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This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper. [ABSTRACT FROM AUTHOR]

The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged for a short time, five important parameters affecting the following scene are selected through correlation analysis, and vehicle-following research is carried out. This paper adopts a driver-following model based on a Genetic Algorithm (GA)-optimized Back Propagation (BP) neural network. Based on the data of next-generation simulation (ngsim), this paper selects vehicle 32 (32 represents the ID of the vehicle in the ngsim project) as the main vehicle in order to study short-term driving habits. A BP neural network is built using MATLAB; 60% of the data of vehicles 32 and 29 is used for the training set, 20% is used for the verification set, and 20% for the test set. Because short-term prediction requires high timeliness, the genetic algorithm is used to optimize the initial weights of the neural network, which not only accelerates the convergence speed but also plays a role in avoiding the local optimal solution. The experimental results show that compared with the traditional stimulus-response vehicle-following model, this model has a following ability that is more in line with the driver's driving habits in terms of ensuring following safety. [ABSTRACT FROM AUTHOR]

The magnetohydrodynamics (MHD) model of the alternating current (AC) arc is complex, so a simplified equivalent heat source (EHS) model can be used to replace the complex model in studying the AC arc's thermal characteristics and cable fire risk. A 2D axisymmetric AC arc MHD simulation model in the short gap of a copper-core cable is established in this paper. The AC arc voltage and current obtained by the model are consistent with experiments. The AC arc's heat source distribution obtained by the MHD model is fitted to obtain the heat source function Q of the AC arc. Q is divided into 16 independent segmented heat sources, and a correction matrix is constructed to optimize the segmented heat sources. A neural network and a genetic algorithm give the prediction model and the optimal correction matrix of the segmented heat source. The EHS model optimized by the optimal correction matrix can obtain a minimum temperature error of 5.8/4.4/4.2% with the MHD model in different AC arc peak currents 2/4/6 A. The probability of a cable fire is calculated by using AC arc's optimized EHS model when different numbers of AC arcs are generated randomly in AC half-waves. The EHS model can replace the complex MHD model to study the thermal characteristics of AC arcs and quickly calculate the probability of a cable fire caused by random AC arcs. [ABSTRACT FROM AUTHOR]

The loan eligibility prediction model utilizes an analytical approach that adjusts previous and current credit user data to provide forecasts. An important challenge in predicting loan eligibility is accurately forecasting loan outcomes via risk assessment and evaluation analysis. In the nowadays the Predictions of loan approvals now require the utilization of machine learning. Financial institutions are seeking methods to automate the loan approval process while minimizing risk in response to the rising demand for credit. This article introduces a novel application of machine learning methods to predict loan approval. The research centers on various algorithmic architectures, neural networks, support vector machines, decision trees, and random forests. Furthermore, the paper addresses the obstacles encountered in applying Artificial Intelligence (machine learning (ML) algorithms) for predicting loan approval. Moreover, for feature selection, the article proposes a dynamic thresholding genetic algorithm (DTGA) based on loan approval prediction. Besides, we emphasize the importance of data quality and feature selection in designing an effective ML model for loan approval prediction. Compared to conventional approaches, performance evaluation of the DTGA demonstrates the GA feature selection can substantially enhance the accuracy of loan approval prediction. Therefore, this article contributes to utilizing ML models for predicting loan approvals and the potential ramifications. Consequently, it enhances the decision-making procedures of financial institutions. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

X-ray fluorescence (XRF) spectroscopy is a non-destructive differential measurement technique widely utilized in elemental analysis. However, due to its inherent high non-linearity and noise issues, it is challenging for XRF spectral analysis to achieve high levels of accuracy. In response to these challenges, this paper proposes a method for XRF spectral analysis that integrates an adaptive genetic algorithm with a backpropagation neural network, enhanced by an attention mechanism, termed as the AGA-BP-Attention method. By leveraging the robust feature extraction capabilities of the neural network and the ability of the attention mechanism to focus on significant features, spectral features are extracted for elemental identification. The adaptive genetic algorithm is subsequently employed to optimize the parameters of the BP neural network, such as weights and thresholds, which enhances the model's accuracy and stability. The experimental results demonstrate that, compared to traditional BP neural networks, the AGA-BP-Attention network can more effectively address the non-linearity and noise issues of XRF spectral signals. In XRF spectral analysis of air pollutant samples, it achieved superior prediction accuracy, effectively suppressing the impact of background noise on spectral element recognition. [ABSTRACT FROM AUTHOR]

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively. [ABSTRACT FROM AUTHOR]

Attitude detection of cooperative robots can help robots recognize environment, understand tasks, and improve the safety, accuracy and efficiency of robot work. For robots with unknown configuration parameters, their configuration can be estimated; it is convenient for further kinematics and dynamics analysis. For an economical cooperative robot, its kinematic parameters can be corrected by attitude detection. An independent robot attitude detection system can be used as a secondary auxiliary system, and the fault diagnosis system of the robot is composed of the high-precision encoder system of the robot body. This paper studies the neural network control system of cooperative robot based on genetic algorithm. In this paper, the robot is taken as the research object. Aiming at its strong conjunction, non-linearity and multivariable characteristics, the problem of robot motion control based on neural network is mainly discussed. On this basis, the basic genetic algorithm and an improved genetic algorithm called messy are used to optimize the structure of the neural network, so as to better realize the motion control problem of the robot. The results of this study show that: After optimization with messy genetic algorithm, not only the tracking effect is better, but also the number of hidden layer nodes of the network is reduced from 12 to 7. This greatly simplifies the structure of the network and makes the design and training of the network relatively simple. [ABSTRACT FROM AUTHOR]

This paper initially involves three main processing parameters: screw speed, feeding speed, and initial material moisture content, exploring the RTD of materials inside the extruder barrel under varying parameters and clarifying the impact of parameter variations on RTD. Finally, machine vision technology was utilized to link extruded product images to texture features, and a texture prediction model based on image features was established using a Back Propagation (BP) neural network. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to optimize the BP neural network. The results indicate that the feeding speed has a stronger impact than the screw speed on the extrusion process, and an increase in the initial material moisture content tends to shorten the RTD. Specifically, an increase in screw speed results in a denser product structure, while higher feeding speeds lead to reduced pore size in the microstructure. As the initial material moisture content increased from 55% to 70%, the average residence time MRT decreased from 265.21 s to 166.62 s. Additionally, elevated moisture content causes a more porous microstructure. After optimizing the texture prediction model of extruded products through the application of Particle Swarm Optimization and Genetic Algorithm models, it was discovered that the Genetic Algorithm was more effective in reducing errors (p < 0.05) than the Particle Swarm Optimization algorithm. It was found that the Particle Swarm Optimization model exhibited better prediction performance. The results of the prediction indicated a significant association between the image features of the product and hardness, resilience, and chewiness, as corroborated by correlation coefficients of 0.93913, 0.94040, and 0.94724, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we present a series of experimental and numerical studies on the performance and modeling of a developed magnetorheological gel (MRG) damper. A bi-directional shear-type damper was designed and fabricated. The MRG damper, which utilizes the gel's high viscosity, can effectively alleviate the settlement problem inherent in magnetorheological fluid damper applications. Then, dynamic performance experiments were carried out to obtain the damping force with sinusoidal and random displacement excitations. Based on the test results, the forward model of the damper was established using a backpropagation neural network. A genetic algorithm was employed to optimize both the network structure parameters and the initial weight and bias values. Different forward models generated using different training datasets were validated and compared with the RBFNN model and Bouc-Wen model using different test datasets. The validation results indicate that the neural network-based forward model greatly outperforms the RBFNN model and Bouc-Wen model in terms of the estimation performance. The influence of the inputs at previous time has also been investigated. Finally, to generate the command current for controlling the damper, inverse neural network models with optimized structure parameters were established using different training datasets. Validation results with different test datasets indicate that, although the predicted current generated by the inverse models has many high-frequency components, it can still act as an effective damper controller, with the resulting damping force calculated using the predicted current coinciding well with the desired behavior. [ABSTRACT FROM AUTHOR]

High forming force is often needed when high-strength and low-plasticity materials are processed by fine blanking. Too high forming force increases the load of the die and greatly increases the risk of die failure. If the forming force is reduced, the material will fracture prematurely, which will lead to poor quality parts. Aiming at this problem, a new force variation load fine-blanking technology is proposed in this paper. During the loading process, the forming force does not remain constant but changes with the blanking stroke. A 2D finite element fine-blanking model was established for the TC4 material. The mechanism of force variation fine blanking is also revealed. This paper proposes a method to design the loading route of the forming force with variable load. This method combines finite element simulation with neural network and a multi-objective genetic algorithm. Finally, the application of variable load fine blanking production and the application of traditional fine blanking production parts are verified by the experimental method. The same results are obtained from both simulation and experiment. It is found that the variable load fine blanking process can greatly reduce the load of the die on the premise of ensuring the quality of fine-blanking parts. [ABSTRACT FROM AUTHOR]

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm. [ABSTRACT FROM AUTHOR]

The world population continues to increase which imposes rising demand in agriculture production. How to improve crop breeding to feed the growing population is a significant challenge. The traditional crop breeding is resource-intensive and time-consuming. Predictive modeling on crop yield can speed up the breeding process and make it resource-efficient. In this paper, a genetic algorithm (GA)-assisted deep learning solution method is proposed for the crop yield prediction. The proposed method consists of two phases, i.e., the global search phase and the local search phase. In the global search phase, GA is used to search for the best initial weights of the neural network. In the local search phase, random perturbation is added to avoid the local optimum and vanishing gradient problems. A case study of crop yield prediction is conducted to compare the proposed method and other gradient-based methods. The results show that the proposed method outperforms the gradient-based methods in terms of convergence speed and prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurately predicting the performance in various sports is crucial in comprehending training characteristics and enhancing physical education and training processes. The increasing significance of sports in China has enhanced the interest for developing prediction models to assess athletes' sports performance, accurately. These models will greatly contribute to improving sports performance and implementing scientific training methods. This paper proposes a prediction model for athletes' sports performance using Neural Networks as the underlying framework. This research aims to enhance sports performance and scientific training by developing a reliable and effective prediction model. First, the proposed model utilizes neural network algorithms, including error backpropagation and genetic algorithms, to train and optimize the prediction model. Second, it analyzes temporal patterns, extracts statistical features, and adapts to new data by ensuring reliability and effectiveness of the proposed model. Third, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models to evaluate the accuracy of the prediction model. In addition, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models. Finally, the results demonstrate that our proposed method achieves a maximum error of 36.12%, with the highest error rate in the BP network prediction being 6.76%. Furthermore, we compared our predicted results with multiple linear regression and other existing prediction methods, and our proposed method demonstrated superior accuracy with an overall prediction accuracy of 97.6%. Our suggested solution surpasses previous approaches with much higher accuracy, reduced latency, improved recall, and increased scalability. [ABSTRACT FROM AUTHOR]

The climate change crisis has resulted in the need to use sustainable methods in architectural design, including building form and orientation decisions that can save a significant amount of energy consumed by a building. Several previous studies have optimized building form and envelope for energy performance, but the isolated effect of varieties of possible architectural forms for a specific climate has not been fully investigated. This paper proposes four novel office building form generation methods (the polygon that varies between pentagon and decagon; the pixels that are complex cubic forms; the letters including H, L, U, T; cross and complex cubic forms; and the round family including circular and oval forms) and evaluates their annual thermal energy use intensity (EUI) for Cairo (hot climate). Results demonstrated the applicability of the proposed methods in enhancing the energy performance of the new forms in comparison to the base case. The results of the optimizations are compared together, and the four families are discussed in reference to their different architectural aspects and performance. Scatterplots are developed for the round family (highest performance) to test the impact of each dynamic parameter on EUI. The round family optimization process takes a noticeably high calculation time in comparison to other families. Therefore, an Artificial Neural Network (ANN) prediction model is developed for the round family after simulating 1726 iterations. Training of 1200 configurations is used to predict annual EUI for the remaining 526 iterations. The ANN predicted values are compared against the trained to determine the time saved and accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Concrete is a highly regarded construction material due to many advantages such as versatility, durability, fire resistance, and strength. Hence, having a prediction of the compressive strength of concrete (CSC) can be highly beneficial. The new generation of machine learning models has provided capable solutions to concrete-related simulations. This paper deals with predicting the CSC using a novel metaheuristic search scheme, namely the slime mold algorithm (SMA). The SMA retrofits an artificial neural network (ANN) to predict the CSC by incorporating the effect of mixture ingredients and curing age. The optimal configuration of the algorithm trained the ANN by taking the information of 824 specimens. The measured root mean square error (RMSE = 7.3831) and the Pearson correlation coefficient (R = 0.8937) indicated the excellent capability of the SMA in the assigned task. The same accuracy indicators (i.e., the RMSE of 8.1321 and R = 0.8902) revealed the competency of the developed SMA-ANN in predicting the CSC for 206 stranger specimens. In addition, the used method outperformed two benchmark algorithms of Henry gas solubility optimization (HGSO) and Harris hawks optimization (HHO) in both training and testing phases. The findings of this research pointed out the applicability of the SMA-ANN as a new substitute to burdensome laboratory tests for CSC estimation. Moreover, the provided solution is compared to some previous studies, and it is shown that the SMA-ANN enjoys higher accuracy. Therefore, an explicit mathematical formula is developed from this model to provide a convenient CSC predictive formula. [ABSTRACT FROM AUTHOR]

Copyright of Al-Khawarizmi Engineering Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: In this paper, we have presented an improved Sub‐Harmonic to Harmonic ratio (SHR) algorithm using the Genetic algorithm (GA) for pitch estimation of audio recordings of various ragas. Then we study the problem of Shadja identification using machine learning with the help of feature extraction and classification. The extraction of features from the raga signal is done with the help of statistical analysis of pitch estimation and these features are classified using a Neural Network (NN). Here, the training of NN is accomplished by Gray Wolf Optimization (GWO) algorithm for, determining the weights. Performance of the proposed Sa detection algorithm is analyzed by comparing the developed NN using Gray Wolf Optimization (GWO) models with the conventional models such as Levenberg Marquardt based NN (LM‐NN), Gradient Descent based (GD‐NN), Particle Swarm Optimization based NN (PSO‐NN) and FireFly based NN (FF‐NN) in terms of positive and negative performance measures. [ABSTRACT FROM AUTHOR]

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a great deal of casualties with airway obstruction in hospitals. The diagnosis of airway obstruction in an emergency mostly relies on the medical experience of physicians. In this paper, we propose the feature selection approach genetic algorithm-mean decrease impurity (GA-MDI) to effectively minimize the number of features as well as ensure the accuracy of prediction. Furthermore, we design a multi-modal neural network, called fully convolutional network with squeeze-and-excitation and multilayer perceptron (FCN-SE + MLP), to help physicians to predict the severity of airway obstruction. We validate the effectiveness of the proposed feature selection approach and multi-modal model on the emergency medical database from the Chinese General Hospital of the PLA. The experimental results show that GA-MDI outperforms the existing feature selection algorithms, while it is also validated that the model FCN-SE + MLP can effectively and accurately achieve the prediction of the severity of airway obstruction, which can assist clinicians in making treatment decisions for airway obstruction casualties. [ABSTRACT FROM AUTHOR]

Multi-modal haptic rendering is an important research direction to improve realism in haptic rendering. It can produce various mechanical stimuli that render multiple perceptions, such as hardness and roughness. This paper proposes a multi-modal haptic rendering method based on a genetic algorithm (GA), which generates force and vibration stimuli of haptic actuators according to the user's target hardness and roughness. The work utilizes a back propagation (BP) neural network to implement the perception model f that establishes the mapping (I = f (G)) from objective stimuli features G to perception intensities I. We use the perception model to design the fitness function of GA and set physically achievable constraints in fitness calculation. The perception model is transformed into the force/vibration control model by GA. Finally, we conducted realism evaluation experiments between real and virtual samples under single or multi-mode haptic rendering, where subjects scored 0-100. The average score was 70.86 for multi-modal haptic rendering compared with 57.81 for hardness rendering and 50.23 for roughness rendering, which proved that the multi-modal haptic rendering is more realistic than the single mode. Based on the work, our method can be applied to render objects in more perceptual dimensions, not only limited to hardness and roughness. It has significant implications for multi-modal haptic rendering. [ABSTRACT FROM AUTHOR]

In the selective laser melting (SLM) process, the experimental approach to determine the optimal process parameters is labor-intensive, material-intensive, and time-consuming. The use of simulation methods also requires more time support and higher hardware requirements. In this paper, a three-dimensional transient heat transfer model and a neural network optimization process parameter model in the process of preparing copper alloys by SLM are developed by combining finite element simulation methods with neural network prediction. The thermal behavior of the multitrack molten pools was investigated by ANSYS APDL, and the effects of different laser powers and scanning speeds on the temperature field and structure dimensions of the molten pools were discussed. The results show that the current single-track has a significant preheating effect on the unmachined single-track and a reheating effect on the machined single-track during the multitrack forming process. The laser power and scanning speed can be controlled to regulate the temperature, 3D size, and heat spread area of the molten pool to avoid over-melting and under-melting. The accuracy of the temperature field model was verified by single-track experiments. A neural network prediction model was constructed to predict the maximum temperature and size of the molten pool by optimizing the backpropagation neural network with a genetic algorithm, providing a methodological guide for the study of SLM process parameters. [ABSTRACT FROM AUTHOR]

Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms. [ABSTRACT FROM AUTHOR]

Ensuring the stable operation of the interconnected dynamics of power systems with time‐varying and nonlinear elements is a complex matter, since it involves continuous adjustments among every part for a suitable and efficient performance. Small disturbances in the load variation procedure also routinely occur. Consequently, the controller parameters must be adjusted to the variable conditions. The nonlinear autoregressive model with exogenous input (NARX) neural network (NN) has been used in many nonlinear dynamic systems. This paper explores the NARX combined with a multiobjective optimization by using genetic algorithms (GAs) to damp local and interarea oscillation modes. The NN model is trained by using a historical database determined by the GA for several load levels. Subsequently, the model can change the stabilizer parameters in real time after the learning phase. This study is used to tune a power system stabilizer (PSS) in a two‐area four‐machine system. The results of extensive simulations indicate a substantial improvement of the GA‐NARX‐PSS design while maintaining a reasonable fault resilience of the synchronous machine for several operating loads. [ABSTRACT FROM AUTHOR]

In this paper, a missing wind speed data temporal interpolation and extrapolation method in the wind energy industry was investigated. Given that traditional methods have previously ignored part of mixed uncertainty of wind speed, a concrete granular computing method is constructed and a new Measure-Correlate-Predict (MCP) method of wind speed data temporal interpolation and extrapolation considering all mixed uncertainties is proposed, based on granular computing theory by adopting the cloud model method, support vector regression method, artificial neural network, genetic algorithm, and fuzzy c-means clustering algorithm as tools. The importance of considering mixed wind speed uncertainty and the suitability of using granular computing method are illustrated, and wind speed mixed uncertainty analysis is implemented, then, recommended values and estimation tools for wind speed measurement uncertainty and combined uncertainty are provided. An interpolation case of two practical meteorological sites in central Southern China was used to implement and validate the method proposed in this paper. The following conclusions are reached: (a) by using the method proposed in this paper, mixed uncertainty of wind speed can be considered, comparing to other MCP methods used for purposes of comparison, a better estimation of the wind speed is provided, and most evaluation metrics employed in this analysis were superior to other methods, that is to say, the accuracy of the wind resource assessment improved, and the risks of wind farm construction were reduced; (b) granular computing method is suitable for the issue of wind speed data interpolation and extrapolation considering wind speed mixed uncertainty; (c) mixed uncertainty of wind speed can be divided into three levels, and recommended values of granularity are minimum interval of records, 0.3-0.8 m/s, and 1-3 m/s, respectively. [ABSTRACT FROM AUTHOR]

Current acoustic modeling methods face problems such as complex processes or inaccurate sound absorption coefficients, etc. Therefore, this paper studies the topic. Firstly, the material samples were prepared, and standing wave tube method experiments were conducted. Material acoustic data were obtained, while a model using improved genetic algorithm and neural network was subsequently proposed. Secondly, the acoustic data obtained from the experiment were analyzed; a neural network structure was designed; and the training, verification, and test data were all divided. In order to facilitate data processing, a symmetrical method was used to inversely normalize all the data. Thirdly, by the design of real coding scheme, fitness function, crossover, and mutation operators, an improved genetic algorithm was proposed to obtain the optimal solution, as the initial weight and threshold, which were then input into the neural network along with the training and verification data. Finally, the test data were input into the trained neural network in order to test the model. The test results and statistical analysis showed that compared with other algorithms, the proposed model has the lower root mean squared error (RMSE) value, the maximum coefficient of determination (R2) value, and shorter convergence time. [ABSTRACT FROM AUTHOR]

In recent years, as the main carrier of deep learning, Deep Neural Network has attracted the attention of experts in computer field. The application of deep neural network can effectively solve complex problems in life. However, in the process of training, the complex relationship caused by noisy data leads to an overfitting, which can impact the robustness of network model. Dropout, as one kind of random regularization techniques, carries a significant effect on restraining the overfitting of deep neural network. The traditional standard dropout can restrain the overfitting in a simple and quick way, but the accuracy is impacted because it cannot accurately locate the appropriate scale. This paper proposes a multi-scale fusion (MSF) dropout method on the basis of standard dropout. At first, several groups of network model with different combinations of dropout rates were trained; then the improved genetic algorithm was used to calculate the optimal scale of each network model; by reducing the corresponding network parameters through the optimal scale, the prediction sub-models were obtained; finally, these sub-models are fused into a final prediction model with certain weight. The present study applies MSF dropout to carry out the experiments in MNIST and CIFAR-10 standard datasets. The result of the study shows that the prediction accuracy is significantly improved compared with the other two kinds of dropout, which verifies the effectiveness of the multi-scale fusion method. [ABSTRACT FROM AUTHOR]

Since the keyboard is the most common method for text input on computers today, the design of the keyboard layout is very significant. Despite the fact that the QWERTY keyboard layout was designed more than 100 years ago, it is still the predominant layout in use today. There have been several attempts to design better layouts, both manually and automatically. In this paper we improve on previous works on automatic keyboard layout optimization, by using a deep neural network to assist in a genetic search algorithm, which enables the use of a sophisticated keyboard evaluation function that would otherwise take a prohibitive amount of time. We also show that a better choice of crossover routine greatly improves the genetic search. Finally, in order to test how users with different levels of experience adapt to new keyboard layouts, we conduct some layout adaptation experiments with 300 participants to examine how users adapt to new keyboard layouts. [ABSTRACT FROM AUTHOR]

This paper reveals two of the challenges faced by Romania and proposes a sustainable and simple solution for its wine industry. First, substantial areas with vineyards that may produce qualitative wine, and second, the very low digitalization rate of industrial sectors. More precisely, this work proposes a solution for digitalizing the fermentation process of white wine, allowing it to be adapted for other control techniques (i.e., knowledge-based systems, intelligent control). Our method consists of implementing a pre-trained multi-layer perceptron neural network, using genetic algorithms capable of predicting the concentration of alcohol and the amount of substrate at a certain point in time that starts from the initial configuration of the fermentation process. The purpose of predicting these process features is to obtain information about status variables so that the process can be automatically driven. The main advantage of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. After comprehensive simulations using experimental data obtained from previous fermentation processes, we concluded that a configuration that is close to the optimal one, for which the prediction accuracy is high, is a neural network (NN) having an input layer with neurons for temperature, time, initial substrate concentration, and the biomass concentration, a hidden layer with 10 neurons, and an output layer with 2 neurons representing the alcohol and substrate concentration, respectively. The best results were obtained with a pre-trained NN, using a genetic algorithm (GA) with a population of 50 individuals for 20 generations, a crossover probability of 0.9, and a probability of mutation of 0.5 that uniformly decreases depending on the generations, based on a beta coefficient of 0.3 and an elitist selection method. In the case of a data set with a larger number of variables, which also contains data regarding pH and CO2, the prediction accuracy is even higher, leading to the conclusion that a larger data set positively influences the performance of the neural network. Furthermore, methods based on artificial intelligence applications like neural networks, along with various heuristic optimization methods such as genetic algorithms, are essential if hardware sensors cannot be used, or if direct measurements cannot be made. [ABSTRACT FROM AUTHOR]

This paper presents laboratory simulator for wheel - legged (Wheg) robot running and application for collecting measurement data. Data is used as a basis for modelling and optimization of energy consumption of running Wheg. The laboratory setup includes instrumented measurement treadmill (IMT) and Wheg drive. The laboratory experimental setup also includes the sensors, drives and software application. Intelligent modelling and optimization of energy usage during Wheg's running is based on a combination of neural networks and genetic algorithms. Neural network has established a correlation between the parameters of running. Using genetic algorithm optimal parameters for running are found. Simulation of neuro-fuzzy control system for minimization of energy usage during running was developed as a function of the angle and Wheg running speed. [ABSTRACT FROM AUTHOR]

Rainfall-runoff modeling is very important for Water Resources Management because accurate and timely prediction can avoid accidents, such as the life risk, economic losses, etc. This paper proposed the novel hybrid optimization algorithm to combine Neural Network (NN) for rainfall-runoff modeling, namely HGASA-NN. Firstly, a novel and specialized hybrid optimization strategy by incorporating Simulated Annealing algorithm (SA) into Genetic Algorithm (GA) was used to train the initial connection weights and thresholds of NN. Secondly, the Back Propagation (BP) algorithm was adjusted the final weights and biases. Finally, a numerical example of daily rainfall-runoff data was used to elucidate the forecasting performance of the proposed HGASA-NN model. The HGASA-NN can make use of not only strong global searching ability of the GASA, but also strong local searching ability of the BP algorithm. The forecasting results indicate that the proposed model yields more accurate forecasting results than the BP-NN and pure GA training NN model. Therefore, the HGASA-NN model is a promising alternative for rainfall-runoff forecasting. [ABSTRACT FROM AUTHOR]

This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor k eff . In the present approach, a neural network is trained as a surrogate model to evaluate the k eff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister k eff and DH values. The k eff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and k eff quantities and (2) to minimize DH and k eff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases. [ABSTRACT FROM AUTHOR]

One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system, and the obtained results indicate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Image segmentation is the method of partitioning an image into a group of pixels that are homogenous in some manner. The homogeneity dependents on some attributes like intensity, color etc. Segmentation being a pre-processing step in image processing have been used in the number of applications like identification of objects to medical images, satellite images and much more. The taxonomy of an image segmentation methods collectively can be divided among two categories Traditional methods and Soft Computing (SC) methods. Unlike Traditional methods, SC methods have the ability to simulate human thinking and are flexible to work with their ownership function, have been predominantly applied to the task of image segmentation. SC techniques are tolerant of partial truth, imprecision, uncertainty, and approximations. Soft Computing approaches also having advantages of providing cost-effective, high performance and steadfast solutions. In this survey paper, our emphasis is on core SC approaches like Fuzzy logic, Artificial Neural Network, and Genetic Algorithm used for image segmentation. The contribution lies in the fact to present this paper to the researchers that explore state-of-the-art elaboration of almost all dimensions associated with the image segmentation. The idea is to encapsulate various aspects like emerging topics, methods, evaluation parameters, the problem associated with different type of images, databases, segmentation applications, and other resources so that, it could be advantageous for researchers to make effort in developing new methods for segmentation. The paper accomplishes with findings and concluding remarks. [ABSTRACT FROM AUTHOR]

The probabilistic self-organizing map (PRSOM) is an improved version of the Kohonen classical model (SOM) that appeared in the late 1990's. In the last years, the interest of probabilistic methods, especially in the fields of clustering and classification has increased, and the PRSOM has been successfully employed in many technological uses, such as: pattern recognition, speech recognition, data compression, medical diagnosis, etc. Mathematically, the PRSOM gives an estimation of the density probability function of a set of samples. And this estimation depends on the parameters given by the architecture of the model. Therefore, the main problem of this model, that we try to approach in this paper, is the architecture choice (the number of neurons and the initialization parameters). In summary, in the present paper, we describe a recent approach of PRSOM trying to find a solution to the problem below. For that, we propose an architecture optimization model that is a mixed integer nonlinear optimization model under linear constraints, resolved by the genetic algorithm. Then to prove the efficiency of the proposed model, we chose to apply it on a speech compression technique based on the determination of the optimal codebook, and finally, we give an implementation and an evaluation of the proposed method that we compare with the results of the classical model. [ABSTRACT FROM AUTHOR]

This paper presents a genetic optimization-based neural network algorithm to conduct in-depth analysis on intelligent evaluation of teaching quality in agricultural and forestry universities. In the genetic optimization stage, the adaptive variation probability is specifically utilized. When searching the global optimal solution, different variation probabilities are used in time according to obtained different fitness values, so as to reduce the population diversity and the number of global individuals. This can enhance the ability of the genetic algorithm to search the global optimal solution. The final evaluation index system is formed by modifying according to the reliability test and the suggestions made. Next, attribute simplification and calculation of index weights are carried out using rough set theory. And the BP neural network model is established with the assistance of historical data, which can be served as the main software constitution of an online platform. The attribute reduction by kernel and reduction calculation can greatly reduce the actual computation, which is beneficial for the relevant managers in universities to consider the diverse attributes and make corresponding adjustments for the decision-making scheme. [ABSTRACT FROM AUTHOR]

An overall state of the economy of a country is reflected through the stock price market. Where high levels of index indicate an improved and growing economic state while low levels mean crisis and downturn. The prediction of stock price index through particle swarm optimization (PSO), artificial neural network and genetic algorithm (GA) can provide a great help. This paper attempts to identify the factors that affect the stock exchange index through the prediction and modeling Iraqi Stock Exchange Index on GAs with PSO approach. The paper aims of predicting the Iraq Stock Exchange Index and presents a general view of items that influence it, through implementing hybrid GA_PSO and compare with other regression model as GA, PSO, ANN. The simulation results the MSE of ANN, GA, PSO, hybrid GA-PSO 0.29332, 0.087423, 0.092345, 0.0077798 in order, that mean the GAPSO achieve the best result. [ABSTRACT FROM AUTHOR]

Exponential growth in soft computing technologies has marked new milestones in powerful representation, modelling paradigms and optimization mechanisms for solving real time issues. With the great expansion of for computing area, a substantial amount of research efforts has been directed at the application of Soft Computing techniques in engineering. Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. The techniques of soft computing are nowadays being used successfully in many domestic, commercial, and industrial applications. This paper we gives an overview of the state of soft computing techniques and describes the various application to produce specification according need of current generation. [ABSTRACT FROM AUTHOR]

In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot's motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot's motion. Use of the basic cognitive elements language, a robot's environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot's environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems. [ABSTRACT FROM AUTHOR]

The accuracy of fused deposition modeling (FDM) prototype is affected by many factors, which process parameters are the most important factor. It is difficult to establish mathematical model accurately; the reason is that process parameters in FDM are coupled and the forming process is nonlinear. In order to define the effect of various process parameters on the forming precision and improve the precision of FDM printing, this paper established the precision prediction model based on process parameters by genetic algorithm optimizing the BP neural network's weight and threshold. Compared with BP prediction model, the result has shown that the precision of the prediction model is better than those of BP prediction model. [ABSTRACT FROM AUTHOR]

• The FEM is utilized to examine the driving force and friction affecting PIGs of varying shapes and sizes. • The motion velocity of the PIGs is calculated based on simulation data, and the factors affecting velocity are also analyzed. • The polynomial fitting, SVM and neural network methods are employed to predict PIG motion velocity. Pipeline transportation is a highly efficient transportation mode, and pipeline inspection gauge (PIG) is a widely used pipeline cleaning, detection and maintenance device. The PIG's ability to fulfill its intended purpose hinges heavily on its motion velocity. And it is vital to predict and control the velocity of PIG to ensure optimal functioning. This paper establishes several models with different rotary valve orifice numbers, orifice diameters, valve openings, and sealing disc compression amounts. The effects of the factors on PIG force state and velocity are analyzed by finite element method (FEM). With these simulation outcomes, the paper establishes velocity prediction models based on polynomial fitting, support vector machine (SVM) and neural network methods. In addition, by combining neural networks with genetic algorithms, the PIG size is optimized. The results demonstrate that compared with polynomial fitting and SVM, neural networks are more convenient and accurate and have a broader application prospect. [ABSTRACT FROM AUTHOR]

The mean volumetric diameter (MVD) and liquid water content (LWC) are two important parameters that affect aircraft icing, but they are difficult to be measured accurately in practice. If these two parameters can be accurately obtained in real time, they can provide some guidance for icing prediction and the establishment of aircraft airworthiness certification standards. In this paper, a prediction model of icing meteorological parameters based on the genetic‑algorithm‑optimized neural network is proposed. We use the ice thickness and icing rate of different combinations of measuring points, ambient temperature, flight speed and wing angle of attack as input parameters, and the icing meteorological parameters MVD and LWC as output parameters, and develop a prediction model of icing meteorological parameters optimized by the genetic algorithm, This model predicts the icing meteorological parameters of the numerical calculation test group data and the icing wind tunnel experiment data. The results show that the relative error of the prediction model for numerical calculation of icing meteorological parameters of the test group is within 10%, and the relative error of the experiment data is within 20%. This method is feasible. [ABSTRACT FROM AUTHOR]

Ransomware attacks continue to pose a significant threat to organizations and individuals worldwide. The attackers' ability to constantly evolve and adapt their tactics challenges traditional cybersecurity approaches to keep pace. Ransomware attacks targeting the healthcare industry accounted for 45% of all reported cyberattacks. The nature and scale of attacks and the increasing healthcare technology adoption will continue to pose ransomware attack risks. However, by collecting and analyzing large volumes of data and applying soft computing techniques, cybersecurity experts can improve their ability to detect and prevent ransomware attacks. As a result, soft computing offers options for detecting and preventing malware attacks. Using methods from the field of soft computing, such as fuzzy logic, neural networks, and genetic algorithms, makes it possible to conduct a thorough analysis of large data sets. These can yield insightful information that can help recognize and react to ransomware attacks. These techniques can also help to decrypt files that have been encrypted using ransomware. [ABSTRACT FROM AUTHOR]

Reliable geology prediction is of great importance in ensuring the stability and safety of tunnels and other underground engineering projects. This paper presents basic neural network and deep neural network models using a genetic algorithm (GA) to predict geological conditions for tunneling. Batch normalization and GA optimization approaches are employed in the deep neural network. A case study of the Jiudingshan Tunnel on the Chuxiong–Dali Highway in Yunnan, China, shows that the neural network method can predict geological conditions well, especially for rock types with voluminous data, for which predictive accuracy exceeds 90%. These results suggest that an appropriately trained neural network can reliably and accurately predict the geological conditions behind the tunnel face. The area under the curve (AUC) and confusion matrix evaluations show that the accuracy performance of the deep neural network exceeds that of the basic neural network. The feature importance of each drilling parameter was also analyzed; the results indicate that a neural network model for geology prediction can achieve predictive accuracy with few drilling parameters. The neural network geology prediction method provides reliable results for dynamic tunnel design. [ABSTRACT FROM AUTHOR]