Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions DOI

S. Selvi,

M. Parvathy

International Journal of Developmental Neuroscience, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 27, 2024

The term 'autism spectrum disorder' describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns poor social interaction. Understanding mental states from FEs is crucial for interpersonal But when there are occlusions like glasses, facial hair or self-occlusion, it becomes harder to identify expressions accurately. This research tackles the issue of identifying parts face occluded suggests an innovative technique tackle this difficulty. Creating strong framework expression recognition (FER) that better handles increases accuracy goal research. Therefore, we propose novel Improved DenseNet-based Residual Cross-Attention Transformer (IDenseNet-RCAformer) system partial occlusion FER problem in autism patients. framework's efficacy assessed using four datasets expressions, some preprocessing procedures conducted increase efficiency. After that, recognizing simple argmax function applied get forecasted landmark position heatmap. Then feature extraction phase, local global representation captured preprocessed images adopting Inception-ResNet-V2 approach, Transformer, respectively. Moreover, both features fused employing FusionNet method, thereby enhancing system's training speed precision. extracted, improved DenseNet mechanism efficiently recognize variety partially A number performance metrics determined analysed demonstrate proposed approach's effectiveness, where IDenseNet-RCAformer performs best with 98.95%. According experimental findings, significantly outperforms prior frameworks terms outcomes.

Язык: Английский

Urban informal settlements interpretation via a novel multi-modal Kolmogorov–Arnold fusion network by exploring hierarchical features from remote sensing and street view images DOI Creative Commons
Haibin Niu, Runyu Fan, Jiajun Chen

и другие.

Science of Remote Sensing, Год журнала: 2025, Номер unknown, С. 100208 - 100208

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

3
Mapping urban construction sites in China through geospatial data fusion: Methods and applications DOI
Chaoqun Zhang, Ziyue Chen, Lei Luo

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 315, С. 114441 - 114441

Опубликована: Сен. 25, 2024

Язык: Английский

Процитировано

3
Supply-Demand risk assessment of urban flood resilience from the perspective of the ecosystem services: A case study in Nanjing, China DOI
Peng Zhang,

Xukan Xu,

Wentong Yang

и другие.

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113397 - 113397

Опубликована: Март 26, 2025

Язык: Английский

Процитировано

0
Mapping and Analyzing Winter Wheat Yields in the Huang-Huai-Hai Plain: A Climate-Independent Perspective DOI Creative Commons
Yachao Zhao, Xin Du, Qiangzi Li

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(8), С. 1409 - 1409

Опубликована: Апрель 16, 2025

Accurate diagnostics of crop yields are essential for climate-resilient agricultural planning; however, conventional datasets often conflate environmental covariates during model training. Here, we present HHHWheatYield1km, a 1 km resolution winter wheat yield dataset China’s Huang-Huai-Hai Plain spanning 2000–2019. By integrating climate-independent multi-source remote sensing metrics with Random Forest model, calibrated against municipal statistical yearbooks, the exhibits strong agreement county-level records (R = 0.90, RMSE 542.47 kg/ha, MRE 9.09%), ensuring independence from climatic influences robust driver analysis. Using Geodetector, reveal pronounced spatial heterogeneity in climate–yield interactions, highlighting distinct regional disparities: precipitation variability exerts strongest constraints on Henan and Anhui, whereas Shandong Jiangsu exhibit weaker dependencies. In Beijing–Tianjin–Hebei, March temperature emerges as critical determinant variability. These findings underscore need tailored adaptation strategies, such enhancing water-use efficiency inland provinces optimizing agronomic practices coastal regions. With its dual ability to resolve pixel-scale dynamics disentangle drivers, HHHWheatYield1km represents resource precision agriculture evidence-based policymaking face changing climate.

Язык: Английский

Процитировано

0
Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods DOI Creative Commons
Saimiao Liu, Wenliang Liu, Yi Zhou

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(10), С. 1709 - 1709

Опубликована: Май 13, 2025

The gridded spatial distribution data of Gross Domestic Product (GDP) has a wide range application values in many fields, such as regional economic analysis, urban planning, sustainable utilization resources, and disaster risk assessment. However, currently the publicly accessible GDP grid datasets face limitations terms temporal coverage, extent, accuracy. Therefore, based on remote sensing land use nighttime light, this study developed two methods: factor averaging method (FAM) (GAM), used Random Forest (RF) eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct model GDP, so produce China’s 1 km 2020. experimental results show following: (1) GAM yields higher R2 than FAM modeling three industries, therefore, it is adopted basis for spatialization modeling. (2) XGBoost achieves RF primary secondary but lower tertiary industry. Consequently, both methods are combined overall model. (3) accuracy evaluated town-level statistics, with an value 0.78, indicating its reliable predictive capability. (4) Compared available datasets, our dataset exhibits consistent patterns aggregation trends. Furthermore, provides more detailed depiction variations within county-level administrative units. proposed offers valuable option generating dataset, visually displaying uneven development across various regions China. It helps uncover disparities among support formulating differentiated policies, promote balanced regions. contributes promoting sustained, inclusive, growth (SDG 8) reducing inequalities countries 10), thereby providing strong planning development.

Язык: Английский

Процитировано

0
Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions DOI

S. Selvi,

M. Parvathy

International Journal of Developmental Neuroscience, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 27, 2024

The term 'autism spectrum disorder' describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns poor social interaction. Understanding mental states from FEs is crucial for interpersonal But when there are occlusions like glasses, facial hair or self-occlusion, it becomes harder to identify expressions accurately. This research tackles the issue of identifying parts face occluded suggests an innovative technique tackle this difficulty. Creating strong framework expression recognition (FER) that better handles increases accuracy goal research. Therefore, we propose novel Improved DenseNet-based Residual Cross-Attention Transformer (IDenseNet-RCAformer) system partial occlusion FER problem in autism patients. framework's efficacy assessed using four datasets expressions, some preprocessing procedures conducted increase efficiency. After that, recognizing simple argmax function applied get forecasted landmark position heatmap. Then feature extraction phase, local global representation captured preprocessed images adopting Inception-ResNet-V2 approach, Transformer, respectively. Moreover, both features fused employing FusionNet method, thereby enhancing system's training speed precision. extracted, improved DenseNet mechanism efficiently recognize variety partially A number performance metrics determined analysed demonstrate proposed approach's effectiveness, where IDenseNet-RCAformer performs best with 98.95%. According experimental findings, significantly outperforms prior frameworks terms outcomes.

Язык: Английский

Процитировано

0