1 project for "artificial intelligence java source code" with 2 filters applied:

  • Power through agendas and documents, make more informed decisions and conduct board meetings faster. Icon
    Power through agendas and documents, make more informed decisions and conduct board meetings faster.

    For team managers searching for a solution to manage their meetings

    iBabs not only captures the entire decision-making process – it takes all the paperwork out of meetings. iBabs empowers everyone who has ever organized or attended, a meeting. With a seemingly simple app that offers complete control and a comprehensive overview of all those fiddly details. With about 3000 organizations and over 300,000 users, iBabs gives you peace of mind. So you can quickly organize effective meetings, and good decisions can be made with confidence. iBabs didn’t just happen overnight. We started analyzing and simplifying board meeting processes many years ago. We understand all the work that goes into meetings, and how to streamline everything so it all flows smoothly. On any device, confidentially, securely and automatically. Make good decisions with confidence.
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  • Best Visitor Management System Icon
    Best Visitor Management System

    Instantly Notify Staff Of Deliveries And Guest Arrivals To Increase Your Efficiency

    <p class="mb-4">Do stacks of paperwork pile up at the front desk area? Or are your receptionists constantly filing reports, guest log-in information and NDAs – taking them away from other important tasks? Not anymore! Our Visitor Management System automates all these processes, streamlining your workflow. Guests can complete inductions, sign NDAs, fill in their contact details and much more using the easy software. These records are then automatically filed and stored, making life easy for receptionists and the HR team. Claim your FREE 7-day trial and experience how VisitUs can transform your workplace!</p>
    Try it Free
  • 1
    JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks...
    Downloads: 0 This Week
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