Artificial Intelligence & Machine Learning Services

  • Services
  • Artificial Intelligence & Machine Learning Services
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As the technology is evolving rapidly day by day, the way of interaction between human and human, and human and devices is getting advanced.

Pixeleye uses Artificial Intelligence and Machine Learning services to provide core business capabilities in unstructured and New Data analytics, autonomous data modelling or processing, and perspective analytics. Pixeleye also helps organizations accelerate business efficiencies through Deep Learning, based on learning data representation algorithms instead of task specific algorithms. The Artificial Intelligence and Machine Learning Services (AI/ML) will focus on the application of machine-based decisioning and auto-remediation to help carriers keep pace with the growth in network size, traffic volume and service complexity, as well as define new approaches to network operations and customer assurance to support the accelerated deployment of new over-the-top services, autonomous vehicles, drones, AR/VR and more. The group will define and share reusable, proven practices, recipes, models and technical requirements for applying AI and machine learning to reduce the cost to plan and operate telecommunications networks.

Framework collaborating across three work streams

ML-based network operations, optimization and planning to enhance intelligence in network operations areas through, for example, predictive maintenance and dynamic resource allocation.

NATURAL LANGUAGE PROCESSING (NLP)

Industry best practices for Web Datamining, Social Media Mining, Parsing, Lemmatization, Tokenization, POS Tagging, Entity Recognition, Text Classification, Topic Modelling.

DEEP LEARNING

Advanced Algorithms like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM), Recurrent Neural Tensor Nets (RNTN), Deep Bayesian Networks, Deep Belief Networks

SUPERVISED MACHINE LEARNING

Advanced techniques like Ensemble Modelling (Bagging, Boosting, and Random Forests), Multilayer Perceptron, Feed-forward Neural Networks and Backpropagation

UNSUPERVISED MACHINE LEARNING

Advanced Techniques like Auto Encoders, Belief Networks, and Restricted Boltzmann Machines (RBM)

ROBOTIC PROCESS AUTOMATION (RPA)

Modern Cognitive Engineering approaches to automate both structured and semi-structured processes

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