Chapter 13 - P300-based brain–computer interface for communication and control

Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems

Nature Inspired Computing (NIC) paradigms, namely, swarm intelligence, evolutionary computation and computational intelligence techniques are widely applied to develop versatile and adaptable systems and create computational methods that can assist human to resolve real world complex problems. It can be achieved by transferring knowledge from natural systems to engineering systems. The objective of this paper is to analyze the recent trends and advancement in the application of metaheuristic algorithms to solve various domains, especially, denoising, edge detection and classification problem. This paper provides a comprehensive list of global optimization algorithms that can be applied to develop innovative system and soft computing applications integrates with computational intelligence techniques. Several fitness functions and parameters implemented in the evaluation of global optimization algorithm to find global optimum for solving diverse applications are thoroughly investigated. The implications for the selection of fitness function to solve specific optimization problems and applications are also discussed.

Micro combined heat and power home supply: Prospective and adaptive management achieved by computational intelligence techniques

Real-world environments are typically dynamic, complex, and multisensory in nature and require the support of top-down attention and memory mechanisms for us to be able to drive a car, make a shopping list, or pour a cup of coffee. Fundamental principles of perception and functional brain organization have been established by research utilizing well-controlled but simplified paradigms with basic stimuli. The last 30 years ushered a revolution in computational power, brain mapping, and signal processing techniques. Drawing on those theoretical and methodological advances, over the years, research has departed more and more from traditional, rigorous, and well-understood paradigms to directly investigate cognitive functions and their underlying brain mechanisms in real-world environments. These investigations typically address the role of one or, more recently, multiple attributes of real-world environments. Fundamental assumptions about perception, attention, or brain functional organization have been challenged-by studies adapting the traditional paradigms to emulate, for example, the multisensory nature or varying relevance of stimulation or dynamically changing task demands. Here, we present the state of the field within the emerging heterogeneous domain of real-world neuroscience. To be precise, the aim of this Special Focus is to bring together a variety of the emerging "real-world neuroscientific" approaches. These approaches differ in their principal aims, assumptions, or even definitions of "real-world neuroscience" research. Here, we showcase the commonalities and distinctive features of the different "real-world neuroscience" approaches. To do so, four early-career researchers and the speakers of the Cognitive Neuroscience Society 2017 Meeting symposium under the same title answer questions pertaining to the added value of such approaches in bringing us closer to accurate models of functional brain organization and cognitive functions.

The aim of this study was to estimate genetic divergence and select BC1F3 populations of dwarf tomato plant within the Santa Cruz segment by computational intelligence techniques. The experiment was conducted in a greenhouse in the Vegetable Crop Experimental Station of the Universidade Federal de Uberlândia (UFU), Monte Carmelo, MG, Brazil. A randomized block experimental design was used with 17 treatments and four replications. The genetic material evaluated comprised thirteen dwarf tomato plant populations obtained by a backcross and two self-fertilizations, plus both parents (recurrent and donor), and two commercial check varieties. The traits evaluated were mean fruit weight (MFW), soluble solids content (SSC), fruit diameter (FD), fruit length (FL), fruit shape (FS), pulp thickness (PT), number of locules (NL), distance between internodes, and acylsugar, β-carotene, and lycopene content. The data were analyzed by means testing, and genetic divergence was measured using Mahalanobis generalized distance by the unweighted pair group method with arithmetic mean (UPGMA) and through computational intelligence using Kohonen self-organizing maps (SOM). Genetic dissimilarity in relation to the donor parent could be confirmed through both methodologies. However, the SOM was able to detect differences and organize the similarities among the populations in a more consistent manner, resulting in a larger number of groups. In addition, the computational intelligence techniques allow the weight of each variable in formation of the groups to be ascertained. Among the BC1F3 populations, UFU-SC#3 and UFU-SC#5 stood out for agronomic traits, and UFU-SC#10 and UFU-SC#11 stood out for quality parameters.

Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today’s time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed.

Computational intelligence techniques have been successfully used for solving control problems in packet-switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introducing new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment.

Short-term stream flow forecasts are required for simulation, optimization, and decision-making purposes in applications ranging from hydropower planning to flood prevention.The particular case of one-day ahead stream flow forecasting is an important but difficult problem that has been increasingly studied using hybrid computational intelligence and machine learning techniques.However, these studies present several limitations.In this work we attempt to address those limitations by (1) replicating and validating previous works; (2) using more objective evaluation criteria; (3) applying several computational intelligence techniques to datasets representative of diverse geographic areas; (4) preprocessing data and performing an extensive parameter optimization in order to improve previous results.

The Group Method of Data Handling (GMDH) is a typical inductive modeling method that is built on principles of self-organization for modeling complex systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recently hybridized with some computational intelligence (CI) techniques resulting in more robust and flexible hybrid intelligent systems for solving complex, real-world problems. The central theme of this book is to present in a very clear manner hybrids of some computational intelligence techniques and GMDH approach. The hybrids discussed in the book include GP-GMDH (Genetic Programming-GMDH) algorithm, GA-GMDH (Genetic Algorithm-GMDH) algorithm, DE-GMDH (Differential Evolution-GMDH) algorithm, and PSO-GMDH (Particle Swarm Optimization) algorithm. Also included is the description of the recently introduced GAME (Group Adaptive Models Evolution algorithm. The hybrid character of models and their self-organizing ability give these hybrid self-organizing modeling systems an advantage over standard data mining models. The modeling and data mining solutions of several real-life problems in the areas of engineering, bioinformatics, finance, and economics are presented in the chapters. The book will benefit amongst others, people who are working in the areas of neural networks, machine learning, artificial intelligence, complex system modeling and analysis, and optimization.

Computational intelligence techniques are attracting more and more attention in NLP and text analysis applications. This paper is devoted to their use in automatic question generation based on text analysis with the goal to develop the computational intelligence framework that should automate or semi-automate the process of quiz and exam question generation. The framework operation is based on information retrieval and NLP algorithms. It incorporates the application of production rules, LSTM neural network models, and other intelligent techniques. It allows generating multiple choice questions, true and false questions as well as Wh-type (What? When? How?) questions. Automation procedures for each type question generation are developed, presented and analyzed. The typical challenges in framework development and application are considered and possible solutions are discussed. The results of the framework application and its use for quiz generation in a real college class are presented and analyzed.

In metal additive manufacturing process, deposition characteristics are very important to determine the quality of the deposition and the process efficiency. To achieve, this an adaptive neural-fuzzy-based computational intelligence (CI) technique is used to model the layer width and layer height in laser-based direct energy deposition technique as this is one of the most promising metal additive manufacturing methodologies. In this present study, neural network-based architecture was applied to develop an optimal neural-fuzzy system for modeling and predicting the width and height of the deposited layer for three controllable process variables such as feed rate, power, and scanning velocity of the laser. Hybridization of steepest descent and least squares method was used as a learning method in the proposed adaptive neural-fuzzy predictive system. To do the comparative study of prediction accuracy of biometal deposition characteristics, trapezoidal and Gaussian membership function were considered after careful study. The predicted deposition characteristics feature values predicted from proposed adaptive neural-fuzzy model have been compared with the real data. This comparative study indicates that the proposed methodology can design optimal data base and rule base of the fuzzy system for predicting the deposition characteristics in metal additive manufacturing process.

In the realm of computational intelligence, various nature-inspired computational methodologies are being used. To solve real-world problems, artificial neural networks (ANNs), swarm intelligence, and fuzzy systems are used, to name a few. In current time, we are surrounded by sensors, various machines, and electronic devices. All of these generate large quantities of data. This large volume of data has changed our lives in a variety of ways, and the field of healthcare is no exception. Clinical trials, personalized gadgets like smart watches, fit-bits, medical insurance records, hospital billing software, medical reports, genetic information, medical IOT devices, medical imaging, scholarly journals, pathology lab networks, and other sources of data generate massive amounts of information in healthcare. The amount of data being generated is increasing all the time. Recent improvements in computational intelligence techniques have enriched the importance of big data analytics, genetics & genomics, medical image processing, computer vision, Internet of Things, drug discovery, disease diagnosis, and disease prevention in the field of healthcare. The main computational intelligence techniques to address these issues will be discussed in this chapter. Healthcare is a critical industry because, in addition to money, people's lives are at risk. As a result, the approaches and models employed must be extremely accurate and efficient. The effectiveness of a machine learning model is determined by the quality of data provided to it during the learning phase. Data errors, such as missing data and unbalanced data, are particularly widespread in the healthcare industry. In this chapter, we'll examine feature engineering as a solution to these kinds of problems. Healthcare is projected to be significantly impacted by genetics and genomics, which will disclose new dimensions, including personalised medicine, disease prediction, and genetic engineering. This case study will be presented in this chapter.

Computational intelligence is a modern research orientation to ensure effective and efficient outcomes for real world problems. As it is highly associated with essentials of statistics, data analytics, and artificial intelligence, technical fields have been intensively focused on using specific techniques to solve different problems. However, predictive analysis has been a modern solution approach for different aspects of advanced problems. Engineering-related applications find computational intelligence techniques useful to have desired outcomes at the end. Among the associated technical issues, Concrete technology finds its way by raising different issues and combining already known methods with the support of computational intelligence. This chapter provides a brief review of computational intelligence techniques to be used in concrete technology. The introduction is related to applications of predicting compressive strength and the ultrasonic pulse velocity. In this chapter, an overview of this book is provided. As an editorial introduction, remarkable original content texts by the book authors are combined in order to provide the most essential knowledge for the readers.

Optimization problems arise in various fields of science, engineering, and industry. In many occasions, such optimization problems, particularly in the present scenario, involve a variety of decision variables and complex structured objectives, and constraints. Often, the classical or traditional optimization techniques face difficulty in solving such real world optimization problems in their original form. Due to deficiencies of classical optimization algorithms in solving large-scale, highly non-linear, and often non-differentiable problems, there is a need to develop efficient and robust computational algorithms, which can solve problems, numerically irrespective of their sizes. Taking inspiration from nature to develop computationally efficient algorithms is one way to deal with real world optimization problems. Broadly, one can put these algorithms in the field of computational sciences and in particular, to computational intelligence. Formally, computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches to solve complex real world problems. The major constituents of CI are Fuzzy Systems (FS), Neural Networks (NN), and Swarm Intelligence (SI) and Evolutionary Computation (EC). Computational intelligence techniques are powerful, efficient, flexible, and reliable. Swarm Intelligence and Evolutionary Computation are two very useful components of computational intelligence that are primarily used to solve optimization problems. This book primarily concerns with various swarm and evolutionary optimization algorithms. This chapter provides a brief introduction to swarm and evolutionary algorithms.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances