Search Results for "extreme learning machine"

...Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. Argo Workflows is the most popular workflow execution engine for Kubernetes. It can run 1000s of workflows a day, each with 1000s of concurrent tasks. ...

The Netcap (NETwork CAPture) framework efficiently converts a stream of network packets into platform-neutral type-safe structured audit records that represent specific protocols or custom abstractions. These audit records can be stored on disk or exchanged over the network, and are well-suited as a data source for machine learning algorithms. Since parsing of untrusted input can be dangerous and network data is potentially malicious, a programming language that provides a garbage-collected memory-safe runtime is used for the implementation.

...The easiest way to create an OTP-designed application in Golang. The goal of this project is to leverage Erlang/OTP experience with Golang performance. The ideal framework for creating complex and distributed solutions (machine learning, data processing pipeline, etc.) being simple and reliable. You don't have to reinvent the wheel. There are ready-to-use implemented design patterns. Two processes can be linked to each other. Termination one terminates another. Any process can monitor the service node. Receives NODE DOWN if node terminated. Ergo Framework almost 5 times outperforms the original Erlang network messaging. ...

...It consists of a cloud part and an edge part, and provides core infrastructure support for networking, application deployment, and metadata synchronization between the cloud and edge. It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image recognition, event processing, and other high-level applications to the Edge. With business logic running at the Edge, much larger volumes of data can be secured & processed locally where the data is produced. With data processed at the Edge, the responsiveness is increased dramatically and data privacy is protected.

...(previously just WiFi). Passive scanning built-in (previously required a separate server). Support for Bluetooth scanning in scanning utility (previously just WiFi). Meta-learning with 10 different machine learning classifiers (previously just three). Client uses Websockets+React which reduces bandwidth (and coding complexity). Rolling compression of MAC addresses for much smaller on-disk databases. Data storage in SQLite-database (previously it was BoltDB). Released under MIT license (more commercially compatible than AGPL). ...