Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

/pdf/medical-diagnostic-systems-using-artificial-intelligence-ai-596qei4se1.pdf

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in practice, it is difficult to choose one appropriately for several reasons. First and foremost, ‘new’ metaheuristics are being proposed at an alarmingly fast rate, rendering impossible to know them all. Moreover, it is necessary to determine a good enough set of parameters for the selected approach. Hence, this work proposes a strategy based on a hyper-heuristic model powered by Simulated Annealing for customising population-based metaheuristics. Our approach considers search operators from 10 well-known techniques as building blocks for new ones. We test this strategy on 107 continuous benchmark functions and in up to 50 dimensions. Besides, we analyse the performance of our approach under different experimental conditions. The resulting data reveal that it is possible to obtain good-performing metaheuristics with diverse configurations for each case of study and in an automatic fashion. In this way, we validate the potential of the proposed framework for devising metaheuristics that solve continuous optimisation problems with different characteristics, similar to those from practical engineering scenarios.

Hyper-Heuristics to customise metaheuristics for continuous optimisation

Biological Functions for Information and Communication Technologies

It is possible for a GA to have two stable fixed points on a single-peak fitness landscape. These can correspond to meta-stable finite populations. This phenomenon is called bistability, and is only known to happen in the presence of recombination, selection, and mutation. This paper models the bistability phenomenon using an infinite population model of a GA based on gene pool recombination. Fixed points and their stability are explicitly calculated. This is possible since the infinite population model of the gene pool GA is much more tractable than the infinite population model for the standard simple GA. For the needle-in-the-haystack fitness function, the fixed point equations reduce to a single variable polynomial equation, and stability of fixed points can be determined from the derivative of the single variable equation. We also show empirically that bistability can occur on a single-peak landscape where there is selective pressure toward the optimum at every point of the search space. ∗ This paper was written while Alden Wright was visiting the School of Computer Science, University of Birmingham, UK.

Bistability in a Gene Pool GA with Mutation.

A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Translation from codons (short substrings of bits) in DNA to amino acids (real value units) is specified by a particular set of translation molecules created by the reaction between tRNA units and amino acid units. This adaptively changes and optimizes the fundamental genotype-phenotype mapping during evolution. Through the struggle between cells containing a DNA unit and small molecular units, the codes in DNA and the translation table described by the small molecular units coevolve, and a specific output function (protein), which is used to evaluate a cell's fitness, is optimized. To demonstrate the effectiveness of the CGA, the algorithm is applied to a set of deceptive problems, and the results by using the CGA are compared to those by using a simple GA. It is shown that the CGA has far better performance for the tested functions than the conventional simple GA.

Chemical genetic algorithms: coevolution between codes and code translation

The effectiveness of crossover in accelerating evolution in genetic algorithms (GAs) is studied with a haploid finite population of bit sequences. A Babel-like fitness landscape is assumed. There is a single bit sequence (schema) that is significantly more advantageous than all the others. We study the time until domination of the advantageous schema (Td). Evolution proceeds with appearance, spread, and domination of the advantageous schema. The most important process determining Td is the appearance (creation) of the advantageous schema. Crossover helps this creation process and enhances the rate of evolution. To study this effect, we first establish an analytical method to estimate Td with or without crossover. Then, we conduct a numerical analysis using the frequency vector representation of the population with the recurrence relations formulated after GA operations. Finally, we carry out direct computer simulations with simple GAs operating on a population of binary strings directly prepared in the computer memory to examine the performance of the two analytical methods. It is shown that Td is reduced greatly by crossover with a mildly high rate when the mutation rate is adjusted to a moderate value and that an advantageous schema has a fairly large order (the number of bits). From these observations, we can determine implementation criteria for GAs, which are useful when we applying GAs to engineering problems having a conspicuously discontinuous fitness landscape.

Errata: Crossover Accelerates Evolution in GAs with a Babel-like Fitness Landscape: Mathematical Analysis

As is well-known, a natural neuron is made up of a huge number of biomolecules from a nanoscopic point of view. A conventional ‘artificial neural network’ (ANN) [1] consists of nodes with static functions, but a more realistic model for the brain could be implemented with functional molecular agents which move around the neural network and cause a change in the neural functionality.

A network-based computational model with learning

As an example of the optimization of an evolutionary system design, a string rewriting system is studied. A set of rewriting rules that defines the growth of a string is experimentarily optimized in terms of maximizing the ‘replicative capacity’, that is the occurrence ratio of self-replicating strings. It is shown that the most optimized rule set allows many strings to self-replicate by using a special character able to copy an original string sequentially. Then, using various different rewriting rule sets, the connectivity between self-replicating strings is studied. A set of ‘hyperblobs’ covering the self-replicating strings is extracted and their connectivity is studied. The experimental results show that a large replicative capacity assures strong connectivity between self-replicating genotypes, making the system highly evolvable.

An example of design optimization for high evolvability: string rewriting grammar

Under the framework of network artificial chemistry (NAC), a method to construct a phenotypic machine from genotypic information is proposed. The genotype is expressed as a sequence of nodes (a node chain) with symbol data. According to an implemented algorithm, a chain is agglomerated, tangled, and finally folded into a node cluster that works as a control-flow machine in the network. Two examples of control-flow clusters, splitase and replicase, are presented and their functionality is demonstrated. Several meanings and future research directions are discussed.

Mathematical folding of node chains in a molecular network.

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