Enhanced User Experience: Features include the FCKeditor enabled by default for ease of use, Apache WSGI for improved performance, an English spellchecker, UTF-8 character encoding for internationalization, and a default theme set to 'rightsidebar' for better user experience. - Security and Maintenance: The appliance includes SSL support out of the box, Postfix MTA bound to localhost for email functionalities like password recovery, and Webmin modules for configuring Apache2 and Postfix. Primary Value and User Solutions: The TurnKey GNU/Linux MoinMoin appliance provides a ready-to-use, secure, and easily maintainable wiki solution, reducing the time and effort required for deployment and configuration. Its flat-file storage system offers flexibility and simplicity, particularly beneficial for users who prefer or require direct access to content files without the complexity of database management. The inclusion of pre-configured features and security measures ensures a robust platform for collaborative content creation and management.

MolBioCloud : Computational Molecular Biology Desktop In The Cloud

MOLTEN

MOLTEN is a cloud-based platform designed to modernize and streamline the internal operations of media and entertainment companies. By integrating rights management, content distribution, and financial operations into a unified system, MOLTEN empowers organizations to efficiently manage and monetize their digital media assets. The platform's user-friendly interface and automation tools enable teams to gain real-time insights, accelerate deal-making, and optimize content monetization strategies. Key Features and Functionality: - Rights Management: Centralizes contracts and rights data, facilitating efficient tracking and monetization of media libraries. - Royalties Management: Automates royalty calculations and ingestions, reducing manual errors and saving time. - Content Distribution: Provides secure storage, delivery, and organization of digital content across global markets, supporting files of any size. - Real-Time Analytics: Offers immediate insights into operations, enabling informed decision-making and strategic planning. - Workflow Automation: Streamlines internal processes, reducing operational costs and enhancing efficiency. Primary Value and Solutions Provided: MOLTEN addresses the complexities faced by media companies in managing and monetizing digital content in the rapidly evolving streaming era. By consolidating disparate data streams and automating core operations, the platform reduces manual workloads, minimizes legal risks, and enhances scalability. This comprehensive solution enables media enterprises to focus on creative endeavors and strategic growth, ensuring they remain competitive in a dynamic industry landscape.