An Aquaponics System Design for Computational Intelligence Teaching

The past decade has witnessed an increasing interest in the application of Computational Intelligence methods to problems derived from the field of cryptography and cryptanalysis. This phenomenon can be attributed both to the effectiveness of these methods to handle hard problems, and to the major importance of automated techniques in the design and cryptanalysis of cryptosystems. This chapter begins with a brief introduction to cryptography and Computational Intelligence methods. A short survey of the applications of Computational Intelligence to cryptographic problems follows, and our contribution in this field is presented. Specifically, some cryptographic problems are viewed as discrete optimization tasks and Evolutionary Computation methods are utilized to address them. Furthermore, the effectiveness of Artificial Neural Networks to approximate some cryptographic functions is studied. Finally, theoretical issues of Ridge Polynomial Networks and cryptography are presented. The experimental results reported suggest that problem formulation and representation are critical determinants of the performance of Computational Intelligence methods in cryptography. Moreover, since strong cryptosystems should not reveal any patterns of the encrypted messages or their inner structure, it appears that Computational Intelligence methods can constitute a first measure of the cryptosystems’ security.

Development of a data acquisition sub-system using DAQ-Middleware

The solution of the modal equation of a planar optical waveguide is a cumbersome job and usually incident angle of successful modes is determined by a graphical solution. In this research work, we applied two computational intelligence methods: Particle Swarm Optimization (PSO) and Genetic algorithm (GA) in a segment-wise approach to solving the modal equation of the tangent function. The motivation for employing Computational Intelligence (CI) lies in its ability to optimize functions without requiring high-level mathematics or complex statistical models, as opposed to traditional analytical methods. This strategic use of computational intelligence significantly reduces the overall computational cost, more nature inspired and probabilistic, providing an efficient alternative. Particularly for functions with complex solutions, the utilization of computational intelligence or soft computing methods becomes imperative to obtain an approximate solution compared to classical numerical optimization methods like Newton-Raphson, bisection etc. that generally deterministic and aim to find the exact optimal solution. In terms of using probability (a core component of chosen algorithm’s searching mechanism) we can incorporate distributions that will enhance the performance. Therefore, while classical root-finding methods are computationally simpler for isolated cases, the use of PSO and GA is motivated by their global search capability, robustness to initialization, and ease of automation, which are advantageous in generalized or large-scale modal solution frameworks. The outcomes derived from both methods (PSO and GA) are meticulously compared with the results obtained through the traditional graphical solution. We have found accuracy of 99.95% for PSO and 99.87% for GA. Notably, the findings reveal a close correlation between the computational intelligence approaches and the graphical method offering a promising avenue for advancing the field with a more computationally feasible approach.

A novel fouling measurement system: Part I. design evaluation and description

The coronavirus disease (COVID-19) has been identified and widely known as an invisible enemy in the history of mankind. The novel coronavirus continued to pose a significant risk to health, and even death of human on earth. Originated December 2019, in the Wuhan province of China, now vastly spreading around the globe like wildfire. The coronavirus pandemic caused the largest global largest recession in the history of mankind, with the largest global population at the time being placed on lockdown, quarantine, and isolation due to vastly spread of the pandemic. The initial stages of this outbreak, all over the countries around the globe, including the USA, faced one major threat—a lack of diagnostic tools and proper testing. In this context, the first step in medical practice is Diagnosis, which is very crucial for clinical decision making. Researchers have used different computational intelligence techniques to classify different types of diseases, such as Diabetes, Cancer, Epilepsy, Lungs, heart disease and Liver, etc., therefore, COVID-19 should not be an exception. This Chapter will systematically talk about the recent state-of-the-art computational intelligence (CI) approaches in the field of medical diagnosis of COVID-19 based on the medical image.

Electricity load demand is the fundamental building block for all utilities planning. The load demand data has nonlinear and non-stationary characteristics, which make it difficult to be predicted accurately by just computational intelligence or ensemble methods. Ensemble methods like Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is a powerful tool to forecast power load demand time series. Heterogeneous ensemble, a combination of two base models, will be distinct or more powerful in forecasting power load demand. In this paper, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is hybridized with three computational intelligence-based predictors: support vector regression (SVR), artificial neural network (ANN) and random forest (RF). The basis of this paper was to conduct a comparative study on the accuracy of the forecasting result from using heterogeneous ensemble method to individual computational intelligence or ensemble method for four different horizons. The performances of the heterogeneous method are compared and discussed. It shows that heterogeneous method has outperformed the individual computational intelligence and ensemble methods. Possible future works are also recommended for power load demand forecasting.

Accurately determining component loads on a helicopter is an important goal in the helicopter structural integrity field. While measuring dynamic component loads directly is possible, these measurement methods are not reliable and are difficult to maintain. This paper explores the potential of using computational intelligence methods to estimate some of these helicopter dynamic loads. Thirty standard time-dependent flight state and control system parameters were used to construct a set of 180 input variables to estimate the main rotor blade normal bending during forward level flight at full speed. Unsupervised nonlinear mapping was used to study the structure of the multidimensional time series from the predictor and target variables. Based on these criteria, black and white box modeling techniques (including ensemble models) for main rotor blade normal bending prediction were applied. They include neural networks, local linear regression and model trees, in combination with genetic algorithms based on residual variance (gamma test) for predictor variables selection. The results from this initial work demonstrate that accurate models for predicting component loads can be obtained using the entire set of predictor variables, as well as with smaller subsets found by computational intelligence based approaches.

Artificial intelligence emerged from human thinking that has both logical and intuitive or subjective sides. The logical side has been developed and utilized, resulting advanced von Neumann type computers and expert systems, both constituting the hard computing domain. However, it is found that hard computing can’t give the solution of very complicated problems by itself. In order to cope with this difficulty, the intuitive and subjective thinking of human mind was explored, resulting the soft computing domain (also called computational intelligence). It includes neural networks, fuzzy logic and probabilistic reasoning, the last gathering evolutionary computation (including genetic algorithms with related efforts in genetic programming and classifier systems, evolution strategies and evolutionary programming), immune networks, chaos computing and parts of learning theory. In different kind of applications, all pure artificial intelligence methods mentioned above proved to be rather complementary than competitive, so that combined methods appeared in order to gather the advantages and to cope with the disadvantages of each pure method. The scope of this chapter is to study and finaly compare some representative classes of pure and combined computational intelligence methods applied in engine knock detection.

To respond to the requirements of the national strategy for cultivating innovative talents, stimulate the enthusiasm of digital media art professional students in learning professional skills, and tap the creative potential of students, this paper introduces the concept of project-based teaching method, the status quo of the application of project-based teaching method in digital media art majors, and analyzes the characteristics of this project-based teaching method, and at the same time build up a theoretical framework of project-based teaching for professional courses in digital media art and design. It also proposes the principles and strategies for introducing the project-based teaching method into the classroom of digital media art majors to improve the teaching quality of higher vocational digital media art major courses and the practical ability of students.

The characteristic of higher vocational education is to pay attention to the cultivation of students' skills, practice and professionalism, and the construction of off-campus practical training base is the key to realize the above skills of students. Both off-campus training bases and on-campus training bases are important components of practical teaching in higher vocational colleges. There are great differences between them in basic functions, functions, operation and management modes. With the reform and rapid development of national vocational education, the advanced concepts of teaching, teaching materials and teaching methods are constantly updated. Under the basic background of ideological and political construction of the course, innovation and entrepreneurship drive, the reconstruction of teaching content, loose leaf teaching materials and project-based teaching method can be flexibly applied in the actual teaching of network marketing comprehensive practical training course. While improving the teaching effect on campus, higher vocational colleges should enhance their ability to serve the regional economy, integrate social services with curriculum construction, and improve teachers' social service ability by means of the integration platform of production and education.

Application of evolutionary algorithm, artificial immune system and particle swarm optimization in the minimization atomic cluster’s total potential energy is presented in this work. These methods of computational intelligence simulate biological processes of the natural environment and organisms such as theory of evolution and biological immune systems and give a strong probability of finding the global optimum. Some examples and discussion on the results of optimization are also presented in this paper.

Computational intelligence, including mathematical modeling and statistical analysis, is always used in economy activities for quantitative analysis. For agriculture is so important that almost all states and regions pay special attention to its development. This paper collects China's 25 provinces agricultural fiscal support data and agriculture production cost data, analyzes the data with panel-data model, and then both 1dimensional and 2-dimensional clustering are utilized to scientifically classify these provinces into different kinds of agriculture-suitable regions. Consequently, based on this kind of quantitative analysis results about agricultural data, this paper proposes 3 kinds of agriculture development strategies.

This paper aims to model the change in market share of 30 domestic and foreign banks, which have been operating between the years 1990 and 2009 in Turkey by taking into consideration 20 financial ratios of those banks. Due to the fragile structure of the banking sector in Turkey, this study plays an important role for determining the changes in market share of banks and taking the necessary measures promptly. For this reason, computational intelligence methods have been used in the study. According to the research results, it is seen that it was not able to properly anticipate the data for the banking sector in the periods of financial crises (2000-2001 and 2008-2009). However, it is seen that, Simple Linear Regression is distinguished as a good algorithm among the computational intelligence algorithms for all periods between the years 1990 and 2009.

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

We present a new approach in astrodynamics and celestial mechanics fields, calledhybrid perturbation theory. A hybrid perturbation theory combines anintegrating technique, general perturbation theory or special perturbation theory or semianalytical method, with aforecasting technique, statistical time series model or computational intelligence method. This combination permits an increase in the accuracy of the integrating technique, through the modeling of higher-order terms and other external forces not considered in the integrating technique. In this paper, neural networks have been used as time series forecasters in order to help two economic general perturbation theories describe the motion of an orbiter only perturbed by the Earth’s oblateness.