Interactive visualisation of oligomer frequency in DNA

Since 1990, bioinformaticians have been exploring applications of the Chaos Game Representation (CGR) for visualisation, statistical characterisation and comparison of DNA sequences. We focus on the development of a new computational algorithm and description of new software tool that enables CGR visualisation of frequencies of K-mers (oligomers) in a flexible way such that it is possible to visualise the whole genome or any of its parts (like genes), and parallel comparison of several sequences, all in real time. User can interactively specify the size and position of visualised region of the DNA sequence, zoom in or out, and change parameters of visualisation. The tool has been written in JAVATM language and is freely available to public

A Quantitative Model for Human Olfactory Receptors

A wide variety of chemicals having distinct odors are smelled by humans. Odor perception initiates in the nose, where it is detected by a large family of olfactory receptors (ORs). Based on divergence of evolutionary model, a sequence of human ORs database has been proposed by D. Lancet et al (2000, 2006). It is quite impossible to infer whether a given sequence of nucleotides is a human OR or not, without any biological experimental validation. In our perspective, a proper quantitative understanding of these ORs is required to justify or nullify whether a given sequence is a human OR or not. In this paper, all human OR sequences have been quantified, and a set of clusters have been made using the quantitative results based on two different metrics. Using this proposed quantitative model, one can easily make probable justification or deterministic nullification whether a given sequence of nucleotides is a probable human OR homologue or not, without seeking any biological experiment. Of course a further biological experiment is essential to validate the probable human OR homologue

Evolutionary algorithms in artificial intelligence: a comparative study through applications

For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system

Analysis of the Performance of Fractal Image Compression on Different Imaging Modalities: A ReviewÂ

No Abstrac

Applications of Artificial Intelligence to Cryptography

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other data types in order to assess the cryptographic strength of an encryption algorithm, e.g. to detect patterns of intercepted data streams that are signatures of encrypted data. This includes some of the authors’ prior contributions to the field which is referenced throughout. Applications are presented which include the authentication of high-value documents such as bank notes with a smartphone. This involves using the antenna of a smartphone to read (in the near field) a flexible radio frequency tag that couples to an integrated circuit with a non-programmable coprocessor. The coprocessor retains ultra-strong encrypted information generated using EC that can be decrypted on-line, thereby validating the authenticity of the document through the Internet of Things with a smartphone. The application of optical authentication methods using a smartphone and optical ciphers is also briefly explored