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Artificial neural networks (ANNs) have very successfully been used in numerical simulations for a series of computational problems ranging from image classification/image recognition, speech recognition, time series analysis, game intelligence, and computational advertising to numerical approximations of partial differential equations (PDEs). Such numerical simulations suggest that ANNs have the capacity to very efficiently approximate high-dimensional functions and, especially, indicate that ANNs seem to admit the fundamental power to overcome the curse of dimensionality when approximating the high-dimensional functions appearing in the above named computational problems. There are a series of rigorous mathematical approximation results for ANNs in the scientific literature. Some of them prove convergence without convergence rates and some of these mathematical results even rigorously establish convergence rates but there are only a few special cases where mathematical results can rigorously explain the empirical success of ANNs when approximating high-dimensional functions. The key contribution of this article is to disclose that ANNs can efficiently approximate high-dimensional functions in the case of numerical approximations of Black-Scholes PDEs. More precisely, this work reveals that the number of required parameters of an ANN to approximate the solution of the Black-Scholes PDE grows at most polynomially in both the reciprocal of the prescribed approximation accuracy

This book is a compilation of eleven quality articles exploring a variety of aspects on applications of ANN. Various authors of the articles from India and abroad have presented their work around the applications of ANN in healthcare and self-medication behaviour, Stock Market Analytics, ANN integrated application for industries including regulatory complaining aspect in Banking Industry, Deep Learning Framework in Medical Diagnosis, Face Recognition, Mobile Learning in Medical Education, Process and Applications of ANN using MATLAB, etc.

Analogicity in computer science is understood in two, not mutually exclusive ways: 1) with regard to the continuity feature (of data or computations), 2) with regard to the analogousness feature (i.e. similarity between certain natural processes and computations). Continuous computations are the subject of three methodological questions considered in the paper: 1a) to what extent do their theoretical models go beyond the model of the universal Turing machine (defining digital computations), 1b) is their computational power greater than that of the universal Turing machine, 1c) under what conditions are continuous computations realizable in practice? The analogue-analogical computations lead to two other issues: 2a) in what sense and to what extent their accuracy depends on the adequacy of certain theories of empirical sciences, 2b) are there analogue-analogical computations in nature that are also continuous? The above issues are an important element of the philosophical discussion on the limitations of contemporary computer science.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades.Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase.

Mezinárodní časopis o problematice neuronových a paralelních výpočetních a informačních systémů

The ability to encode and convert heritable information into molecular function is a defining feature of life as we know it. The conversion of information into molecular function is performed by the translation process, in which triplets of nucleotides in a nucleic acid polymer (mRNA) encode specific amino acids in a protein polymer that folds into a three-dimensional structure. The folded protein then performs one or more molecular activities, often as one part of a complex and coordinated physiological network. Prebiotic systems, lacking the ability to explicitly translate information between genotype and phenotype, would have depended upon either chemosynthetic pathways to generate its components—constraining its complexity and evolvability— or on the ambivalence of RNA as both carrier of information and of catalytic functions—a possibility which is still supported by a very limited set of catalytic RNAs. Thus, the emergence of translation during early evolutionary history may have allowed life to unmoor from the setting of its origin. The origin of translation machinery also represents an entirely novel and distinct threshold of behavior for which there is no abiotic counterpart—it could be the only known example of computing that emerged naturally at the chemical level. Here we describe translation machinery’s decoding system as the basis of cellular translation’s information-processing capabilities, and the four operation types that find parallels in computer systems engineering that this biological machinery exhibits.

Artificial Intelligence for Developers in easy steps is for coders who want to enhance their skillset quickly and easily. Artificial Intelligence (AI) is here to stay, and this guide reveals how AI works and illustrates how to build AI applications. It even covers no-code AI tools. This primer comes with free downloadable source code to get you started straightaway. Topics covered include:Creating a chatbot.Building an expert system. Understanding the flatworld, fuzzy logic, and subsumption architecture. Genetic algorithms, neural networks, generative AI, and low code. Aimed at aspiring developers and students who are familiar with Python and now want to master AI concepts and build intelligent AI solutions. AI programming is mainstream now. Update your coding skills and stay on top!

Virtual objects, such as online shops, the elements that go to make up virtual life in computer games, virtual maps, e-books, avatars, cryptocurrencies, chatbots, holograms, etc., are a phenomenon we now encounter at every turn: they have become a part of our life and our world. Philosophers—and ontologists in particular—have sought to answer the question of what, exactly, they are. They fall into two camps: some, pointing to the chimerical character of virtuality, hold that virtual objects are like dreams, illusions and fictions, while others, citing the real impact of virtuality on our world, take them to be real—an actual part of the real world, just like other real objects. In this article, we defend the thesis that both sides are wrong. Using Roman Ingarden’s phenomenological ontology, we advocate a position according to which a virtual object is a computationally grounded intentional object that has its existential foundation in computational processes, which are compliant with a certain model of computation. We point out that virtuality is framed by some kind of ideal mathematical objects: i.e., mathematical models of computation, which in turn fall, each of them, under their respective ideas. We also refer to the idea of natural computation, which in conjunction with the ontological analysis carried out leads to the thesis that an object can be more or less virtual.

In the past, control systems for automation and robotics and the expert systems employed in artificial intelligence were generally based on classical, or Boolean, logic. However, this proved to be inadequate by virtue of its binary nature, for portraying the uncertainties and inconsistencies of the 'real' world, and so from the late 1990s, research has been ongoing into the application of paraconsistent, or non-classical logics in these fields. This book aggregates much of this research, from 1999 up to the present. Organized to facilitate an understanding of the theory and the development of the applied methods, \"Uncertainty Treatment Using Praconsistent Logic\" presents the material in a sequential fashion and is divided into three parts. Notions of Paraconsistent Annotated Logic (PAL) summarizes the basic theory and fundamentals of the subject. The second part, Paraconsistent Analysis Networks (PANets), describes the utilization of paraconsistent logic in constructing networks which can deal with representative data from uncertain information. The final section, Paraconsistent Artificial Neural Networks (PANNets), is composed of six chapters which chart the applications of PAL, from a comparison between Paraconsistent Analysis Nodes (PANs) and the action of the human brain through to complex PANNet architecture capable of processing signals inspired by human brain function. This invaluable state-of-the-art overview will be of interest to all those involved with the development of robotics or artificial intelligence and will serve as reference for future application of paraconsistent logics in all computer and electronic systems.

Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automatically and dynamically adjusting evolutionary parameters such as crossover rates and mutation rates. In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates. Firstly, our main objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using De Jong's F1–F5 benchmark test problems, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm actually outperformed the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.