## What is/are Optimized Artificial?

Optimized Artificial - Design/methodology/approach This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN).^{[1]}First, a genetic algorithm (GA)-optimized artificial immune system (AIS) technique is presented, which uses signal processing to extract multi-perspective system features and selects low-dimensional features for intelligent fault detection.

^{[2]}In addition, depending on each binder mass percentage, the mechanical properties of the produced concretes were evaluated by developing an optimized artificial neural network (ANN) combined with the genetic algorithm (GA) and compared with the available experimental test database.

^{[3]}In this work, we propose an optimized artificial bee colony algorithm (OABC) to make it more suitable for the problem.

^{[4]}To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine , Namibia and Phoenix Mine, USA.

^{[5]}The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR.

^{[6]}For this purpose, an optimized artificial neural network, combined with a novel metaheuristic shuffled frog leaping optimization algorithm, was employed and compared with a well-known genetic algorithm and multiple linear regression.

^{[7]}The influence of ITCZ on IR by performing a metric relevance analysis on weights of optimized Artificial Neural Networks was computed.

^{[8]}With the upliftment of big data technology, designing optimized artificial intelligence algorithms is a must need.

^{[9]}The AI approach presented in this paper uses the power of optimized artificial neural networks (ANNs) to model the behavior of the non-linear, multi-input/output drilling system.

^{[10]}The paper proposes a hybrid model of Optimized Artificial Neural Network-Artificial Bee Colony (ABC) that could be employed to improve the prediction by using machinelearning approach to obtain the precise diagnosis of heart.

^{[11]}An optimized artificial neural network (ANN) combined with a firefly algorithm was developed to estimate these indexes over the self-healing process.

^{[12]}In this paper, an optimized artificial neural network using genetic algorithm are developed to predict the mechanical behaviour for aluminium silicon- zirconium oxide composites.

^{[13]}Reviews are analyzed using optimized Artificial Neural Network (ANN) which shows notified improvement than traditional ANN on real-time extracted data of reviews.

^{[14]}Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario.

^{[15]}Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries.

^{[16]}Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model.

^{[17]}A cyber-physical system over field-programmable gate array with optimized artificial intelligence algorithm is beneficial for society.

^{[18]}The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO.

^{[19]}Aiming at the problems of low diagnostic accuracy and large errors in traditional fault diagnosis methods, this paper designs an aero-engine gas path fault diagnosis method based on the Optimized Artificial Bee Colony-Back Propagation Neural Network (OPABC-BP) algorithm.

^{[20]}In order to determine the resilient modulus of compacted subgrade soils quickly and accurately, an optimized artificial neural network (ANN) approach based on the multi-population genetic algorithm (MPGA) was proposed in this study.

^{[21]}In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment.

^{[22]}Time of the optimized artificial ageing temperature (125 °C for 45 Hrs) was obtained using hardness testing and DSC study.

^{[23]}Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs.

^{[24]}In order to predict RQS, three optimized artificial neural network (ANN) models based on genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competition algorithm (ICA) were developed.

^{[25]}In this paper, an optimized artificial neural network (OANN) was developed to enhance the existing artificial neural network (ANN) model by updating the initial weights in the network using a Genetic Algorithm (GA).

^{[26]}This study introduces an empirical equation developed based on the optimized artificial neural networks (ANNs) for estimation of the rate of penetration (ROP) in real-time while horizontally drilling natural gas-bearing sandstone reservoirs based on the surface measurable drilling parameters of the mud injection rate, drillstring rotation speed (DSR), standpipe pressure, torque, and weight on bit (WOB) in combination with ROPc, which is a new parameter developed in this study based on regression analysis.

^{[27]}Three AI models which are ordinary artificial neural network (ANN), particle swarm optimized artificial neural network (PSO-ANN) and Dragonfly optimized artificial neural network (DA-ANN) are proposed.

^{[28]}This paper proposes a two-layer path-planning method, where an optimized artificial potential field (APF) method and an improved dynamic window approach (DWA) are used at the global and local layer, respectively.

^{[29]}An optimized artificial neural network (ANN) model based on the artificial bee colony (ABC) algorithm is proposed in this paper to acquire an informational model from the available literature database of experimental test measurements on TSACWs.

^{[30]}Sedirea japonica is becoming endangered, and even extinct, due to habitat destruction and illegal collection, and the development of an optimized artificial propagation system is necessary for its conservation and reintroduction.

^{[31]}This paper presents a methodology of predicting soil plasticity index by CPT using optimized artificial neural networks (SNNs) for reducing laboratory work that represents a significant saving of both time and money.

^{[32]}Besides, by using the available experimental test database, an optimized artificial neural network (ANN) combined with the particle swarm optimization (PSO) was developed to estimate the residual CS of modified rubberized concrete after immersion one year in MgSO4 and H2SO4 solutions.

^{[33]}The root mean square error of the optimized artificial neural network model to validation set is only 0.

^{[34]}In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield.

^{[35]}An optimized artificial neural network of 7 inputs, including slope, slope curvature, flow accumulation, NDVI, geological units, soil type, and rainfall data along with eight, sixteen and one neurons for the first, second and output hidden layers, respectively, were designed and developed.

^{[36]}In this paper, we present a more accurate prediction methodology for short-term energy consumption utilizing optimized artificial neural networks (ANNs) for Hormozgan.

^{[37]}With the optimized artificial cilia rotation, superior performance was achieved with a maximum degradation rate of 81.

^{[38]}Support Vector Machines and Genetically-Optimized Artificial Neural Networks, pronounced machine learning algorithms, are fine-tuned to combine the two set of features in one automated image classification system.

^{[39]}The objective of this work is to evaluate the feasibility of an optimized artificial neural network and particle swarm optimization for estimating the energetic performance of a building integrated photovoltaic/thermal system.

^{[40]}In this study, the effect of Al2O3/water nanofluid as the working media on thermal performance of WHP investigated and compared with pure water by designing an optimized Artificial Neural Network (ANN).

^{[41]}To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN).

^{[42]}Then the flood risk grade was evaluated based on the optimized artificial neural network (ANN).

^{[43]}This study intended to use optimized artificial neural network (ANN) for the design of pure cohesive slopes (by means of considering sufficient safety factors (SF) of stability).

^{[44]}In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm optimized with ANN (GA-ANN) and particle swarm optimization optimized with ANN (PSO-ANN), differential evolution algorithm (DEA), adaptive neuro-fuzzy inference system (ANFIS), general regression neural network (GRNN), and feedforward neural network (FFNN) were optimized and applied to predict the ultimate bearing capacity ( F ult ) of shallow footing on two-layered soil condition.

^{[45]}An optimized artificial neural network model with three layers and 14 neurons in hidden layer could successfully predict BB9 adsorption (R2 = 0.

^{[46]}In this work, the real-time drilling data was applied to predict the formation type and lithology while drilling that formation using a genetic algorithm and Taguchi design of experiment optimized artificial neural network.

^{[47]}In this study, an optimized artificial neural network (ANN) trained with three different optimization algorithms namely; particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA) was proposed for predicting the electric conductivity (EC).

^{[48]}After that, the new systems were implemented using optimized artificial intelligence techniques.

^{[49]}Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service.

^{[50]}

## particle swarm optimization

In order to predict RQS, three optimized artificial neural network (ANN) models based on genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competition algorithm (ICA) were developed.^{[1]}The objective of this work is to evaluate the feasibility of an optimized artificial neural network and particle swarm optimization for estimating the energetic performance of a building integrated photovoltaic/thermal system.

^{[2]}In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm optimized with ANN (GA-ANN) and particle swarm optimization optimized with ANN (PSO-ANN), differential evolution algorithm (DEA), adaptive neuro-fuzzy inference system (ANFIS), general regression neural network (GRNN), and feedforward neural network (FFNN) were optimized and applied to predict the ultimate bearing capacity ( F ult ) of shallow footing on two-layered soil condition.

^{[3]}In this study, an optimized artificial neural network (ANN) trained with three different optimization algorithms namely; particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA) was proposed for predicting the electric conductivity (EC).

^{[4]}

## experimental test database

In addition, depending on each binder mass percentage, the mechanical properties of the produced concretes were evaluated by developing an optimized artificial neural network (ANN) combined with the genetic algorithm (GA) and compared with the available experimental test database.^{[1]}Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs.

^{[2]}Besides, by using the available experimental test database, an optimized artificial neural network (ANN) combined with the particle swarm optimization (PSO) was developed to estimate the residual CS of modified rubberized concrete after immersion one year in MgSO4 and H2SO4 solutions.

^{[3]}

## An Optimized Artificial

An optimized artificial neural network (ANN) combined with a firefly algorithm was developed to estimate these indexes over the self-healing process.^{[1]}An optimized artificial neural network (ANN) model based on the artificial bee colony (ABC) algorithm is proposed in this paper to acquire an informational model from the available literature database of experimental test measurements on TSACWs.

^{[2]}An optimized artificial neural network of 7 inputs, including slope, slope curvature, flow accumulation, NDVI, geological units, soil type, and rainfall data along with eight, sixteen and one neurons for the first, second and output hidden layers, respectively, were designed and developed.

^{[3]}An optimized artificial neural network model with three layers and 14 neurons in hidden layer could successfully predict BB9 adsorption (R2 = 0.

^{[4]}

## Utilizing Optimized Artificial

In this paper, we present a more accurate prediction methodology for short-term energy consumption utilizing optimized artificial neural networks (ANNs) for Hormozgan.^{[1]}To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN).

^{[2]}

## Novel Optimized Artificial

In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield.^{[1]}To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space.

^{[2]}

## optimized artificial neural

Design/methodology/approach This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN).^{[1]}In addition, depending on each binder mass percentage, the mechanical properties of the produced concretes were evaluated by developing an optimized artificial neural network (ANN) combined with the genetic algorithm (GA) and compared with the available experimental test database.

^{[2]}To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine , Namibia and Phoenix Mine, USA.

^{[3]}The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR.

^{[4]}For this purpose, an optimized artificial neural network, combined with a novel metaheuristic shuffled frog leaping optimization algorithm, was employed and compared with a well-known genetic algorithm and multiple linear regression.

^{[5]}The influence of ITCZ on IR by performing a metric relevance analysis on weights of optimized Artificial Neural Networks was computed.

^{[6]}The AI approach presented in this paper uses the power of optimized artificial neural networks (ANNs) to model the behavior of the non-linear, multi-input/output drilling system.

^{[7]}The paper proposes a hybrid model of Optimized Artificial Neural Network-Artificial Bee Colony (ABC) that could be employed to improve the prediction by using machinelearning approach to obtain the precise diagnosis of heart.

^{[8]}An optimized artificial neural network (ANN) combined with a firefly algorithm was developed to estimate these indexes over the self-healing process.

^{[9]}In this paper, an optimized artificial neural network using genetic algorithm are developed to predict the mechanical behaviour for aluminium silicon- zirconium oxide composites.

^{[10]}Reviews are analyzed using optimized Artificial Neural Network (ANN) which shows notified improvement than traditional ANN on real-time extracted data of reviews.

^{[11]}Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario.

^{[12]}Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries.

^{[13]}Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model.

^{[14]}The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO.

^{[15]}In order to determine the resilient modulus of compacted subgrade soils quickly and accurately, an optimized artificial neural network (ANN) approach based on the multi-population genetic algorithm (MPGA) was proposed in this study.

^{[16]}Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs.

^{[17]}In order to predict RQS, three optimized artificial neural network (ANN) models based on genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competition algorithm (ICA) were developed.

^{[18]}In this paper, an optimized artificial neural network (OANN) was developed to enhance the existing artificial neural network (ANN) model by updating the initial weights in the network using a Genetic Algorithm (GA).

^{[19]}This study introduces an empirical equation developed based on the optimized artificial neural networks (ANNs) for estimation of the rate of penetration (ROP) in real-time while horizontally drilling natural gas-bearing sandstone reservoirs based on the surface measurable drilling parameters of the mud injection rate, drillstring rotation speed (DSR), standpipe pressure, torque, and weight on bit (WOB) in combination with ROPc, which is a new parameter developed in this study based on regression analysis.

^{[20]}Three AI models which are ordinary artificial neural network (ANN), particle swarm optimized artificial neural network (PSO-ANN) and Dragonfly optimized artificial neural network (DA-ANN) are proposed.

^{[21]}An optimized artificial neural network (ANN) model based on the artificial bee colony (ABC) algorithm is proposed in this paper to acquire an informational model from the available literature database of experimental test measurements on TSACWs.

^{[22]}This paper presents a methodology of predicting soil plasticity index by CPT using optimized artificial neural networks (SNNs) for reducing laboratory work that represents a significant saving of both time and money.

^{[23]}Besides, by using the available experimental test database, an optimized artificial neural network (ANN) combined with the particle swarm optimization (PSO) was developed to estimate the residual CS of modified rubberized concrete after immersion one year in MgSO4 and H2SO4 solutions.

^{[24]}The root mean square error of the optimized artificial neural network model to validation set is only 0.

^{[25]}An optimized artificial neural network of 7 inputs, including slope, slope curvature, flow accumulation, NDVI, geological units, soil type, and rainfall data along with eight, sixteen and one neurons for the first, second and output hidden layers, respectively, were designed and developed.

^{[26]}In this paper, we present a more accurate prediction methodology for short-term energy consumption utilizing optimized artificial neural networks (ANNs) for Hormozgan.

^{[27]}Support Vector Machines and Genetically-Optimized Artificial Neural Networks, pronounced machine learning algorithms, are fine-tuned to combine the two set of features in one automated image classification system.

^{[28]}The objective of this work is to evaluate the feasibility of an optimized artificial neural network and particle swarm optimization for estimating the energetic performance of a building integrated photovoltaic/thermal system.

^{[29]}In this study, the effect of Al2O3/water nanofluid as the working media on thermal performance of WHP investigated and compared with pure water by designing an optimized Artificial Neural Network (ANN).

^{[30]}To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN).

^{[31]}Then the flood risk grade was evaluated based on the optimized artificial neural network (ANN).

^{[32]}This study intended to use optimized artificial neural network (ANN) for the design of pure cohesive slopes (by means of considering sufficient safety factors (SF) of stability).

^{[33]}In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm optimized with ANN (GA-ANN) and particle swarm optimization optimized with ANN (PSO-ANN), differential evolution algorithm (DEA), adaptive neuro-fuzzy inference system (ANFIS), general regression neural network (GRNN), and feedforward neural network (FFNN) were optimized and applied to predict the ultimate bearing capacity ( F ult ) of shallow footing on two-layered soil condition.

^{[34]}An optimized artificial neural network model with three layers and 14 neurons in hidden layer could successfully predict BB9 adsorption (R2 = 0.

^{[35]}In this work, the real-time drilling data was applied to predict the formation type and lithology while drilling that formation using a genetic algorithm and Taguchi design of experiment optimized artificial neural network.

^{[36]}In this study, an optimized artificial neural network (ANN) trained with three different optimization algorithms namely; particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA) was proposed for predicting the electric conductivity (EC).

^{[37]}Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service.

^{[38]}In this study, we optimized artificial neural network (ANN) with imperialist competition algorithm (ICA) for the problem of slope stability design charts.

^{[39]}From the 441 observations, optimized artificial neural network accurately predicted oocyte number for 71% of women within ±1 eggs SD.

^{[40]}In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT).

^{[41]}The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper.

^{[42]}Method: In this research, we input Continuous Glucose Monitoring (CGM) data to Optimized Artificial Neural Networks (OANN) in order to predict BGL of Type 1 Diabetes (T1D).

^{[43]}Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model.

^{[44]}The new optimized Artificial Neural Network technique (ANN) is used.

^{[45]}

## optimized artificial intelligence

With the upliftment of big data technology, designing optimized artificial intelligence algorithms is a must need.^{[1]}A cyber-physical system over field-programmable gate array with optimized artificial intelligence algorithm is beneficial for society.

^{[2]}In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield.

^{[3]}After that, the new systems were implemented using optimized artificial intelligence techniques.

^{[4]}

## optimized artificial bee

In this work, we propose an optimized artificial bee colony algorithm (OABC) to make it more suitable for the problem.^{[1]}Aiming at the problems of low diagnostic accuracy and large errors in traditional fault diagnosis methods, this paper designs an aero-engine gas path fault diagnosis method based on the Optimized Artificial Bee Colony-Back Propagation Neural Network (OPABC-BP) algorithm.

^{[2]}In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment.

^{[3]}

## optimized artificial potential

This paper proposes a two-layer path-planning method, where an optimized artificial potential field (APF) method and an improved dynamic window approach (DWA) are used at the global and local layer, respectively.^{[1]}To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space.

^{[2]}