With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way fo...With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.展开更多
Utopian visions between China and Western society differed in their early stage. Words reflecting early Chinese utopian visions scattered in many ancient classics. Most of them were general depiction of an ideal socie...Utopian visions between China and Western society differed in their early stage. Words reflecting early Chinese utopian visions scattered in many ancient classics. Most of them were general depiction of an ideal society featured with equality, sympathy, preference for community autonomy and the social order ＂the whole world as one community＂. Early Western society witnessed many utopian monographs. Most of them offered detailed construction of social frame with emphasis on social function division, request for ideal authority, and property co-ownership as core of an ideal society.展开更多
There are increasing calls for engaging citizens in the development of future outlooks. At the same time, large-scale public engagement activities warrant appropriate methods for analyzing their outcomes. This paper r...There are increasing calls for engaging citizens in the development of future outlooks. At the same time, large-scale public engagement activities warrant appropriate methods for analyzing their outcomes. This paper reviews how topic modeling could provide such a methodology, which both accounts for all textual data collected in public engagement activities, however large in scope, yet also allows for meaningful topical analysis. It compares topic modeling results concerning a corpus of 179 citizen visions from 30 European countries on desirable and sustainable futures to those acquired through deliberative analysis. While both methodologies contend that European citizens＇ outlook consists of education, sustainability in the economy, health concerns, and fairness in communities, and the particular strengths of topic modeling relate to its documentability, repeatability, cost efficiency, and scalability. Topic modeling can also be considered to support public engagement analytically from the perspective of knowledge formation rather than that of common sense.展开更多
Architecture and the city are two major constituents of human development which, today more than ever, have to be present in the long-term. The Year of France in China is, for the first time, the occasion to present t...Architecture and the city are two major constituents of human development which, today more than ever, have to be present in the long-term. The Year of France in China is, for the first time, the occasion to present to the Chinese public a vision of the contemporary French architectural production, not only in France but also in China.展开更多
AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A...AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.展开更多
Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step...Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.展开更多
AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbi...AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbil,Iraq.The target population consisted of high school students selected through a multi-stage cluster sampling method.Comprehensive visual examinations were performed for all students,including measurement of uncorrected and corrected visual acuity,objective and subjective refraction,and distance and near cover tests.NPC was evaluated using a single 6/12 visual target mounted on a centrally positioned Gulden fixation stick.Ocular symptoms were investigated through interviews.RESULTS:Of the 996 selected students,921 participated in the study.Of them,543(58.96%)were female,and their ages ranged from 13 to 22y.The prevalence of tropia was 3.58%[95%confidence interval(CI):2.38%-4.78%],observed in 3.44%of males and 3.68%of females.Exotropia(1.95%,95%CI:1.06%-2.85%)was more common than esotropia(1.52%,95%CI:0.73%-2.31%).The 15.42%(95%CI:13.09%-17.75%)of students had phoria.Exophoria(13.79%,95%CI:11.56%-16.02%)was significantly more prevalent than esophoria(1.63%,95%CI:0.81%-2.45%).The prevalence of NPC abnormality in the total study population was 24.97%(95%CI:22.18%-27.77%).It was 26.72%(95%CI:22.26%-31.18%)in males and 23.76%(95%CI:20.18%-27.34%)in females(P=0.307).The most common symptom in phoria was headache(86.62%,95%CI:81.02%-92.22%),followed by tired or sore eyes(61.97%,95%CI:53.99%-69.96%).The most common symptoms in tropia were blurry vision(93.94%,95%CI:79.77%-99.26%)and difficulty concentrating(87.88%,95%CI:76.74%-99.01%).CONCLUSION:Among Erbil’s high school students,the prevalence of strabismus,particularly the exodeviation type,is relatively high,and a significant percentage of students have NPC abnormalities.Addressing and correcting these binocular vision problems,due to their associated visual symptoms,can lead to an improvement in students’quality of life and academic performance.展开更多
Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in ...Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in many applications,such as smart home,healthcare,human computer interaction,sports analysis,and especially,intelligent surveillance.In this paper,we propose a robust and efficient HAR system by leveraging deep learning paradigms,including pre-trained models,CNN architectures,and their average-weighted fusion.However,due to the diversity of human actions and various environmental influences,as well as a lack of data and resources,achieving high recognition accuracy remain elusive.In this work,a weighted average ensemble technique is employed to fuse three deep learning models:EfficientNet,ResNet50,and a custom CNN.The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities.Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures.The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%,compared to 97%,96%,and 95%for the customized CNN,EfficientNet,and ResNet50 models,respectively.展开更多
Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food proce...Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
基金supported in part by National Natural Science Foundation of China under Grants 61631005, 61801101, U1801261, and 61571100
文摘With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.
文摘Utopian visions between China and Western society differed in their early stage. Words reflecting early Chinese utopian visions scattered in many ancient classics. Most of them were general depiction of an ideal society featured with equality, sympathy, preference for community autonomy and the social order ＂the whole world as one community＂. Early Western society witnessed many utopian monographs. Most of them offered detailed construction of social frame with emphasis on social function division, request for ideal authority, and property co-ownership as core of an ideal society.
文摘There are increasing calls for engaging citizens in the development of future outlooks. At the same time, large-scale public engagement activities warrant appropriate methods for analyzing their outcomes. This paper reviews how topic modeling could provide such a methodology, which both accounts for all textual data collected in public engagement activities, however large in scope, yet also allows for meaningful topical analysis. It compares topic modeling results concerning a corpus of 179 citizen visions from 30 European countries on desirable and sustainable futures to those acquired through deliberative analysis. While both methodologies contend that European citizens＇ outlook consists of education, sustainability in the economy, health concerns, and fairness in communities, and the particular strengths of topic modeling relate to its documentability, repeatability, cost efficiency, and scalability. Topic modeling can also be considered to support public engagement analytically from the perspective of knowledge formation rather than that of common sense.
文摘Architecture and the city are two major constituents of human development which, today more than ever, have to be present in the long-term. The Year of France in China is, for the first time, the occasion to present to the Chinese public a vision of the contemporary French architectural production, not only in France but also in China.
基金Supported by Ongoing Research Funding Program(ORFFT-2025-054-1),King Saud University,Riyadh,Saudi Arabia.
文摘AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.
文摘Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.
文摘AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbil,Iraq.The target population consisted of high school students selected through a multi-stage cluster sampling method.Comprehensive visual examinations were performed for all students,including measurement of uncorrected and corrected visual acuity,objective and subjective refraction,and distance and near cover tests.NPC was evaluated using a single 6/12 visual target mounted on a centrally positioned Gulden fixation stick.Ocular symptoms were investigated through interviews.RESULTS:Of the 996 selected students,921 participated in the study.Of them,543(58.96%)were female,and their ages ranged from 13 to 22y.The prevalence of tropia was 3.58%[95%confidence interval(CI):2.38%-4.78%],observed in 3.44%of males and 3.68%of females.Exotropia(1.95%,95%CI:1.06%-2.85%)was more common than esotropia(1.52%,95%CI:0.73%-2.31%).The 15.42%(95%CI:13.09%-17.75%)of students had phoria.Exophoria(13.79%,95%CI:11.56%-16.02%)was significantly more prevalent than esophoria(1.63%,95%CI:0.81%-2.45%).The prevalence of NPC abnormality in the total study population was 24.97%(95%CI:22.18%-27.77%).It was 26.72%(95%CI:22.26%-31.18%)in males and 23.76%(95%CI:20.18%-27.34%)in females(P=0.307).The most common symptom in phoria was headache(86.62%,95%CI:81.02%-92.22%),followed by tired or sore eyes(61.97%,95%CI:53.99%-69.96%).The most common symptoms in tropia were blurry vision(93.94%,95%CI:79.77%-99.26%)and difficulty concentrating(87.88%,95%CI:76.74%-99.01%).CONCLUSION:Among Erbil’s high school students,the prevalence of strabismus,particularly the exodeviation type,is relatively high,and a significant percentage of students have NPC abnormalities.Addressing and correcting these binocular vision problems,due to their associated visual symptoms,can lead to an improvement in students’quality of life and academic performance.
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R765),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in many applications,such as smart home,healthcare,human computer interaction,sports analysis,and especially,intelligent surveillance.In this paper,we propose a robust and efficient HAR system by leveraging deep learning paradigms,including pre-trained models,CNN architectures,and their average-weighted fusion.However,due to the diversity of human actions and various environmental influences,as well as a lack of data and resources,achieving high recognition accuracy remain elusive.In this work,a weighted average ensemble technique is employed to fuse three deep learning models:EfficientNet,ResNet50,and a custom CNN.The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities.Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures.The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%,compared to 97%,96%,and 95%for the customized CNN,EfficientNet,and ResNet50 models,respectively.
文摘Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
