Computational Intelligence Techniques Research Articles

Correction: Pourdaryaei et al. Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market. Energies 2021, 14, 6104

There was an error in the original publication [...]

COMPUTATIONAL INTELLIGENCE BASED ROUTING SOLUTIONS FOR MOBILE AD HOC NETWORKS

Mobile Ad Hoc Networks (MANETs) refers to a wireless network where nodes dynamically establish and maintain connections with each other, directly without the need for a centralized infrastructure. MANETs are characterized by their decentralized nature, presenting challenges for routing protocols concerning rapid data transmission, minimized delay and Quality of Service (QoS) provisioning. Traditional routing protocols may struggle to perform optimally in MANETs due to frequent changes in network topology and resource constraints. The development of intelligent routing protocols tailored to the specific characteristics of MANETs is vital for dynamic and resource-constrained environments. Computational intelligence (CI) techniques have gained attention for designing intelligent routing protocols for MANETs. This paper provides a comprehensive overview of majorly used CI methodologies in MANETs. The paper reviews existing research on intelligent routing protocols based on CI techniques in MANETs and identifies future research directions in this field to address the unique challenges of MANETs and improve network performance and reliability.

An application of Computational Intelligence in group formation for digital drama education

AbstractUnveiling the intersection of technology and art, this research aims to explore the vital role of Computational Intelligence (CI) techniques in shaping digital theater performances within virtual worlds by managing student populations engaged in these productions. The realm of digital theater offers opportunities for creative expression and collaboration. This necessitates the integration of Computational Intelligence (CI) to establish cohesive teams in digital theater. By doing so, effective collaboration is promoted, and creativity is dynamically enhanced in virtual cultural environments and digital worlds. Central to this study is the use of a CI algorithm called Flying Fox Optimizer, designed to form homogeneous student groups, and optimize theater dynamics. The algorithm utilizes fuzzy logic, eliminating the requirement for fine-tuning by the user. The results reveal that through this mechanism, theater educators of the future can effortlessly create student groups that foster seamless collaboration in virtual theater education.

Implementation of Health Monitoring System of Induction Machine Using Computational Intelligence

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.  

Heat transfer analysis for magnetohydrodynamic peristalsis of Reiner–Philippoff fluid: Application of an artificial neural network

Present communication explores a novel application of the computational intelligence technique, namely, the Levenberg–Marquardt scheme under a Backpropagated Neural Network (LM-BNN) to solve the mathematical model for the magnetohydrodynamic peristaltic transport of Reiner–Philippoff (R–Ph) pseudoplastic fluid considering the influences of Ohmic heating, mixed convection, and viscous dissipation through a symmetric channel. The R–Ph fluid model is used in this investigation to elucidate the non-Newtonian behavior of the fluid under consideration. The Reiner–Philippoff fluid model delineates the intricate relationship between stress and deformation rate within the fluid. There are a few studies available on the peristaltic transport of the Reiner–Philippoff fluid that do not incorporate Joule heating, mixed convection, and magnetic field effects. Therefore, a novel mathematical model is developed to employ an artificial neural network technique with a different approach that has not been examined before. The governing equations of the problem are simplified using long wavelength and low Reynolds number approximations, and the resulting system is numerically solved using the BVP4c scheme in MATLAB based on the shooting algorithm. Furthermore, a dataset is constructed through the BVP4c technique for the proposed LM-BNN, considering eight scenarios of peristaltic motion of the Reiner–Philippoff fluid model by varying the Bingham number, the Brinkman number, the Grashof number, the R–Ph fluid parameter, and the Hartmann number. The numerical dataset is divided into 15% for testing, 15% for training, and 70% for validation, which are utilized in LM-BNN to analyze the numerical solutions and Levenberg–Marquardt neural networks (LM-NNs) predicted results. The consistency and effectiveness of LM-BNN are validated through regression analysis, stresses at the wall, error histogram, correlation index, heat transfer, and mean squared error based fitness curves, which vary from 10−3→10−11. Variations in several flow parameters affecting temperature and velocity profiles are explained physically through graphs. Additionally, an analysis of heat transfer and stresses at the wall, including absolute errors, is provided through tables. The outcomes reveal that the improving Grashof number and the Hartmann number tend to increase the temperature profile. Tabular results indicate that rates of heat transfer improve when assigning higher values to the Hartmann number, the Brinkman number, and the Grashof number, whereas stresses at the wall decrease for the Reiner–Philippoff fluid parameter and the Bingham number. The error analysis of numerical simulations is a valuable step in determining whether the data obtained are reliable and accurate. In terms of absolute error, the disagreement between numerical solutions and those predicted by LM-NNs is approximately 10−5→10−11. It is clear from this error analysis that LM-NNs predicted results are consistent and reliable.

A novel decision support system based on computational intelligence and machine learning: Towards zero-defect manufacturing in injection molding

Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.

Performance optimization of generator in steam turbine power plants using computational intelligence techniques

Performance optimization of generator in steam turbine power plants using computational intelligence techniques

Implementation of AI for forecasting of wind speed and solar irradiation using different Machine Learning techniques

High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very import ant. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describe the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Indore has been highlighted in this research.

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration.

Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies: Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output. The result shows that the optimum computational intelligence techniques include the 3-9-1 topology trained with Bayesian regulation function for ANN, Gaussian kernel function for SVM, Matérn covariance function combined with zero mean function for GPR, boosting method for EoT and 4 minimum leaf size for DT. GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model, respectively. GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs. Therefore, the computational intelligence models, especially GPR, can be applied for an effective description of moisture evolution during small-scale and industrial dehydration of glutinous rice. © 2024 Society of Chemical Industry.

Probabilistic characterisation of rock mass deformation modulus from P-wave velocity based on information criteria

ABSTRACT Determination of rock mass deformation modulus (Erm ) is an important but very challenging task in rock mechanics and rock engineering. Direct measurements of Erm by in-situ tests are expensive, time-consuming, and sometimes even impossible. Since the nondestructive and easy geophysical method is commonly used to measure the P-wave velocity (Vp ) in the field, the study has developed an empirical correlation between Erm and Vp based on 834 collected datasets by considering both inherent and transformation uncertainties. The information criteria are employed to select the best model for deriving a general empirical correlation. The maximum likelihood estimation (MLE) method and computational intelligence techniques like particle swarm optimisation (PSO), artificial bee colony (ABC), and differential evolution (DE) algorithms are used to specify the incorporated parameters of each candidate model. Based on the derived general empirical equation, a Bayesian equivalent sample approach using Markov Chain Monte Carlo (MCMC) simulation is applied to conduct probabilistic characterisation of Erm by combining prior knowledge and project-specific observed Vp data. Comparative studies of the results from theory and in-situ measured Erm values suggest that the proposed method is effective and validate the applicability of the derived general empirical correlation.

A Systematic Review of Computational Intelligence Techniques for Channel Selection in P300-Based Brain Computer Interface Speller

Electroencephalography (EEG) based P300 speller aid in restoring the communication and control capabilities in patients suffering from motor disabilities. However, the quality and quantity of the data collected from EEG recordings have a substantial influence on the P300 speller's performance. Hence, selecting the optimum number of recording electrodes, i.e., channels for each user, is a significant difficulty for the P300 speller. There are two fundamental objectives of the channel selection process: 1) To extract the most crucial information from the relevant channels, hence reducing the computing complexity of P300/Non-P300 signal processing operation, and 2) To lessen the potential overfitting that could result from using unwanted channels to boost performance. For obtaining the best channel subsets, different channel selection techniques, including manual, filtering, wrapper, and embedded approaches, have been applied by past researchers. This study presents a comprehensive review of recent developments, present status, challenges, and possible solution associated with channel selection strategies in P300 speller.

Outdoor activity classification using smartphone based inertial sensor measurements

AbstractHuman Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model’s performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model’s capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports.

Monitoring and fault detection: a comparative study using computational intelligence techniques

In this work, two different Computational Intelligence Techniques were used to provide a comparative study by using Neural Networks and Neuro-fuzzy applied in Monitoring and Fault Detection in sensor of an experimental reactor. Both methodologies were developed and tested using a model composed by 9 variables: N2 (% power), T3 (pool water temperature), T4 (decay tank inlet temperature), T7 (primary loop outlet temperature), T8 (secondary loop inlet temperature), T9 (secondary loop outlet temperature), F1M3 (primary loop flowrate), F2M3 (secondary loop flow rate) and R1M3 (nuclear dose rate). It was used data from the first week experimental reactor IEA-R1 operation from October 2012 in both Monitoring Systems, which was divided in subsets in a following way: 60% for training, 20% for tens and 20% for validation. The results obtained using Neuro-fuzzy were better than the ones with Neural Networks.

KBSS: an efficient approach of extracting text contents from lecture videos - computational intelligence techniques

KBSS: an efficient approach of extracting text contents from lecture videos - computational intelligence techniques

KBSS: an efficient approach of extracting text contents from lecture videos - computational intelligence techniques

KBSS: an efficient approach of extracting text contents from lecture videos - computational intelligence techniques

Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques

<abstract> <p>Emphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Current Control (PCC) strategy applied to a Three-Phase Inverter-fed Induction Motor (IM), with a particular focus on the Sequential Model methodology. The Sequential Model MPC algorithm employed a cost functional approach, predicated on the square of the discrepancy between reference and stator-measured currents of the IM in d-q reference frame. This method, implemented and tested in both MATLAB/Simulink and Python environments, utilized a minimization principle to guide the switching states of the inverter, thereby ensuring the accuracy of voltage signals for the induction motor. The projected study further included a comparative analysis of the electromagnetic torque, load currents, rotor speed, and angle deviations derived from the Sequential Model with those obtained through the Ant Colony Optimization (ACO) and Nelder-Mead methods. The results distinctly illustrated the robust adaptability of the Sequential Model methodology, outperforming the ACO and Nelder-Mead techniques in certain aspects such as minimum current errors, better speed regulations, and rotor angle trajectories.</p> </abstract>

Correction to: Feasibility of computational intelligent techniques for the estimation of spring constant at joint of structural glass plates: a dome-shaped glass panel structure

Correction to: Feasibility of computational intelligent techniques for the estimation of spring constant at joint of structural glass plates: a dome-shaped glass panel structure

Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms

The bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.

Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review

Extracellular electron transfer (EET) is a biological mechanism that plays a crucial role in various bioelectrochemical systems (BESs) and has substantial implications for renewable energy production. By utilizing the metabolic capacities of exoelectrogens, BESs offer a viable and environmentally friendly approach to electricity generation and chemical production; however, the diminished effectiveness of EET remains a hindrance to their optimal application in practical contexts. This paper examines the various strategies that have the potential to be employed to enhance the efficiency of EET systems and explores the potential for the integration of BESs technology with contemporary technologies, resulting in the development of an enhanced and sustainable system. It also examines how quorum sensing, electrode modifications, electron shuttles, and mediators can aid in improving EET performance. Many technological innovations, such as additive manufacturing, the science of nanotechnology, the technique of genetic engineering, computational intelligence, and other combinations of technologies that can be used to augment the efficacy of BESs are also discussed. Our findings will help readers understand how BESs, though an evolving technology, can play an important role in addressing our environmental concerns. Technical constraints are identified, and future directions in the field of EET are suggested.

Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively.