Internet of Things and Bayesian Networks


As big data becomes more of cliche with every passing day, do you feel Internet of Things is the next marketing buzzword to grapple our lives. So what exactly is Internet of Thing (IoT) and why are we going to hear more about it in the coming days. Internet of thing (IoT) today denotes advanced connectivity of devices,systems and services that goes beyond machine to machine communications and covers a wide variety of domains and applications specifically in the manufacturing and power, oil and gas utilities. An application in IoT can be an automobile that has built in sensors to alert the driver when the tyre pressure is low. Built-in sensors on equipment's present in the power plant which transmit real time data and thereby enable to better transmission planning,load balancing.

Overview of Udacity Artificial Intelligence Engineer Nanodegree, Term 1


After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs.

Text Mining Support in Semantic Annotation and Indexing of Multimedia Data

AAAI Conferences

This short paper is describing a demonstrator that is complementing the paper "Towards Cross-Media Feature Extraction" in these proceedings. The demo is exemplifying the use of textual resources, out of which semantic information can be extracted, for supporting the semantic annotation and indexing of associated video material in the soccer domain. Entities and events extracted from textual data are marked-up with semantic classes derived from an ontology modeling the soccer domain. We show further how extracted Audio-Video features by video analysis can be taken into account for additional annotation of specific soccer event types, and how those different types of annotation can be combined.


AAAI Conferences

Rule induction methods axe classified into two categories, induction of deterministic rules and probabilistic ones(Michalski 1986; Pawlak 1991; Tsumoto and Tanaka 1996). While deterministic rules are supported by positive examples, probabilistic ones are supported by large positive examples and small negative samples. That is, both kinds of rules select positively one decision if a case satisfies their conditional parts. However, domain experts do not use only positive reasoning but also negative reasoning, since a domain is not always deterministic. For example, when a patient does not have a headache, migraine should not be suspected: negative reasoning plays an important role in cutting the search space of a differential diagnosis(Tsumoto and Tanaka 1996). 1 Therefore, negative rules should be induced from databases in order to induce rules which will be easier for domain experts to 1The essential point is that if extracted patterns do not reflect experts' reasoning process, domain experts have difficulties in interpreting them. Without interpretation of domain experts, a discovery procedure would not proceed, which also means that the interaction between human experts and computers is indispensable to computer-assisted discovery.

Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model Machine Learning

In econometrics and finance, the vector error correction model (VECM) is an important time series model for cointegration analysis, which is used to estimate the long-run equilibrium variable relationships. The traditional analysis and estimation methodologies assume the underlying Gaussian distribution but, in practice, heavy-tailed data and outliers can lead to the inapplicability of these methods. In this paper, we propose a robust model estimation method based on the Cauchy distribution to tackle this issue. In addition, sparse cointegration relations are considered to realize feature selection and dimension reduction. An efficient algorithm based on the majorization-minimization (MM) method is applied to solve the proposed nonconvex problem. The performance of this algorithm is shown through numerical simulations.