An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure

Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers

Due to the ANNs architecture, the ISA methods that can manipulate synaptic weights selectedare Connection Weights (CW) and Garson’s Algorithm (GA). The ANNs-based classifiers thatcan provide such manipulation are Multi-Layer Perceptron (MLP) and Evolving Fuzzy NeuralNetworks (EFuNNs). The goals for this work are firstly to identify which of the twoclassifiers works best with the filtered/ranked data, secondly is to test the FR method by usinga selected dataset taken from the UCI Machine Learning Repository and in an onlineenvironment and lastly to attest the FR results by using another selected dataset taken fromthe same source and in the same environment. There are three groups of experimentsconducted to accomplish these goals. The results are promising when FR is applied, someefficiency and accuracy are noticeable compared to the original data.Keywords: artificial neural networks, input significance analysis; feature selection; featureranking; connection weights; Garson’s algorithm; multi-layer perceptron; evolving fuzzyneural networks

Neuro-fuzzy feature evaluation with theoretical analysis

The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for its evaluation along with their theoretical analysis. A concept of weighted membership function is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. It is shown theoretically that the evaluation index has a fixed upper bound and a varying lower bound, and it monotonically increases with the lower bound. A relation between the evaluation index, interclass distance and weighting coefficients is established. Effectiveness of the algorithms for evaluating features both individually and in a group (considering their independence and dependency) is demonstrated along with comparisons on speech, Iris, medical and mango-leaf data. The results are also validated using scatter diagram and k-NN classifier

A Review of Artificial Intelligence in the Internet of Things

Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields