Feature selection using enhanced particle swarm optimisation for classification models.

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets

Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier

Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS

Control System for Electrical Power Grids with Renewables using Artificial Intelligence Methods

Modern electrical and electronic devices are very sensitive to the power supply and require steady and stable electric power. Factories may also need electric power within a specific standard range of voltage, frequency, and current to avoid defects in the production. For these reasons electric power utilities must produce an electric power of a specific standard of power quality parameters [EN50160]. Nowadays, renewable energy sources, such as wind energy and solar energy are used to generate electric power as free and clean power sources as well to reduce fuel consumption and environmental pollution as much as possible. Renewable energy, e.g. wind speed or solar irradiance, are not stable or not constant energies over the time. Therefore smart control systems (SCSs) are needed for operate the power system in optimal way which help for producing a power with good quality from renewable sources. The forecasting and prediction models play a main role in these issues and contribute as the important part of the smart control system (SCS). The main task of the SCS is to keep the generated power equal to the consumed power as well as to consider standard levels of power quality parameters as much as possible. Some of previous studies have focused on forecasting power quality parameters, power load, wind speed and solar irradiance using machine learning models as neural networks, support vector machines, fuzzy sets, and neuro fuzzy. This thesis proposes designing forecasting systems using machine learning techniques in order to be use in control and operate an electrical power system. In this study, design and tested forecasting systems related to the power and renewable energies. These systems include wind speed forecasting, power load forecasting and power quality parameters forecasting. The main part of this thesis is focus in power quality parameters forecasting in short-term, these parameters are: power frequency, magnitude of the supply voltage, total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), and short term flicker severity (Pst) according to the definition in [EN50160]. The output of the forecasting models of power quality parameters can be used in shifting the load to run in switch time which will help for correct and optimize the quality of the power.Modern electrical and electronic devices are very sensitive to the power supply and require steady and stable electric power. Factories may also need electric power within a specific standard range of voltage, frequency, and current to avoid defects in the production. For these reasons electric power utilities must produce an electric power of a specific standard of power quality parameters [EN50160]. Nowadays, renewable energy sources, such as wind energy and solar energy are used to generate electric power as free and clean power sources as well to reduce fuel consumption and environmental pollution as much as possible. Renewable energy, e.g. wind speed or solar irradiance, are not stable or not constant energies over the time. Therefore smart control systems (SCSs) are needed for operate the power system in optimal way which help for producing a power with good quality from renewable sources. The forecasting and prediction models play a main role in these issues and contribute as the important part of the smart control system (SCS). The main task of the SCS is to keep the generated power equal to the consumed power as well as to consider standard levels of power quality parameters as much as possible. Some of previous studies have focused on forecasting power quality parameters, power load, wind speed and solar irradiance using machine learning models as neural networks, support vector machines, fuzzy sets, and neuro fuzzy. This thesis proposes designing forecasting systems using machine learning techniques in order to be use in control and operate an electrical power system. In this study, design and tested forecasting systems related to the power and renewable energies. These systems include wind speed forecasting, power load forecasting and power quality parameters forecasting. The main part of this thesis is focus in power quality parameters forecasting in short-term, these parameters are: power frequency, magnitude of the supply voltage, total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), and short term flicker severity (Pst) according to the definition in [EN50160]. The output of the forecasting models of power quality parameters can be used in shifting the load to run in switch time which will help for correct and optimize the quality of the power.410 - Katedra elektroenergetikyvyhově

Disease Prediction Using Data mining Classification Algorithm

Knowledge discovery in databases has built up its prosperity rate in different noticeable fields, for example, e-business, advertising, retail and medical. Medical Data mining has extraordinary intensity for investigating the outside of anyone's ability to see designs in the separate medical Data collections. This venture plans to arrange the disease Data indexes and deliver the reports in light of their manifestations. The Data collections are ordered by utilizing multilayered encourage forward neural systems. The datasets for the diseases are procured from UCI, an online vault of vast Data collections

Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking

Particle Swarm Optimization (PSO) has demonstrated its effectiveness in solving the optimization problems. Nevertheless, the PSO algorithm still has the limitation in finding the optimum solution. This is due to the lack of exploration and exploitation of the particle throughout the search space. This problem may also cause the premature convergence, the inability to escape the local optima, and has a lack of self-adaptation in their performance. Therefore, a new variant of PSO called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) was proposed to overcome these drawbacks. In this algorithm, the worst particle will be relocating to a new position to ensure the concept of exploration and exploitation remains in the search space. This is the way to avoid the particles from being trapped in local optima and exploit in a suboptimal solution. The concept of exploration will continue when the particle is relocated to a new position. In addition, to evaluate the performance of MEESPSO, we conducted the experiment on 12 benchmark functions. Meanwhile, for the dynamic environment, the method of MEESPSO with Hue, Saturation, Value (HSV)-template matching was proposed to improve the accuracy and precision of object tracking. Based on 12 benchmarks functions, the result shows a slightly better performance in term of convergence, consistency and error rate compared to another algorithm. The experiment for object tracking was conducted in the PETS09 and MOT20 datasets in a crowded environment with occlusion, similar appearance, and deformation challenges. The result demonstrated that the tracking performance of the proposed method was increased by more than 4.67% and 15% in accuracy and precision compared to other reported works

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Methodology to Predict Daily Groundwater Levels by the Implementation of Machine Learning and Crop Models

The continuous decline of groundwater levels caused by variations in climatic conditions and crop water demands is an increased concern for the agricultural community. It is necessary to understand the factors that control these changes in groundwater levels so that we can better address declines and develop improved conservation practices that will lead to a more sustainable use of water. In this study, two machine learning techniques namely support vector regression (SVR) and the nonlinear autoregressive with exogenous inputs (NARX) neural network were implemented to predict daily groundwater levels in a well located in the Mississippi Delta Region (MDR). Results of the NARX model indicate that a Bayesian regularization algorithm with two hidden nodes and 100 time delays was the best architecture to forecast groundwater levels. In another study, the SVR and the NARX model were compared for the prediction of groundwater withdrawal and recharge periods separately. Results from this study showed that input data classified by seasons lead to incremental improvements in the model accuracy, and that the SVR was the most efficient machine learning model with a Mean Squared Error (MSE) of 0.00123 m for the withdrawal season. Analysis of input variables such as previous daily groundwater levels (Gw), precipitation (Pr), and evapotranspiration (ET) showed that the combination of Gw+Pr provides the optimal set for groundwater prediction and that ET degraded the modeling performance, especially during recharge seasons. Finally, the CROPGRO-Soybean crop model was used to simulate the impacts of different volumes of irrigation on the crop height and yield, and to generate the daily irrigation requirements for soybean crops in the MDR. Four irrigation threshold scenarios (20%, 40%, 50% and 60%) were obtained from the CROGRO-Soybean model and used as inputs in the SVR to evaluate the predicted response of daily groundwater levels to different irrigation demands. This study demonstrated that conservative irrigation management, by selecting a low irrigation threshold, can provide good yields comparable to what is produced by a high volume irrigation management practice. Thus, lower irrigation volumes can have a big impact on decreasing the amount of groundwater withdrawals, while still maintaining comparable yields

Methodology to Predict Daily Groundwater Levels by the Implementation of Machine Learning and Crop Models

The continuous decline of groundwater levels caused by variations in climatic conditions and crop water demands is an increased concern for the agricultural community. It is necessary to understand the factors that control these changes in groundwater levels so that we can better address declines and develop improved conservation practices that will lead to a more sustainable use of water. In this study, two machine learning techniques namely support vector regression (SVR) and the nonlinear autoregressive with exogenous inputs (NARX) neural network were implemented to predict daily groundwater levels in a well located in the Mississippi Delta Region (MDR). Results of the NARX model indicate that a Bayesian regularization algorithm with two hidden nodes and 100 time delays was the best architecture to forecast groundwater levels. In another study, the SVR and the NARX model were compared for the prediction of groundwater withdrawal and recharge periods separately. Results from this study showed that input data classified by seasons lead to incremental improvements in the model accuracy, and that the SVR was the most efficient machine learning model with a Mean Squared Error (MSE) of 0.00123 m for the withdrawal season. Analysis of input variables such as previous daily groundwater levels (Gw), precipitation (Pr), and evapotranspiration (ET) showed that the combination of Gw+Pr provides the optimal set for groundwater prediction and that ET degraded the modeling performance, especially during recharge seasons. Finally, the CROPGRO-Soybean crop model was used to simulate the impacts of different volumes of irrigation on the crop height and yield, and to generate the daily irrigation requirements for soybean crops in the MDR. Four irrigation threshold scenarios (20%, 40%, 50% and 60%) were obtained from the CROGRO-Soybean model and used as inputs in the SVR to evaluate the predicted response of daily groundwater levels to different irrigation demands. This study demonstrated that conservative irrigation management, by selecting a low irrigation threshold, can provide good yields comparable to what is produced by a high volume irrigation management practice. Thus, lower irrigation volumes can have a big impact on decreasing the amount of groundwater withdrawals, while still maintaining comparable yields