Storing all relevant information about a person, including the devices he is wearing and medical tests he has undergone.
This solution does :
The input for the Health Care are:
Assessment for Cancer, Cardiac and Stroke risk
An Interactive therapy app.
This solution does :
We build solutions that convert the commands in the mind to a motion. This has major applications in electronic devices such as wheel chairs that help quardriplegic/paraplegic gain freedom from constant care in terms of mobiility. We will also be able to provide solutions to Austism where communication is a big challenge. The electrical signals in the mid are analyzed and classified into commands. The core algorithms relies on pattern matching, noise elimination in the signals and converting the signals to the device controls. These solutions are even more advanced than the gestures.
This solution enables health insurers/payers to dramatically improve their financial performance by improving the pricing of their products. With slowing premium growth, broad similarity across plan and benefit offerings, market consolidation, and diminished ability to control medical costs – is driving an increased emphasis on strategic pricing. This requires timely, comprehensive data, sophisticated analytical methodologies, expert underwriting tools, and a new level of collaboration among actuaries, underwriters, sales, and account management to achieve these levels of performance improvement. This asset directly impacts the functional areas of Actuarial, Underwriting and Sales/Account Management.This solution is used in the cases where
This Solution to helps reduce inappropriate payment of medical and pharmaceutical claims. This solution takes a broad view of the claims process, because incorrect payments are the result of a wide range of potential breakdowns in a complex process with multiple decision points. A pattern overpayment types common in healthcare claims operations provides the basis for utilizing Machine Learning techniques.
This solution focuses on identification, adjustments, and avoidance of incorrect payments. Underpayments are costly to fix and create significant ill will with providers.
The businesses are increasingly moving their marketing activities from traditional mar- keting channels to digital marketing channels. This new shift gives an opportunity for the marketing teams to personalize and customize services to any user in question. The Ads are transmitted to match the needs of a specific customer base. Ad Server platforms makes this possible. The Ad Server platforms has access to data related to Users, Stores, User AD Clicks, Internet Publishers, Advertisers, Campaigns etc.,
The purpose of this project however is to develop and deploying core learning algorithms for determining Home locations for the users based on the Ad server Clicks and Matching Store locations/Ads for the Users. These Algo- rithms for the Ad Servers optimizes marketing campaigns and website behavior to improve customer responses and conversions.This Data is not available as an open data, hence there is a need for synthesizing the data. This project also creates the necessary transaction data using Monte Carlo Simulation.
In this solution, speech is synchronously analyzed as it is been delivered. The process involves capturing the voice stream, converting this to text, applying various text processing techniques, identifying the Parts of Speech and analyzing the sentiments there of. In a general sense, text processing is applied on a corp[us of text that is persisted. Our approach is to analyze the situation or sentiment as a speaker is speaking on the subject. Our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases.This is accomplished by using Storm and analyzing the stream of voice
Recommender systems are information filtering systems that maximize the personalization of objects of interest to a user. Traditional methods are collaborative or content-based filtering. Collaborative filtering approaches use a user's past behavior as well as similar decisions made by other users. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. In this recommender solution, we will consider locational data of a user to be critical for personalization. This system will exploit community opinions to produce suggestions. This requires the recommender system to extensively use graph and network theory. We will predominantly use content filtering techniques for the user key word search by considering spatial features.
Knowing the geo-location of a person in real-time can be beneficial for various applications such as real-time tracking, mobility analysis, location targeting, emergency response etc., The focus of this application is to gather real time location details from individuals belonging to a specific group of Mobile consumers - Google Hangouts, Whatsapp or any other Closed User Group (CUG). Visualizing mobility of various users helps in proximity analysis, collaboration and chance meetings. The geo location data is collected using a fusion technology, which will automatically select best location provided from On-Board GPS Chip and the Mobile network provided geo location to minimize the strain on the device battery. The data collected is stored using MongoDB, a document database which is known for high availability and performance, the NOSQL data base is highly scalable and is easy to be used among different mobile or desktop platforms