Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
Background Adolescents’subjective well-being(SWB)is strongly linked to mental health,academic achievement,social relationships,and quality of life,and is a key predictor of life outcomes in adulthood.Mental health an...Background Adolescents’subjective well-being(SWB)is strongly linked to mental health,academic achievement,social relationships,and quality of life,and is a key predictor of life outcomes in adulthood.Mental health and addictive behaviors are the two main factors influencing SWB.This study aimed to identify key mental health and addictive behavior factors associated with adolescent SWB through machine learning models.Methods The data for this study comes from the Health Behaviour in School-aged Children(HBSC)survey 2017/18.The study data contains health data from 60,450 adolescents aged 10–16 years.The study used the XGBoost machine learning model to analyze the impact of mental health and addictive behaviors on adolescent SWB.Gain was used to analyze the significance of the variables.The direction of action of the variables and the interaction between the variables were analyzed using the SHapley Additive exPlanations(SHAP)method.Results The model in this study has an accuracy of 86.7%and an AUC value of 0.85,showing its good predictive performance.Six key variables were filtered through Gain analysis.Feeling low and health as the two most important factors affecting SWB,with these two variables contributing 51.38%and 19.65%,respectively.Friends and thinking body as major factors influencing SWB in mental health.Smoking lifetime and sweets as major factors influencing SWB in addictive behaviors.The interactions and characteristic dependencies between these variables were further analyzed.The results showed that feeling low,friends,and sweets had a positive effect on SWB,while health and smoking lifetime showed a negative effect.In addition,a moderate thinking body contributes to SWB,whereas being too fat and too thin are both associated with decreased levels of SWB.Conclusion Mental health and addictive behavioral factors such as feeling low,friends,sweets,and smoking lifetime were significant factors influencing SWB.This provides a scientific basis for the development of public health policies and interventions aimed at enhancing adolescent well-being.展开更多
This study constructs a theoretical and analytical framework for examining the interplay between the information environment and autonomous English learning by integrating Zimmerman’s self-regulated learning theory w...This study constructs a theoretical and analytical framework for examining the interplay between the information environment and autonomous English learning by integrating Zimmerman’s self-regulated learning theory with the Technology Acceptance Model(TAM).This integrated framework reveals the underlying connections between learners’self-regulatory processes and their acceptance of technological tools.Using vocational college students as the target popula-tion,this study employs both quantitative and qualitative methods to investi-gate the roles of the information environment,autonomous learning behaviors,learning psychology,and cross-cultural communication competence.The results demonstrate that the information environment significantly and positively influences both autonomous learning behaviors and learning psychology.Furthermore,cross-cultural communication competence is found to mediate this relationship.On the basis of these findings,several strategies are proposed:educational institutions should enhance intelligent resource databases and promote the develop-ment of AI-assisted learning systems;educators are encouraged to strengthen their competence in digital instructional design;and learners should be supported in improving their metacognitive strategies and ability to integrate technological tools effectively.展开更多
To address the issue of low recognition accuracy for eight types of behaviors including standing,walking,drinking,lying,eating,mounting,fighting and limping in complex multi-cow farm environments,a multi-target cow be...To address the issue of low recognition accuracy for eight types of behaviors including standing,walking,drinking,lying,eating,mounting,fighting and limping in complex multi-cow farm environments,a multi-target cow behavior recognition method based on an improved YOLOv11n algorithm was proposed.The detection capability for small targets in images was enhanced by incorporating a DASI module into the backbone network and a MDCR module into the neck network,based on YOLOv11.The improved YOLOv11 algorithm increased the mean average precision from the original 89.5%to 93%,with particularly notable improvements of 8.7%and 6.3%in the average precision for recognizing drinking and walking behaviors,respectively.These results fully demonstrate that the proposed method enhances the model s ability to recognize cow behaviors.展开更多
The hot deformation behavior of magnesium(Mg)alloys is significantly governed by the multi-physics coupling effects of temperature(T),strain rate(ε)and strain(ε),resulting in flow behavior that exhibits pronounced n...The hot deformation behavior of magnesium(Mg)alloys is significantly governed by the multi-physics coupling effects of temperature(T),strain rate(ε)and strain(ε),resulting in flow behavior that exhibits pronounced nonlinearity and multi-scale complexity.This study systematically investigates the hot deformation behavior of Mg-Y-Nd-(Sm)-Zr alloys.Sm alloying promotes recrystallization.The flow stress of Sm-containing alloys declines sharply towards a steady state after reaching its peak value.To overcome the limitations of the Arrhenius-type constitutive(AC)model in predicting complex nonlinear flow behavior,the AC and data hybrid informed neural network(ACINN)model is developed.This approach enhances the predictive accuracy and extends the applicability of the traditional AC model.The evolution of microstructure and recrystallization behavior under hot deformation conditions are investigated based on results from electron backscatter diffraction(EBSD)and transmission electron microscopy(TEM).The relationship between the power dissipation factor(η)and recrystallization behavior is further examined using K-means clustering analysis.The results demonstrate that dynamic recrystallization(DRX)behavior varies with theηvalue,comprising four distinct regimes:dynamic recovery(DRV),discontinuous dynamic recrystallization(DDRX)dominance,continuous dynamic recrystallization(CDRX)dominance and complete dynamic recrystallization.This analysis presents a new perspective for studying the hot deformation processes of Mg alloys.展开更多
Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the ...Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.展开更多
Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers.In this work,a novel twolayer reinforcement learning behavi...Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers.In this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error learning.Specifically,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission priority.Compared with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks.In the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control policy.Compared with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption.All error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.展开更多
The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that ha...The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that have shaped complex beha- viors and cognitive abilities. A strategy to investigate the change of behavior across generations is to assume that genetic con- straints have a negligible role in evolution （the phenotypic gambit） and focus on the phenotype as a proxy for genetic evolution. Empirical evidence and technologic advances in genomics question the choice of neglecting the genetic underlying the dynamics of behavioral evolution. I first discuss the relevance of genetic factors - e.g. genetic variability, genetic linkage, gene interactions - in shaping evolution, showing the importance of taking genetic factors into account when dealing with evolutionary dynamics. I subsequently describe the recent advancements in genetics and genomics that make the investigation of the ongoing evolutionary process of behavioral traits finally attainable. In particular, by applying genomic resequencing to experimental evolution - a me- thod called Evolve ＆ Resequence - it is possible to monitor at the same time phenotypic and genomie changes in populations exposed to controlled selective pressures. Experimental evolution of associative learning, a well-known trait that promptly re- sponds to selection, is a convenient model to illustrate this approach applied to behavior and cognition. Taking into account the recent achievements of the field, I discuss how to design and conduct an effective Evolve ＆ Resequence study on associative learning in Drosophila. By integrating phenotypic and genomic data in the investigation of evolutionary dynamics, new insights can be gained on longstanding questions such as the modularity of mind and its evolution .展开更多
Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing ...Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement characteristics.First,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight MPs.We used the softmax classifier to obtain the behavior-movement hierarchical probability model.Secondly,we specified the MPs using movement parameters that are static and dynamic.We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,respectively.These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden layers.Finally,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,respectively.We implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.展开更多
With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades,social media platforms(such as Facebook,Twitter,and Instagram)have consumed a large proportion o...With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades,social media platforms(such as Facebook,Twitter,and Instagram)have consumed a large proportion of time in our daily lives.People tend to stay alive on their social media with recent updates,as it has become the primary source of interactionwithin social circles.Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities.Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression,anxiety,suicide commitment,and mental disorder,particularly in the young adults who have excessively spent time on socialmedia which necessitates a thorough psychological analysis of all these platforms.This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates.In this paper,we start with depression detection in the first instance and then expand on analyzing six other psychotic issues(e.g.,depression,anxiety,psychopathic deviate,hypochondria,unrealistic,and hypomania)commonly found in adults due to extreme use of social media networks.To classify the psychotic issues with the user’s mental state,we have employed different Machine Learning(ML)classifiers i.e.,Random Forest(RF),Support Vector Machine(SVM),Naïve Bayes(NB),and K-Nearest Neighbor(KNN).The used ML models are trained and tested by using different combinations of features selection techniques.To observe themost suitable classifiers for psychotic issue classification,a cost-benefit function(sometimes termed as‘Suitability’)has been used which combines the accuracy of the model with its execution time.The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.展开更多
This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities ...This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities at such a network.The represented sequences are thenfitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users.Thus,the model can recognize frequencies of regular behavior to profile the user manner in the network.The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular behavior.The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network.Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including users.Therefore,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow.In contrast,the irregular patterns can trigger an alert for a potential cyber-attack.The framework has been fully described where the evaluation metrics have also been introduced.The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1.The paper has been concluded with pro-viding the potential directions for future improvements.展开更多
Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its forma...Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.展开更多
Stress is mental tension caused by difficult situations,often experienced by hospital workers and IT professionals who work long hours.It is essential to detect the stress in shift workers to improve their health.Howe...Stress is mental tension caused by difficult situations,often experienced by hospital workers and IT professionals who work long hours.It is essential to detect the stress in shift workers to improve their health.However,existing models measure stress with physiological signals such as PPG,EDA,and blink data,which could not identify the stress level accurately.Additionally,the works face challenges with limited data,inefficient spatial relationships,security issues with health data,and long-range temporal dependencies.In this paper,we have developed a federated learning-based stress detection system for IT and hospital workers,integrating physiological and behavioral indicators for accurate stress detection.Furthermore,the study introduces a hybrid deep learning classifier called ResTFTNet to capture spatial features and complex temporal relationships to detect stress effectively.The proposed work involves two localmodels and a globalmodel,to develop a federated learning framework to enhance stress detection.Thedatasets are pre-processed using the bandpass filter noise removal technique and normalization.The Recursive Feature Elimination feature selection method improves themodel performance.FL aggregates thesemodels using FedAvg to ensure privacy by keeping data localized.After evaluating ResTFTNet with existing models,including Convolution Neural Network,Long-Short-Term-Memory,and Support VectorMachine,the proposed model shows exceptional performance with an accuracy of 99.3%.This work provides an accurate and privacy-preserving method for detecting stress in hospital and IT staff.展开更多
With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness...With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.展开更多
In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agen...In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agents lead to complex collective behaviors.This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior,overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments.Based on this foundation,a novel Graph Convolutional Networks(GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.Simulation experiments demonstrate that,compared to traditional single-agent algorithms,the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior.Additionally,a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed,providing a robust tool for exploring dynamic behavioral changes under various conditions.展开更多
Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH_(4))_(2),this study proposes,for the first time,a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH_(4))_(2).Nota...Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH_(4))_(2),this study proposes,for the first time,a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH_(4))_(2).Notably,numerous data points are collected from temperatureprogrammed,isothermal,and cyclic dehydrogenation behaviors,a neural network model is proposed by using multi-head attention mechanisms,which exhibits the highest predictive performance compared to traditional machine learning models.The study also ranks different variables influencing dehydrogenation processes,employing interpretable analysis to identify critical variable thresholds,offering guidance for the experimental parameter design.The model can also be adapted to scenarios involving co-doping of hydrides and catalysts in Mg(BH_(4))_(2) system and proved high accuracy and scalability in predicting dehydrogenation curves under diverse conditions.Employing the model,performance predictions for a series of undeveloped Mg(BH_(4))_(2) co-doping systems can be made,and superior dehydrogenation catalytic effects of fluorinated graphite(FGi)are uncovered.Real-world experimental validation of the optimal Mg(BH_(4))_(2)-LiBH_(4)-FGi system confirms consistency with model predictions,and performance enhancement attributes to experimental parameter optimization.Further characterizations provide mechanistic insights into the synergistic interactions of FGi and LiBH_(4).This work paves the way for advancing utilization of machine learning in the high-capacity hydrogen storage field.展开更多
Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was esta...Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was established by passive smoking method. The rats offspring were divided into the FGR group and the control group, then randomly divided into the trained and untrained group, respectively. Morris water maze test was proceeded on postnatal month（PM2/4） as a behavior training method, then the learning-memory of rats was detected through dark-avoidance and step-down tests. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas were detected by immunohistochemical method. Results： In the dark-avoidance and step-down tests, the performance record of rats with FGR was worse than that of control rats, and the behavior-trained rats was better than the untrained rats, when the FGR model and training factors were analyzed singly. The model factor and training factor had significant interaction（P 〈 0.05）. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas of rats with FGR reduced. In contrast, the expressions of GluR1 and NR2B subunits in CA1 area of behavior-trained rats increased, when the FGR model and training factors were analyzed singly. Conclusion： These findings suggested that the effect of behavior training on the expressions of NR2B and GluR1 subunits in CA1 area should be the mechanistic basis for the training-induced improvement in learning-memory abilities.展开更多
Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications.At the same time,machine learning(ML)and data min...Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications.At the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification processes.With this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT environment.The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,etc.which are then connected to the cloud server for further processing.In addition,the modelling and extraction of behaviour take place.Moreover,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal behaviour.Finally,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE model.In order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.展开更多
In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave rada...In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.展开更多
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
基金funded by the National Social Science Fund of China(GrantNo.24CTJ019).
文摘Background Adolescents’subjective well-being(SWB)is strongly linked to mental health,academic achievement,social relationships,and quality of life,and is a key predictor of life outcomes in adulthood.Mental health and addictive behaviors are the two main factors influencing SWB.This study aimed to identify key mental health and addictive behavior factors associated with adolescent SWB through machine learning models.Methods The data for this study comes from the Health Behaviour in School-aged Children(HBSC)survey 2017/18.The study data contains health data from 60,450 adolescents aged 10–16 years.The study used the XGBoost machine learning model to analyze the impact of mental health and addictive behaviors on adolescent SWB.Gain was used to analyze the significance of the variables.The direction of action of the variables and the interaction between the variables were analyzed using the SHapley Additive exPlanations(SHAP)method.Results The model in this study has an accuracy of 86.7%and an AUC value of 0.85,showing its good predictive performance.Six key variables were filtered through Gain analysis.Feeling low and health as the two most important factors affecting SWB,with these two variables contributing 51.38%and 19.65%,respectively.Friends and thinking body as major factors influencing SWB in mental health.Smoking lifetime and sweets as major factors influencing SWB in addictive behaviors.The interactions and characteristic dependencies between these variables were further analyzed.The results showed that feeling low,friends,and sweets had a positive effect on SWB,while health and smoking lifetime showed a negative effect.In addition,a moderate thinking body contributes to SWB,whereas being too fat and too thin are both associated with decreased levels of SWB.Conclusion Mental health and addictive behavioral factors such as feeling low,friends,sweets,and smoking lifetime were significant factors influencing SWB.This provides a scientific basis for the development of public health policies and interventions aimed at enhancing adolescent well-being.
文摘This study constructs a theoretical and analytical framework for examining the interplay between the information environment and autonomous English learning by integrating Zimmerman’s self-regulated learning theory with the Technology Acceptance Model(TAM).This integrated framework reveals the underlying connections between learners’self-regulatory processes and their acceptance of technological tools.Using vocational college students as the target popula-tion,this study employs both quantitative and qualitative methods to investi-gate the roles of the information environment,autonomous learning behaviors,learning psychology,and cross-cultural communication competence.The results demonstrate that the information environment significantly and positively influences both autonomous learning behaviors and learning psychology.Furthermore,cross-cultural communication competence is found to mediate this relationship.On the basis of these findings,several strategies are proposed:educational institutions should enhance intelligent resource databases and promote the develop-ment of AI-assisted learning systems;educators are encouraged to strengthen their competence in digital instructional design;and learners should be supported in improving their metacognitive strategies and ability to integrate technological tools effectively.
基金Supported by The Three Vertical Basic Cultivation Project of Heilongjiang Bayi Agricultural University(ZRCPY202314).
文摘To address the issue of low recognition accuracy for eight types of behaviors including standing,walking,drinking,lying,eating,mounting,fighting and limping in complex multi-cow farm environments,a multi-target cow behavior recognition method based on an improved YOLOv11n algorithm was proposed.The detection capability for small targets in images was enhanced by incorporating a DASI module into the backbone network and a MDCR module into the neck network,based on YOLOv11.The improved YOLOv11 algorithm increased the mean average precision from the original 89.5%to 93%,with particularly notable improvements of 8.7%and 6.3%in the average precision for recognizing drinking and walking behaviors,respectively.These results fully demonstrate that the proposed method enhances the model s ability to recognize cow behaviors.
基金supported by the National Natural Science Foundation of China(nos.52201119,52371108)Frontier Exploration Project of Longmen Laboratory,China(no.LMQYTSKT014)The Joint Fund of Henan Science and Technology R&D Plan of China(no.242103810056).
文摘The hot deformation behavior of magnesium(Mg)alloys is significantly governed by the multi-physics coupling effects of temperature(T),strain rate(ε)and strain(ε),resulting in flow behavior that exhibits pronounced nonlinearity and multi-scale complexity.This study systematically investigates the hot deformation behavior of Mg-Y-Nd-(Sm)-Zr alloys.Sm alloying promotes recrystallization.The flow stress of Sm-containing alloys declines sharply towards a steady state after reaching its peak value.To overcome the limitations of the Arrhenius-type constitutive(AC)model in predicting complex nonlinear flow behavior,the AC and data hybrid informed neural network(ACINN)model is developed.This approach enhances the predictive accuracy and extends the applicability of the traditional AC model.The evolution of microstructure and recrystallization behavior under hot deformation conditions are investigated based on results from electron backscatter diffraction(EBSD)and transmission electron microscopy(TEM).The relationship between the power dissipation factor(η)and recrystallization behavior is further examined using K-means clustering analysis.The results demonstrate that dynamic recrystallization(DRX)behavior varies with theηvalue,comprising four distinct regimes:dynamic recovery(DRV),discontinuous dynamic recrystallization(DDRX)dominance,continuous dynamic recrystallization(CDRX)dominance and complete dynamic recrystallization.This analysis presents a new perspective for studying the hot deformation processes of Mg alloys.
基金Under the auspices of the National Natural Science Foundation of China(No.41571144)。
文摘Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.
基金the National Natural Science Foundation of China(61603094)。
文摘Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers.In this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error learning.Specifically,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission priority.Compared with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks.In the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control policy.Compared with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption.All error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.
文摘The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that have shaped complex beha- viors and cognitive abilities. A strategy to investigate the change of behavior across generations is to assume that genetic con- straints have a negligible role in evolution （the phenotypic gambit） and focus on the phenotype as a proxy for genetic evolution. Empirical evidence and technologic advances in genomics question the choice of neglecting the genetic underlying the dynamics of behavioral evolution. I first discuss the relevance of genetic factors - e.g. genetic variability, genetic linkage, gene interactions - in shaping evolution, showing the importance of taking genetic factors into account when dealing with evolutionary dynamics. I subsequently describe the recent advancements in genetics and genomics that make the investigation of the ongoing evolutionary process of behavioral traits finally attainable. In particular, by applying genomic resequencing to experimental evolution - a me- thod called Evolve ＆ Resequence - it is possible to monitor at the same time phenotypic and genomie changes in populations exposed to controlled selective pressures. Experimental evolution of associative learning, a well-known trait that promptly re- sponds to selection, is a convenient model to illustrate this approach applied to behavior and cognition. Taking into account the recent achievements of the field, I discuss how to design and conduct an effective Evolve ＆ Resequence study on associative learning in Drosophila. By integrating phenotypic and genomic data in the investigation of evolutionary dynamics, new insights can be gained on longstanding questions such as the modularity of mind and its evolution .
基金supported in part by the National Natural Science Foundation of China(62022014)in part by the National Key Research and Development Program of China(2017YFE0117000)。
文摘Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement characteristics.First,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight MPs.We used the softmax classifier to obtain the behavior-movement hierarchical probability model.Secondly,we specified the MPs using movement parameters that are static and dynamic.We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,respectively.These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden layers.Finally,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,respectively.We implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.
文摘With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades,social media platforms(such as Facebook,Twitter,and Instagram)have consumed a large proportion of time in our daily lives.People tend to stay alive on their social media with recent updates,as it has become the primary source of interactionwithin social circles.Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities.Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression,anxiety,suicide commitment,and mental disorder,particularly in the young adults who have excessively spent time on socialmedia which necessitates a thorough psychological analysis of all these platforms.This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates.In this paper,we start with depression detection in the first instance and then expand on analyzing six other psychotic issues(e.g.,depression,anxiety,psychopathic deviate,hypochondria,unrealistic,and hypomania)commonly found in adults due to extreme use of social media networks.To classify the psychotic issues with the user’s mental state,we have employed different Machine Learning(ML)classifiers i.e.,Random Forest(RF),Support Vector Machine(SVM),Naïve Bayes(NB),and K-Nearest Neighbor(KNN).The used ML models are trained and tested by using different combinations of features selection techniques.To observe themost suitable classifiers for psychotic issue classification,a cost-benefit function(sometimes termed as‘Suitability’)has been used which combines the accuracy of the model with its execution time.The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.
基金supported by the fund received from Al Baha University,8/1440.
文摘This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities at such a network.The represented sequences are thenfitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users.Thus,the model can recognize frequencies of regular behavior to profile the user manner in the network.The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular behavior.The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network.Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including users.Therefore,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow.In contrast,the irregular patterns can trigger an alert for a potential cyber-attack.The framework has been fully described where the evaluation metrics have also been introduced.The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1.The paper has been concluded with pro-viding the potential directions for future improvements.
基金This work was partially supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61375121)the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301)+1 种基金the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Jiangsu Province(BE2020633)the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.
文摘Stress is mental tension caused by difficult situations,often experienced by hospital workers and IT professionals who work long hours.It is essential to detect the stress in shift workers to improve their health.However,existing models measure stress with physiological signals such as PPG,EDA,and blink data,which could not identify the stress level accurately.Additionally,the works face challenges with limited data,inefficient spatial relationships,security issues with health data,and long-range temporal dependencies.In this paper,we have developed a federated learning-based stress detection system for IT and hospital workers,integrating physiological and behavioral indicators for accurate stress detection.Furthermore,the study introduces a hybrid deep learning classifier called ResTFTNet to capture spatial features and complex temporal relationships to detect stress effectively.The proposed work involves two localmodels and a globalmodel,to develop a federated learning framework to enhance stress detection.Thedatasets are pre-processed using the bandpass filter noise removal technique and normalization.The Recursive Feature Elimination feature selection method improves themodel performance.FL aggregates thesemodels using FedAvg to ensure privacy by keeping data localized.After evaluating ResTFTNet with existing models,including Convolution Neural Network,Long-Short-Term-Memory,and Support VectorMachine,the proposed model shows exceptional performance with an accuracy of 99.3%.This work provides an accurate and privacy-preserving method for detecting stress in hospital and IT staff.
基金supported by the Natural Science Foundation Project of Fujian Province,China(Grant No.2023J011439 and No.2019J01859).
文摘With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.
基金supported by the National Natural Science Foundation of China under Grant 62273351 and Grant 62303020.
文摘In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agents lead to complex collective behaviors.This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior,overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments.Based on this foundation,a novel Graph Convolutional Networks(GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.Simulation experiments demonstrate that,compared to traditional single-agent algorithms,the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior.Additionally,a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed,providing a robust tool for exploring dynamic behavioral changes under various conditions.
基金the National Natural Science Foundation of China(No.52171223 and U20A20237).
文摘Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH_(4))_(2),this study proposes,for the first time,a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH_(4))_(2).Notably,numerous data points are collected from temperatureprogrammed,isothermal,and cyclic dehydrogenation behaviors,a neural network model is proposed by using multi-head attention mechanisms,which exhibits the highest predictive performance compared to traditional machine learning models.The study also ranks different variables influencing dehydrogenation processes,employing interpretable analysis to identify critical variable thresholds,offering guidance for the experimental parameter design.The model can also be adapted to scenarios involving co-doping of hydrides and catalysts in Mg(BH_(4))_(2) system and proved high accuracy and scalability in predicting dehydrogenation curves under diverse conditions.Employing the model,performance predictions for a series of undeveloped Mg(BH_(4))_(2) co-doping systems can be made,and superior dehydrogenation catalytic effects of fluorinated graphite(FGi)are uncovered.Real-world experimental validation of the optimal Mg(BH_(4))_(2)-LiBH_(4)-FGi system confirms consistency with model predictions,and performance enhancement attributes to experimental parameter optimization.Further characterizations provide mechanistic insights into the synergistic interactions of FGi and LiBH_(4).This work paves the way for advancing utilization of machine learning in the high-capacity hydrogen storage field.
基金the National Natural Science Foundationof China(30471826)
文摘Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was established by passive smoking method. The rats offspring were divided into the FGR group and the control group, then randomly divided into the trained and untrained group, respectively. Morris water maze test was proceeded on postnatal month（PM2/4） as a behavior training method, then the learning-memory of rats was detected through dark-avoidance and step-down tests. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas were detected by immunohistochemical method. Results： In the dark-avoidance and step-down tests, the performance record of rats with FGR was worse than that of control rats, and the behavior-trained rats was better than the untrained rats, when the FGR model and training factors were analyzed singly. The model factor and training factor had significant interaction（P 〈 0.05）. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas of rats with FGR reduced. In contrast, the expressions of GluR1 and NR2B subunits in CA1 area of behavior-trained rats increased, when the FGR model and training factors were analyzed singly. Conclusion： These findings suggested that the effect of behavior training on the expressions of NR2B and GluR1 subunits in CA1 area should be the mechanistic basis for the training-induced improvement in learning-memory abilities.
文摘Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications.At the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification processes.With this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT environment.The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,etc.which are then connected to the cloud server for further processing.In addition,the modelling and extraction of behaviour take place.Moreover,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal behaviour.Finally,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE model.In order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.
基金Project supported by the National Natural Science Foundation of China (Grant No. 52072108)the Natural Science Foundation of Anhui Province, China (Grant No. 2208085ME148)the Open Fund for State Key Laboratory of Cognitive Intelligence, China (Grant No. W2022JSKF0504)。
文摘In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.
