Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ...Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.展开更多
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
Background:Donor nerve selection is a crucial factor in determining clinical outcomes of facial reanimation.Although dual innervation approaches using two neurotizers have shown promise,there is a lack of evidence-bas...Background:Donor nerve selection is a crucial factor in determining clinical outcomes of facial reanimation.Although dual innervation approaches using two neurotizers have shown promise,there is a lack of evidence-based comparison in the literature.Furthermore,no animal model of dual reinnervation has yet been published.This study aimed to establish such a model and verify its technical and anatomical feasibility by performing dual-innervated reanimation approaches in Wistar rats.Methods:Fifteen Wistar rats were divided into four experimental groups and one control group.The sural nerve was exposed and used as a cross-face nerve graft(CFNG),which was then anastomosed to the contralateral buccal branch of the facial nerve through a subcutaneous tunnel on the forehead.The CFNG,the masseteric nerve(MN),and the recipient nerve were coapted in one or two stages.The length and width of the utilized structures were measured under an operating microscope.Return of whisker motion was visually confirmed.Results:Nine out of the eleven rats that underwent surgery survived the procedure.Whisker motion was observed in all experimental animals,indicating successful reinnervation.The mean duration of the surgical procedures did not differ significantly between the experimental groups,ensuring similar conditions for all groups.Conclusions:Our experimental study confirmed that the proposed reanimation model in Wistar rats is anatomically and technically feasible,with a high success rate,and shows good prospects for future experiments.展开更多
Facial morphology,a complex trait influenced by genetics,holds great significance in evolutionary research.However,due to limited fossil evidence,the facial characteristics of Neanderthals and Denisovans have remained...Facial morphology,a complex trait influenced by genetics,holds great significance in evolutionary research.However,due to limited fossil evidence,the facial characteristics of Neanderthals and Denisovans have remained largely unknown.In this study,we conduct a large-scale multi-ethnic meta-analysis of the genome-wide association study(GWAS),including 9674 East Asians and 10,115 Europeans,quantitatively assessing 78 facial traits using 3D facial images.We identify 71 genomic loci associated with facial features,including 21 novel loci.We develop a facial polygenic score(FPS)that enables the prediction of facial features based on genetic information.Interestingly,the distribution of FPSs among populations from diverse continental groups exhibits relevant correlations with observed facial features.Furthermore,we apply the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA and align predictions with the fossil records.Our results suggest that Neanderthals and Denisovans likely share similar facial features,such as a wider but shorter nose and a wider endocanthion distance.The decreased mouth width is characterized specifically in Denisovans.The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.展开更多
This study aims to investigate the antioxidant activity of Tibetan gentian(Gentiana spp.)extract and its essence when compounded with a facial mask matrix.It also evaluates the efficacy of facial masks containing gent...This study aims to investigate the antioxidant activity of Tibetan gentian(Gentiana spp.)extract and its essence when compounded with a facial mask matrix.It also evaluates the efficacy of facial masks containing gentian extract on sensitive facial skin and analyzes the comprehensive performance of the mask.A total of 90 patients with facial sensitive skin,enrolled between October 2022 and December 2024,were randomly assigned to either a control group or an observation group,with 45 patients in each.The control group used standard facial masks,while the observation group used masks containing gentian extract.Both groups underwent a 4-week intervention.The effi cacy,lactic acid stinging test indicators,and skin physiological function parameters were compared between the two groups.Results showed that the overall eff ectiveness rate in the observation group reached 93.26%,signifi cantly higher than 71.20%in the control group(P<0.05).After the intervention,both groups showed notable improvements compared to baseline in lactic acid stinging test scores and physiological skin indicators.Specifi cally,the observation group had signifi cantly lower stinging scores and a longer latency before the onset of stinging compared to the control group.Moreover,the skin pH values were lower,while sebum levels and stratum corneum hydration were higher than those in the control group(P<0.05).No serious adverse events occurred in either group.These fi ndings suggest that facial masks containing gentian extract eff ectively alleviate symptoms of sensitive facial skin,enhance skin barrier function and tolerance,and are safe for use.展开更多
BACKGROUND Anxiety and depression are common psychological reactions in teenagers with facial burns and have a significant impact on their rehabilitation and quality of life.AIM To analyze anxiety and depressive sympt...BACKGROUND Anxiety and depression are common psychological reactions in teenagers with facial burns and have a significant impact on their rehabilitation and quality of life.AIM To analyze anxiety and depressive symptoms in teenagers with facial burns.METHODS We selected 50 young patients with facial burns who were treated at our hospital between October 2023 and October 2024.The Hamilton Anxiety Scale and Beck Depression Inventory were used to evaluate anxiety and depressive symptoms.Additionally,we evaluated patients'social support levels and self-esteem.Pearson's correlation analysis was used to evaluate factors related to depression and anxiety.RESULTS The overall average Hamilton Anxiety Scale score was 23.4±6.2,and 16(32%)and 34(68%)patients showed mild to moderate and moderate to severe anxiety,respectively.The overall average Beck Depression Inventory score was 18.7±7.5,and 23(46%)and 27(54%)patients had mild to moderate and moderate to severe depression,respectively.Furthermore,Pearson's correlation analysis showed a significant positive correlation between burn severity and anxiety(r=0.48,P<0.01)and depression(r=0.42,P<0.01)symptoms.Self-esteem scores and social support were significantly negatively correlated with anxiety(r=-0.55 and r=-0.40,respectively;P<0.01)and depression(r=-0.60 and r=-0.38,respectively;P<0.01 for both).CONCLUSION Adolescents with facial burns commonly experience anxiety and depressive symptoms,the severity of which is closely related to burn severity,social support,and self-esteem.展开更多
Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significa...Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.展开更多
The effects of different kinds of cosurfactants on the properties of the crystalline amino acid cleanser based on potassium cocoyl-glycine were studied by analyzing the foam properties,high-temperature stability and c...The effects of different kinds of cosurfactants on the properties of the crystalline amino acid cleanser based on potassium cocoyl-glycine were studied by analyzing the foam properties,high-temperature stability and crystallization temperature.The results showed that PEG-80 sorbitan laurate makes the composite foaming system slower and less,but the foam stability and high-temperature stability are better.The addition of lauryl hydroxysultaine can make the foaming speed faster and the foam volume larger,and this material can improve the crystallization of potassium cocoyl-glycine,so that the high-temperature stability of the composite system is better.The addition of anionic surfactant like sodium methyl cocoyl taurate or sodium lauroyl glutamate is helpful for foam fineness and foam stability,but may have a negative effect on high-temperature stability.The addition of lauryl glucoside is disadvantage on foam stability and high-temperature stability,so it is not suitable for this system.Cosurfactants can be selected on demand when developing the related products.展开更多
Hearing and Speech impairment can be congenital or acquired.Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges.However,the development of a...Hearing and Speech impairment can be congenital or acquired.Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges.However,the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of study.Assistive learning devices enable students to access institutional resources and facilities fully.The proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and classmates.This tool converts the audio signals into sign language videos for the speech and hearing-impaired to follow and converts the sign language to text format for the teachers to follow.This educational tool for the speech and hearing-impaired is implemented by customized deep learning models such as Convolution neural networks(CNN),Residual neural Networks(ResNet),and stacked Long short-term memory(LSTM)network models.This assistive learning tool is a novel framework that interprets the static and dynamic gesture actions in American Sign Language(ASL).Such communicative tools empower the speech and hearing impaired to communicate effectively in a classroom environment and foster inclusivity.Customized deep learning models were developed and experimentally evaluated with the standard performance metrics.The model exhibits an accuracy of 99.7% for all static gesture classification and 99% for specific vocabulary of gesture action words.This two-way communicative and educational tool encourages social inclusion and a promising career for disabled students.展开更多
Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included p...Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.展开更多
Facial recognition payment(FRP),a new method of contactless payment,has attracted considerable attention over the past few years.However,the research on this topic remains nascent.This study assessed the drivers of cu...Facial recognition payment(FRP),a new method of contactless payment,has attracted considerable attention over the past few years.However,the research on this topic remains nascent.This study assessed the drivers of customers’FRP continuance intention from the perspectives of coolness and inspiration.We use online survey data from 610 Chinese FRP customers as the basis for our conceptual model.The results show that the coolness factors of subculture,attractiveness,utility,and originality have positive and significant effects on customers’inspired-by states and that subculture and utility also promote inspired-to.Inspired-by is positively associated with inspiredto,which in turn enhances customers’FRP continuance intention.Furthermore,the relationship between inspired-to and FRP continuance intention is negatively moderated by financial risk.In addition to contributing to the literature on FRP,coolness,and customer inspiration,this study offers several suggestions for implementing and developing FRP systems.展开更多
Gain-of-function mutations in fibroblast growth factor receptor(FGFR) genes lead to chondrodysplasia and craniosynostoses. FGFR signaling has a key role in the formation and repair of the craniofacial skeleton. Here, ...Gain-of-function mutations in fibroblast growth factor receptor(FGFR) genes lead to chondrodysplasia and craniosynostoses. FGFR signaling has a key role in the formation and repair of the craniofacial skeleton. Here, we analyzed the impact of Fgfr2- and Fgfr3- activating mutations on mandibular bone formation and endochondral bone repair after non-stabilized mandibular fractures in mouse models of Crouzon syndrome(Crz) and hypochondroplasia(Hch).展开更多
The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise f...The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures.Effective transitions to engagement occur upon problem resolution,while unresolved issues lead to frustration and subsequent boredom.This study proposes a Convolutional Neural Networks(CNN)based approach utilizing the Multi⁃source Academic Affective Engagement Dataset(MAAED)to categorize facial expressions into boredom,confusion,frustration,and yawning.This method provides an efficient and objective way to assess student engagement by extracting features from facial images.Recognizing and addressing negative affective states,such as confusion and boredom,is fundamental in creating supportive learning environments.Through automated frame extraction and model comparison,this study demonstrates reduced loss values with improving accuracy,showcasing the effectiveness of this method in objectively evaluating student engagement.Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences.展开更多
Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semant...Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.展开更多
Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female parti...Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female participants(anemic and healthy)were recruited from Shuguang Hospital.Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System(FDAS)and Tongue Diagnosis Analysis System version 2.0(TDAS v2.0).Least Absolute Shrinkage and Selection Operator(LASSO)regression was used for feature selection.Ten machine learning models and one deep learning model(ResNet50V2+Conv1D)were developed and evaluated.Results Anemic women showed lower a-values,higher L-and b-values across all age groups.Texture features analysis showed that women aged 30–39 with anemia had higher angular second moment(ASM)and lower entropy(ENT)values in facial images,while those aged 40–49 had lower contrast(CON),ENT,and MEAN values in tongue images but higher ASM.Anemic women exhibited age-related trends similar to healthy women,with decreasing L-values and increasing a-,b-,and ASM-values.LASSO identified 19 key features from 62.Among classifiers,the Artificial Neural Network(ANN)model achieved the best performance[area under the curve(AUC):0.849,accuracy:0.781].The ResNet50V2 model achieved comparable results[AUC:0.846,accuracy:0.818].Conclusion Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.展开更多
In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiologi...In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.展开更多
Ten years ago,Drolma-then selling facial masks on the streets of Lhasa-could hardly have imagined that the"Tibetan medicinal facial mask"sheand her partner were developing would one day appear in the central...Ten years ago,Drolma-then selling facial masks on the streets of Lhasa-could hardly have imagined that the"Tibetan medicinal facial mask"sheand her partner were developing would one day appear in the central exhibition hall of an international showcase.展开更多
BACKGROUND Aging is an inevitable aspect of human life,characterized by the gradual decline in the function of individual cells and structural components,including bones,muscles,and ligaments.AIM To evaluate the clini...BACKGROUND Aging is an inevitable aspect of human life,characterized by the gradual decline in the function of individual cells and structural components,including bones,muscles,and ligaments.AIM To evaluate the clinical effects of radiofrequency technology in treating facial skin wrinkles and laxity.METHODS This study included 60 female patients,aged 36-58 years(mean age 47.71±1.56 years),who received focused radiofrequency technology treatment for facial wrinkles and laxity in the Department of Medical Cosmetology at our hospital between January 2021 and June 2022.Each patient underwent three treatment sessions,one every two months.Facial photographs were taken before treatment and one week after the final session.A single physician assessed wrinkle severity using a standardized wrinkle severity scale,and patients completed a satisfaction questionnaire one week after the last treatment.RESULTS After three consecutive radiofrequency treatments,performed every two months,patients exhibited significantly reduced wrinkles and skin laxity compared to baseline.One week after the third treatment,the mean facial wrinkle severity score had significantly decreased from 3.00±0.79 to 2.71±0.47(t=2.58,P<0.05).Additionally,88.24%of patients reported noticeable improvements in facial wrinkles and skin laxity.No serious adverse reactions occurred during or follow-ing treatment.CONCLUSION Radiofrequency technology demonstrates significant clinical efficacy in improving facial skin wrinkles and laxity.展开更多
Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
基金supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).
文摘Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金Grant Agency of Masaryk University,Grant/Award Number:MUNI/11/SUP/01/2020 and MUNI/A/1457/2021。
文摘Background:Donor nerve selection is a crucial factor in determining clinical outcomes of facial reanimation.Although dual innervation approaches using two neurotizers have shown promise,there is a lack of evidence-based comparison in the literature.Furthermore,no animal model of dual reinnervation has yet been published.This study aimed to establish such a model and verify its technical and anatomical feasibility by performing dual-innervated reanimation approaches in Wistar rats.Methods:Fifteen Wistar rats were divided into four experimental groups and one control group.The sural nerve was exposed and used as a cross-face nerve graft(CFNG),which was then anastomosed to the contralateral buccal branch of the facial nerve through a subcutaneous tunnel on the forehead.The CFNG,the masseteric nerve(MN),and the recipient nerve were coapted in one or two stages.The length and width of the utilized structures were measured under an operating microscope.Return of whisker motion was visually confirmed.Results:Nine out of the eleven rats that underwent surgery survived the procedure.Whisker motion was observed in all experimental animals,indicating successful reinnervation.The mean duration of the surgical procedures did not differ significantly between the experimental groups,ensuring similar conditions for all groups.Conclusions:Our experimental study confirmed that the proposed reanimation model in Wistar rats is anatomically and technically feasible,with a high success rate,and shows good prospects for future experiments.
基金funded by the following grants and contracts:Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38020400 to S.W.)the National Natural Science Foundation of China(32325013 to S.W.,32271186 to J.T.,31900408 to M.Z.)+5 种基金the CAS Project for Young Scientists in Basic Research(YSBR-077 to S.W.)Shanghai Science and Technology Commission Excellent Academic Leaders Program(22XD1424700 to S.W.)CAMS Innovation Fund for Medical Sciences(2019-I2M-5-066 to L.J.and J.W.)Shanghai Municipal Science and Technology Major Project(2017SHZDZX01 to L.J.and S.W.)the National Science and Technology Basic Research Project(2015FY111700 to L.J.)the 111 Project(B13016 to L.J.).
文摘Facial morphology,a complex trait influenced by genetics,holds great significance in evolutionary research.However,due to limited fossil evidence,the facial characteristics of Neanderthals and Denisovans have remained largely unknown.In this study,we conduct a large-scale multi-ethnic meta-analysis of the genome-wide association study(GWAS),including 9674 East Asians and 10,115 Europeans,quantitatively assessing 78 facial traits using 3D facial images.We identify 71 genomic loci associated with facial features,including 21 novel loci.We develop a facial polygenic score(FPS)that enables the prediction of facial features based on genetic information.Interestingly,the distribution of FPSs among populations from diverse continental groups exhibits relevant correlations with observed facial features.Furthermore,we apply the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA and align predictions with the fossil records.Our results suggest that Neanderthals and Denisovans likely share similar facial features,such as a wider but shorter nose and a wider endocanthion distance.The decreased mouth width is characterized specifically in Denisovans.The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.
文摘This study aims to investigate the antioxidant activity of Tibetan gentian(Gentiana spp.)extract and its essence when compounded with a facial mask matrix.It also evaluates the efficacy of facial masks containing gentian extract on sensitive facial skin and analyzes the comprehensive performance of the mask.A total of 90 patients with facial sensitive skin,enrolled between October 2022 and December 2024,were randomly assigned to either a control group or an observation group,with 45 patients in each.The control group used standard facial masks,while the observation group used masks containing gentian extract.Both groups underwent a 4-week intervention.The effi cacy,lactic acid stinging test indicators,and skin physiological function parameters were compared between the two groups.Results showed that the overall eff ectiveness rate in the observation group reached 93.26%,signifi cantly higher than 71.20%in the control group(P<0.05).After the intervention,both groups showed notable improvements compared to baseline in lactic acid stinging test scores and physiological skin indicators.Specifi cally,the observation group had signifi cantly lower stinging scores and a longer latency before the onset of stinging compared to the control group.Moreover,the skin pH values were lower,while sebum levels and stratum corneum hydration were higher than those in the control group(P<0.05).No serious adverse events occurred in either group.These fi ndings suggest that facial masks containing gentian extract eff ectively alleviate symptoms of sensitive facial skin,enhance skin barrier function and tolerance,and are safe for use.
文摘BACKGROUND Anxiety and depression are common psychological reactions in teenagers with facial burns and have a significant impact on their rehabilitation and quality of life.AIM To analyze anxiety and depressive symptoms in teenagers with facial burns.METHODS We selected 50 young patients with facial burns who were treated at our hospital between October 2023 and October 2024.The Hamilton Anxiety Scale and Beck Depression Inventory were used to evaluate anxiety and depressive symptoms.Additionally,we evaluated patients'social support levels and self-esteem.Pearson's correlation analysis was used to evaluate factors related to depression and anxiety.RESULTS The overall average Hamilton Anxiety Scale score was 23.4±6.2,and 16(32%)and 34(68%)patients showed mild to moderate and moderate to severe anxiety,respectively.The overall average Beck Depression Inventory score was 18.7±7.5,and 23(46%)and 27(54%)patients had mild to moderate and moderate to severe depression,respectively.Furthermore,Pearson's correlation analysis showed a significant positive correlation between burn severity and anxiety(r=0.48,P<0.01)and depression(r=0.42,P<0.01)symptoms.Self-esteem scores and social support were significantly negatively correlated with anxiety(r=-0.55 and r=-0.40,respectively;P<0.01)and depression(r=-0.60 and r=-0.38,respectively;P<0.01 for both).CONCLUSION Adolescents with facial burns commonly experience anxiety and depressive symptoms,the severity of which is closely related to burn severity,social support,and self-esteem.
基金supported by the grants from Yunnan Province(202305AH340006,202305AH340007)CAS Light of West China Program(xbzg-zdsys-202213)。
文摘Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.
文摘The effects of different kinds of cosurfactants on the properties of the crystalline amino acid cleanser based on potassium cocoyl-glycine were studied by analyzing the foam properties,high-temperature stability and crystallization temperature.The results showed that PEG-80 sorbitan laurate makes the composite foaming system slower and less,but the foam stability and high-temperature stability are better.The addition of lauryl hydroxysultaine can make the foaming speed faster and the foam volume larger,and this material can improve the crystallization of potassium cocoyl-glycine,so that the high-temperature stability of the composite system is better.The addition of anionic surfactant like sodium methyl cocoyl taurate or sodium lauroyl glutamate is helpful for foam fineness and foam stability,but may have a negative effect on high-temperature stability.The addition of lauryl glucoside is disadvantage on foam stability and high-temperature stability,so it is not suitable for this system.Cosurfactants can be selected on demand when developing the related products.
基金sponsored by Prince Sattam Bin Abdulaziz University(PSAU)as part of funding for its SDG Roadmap Research Funding Programme project number PSAU-2023-SDG-2023/SDG/31.
文摘Hearing and Speech impairment can be congenital or acquired.Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges.However,the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of study.Assistive learning devices enable students to access institutional resources and facilities fully.The proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and classmates.This tool converts the audio signals into sign language videos for the speech and hearing-impaired to follow and converts the sign language to text format for the teachers to follow.This educational tool for the speech and hearing-impaired is implemented by customized deep learning models such as Convolution neural networks(CNN),Residual neural Networks(ResNet),and stacked Long short-term memory(LSTM)network models.This assistive learning tool is a novel framework that interprets the static and dynamic gesture actions in American Sign Language(ASL).Such communicative tools empower the speech and hearing impaired to communicate effectively in a classroom environment and foster inclusivity.Customized deep learning models were developed and experimentally evaluated with the standard performance metrics.The model exhibits an accuracy of 99.7% for all static gesture classification and 99% for specific vocabulary of gesture action words.This two-way communicative and educational tool encourages social inclusion and a promising career for disabled students.
基金National Natural Science Foundation of China(82305090)Shanghai Municipal Health Commission(20234Y0168)National Key Research and Development Program of China (2017YFC1703301)。
文摘Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.
基金This study was supported by the National Natural Science Foundation of China(72202185,72302145)the Postdoctoral Science Foundation of China(2023M742232)the Innovation Research 2035 Pilot Plan of Southwest University(SWUPilotPlan026).
文摘Facial recognition payment(FRP),a new method of contactless payment,has attracted considerable attention over the past few years.However,the research on this topic remains nascent.This study assessed the drivers of customers’FRP continuance intention from the perspectives of coolness and inspiration.We use online survey data from 610 Chinese FRP customers as the basis for our conceptual model.The results show that the coolness factors of subculture,attractiveness,utility,and originality have positive and significant effects on customers’inspired-by states and that subculture and utility also promote inspired-to.Inspired-by is positively associated with inspiredto,which in turn enhances customers’FRP continuance intention.Furthermore,the relationship between inspired-to and FRP continuance intention is negatively moderated by financial risk.In addition to contributing to the literature on FRP,coolness,and customer inspiration,this study offers several suggestions for implementing and developing FRP systems.
基金National Research Agency under the Investments for the Future program (ANR-10-IAHU-01)Filière Nationale TeteCou for financial support。
文摘Gain-of-function mutations in fibroblast growth factor receptor(FGFR) genes lead to chondrodysplasia and craniosynostoses. FGFR signaling has a key role in the formation and repair of the craniofacial skeleton. Here, we analyzed the impact of Fgfr2- and Fgfr3- activating mutations on mandibular bone formation and endochondral bone repair after non-stabilized mandibular fractures in mouse models of Crouzon syndrome(Crz) and hypochondroplasia(Hch).
文摘The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures.Effective transitions to engagement occur upon problem resolution,while unresolved issues lead to frustration and subsequent boredom.This study proposes a Convolutional Neural Networks(CNN)based approach utilizing the Multi⁃source Academic Affective Engagement Dataset(MAAED)to categorize facial expressions into boredom,confusion,frustration,and yawning.This method provides an efficient and objective way to assess student engagement by extracting features from facial images.Recognizing and addressing negative affective states,such as confusion and boredom,is fundamental in creating supportive learning environments.Through automated frame extraction and model comparison,this study demonstrates reduced loss values with improving accuracy,showcasing the effectiveness of this method in objectively evaluating student engagement.Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences.
基金supported by the National Natural Science Foundation of China (Nos. NSFC 61925105, 62322109, 62171257 and U22B2001)the Xplorer Prize in Information and Electronics technologiesthe Tsinghua University (Department of Electronic Engineering)-Nantong Research Institute for Advanced Communication Technologies Joint Research Center for Space, Air, Ground and Sea Cooperative Communication Network Technology
文摘Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.
基金Funding This research was funded by funding from the National Natural Science Foundation of China(No.82305090,No.82104738)Key Discipline Construction Project of High-level Traditional Chinese Medicine of the National Administration of Traditional Chinese Medicine-Traditional Chinese Medical Diagnostics(ZYYZDXK-2023069)+5 种基金Shanghai Municipal Health Commission Project(No.20234Y0168,No.2024QN018)Shanghai Science and Technology Commission Rising Star Cultivation Project(No.22YF1448900)Capacity Building of Local Colleges and Universities under the Shanghai Municipal Science and Technology Commission(21010504400)General Program of China Postdoctoral Science Foundation(2023M732337)Shanghai“Super Postdoctoral”Incentive Plan(2022509)Science and Technology Development Project of Shanghai University of Traditional Chinese Medicine(23KFL005).
文摘Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female participants(anemic and healthy)were recruited from Shuguang Hospital.Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System(FDAS)and Tongue Diagnosis Analysis System version 2.0(TDAS v2.0).Least Absolute Shrinkage and Selection Operator(LASSO)regression was used for feature selection.Ten machine learning models and one deep learning model(ResNet50V2+Conv1D)were developed and evaluated.Results Anemic women showed lower a-values,higher L-and b-values across all age groups.Texture features analysis showed that women aged 30–39 with anemia had higher angular second moment(ASM)and lower entropy(ENT)values in facial images,while those aged 40–49 had lower contrast(CON),ENT,and MEAN values in tongue images but higher ASM.Anemic women exhibited age-related trends similar to healthy women,with decreasing L-values and increasing a-,b-,and ASM-values.LASSO identified 19 key features from 62.Among classifiers,the Artificial Neural Network(ANN)model achieved the best performance[area under the curve(AUC):0.849,accuracy:0.781].The ResNet50V2 model achieved comparable results[AUC:0.846,accuracy:0.818].Conclusion Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
基金supported by the Science and Technology Bureau of Xi’an project(24KGDW0049)the Key Research and Development Programof Shaanxi(2023-YBGY-264)the Key Research and Development Program of Guangxi(GK-AB20159032).
文摘In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.
文摘Ten years ago,Drolma-then selling facial masks on the streets of Lhasa-could hardly have imagined that the"Tibetan medicinal facial mask"sheand her partner were developing would one day appear in the central exhibition hall of an international showcase.
文摘BACKGROUND Aging is an inevitable aspect of human life,characterized by the gradual decline in the function of individual cells and structural components,including bones,muscles,and ligaments.AIM To evaluate the clinical effects of radiofrequency technology in treating facial skin wrinkles and laxity.METHODS This study included 60 female patients,aged 36-58 years(mean age 47.71±1.56 years),who received focused radiofrequency technology treatment for facial wrinkles and laxity in the Department of Medical Cosmetology at our hospital between January 2021 and June 2022.Each patient underwent three treatment sessions,one every two months.Facial photographs were taken before treatment and one week after the final session.A single physician assessed wrinkle severity using a standardized wrinkle severity scale,and patients completed a satisfaction questionnaire one week after the last treatment.RESULTS After three consecutive radiofrequency treatments,performed every two months,patients exhibited significantly reduced wrinkles and skin laxity compared to baseline.One week after the third treatment,the mean facial wrinkle severity score had significantly decreased from 3.00±0.79 to 2.71±0.47(t=2.58,P<0.05).Additionally,88.24%of patients reported noticeable improvements in facial wrinkles and skin laxity.No serious adverse reactions occurred during or follow-ing treatment.CONCLUSION Radiofrequency technology demonstrates significant clinical efficacy in improving facial skin wrinkles and laxity.
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
