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Automate contact and company data extraction
Build lead generation pipelines that pull emails, phone numbers, and company details from directories, maps, social platforms. Full API access.
Generate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
Sequential and parallel implementation of string quicksorts(-select) . Articles: On the number of string lookups in BSTs (and related algorithms) with digital access/ Combining digital access and parallel partition for quicksort and quickselect.
This is implementation of parallel genetic algorithm with "ring" insular topology. Algorithm provides a dynamic choice of genetic operators in the evolution of. The library supports the 26 genetic operators. This is cross-platform GA written in С++.
QuickMP (Quick Multi-Processing) is a simple cross-platform C++ API for generating parallel for loops in shared-memory programs, similar to OpenMP. It provides automatic scalable performance based on the number of available processors.
As a healthcare provider, you should be paid promptly for the services you provide to patients. Slow, inefficient, and error-prone manual coding keeps you from the financial peace you deserve. XpertDox’s autonomous coding solution accelerates the revenue cycle so you can focus on providing great healthcare.
PyLife is an implementation of the game of life algorithm featuring parallel programming. It uses MPI and python to achieve a consistent software architecture and reliably performance.
The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.
The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).