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Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning
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作者 K.Akilandeswari Nithya Rekha Sivakumar +2 位作者 Hend Khalid Alkahtani Shakila Basheer Sara Abdelwahab Ghorashi 《Computers, Materials & Continua》 SCIE EI 2024年第1期1189-1205,共17页
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor... In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods. 展开更多
关键词 Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector
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Project-based Language Learning: an Activity Theory Analysis in SOE Language Learning
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作者 陈苡晴 《海外英语》 2016年第10期215-217,220,共4页
This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activ... This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activity Theory. Moreover,Data has been collected and categorized based on the components of complex human activity: the subject, object, tools(signs,symbols, and language), the community in which the activity take place, division of labor, and rules. The findings theoretically support the outcome of project-based language learning which align with the object of the activity. 展开更多
关键词 activity THEORY PROJECT-BASED learning SOE LANGUAGE learning
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An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features
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作者 Wenhao Jing Zihao Jiao +2 位作者 Mengmeng Song Ya Liu Liejin Guo 《Green Energy & Environment》 SCIE EI CAS CSCD 2024年第10期1489-1496,共8页
Machine learning combined with density functional theory(DFT)enables rapid exploration of catalyst descriptors space such as adsorption energy,facilitating rapid and effective catalyst screening.However,there is still... Machine learning combined with density functional theory(DFT)enables rapid exploration of catalyst descriptors space such as adsorption energy,facilitating rapid and effective catalyst screening.However,there is still a lack of models for predicting adsorption energies on oxides,due to the complexity of elemental species and the ambiguous coordination environment.This work proposes an active learning workflow(LeNN)founded on local electronic transfer features(e)and the principle of coordinate rotation invariance.By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures,LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments.As a result,it enables the prediction of^(*)H adsorption energy on binary oxide surfaces with a mean absolute error(MAE)below 0.18 eV.Moreover,we incorporate local coverage(θ_(l))and leverage neutral network ensemble to establish an active learning workflow,attaining a prediction MAE below 0.2 eV for 5419 multi-^(*)H adsorption structures.These findings validate the universality and capability of the proposed features in predicting^(*)H adsorption energy on binary oxide surfaces. 展开更多
关键词 Machine learning Adsorption energy Binary oxide Electron transfer active learning
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Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs
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作者 Bohan Chen Kevin Miller +1 位作者 Andrea L.Bertozzi Jon Schwenk 《Communications on Applied Mathematics and Computation》 EI 2024年第2期1013-1033,共21页
Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi... Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels. 展开更多
关键词 Image segmentation Graph learning Batch active learning Hyperspectral image
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Model Change Active Learning in Graph-Based Semi-supervised Learning
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作者 Kevin S.Miller Andrea L.Bertozzi 《Communications on Applied Mathematics and Computation》 EI 2024年第2期1270-1298,共29页
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to bes... Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art. 展开更多
关键词 active learning Graph-based methods Semi-supervised learning(SSL) Graph Laplacian
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Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System
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作者 Ameni Ellouze Nesrine Kadri +1 位作者 Alaa Alaerjan Mohamed Ksantini 《Computers, Materials & Continua》 SCIE EI 2024年第4期351-372,共22页
Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell t... Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women. 展开更多
关键词 Human physical activities smartphone sensors deep learning distributed monitoring recommendation system uncertainty HEALTHY CALORIES
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A Modified Iterative Learning Control Approach for the Active Suppression of Rotor Vibration Induced by Coupled Unbalance and Misalignment
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作者 Yifan Bao Jianfei Yao +1 位作者 Fabrizio Scarpa Yan Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第1期242-253,共12页
This paper proposes a modified iterative learning control(MILC)periodical feedback-feedforward algorithm to reduce the vibration of a rotor caused by coupled unbalance and parallel misalignment.The control of the vibr... This paper proposes a modified iterative learning control(MILC)periodical feedback-feedforward algorithm to reduce the vibration of a rotor caused by coupled unbalance and parallel misalignment.The control of the vibration of the rotor is provided by an active magnetic actuator(AMA).The iterative gain of the MILC algorithm here presented has a self-adjustment based on the magnitude of the vibration.Notch filters are adopted to extract the synchronous(1×Ω)and twice rotational frequency(2×Ω)components of the rotor vibration.Both the notch frequency of the filter and the size of feedforward storage used during the experiment have a real-time adaptation to the rotational speed.The method proposed in this work can provide effective suppression of the vibration of the rotor in case of sudden changes or fluctuations of the rotor speed.Simulations and experiments using the MILC algorithm proposed here are carried out and give evidence to the feasibility and robustness of the technique proposed. 展开更多
关键词 Rotor vibration suppression Modified iterative learning control UNBALANCE Parallel misalignment active magnetic actuator
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Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences 被引量:1
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作者 Umair Muneer Butt Hadiqa Aman Ullah +3 位作者 Sukumar Letchmunan Iqra Tariq Fadratul Hafinaz Hassan Tieng Wei Koh 《Computers, Materials & Continua》 SCIE EI 2023年第3期5017-5033,共17页
Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments... Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets. 展开更多
关键词 Human activity recognition deep learning transfer learning neural network ensemble learning SPATIO-TEMPORAL
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Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services 被引量:2
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作者 E.Dhiravidachelvi M.Suresh Kumar +4 位作者 L.D.Vijay Anand D.Pritima Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期961-977,共17页
Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,... Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures. 展开更多
关键词 Artificial intelligence human activity recognition deep learning deep belief network hyperparameter tuning healthcare
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Classroom Activity and Intrinsic Motivationin EFL Teaching and Learning
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作者 郑玉全 《俪人(教师)》 2015年第18期156-157,共2页
The question of how to motivate language learners has been a neglected area in L2 motivation research, and even thefew available analyses lack an adequate research base. This article presents the results of an empiric... The question of how to motivate language learners has been a neglected area in L2 motivation research, and even thefew available analyses lack an adequate research base. This article presents the results of an empirical survey aimed at initiatinginterviews and conducting follow-up questionnaire to obtain classroom data on motivational classroom teaching activities and theactual effect of these strategies. This current study provides new insights into English classroom teaching with further researchinvestigation and teaching implication to promote students' integrative motivation through classroom teaching activities. 展开更多
关键词 INTRINSIC MOTIVATION class activity CLASSROOM teaching and learning
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Learning Activity Sequencing in Personalized Education System
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作者 ZHU Fan CAO Jiaheng 《Wuhan University Journal of Natural Sciences》 CAS 2008年第4期461-465,共5页
Personalized education provides an open learning environment which enriches the advanced technologies to establish a paradigm shift, active and dynamic teaching and learning patterns. E-learning has a various establis... Personalized education provides an open learning environment which enriches the advanced technologies to establish a paradigm shift, active and dynamic teaching and learning patterns. E-learning has a various established approaches to the creation and sequencing of content-based, single learner, and self-paced learning objects. However, there is little understanding of how to create sequences of learning activities which involve groups of learners interacting within a structured set of collaborative environments. In this paper, we present an approach for learning activity sequencing based on ontology and activity graph in personalized education system. Modeling and management of learning activity and learner are depicted, and an algorithm is proposed to realize learning activity sequencing and learner ontology dynamically updating. 展开更多
关键词 learning activity sequencing ONTOLOGY personalized education
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Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model
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作者 Bareen Shamsaldeen Tahir Zainab Salih Ageed +1 位作者 Sheren Sadiq Hasan Subhi R.M.Zeebaree 《Computers, Materials & Continua》 SCIE EI 2023年第5期4009-4024,共16页
Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount ... Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older people.The extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing.The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning(DL).The study develops aModifiedWild Horse Optimization withDLAided Symmetric Human Activity Recognition(MWHODL-SHAR)model.The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,etc.In the presented MWHODL-SHAR technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTM)model is applied for activity recognition.The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm.The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset.An extensive comparison study revealed the betterment of theMWHODL-SHAR technique over other recent approaches. 展开更多
关键词 Human activity recognition SYMMETRY deep learning machine learning pattern recognition time series classification
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IoT-Deep Learning Based Activity Recommendation System
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作者 Sharmilee Kannan R.U.Anitha +1 位作者 M.Divayapushpalakshmi K.S.Kalaivani 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期2001-2016,共16页
The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,... The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical settings.The Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health care.Healthcare Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have increased.The existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports.Patients’health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies.This research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it accordingly.The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures.Through a healthcare DSS,this framework is clever enough to detect and analyze a patient’s data.Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care.The data reported in this study demonstrate the notion.This device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals. 展开更多
关键词 Deep learning IOT healthcare system activity recommender system body sensors
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Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People
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作者 Mesfer Al Duhayyim 《Computers, Materials & Continua》 SCIE EI 2023年第6期6287-6303,共17页
Elderly or disabled people can be supported by a human activity recognition(HAR)system that monitors their activity intervenes and pat-terns in case of changes in their behaviors or critical events have occurred.An au... Elderly or disabled people can be supported by a human activity recognition(HAR)system that monitors their activity intervenes and pat-terns in case of changes in their behaviors or critical events have occurred.An automated HAR could assist these persons to have a more indepen-dent life.Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system.With the fast development of neural networks,computing,and machine learning algorithms,HAR system based on wearable sensors has gained popularity in several areas,such as medical services,smart homes,improving human communication with computers,security systems,healthcare for the elderly,mechanization in industry,robot monitoring system,monitoring athlete train-ing,and rehabilitation systems.In this view,this study develops an improved pelican optimization with deep transfer learning enabled HAR(IPODTL-HAR)system for disabled persons.The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living.The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data.Besides,EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer.Finally,the IPO with deep belief network(DBN)model is utilized for the recognition and classification of human activities.The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively.The experimental validation of the IPODTL-HAR method is tested using benchmark dataset.Extensive comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures. 展开更多
关键词 Human activity recognition disabled person artificial intelligence computer vision deep learning
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Research on the Relationship Between Learning Motivation and Neural Activity in the Learning Process of Instructional Video:A NIRS Study
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作者 CHEN Meifen QING Cuihua +1 位作者 SHEN Ruizhu WU Bo 《Psychology Research》 2021年第4期148-160,共13页
As the intrinsic driving force to promote learner’s learning,learning motivation is one of the key factors that affect learning engagement and efficiency.In terms of optimizing instructional videos and strengthening ... As the intrinsic driving force to promote learner’s learning,learning motivation is one of the key factors that affect learning engagement and efficiency.In terms of optimizing instructional videos and strengthening learning effects,it is particularly important to understand the cognitive neural mechanism and influencing factors of the changes of learning motivation.By using the near-infrared spectrometer technology,the paper has collected the state of neural activity while learners were learning different instructional videos,and has analyzed the relationship between the learning motivation of instructional videos and the state of neural activity in the learning process from the angle of cognitive neuroscience.It is found that both the intrinsic and extrinsic learning motivation of instructional videos will affect the state of neural activity in the learning process;the learning process will also affect the intensity of learning motivation,while the preparation of fine instructional videos will also cause the transfer of learning motivation. 展开更多
关键词 learning motivation learning process state of neural activity NIRS
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Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition
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作者 Mohammed Alonazi Mrim M.Alnfiai 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期3135-3150,共16页
Human-Computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for com... Human-Computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for computer systems.HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science.Usability is an aspect of HCI dedicated to guar-anteeing that human–computer communication is,amongst other things,efficient,effective,and sustaining for the user.Simultaneously,Human activity recognition(HAR)aim is to identify actions from a sequence of observations on the activities of subjects and the environmental conditions.The vision-based HAR study is the basis of several applications involving health care,HCI,and video surveillance.This article develops a Fire Hawk Optimizer with Deep Learning Enabled Activ-ity Recognition(FHODL-AR)on HCI driven usability.In the presented FHODL-AR technique,the input images are investigated for the identification of different human activities.For feature extraction,a modified SqueezeNet model is intro-duced by the inclusion of few bypass connections to the SqueezeNet among Fire modules.Besides,the FHO algorithm is utilized as a hyperparameter optimization algorithm,which in turn boosts the classification performance.To detect and cate-gorize different kinds of activities,probabilistic neural network(PNN)classifier is applied.The experimental validation of the FHODL-AR technique is tested using benchmark datasets,and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches. 展开更多
关键词 activity recognition fire hawks optimizer deep learning USABILITY human computer interaction
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Recognition for Frontal Emergency Stops Dangerous Activity Using Nano IoT Sensor and Transfer Learning
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作者 Wei Sun Zhanhe Du 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期1181-1195,共15页
Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous acti... Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activ-ity.Therefore,a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor(NIoTS)and transfer learning is proposed.First,the NIoTS is installed in the athlete’s leg muscles to collect activity signals.Second,the noise component in the activity signal is removed using the de-noising method based on mathematical morphology.Finally,the depth feature of the activity signal is extracted through the deep transfer learning model,and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared.If the European distance is small,it can be judged as the frontal emergency stops dangerous activity,and the frontal emergency stops dangerous activity recognition is realized.The results show that the average time delay of activity signal acquisition of the algorithm is low,the signal-to-noise ratio of the action signal is high,and the activity signal mean square error is low.The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5.The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s,it can accurately and quickly recognize the frontal emergency stops the dangerous activity. 展开更多
关键词 Frontal emergency stops RECOGNITION nano internet of things sensor transfer learning dangerous activity distinguish
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Deep learning and transfer learning for device-free human activity recognition:A survey
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作者 Jianfei Yang Yuecong Xu +2 位作者 Haozhi Cao Han Zou Lihua Xie 《Journal of Automation and Intelligence》 2022年第1期34-47,共14页
Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made si... Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models,but it suffers from the limitation of manual feature design.Deep learning overcomes such issues by automatic high-level feature extraction,but its performance degrades due to the requirement of massive annotated data and cross-site issues.To deal with these problems,transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics.This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition.We begin with the motivation of deep learning and transfer learning,and then introduce the major sensor modalities.Then the deep and transfer learning techniques for device-free human activity recognition are introduced.Eventually,insights on existing works and grand challenges are summarized and presented to promote future research. 展开更多
关键词 Human activity recognition Deep learning Transfer learning Domain adaptation Action recognition Device-free
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Investigation of Relation between Solar Activity and Earthquakes with Deep Learning Method
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作者 Leilei Li Hong Gu +2 位作者 Ryosuke Kikuyama Ryuei Nishii Pan Qin 《International Journal of Geosciences》 2021年第8期704-713,共10页
Solar activity (SA) has been hypothesized to be a trigger of earthquakes, although it is not as intuitively associated as other potential triggers such as </span><span style="font-family:Verdana;"&g... Solar activity (SA) has been hypothesized to be a trigger of earthquakes, although it is not as intuitively associated as other potential triggers such as </span><span style="font-family:Verdana;">tidal stress, rainfall, and the building of artificial water reservoirs. Here, we in</span><span style="font-family:Verdana;">ves</span><span style="font-family:Verdana;">tigate the relation between SA and global earthquake numbers (GEN) by using</span><span style="font-family:Verdana;"> a deep learning method to test the hypothesis. We use the daily data of GEN </span><span style="font-family:Verdana;">and SA (1996/01/01</span></span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">2019/12/31) to construct a temporal convolution netw</span><span style="font-family:""><span style="font-family:Verdana;">ork (</span><span style="font-family:Verdana;">TCN). From the computational results, we confirm that the TCN captures th</span><span style="font-family:Verdana;">e </span><span style="font-family:Verdana;">relation between SA and earthquakes with magnitudes from 4.0 to 4.9. We als</span><span style="font-family:Verdana;">o </span><span style="font-family:Verdana;">find that the TCN achieves better fitting and prediction performance compar</span><span style="font-family:Verdana;">ed with previous work</span></span><span style="font-family:Verdana;">. 展开更多
关键词 Deep learning EARTHQUAKES PREDICTION Solar activities Temporal Convolution Network
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Learning Performance of Linear and Exponential Activity Function with Multi-layered Neural Networks
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作者 Betere Job Isaac Hiroshi Kinjo +1 位作者 Kunihiko Nakazono Naoki Oshiro 《Journal of Electrical Engineering》 2018年第5期289-294,共6页
This paper presents a study on the improvement of MLNNs(multi-layer neural networks)performance by an activity function for multi logic training patterns.Our model network has L hidden layers of two inputs and three,f... This paper presents a study on the improvement of MLNNs(multi-layer neural networks)performance by an activity function for multi logic training patterns.Our model network has L hidden layers of two inputs and three,four to six output training using BP(backpropagation)neural network.We used logic functions of XOR(exclusive OR),OR,AND,NAND(not AND),NXOR(not exclusive OR)and NOR(not OR)as the multi logic teacher signals to evaluate the training performance of MLNNs by an activity function for information and data enlargement in signal processing(synaptic divergence state).We specifically used four activity functions from which we modified one and called it L&exp.function as it could give the highest training abilities compared to the original activity functions of Sigmoid,ReLU and Step during simulation and training in the network.And finally,we propose L&exp.function as being good for MLNNs and it may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple training logic patterns hence can be adopted in machine deep learning. 展开更多
关键词 MULTI-LAYER NEURAL networks learning performance multi logic training patterns activity FUNCTION BP NEURAL network deep learning
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