The Clafisol solution covers a wide spectrum of vehicle tracking and fleet monitoring applications. It is possible to use an Android or IoS device to act as the main tracking device and gateway. This is particularly useful for light personal mobile transport vehicles such as bicycles, electric bicycles, tricycles and small scooters.
We are making retailer forecast demand and supply decision well in advance via an item-to-item collaborative filtering algorithms implemented in our recommendation module in the data pool resource search engine. This collaborative filtering algorithm is solving the problem of existing algorithms unable to scale to the massive volume of data eCommerce platform deals with. In our Item-to-Item collaborative filtering algorithms, we are focusing on rating distribution per item that allows for more stable ratings distributions and equates to the ability to scale to huge data sets. Discriminant analysis has also been incorporated into machine learning algorithms for addressing and improving segmentation and classification. State-of-the-art treats transaction as an isolated event and relies on historical data and manual decision-making
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leading to ignoring factors like seasonal changes, market cannibalization, etc which are too complex for traditional forecasting tool-sets. We are using machine learning in the direction for allowing retailers to combine historical and real-time data, and identify patterns that humans and traditional forecasting tools are missing currently.