With the developing and rapidly changing technology,marketing strategies have necessarily changed in order to meet the demands and needs of consumers.The inability of businesses to keep up with this changing system pu...With the developing and rapidly changing technology,marketing strategies have necessarily changed in order to meet the demands and needs of consumers.The inability of businesses to keep up with this changing system pushes them out of the process.In daily life,where consumption never ends,marketing strategies are also consumed very quickly.Although the name changes according to age,the main goal is always more profitability.Digitalization of sales and marketing has made shopping in virtual environments widespread.Most customer services are performed by chatbots.It is seen that these studies are also carried out in the field of health services.From Siri to augmented reality applications,they are in our lives.These intelligent systems date back to the 1970s.So,where are the artificial intelligence and intelligent robots that have taken their place in almost every sector,from health to defense,which has been the favorite of recent years?Although the answer to this question has only recently begun to be researched,it seems that it will be one of the most important issues in the near future.In this study,which seeks a definitive answer to this question,the place and future of artificial intelligence in marketing strategies are discussed.In addition to contributing to the academic world,the study is thought to be useful in artificial intelligence studies.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallsca...The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallscale turbine blades using the Artificial Bee Colony(ABC)Algorithm based on the stochastic method to optimize both mass and cost(objective functions).The study used computational fluid dynamics(CFD)and structural analysis to consider the fluid-structure interaction.The optimization algorithm defined several variables:structural constraints,the type of composite material,and the number of composite layers to form a mathematical model.The numerical modeling was performed using the Ansys Fluent software and its Fluid-Structure Interaction(FSI)module.The ANSYS Composite PrePost(ACP)advanced composite modeling method was utilized in the structural design of composite materials.This study showed that the structurally optimized small-scale turbine blades provided a sustainable solution with improved efficiency compared to traditional designs.Furthermore,using CFD,structural analysis,and material characterization techniques first considered in this study highlights the importance of considering structural behavior when optimizing turbine blade designs.展开更多
The rapid development of artificial intelligence(AI)technology in today’s society has brought varying degrees of influence to various industries and fields,and this transformation is also playing a profound role in t...The rapid development of artificial intelligence(AI)technology in today’s society has brought varying degrees of influence to various industries and fields,and this transformation is also playing a profound role in the field of education.This paper intends to analyze the social background,cultural background,and policy background of introducing AI technology into the primary school music education system,deeply explore the important value and practical path of applying AI technology in the field of primary school music education,and provide corresponding optimization strategies for teaching,with a view to providing some theoretical references for promoting the in-depth integration of AI and primary school music education.展开更多
With the acceleration of the iterative process of artificial intelligence technology,small and medium-sized enterprises have encountered innovative solutions in the field of financial decision-making,significantly enh...With the acceleration of the iterative process of artificial intelligence technology,small and medium-sized enterprises have encountered innovative solutions in the field of financial decision-making,significantly enhancing their dynamic response capabilities to complex and changing market environments.This article summarizes the specific application scenarios and development trends of artificial intelligence in the financial management practice of small and medium-sized enterprises,and explores the path and positive impact of artificial intelligence on the optimization of financial decision-making in small and medium-sized enterprises from the current application status of artificial intelligence in financial decision-making,the unique challenges of financial management in small and medium-sized enterprises and the impact of AI technology on it,the application of artificial intelligence in financial decision-making optimization,and prospects.展开更多
The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the f...The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information,abilities,and critical thinking needed to solve energy-related problems sustainably.This research proposes AI-powered personalized learning,innovative real-time integration of diverse data,and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts.The sheep flock-optimized innovative recurrent neural network(SFO-IRNN)will recommend relevant topics and resources based on students’performance.Renewable energy teaching data from assessmethments are combined with real-time IoT-based renewable energy data.This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning.The data was preprocessed by handling missing values and min-max scaling.The data features were extracted using Fourier Transform(FT).Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy,F1-score,recall,and precision on different subsets of student data,which improves its robustness and prevents overfitting.The findings showed that the proposed method is significantly better,which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies.In addition,integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning.The research shows that AI provides context-aware guidance on sustainable energy infrastructure,enhancing interactive and personalized learning.展开更多
Air pollution,a critical environmental issue,necessitates urgent action.It originates from both human activities,like industrial emissions and vehicle pollution,and natural events such as sandstorms,leading to increas...Air pollution,a critical environmental issue,necessitates urgent action.It originates from both human activities,like industrial emissions and vehicle pollution,and natural events such as sandstorms,leading to increased atmospheric pollutants such as sulfur dioxide(SO_(2)),nitrogen dioxide(NO_(2)),ammonia ion(NH_(4)^(+)),black carbon,ozone,and fine particulate matter(PM_(2.5)).Leveraging China's extensive air quality monitoring data,artificial intelligence(AI)was used in this study to enhance air quality prediction and management.The study aims to utilize the vast air monitoring data more effectively by developing advanced air quality assessment methods and AI models.An AI-based method presented in this study was applied to train extensive air quality data,enabling an intelligent air quality index(AQI)that swiftly and accurately reflects air quality status,to assess impacts on sensitive groups,and to predict future trends.This smart prediction and optimization(SPO)approach not only utilizes existing monitoring network data efficiently but also offers precise future air quality forecasts,providing valuable strategies for pollution prevention and air quality improvement.Data on various pollutants were collected from four regions in China between August 2021 and July 2022,using diverse modeling techniques and machine learning methodologies.The models achieved a high accuracy level of around 99%,indicating the significant portion of air quality that falls into the unhealthy category,especially impacting sensitive groups and reflecting the adverse atmospheric conditions in the studied regions.展开更多
This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa...This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.展开更多
In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel referenc...In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
In this study,artificial neural networks(ANNs)were implemented to determine design parameters for an impressed current cathodic protection(ICCP)prototype.An ASTM A36 steel plate was tested in 3.5%NaCl solution,seawate...In this study,artificial neural networks(ANNs)were implemented to determine design parameters for an impressed current cathodic protection(ICCP)prototype.An ASTM A36 steel plate was tested in 3.5%NaCl solution,seawater,and NS4 using electrochemical impedance spectroscopy(EIS)to monitor the evolution of the substrate surface,which affects the current required to reach the protection potential(Eprot).Experimental data were collected as training datasets and analyzed using statistical methods,including box plots and correlation matrices.Subsequently,ANNs were applied to predict the current demand at different exposure times,enabling the estimation of electrochemical parameters(limiting voltage values)that can be used to optimize a self-regulating ICCP system.The obtained electrochemical parameters were then used,through Particle Swarm Optimization(PSO),to fine-tune an ANN-based proportional-integral-derivative(PID)controller for the ICCP system.展开更多
Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(AN...Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic alg...In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.展开更多
The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is...The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.展开更多
In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algor...In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.展开更多
An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed...An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
文摘With the developing and rapidly changing technology,marketing strategies have necessarily changed in order to meet the demands and needs of consumers.The inability of businesses to keep up with this changing system pushes them out of the process.In daily life,where consumption never ends,marketing strategies are also consumed very quickly.Although the name changes according to age,the main goal is always more profitability.Digitalization of sales and marketing has made shopping in virtual environments widespread.Most customer services are performed by chatbots.It is seen that these studies are also carried out in the field of health services.From Siri to augmented reality applications,they are in our lives.These intelligent systems date back to the 1970s.So,where are the artificial intelligence and intelligent robots that have taken their place in almost every sector,from health to defense,which has been the favorite of recent years?Although the answer to this question has only recently begun to be researched,it seems that it will be one of the most important issues in the near future.In this study,which seeks a definitive answer to this question,the place and future of artificial intelligence in marketing strategies are discussed.In addition to contributing to the academic world,the study is thought to be useful in artificial intelligence studies.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金Scientific Research Projects Unit of Erciyes University under the contract numbers:FDK-2019-8616 and FDK-2025-14774(https://bap.erciyes.edu.tr/,accessed on 12 October 2025)The Scientific and Technological Research Council of Turkey(TUB˙ITAK)for the Doctoral Scholarship for Priority Areas 2211/C for Ramazan OZKAN(https://tubitak.gov.tr,accessed on 12 October 2025).
文摘The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallscale turbine blades using the Artificial Bee Colony(ABC)Algorithm based on the stochastic method to optimize both mass and cost(objective functions).The study used computational fluid dynamics(CFD)and structural analysis to consider the fluid-structure interaction.The optimization algorithm defined several variables:structural constraints,the type of composite material,and the number of composite layers to form a mathematical model.The numerical modeling was performed using the Ansys Fluent software and its Fluid-Structure Interaction(FSI)module.The ANSYS Composite PrePost(ACP)advanced composite modeling method was utilized in the structural design of composite materials.This study showed that the structurally optimized small-scale turbine blades provided a sustainable solution with improved efficiency compared to traditional designs.Furthermore,using CFD,structural analysis,and material characterization techniques first considered in this study highlights the importance of considering structural behavior when optimizing turbine blade designs.
文摘The rapid development of artificial intelligence(AI)technology in today’s society has brought varying degrees of influence to various industries and fields,and this transformation is also playing a profound role in the field of education.This paper intends to analyze the social background,cultural background,and policy background of introducing AI technology into the primary school music education system,deeply explore the important value and practical path of applying AI technology in the field of primary school music education,and provide corresponding optimization strategies for teaching,with a view to providing some theoretical references for promoting the in-depth integration of AI and primary school music education.
文摘With the acceleration of the iterative process of artificial intelligence technology,small and medium-sized enterprises have encountered innovative solutions in the field of financial decision-making,significantly enhancing their dynamic response capabilities to complex and changing market environments.This article summarizes the specific application scenarios and development trends of artificial intelligence in the financial management practice of small and medium-sized enterprises,and explores the path and positive impact of artificial intelligence on the optimization of financial decision-making in small and medium-sized enterprises from the current application status of artificial intelligence in financial decision-making,the unique challenges of financial management in small and medium-sized enterprises and the impact of AI technology on it,the application of artificial intelligence in financial decision-making optimization,and prospects.
文摘The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information,abilities,and critical thinking needed to solve energy-related problems sustainably.This research proposes AI-powered personalized learning,innovative real-time integration of diverse data,and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts.The sheep flock-optimized innovative recurrent neural network(SFO-IRNN)will recommend relevant topics and resources based on students’performance.Renewable energy teaching data from assessmethments are combined with real-time IoT-based renewable energy data.This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning.The data was preprocessed by handling missing values and min-max scaling.The data features were extracted using Fourier Transform(FT).Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy,F1-score,recall,and precision on different subsets of student data,which improves its robustness and prevents overfitting.The findings showed that the proposed method is significantly better,which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies.In addition,integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning.The research shows that AI provides context-aware guidance on sustainable energy infrastructure,enhancing interactive and personalized learning.
基金supported by the National Key R&D Program of China(No.2023YFC3707201)the National Natural Science Foundation of China(No.52320105003)+2 种基金the Informatization Plan of Chinese Academy of Sciences(No.CASWX2023PY-0103)CAS-ANSO Co-funding Research Project(No.CAS-ANSO-CF-2024)the Fundamental Research Funds for the Central Universities(No.E3ET1803)。
文摘Air pollution,a critical environmental issue,necessitates urgent action.It originates from both human activities,like industrial emissions and vehicle pollution,and natural events such as sandstorms,leading to increased atmospheric pollutants such as sulfur dioxide(SO_(2)),nitrogen dioxide(NO_(2)),ammonia ion(NH_(4)^(+)),black carbon,ozone,and fine particulate matter(PM_(2.5)).Leveraging China's extensive air quality monitoring data,artificial intelligence(AI)was used in this study to enhance air quality prediction and management.The study aims to utilize the vast air monitoring data more effectively by developing advanced air quality assessment methods and AI models.An AI-based method presented in this study was applied to train extensive air quality data,enabling an intelligent air quality index(AQI)that swiftly and accurately reflects air quality status,to assess impacts on sensitive groups,and to predict future trends.This smart prediction and optimization(SPO)approach not only utilizes existing monitoring network data efficiently but also offers precise future air quality forecasts,providing valuable strategies for pollution prevention and air quality improvement.Data on various pollutants were collected from four regions in China between August 2021 and July 2022,using diverse modeling techniques and machine learning methodologies.The models achieved a high accuracy level of around 99%,indicating the significant portion of air quality that falls into the unhealthy category,especially impacting sensitive groups and reflecting the adverse atmospheric conditions in the studied regions.
基金This paper is supported by the Nature Science Foundation of Heilongjiang Province.
文摘This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.
基金The National Natural Science Foundation of China(No.51306082,51476027)
文摘In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
文摘In this study,artificial neural networks(ANNs)were implemented to determine design parameters for an impressed current cathodic protection(ICCP)prototype.An ASTM A36 steel plate was tested in 3.5%NaCl solution,seawater,and NS4 using electrochemical impedance spectroscopy(EIS)to monitor the evolution of the substrate surface,which affects the current required to reach the protection potential(Eprot).Experimental data were collected as training datasets and analyzed using statistical methods,including box plots and correlation matrices.Subsequently,ANNs were applied to predict the current demand at different exposure times,enabling the estimation of electrochemical parameters(limiting voltage values)that can be used to optimize a self-regulating ICCP system.The obtained electrochemical parameters were then used,through Particle Swarm Optimization(PSO),to fine-tune an ANN-based proportional-integral-derivative(PID)controller for the ICCP system.
文摘Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
文摘In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.
基金supported by the Aviation Science Foundation of China(20105196016)the Postdoctoral Science Foundation of China(2012M521807)
文摘The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.
基金Project(51305467)supported by the National Natural Science Foundation of ChinaProject(12JJ4050)supported by the Natural Science Foundation of Hunan Province,China
文摘In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.
基金supported by the National Natural Science Foundation of China (Nos.61973158, 61673209)the Aeronautical Science Foundation (No.2016ZA52009)
文摘An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
