Ontology Management System Using Part-of-Speech Tagging Algorithm – The state-of-the-art has been made since 2013, when the project of building the first-ever automatic conversational AI system (ALA) in the UK was put on hold due to the need to deliver an AI system for a job interview. It is considered that, at present, the automated system with the capacity to generate a human-level conversational reply is still at an early stage, given the fact that it is not part of a large-scale job interview task. The aim of this paper is to provide a short summary of the process of ALA and develop a theory for the system.

One of the most common questions posed in the recent years has been to solve the problem of solving one-dimensional (1D) graphs. In this paper, a novel type of Markov decision process (MDP) is proposed by exploiting the knowledge learned during the learning process. We propose a new approach for this problem which has two important properties. First, it is inspired by the concept of Markov chains. Second, it is able to learn and exploit features of graph in order to improve the posterior over the expected model, which is a knowledge base. To our knowledge, this approach is the first to tackle the problem of finding high-dimensional states of a graph. We first show the proposed approach improves convergence on the existing Markov chains for graph-structured tasks. Finally, we present a fast and efficient algorithm to solve the MDP to its maximum. The algorithm is based on a novel Markov chain construction algorithm, which can be adapted to any graph to improve the posterior. Our algorithm yields a state-of-the-art performance against a variety of known MDPs.

A Unified Collaborative Strategy for Data Analysis and Feature Extraction

The Online Stochastic Discriminator Optimizer

Ontology Management System Using Part-of-Speech Tagging Algorithm

Classification of Mammal Microbeads on Electron Microscopy Using Fuzzy Visual Coding

A Multiunit Approach to Optimization with Couples of UnitsOne of the most common questions posed in the recent years has been to solve the problem of solving one-dimensional (1D) graphs. In this paper, a novel type of Markov decision process (MDP) is proposed by exploiting the knowledge learned during the learning process. We propose a new approach for this problem which has two important properties. First, it is inspired by the concept of Markov chains. Second, it is able to learn and exploit features of graph in order to improve the posterior over the expected model, which is a knowledge base. To our knowledge, this approach is the first to tackle the problem of finding high-dimensional states of a graph. We first show the proposed approach improves convergence on the existing Markov chains for graph-structured tasks. Finally, we present a fast and efficient algorithm to solve the MDP to its maximum. The algorithm is based on a novel Markov chain construction algorithm, which can be adapted to any graph to improve the posterior. Our algorithm yields a state-of-the-art performance against a variety of known MDPs.