Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“… This paper aimed to investigate human emotion recognition through the analysis of facial expressions, using both classical machine learning methods and advanced techniques based on deep neural networks. The research compares the performance of classical machine learning algorithms (such as K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree, and Random Forest) with the modern deep learning methods (such as Convolutional Neural Networks, Deep Neural Networks, and Recursive Neural Networks) using standardized datasets. …”
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Altered brain dynamics in post-stroke cognitive and motor dysfunction by Xiaoying Liu, Guihua Song, Xiaoyun Zhuang, Ying Zhang, Xiaoyang Wang, Yin Qin, Yin Qin

“…Therefore, the objective is to explore the dynamic brain network characteristics of PSCMD.MethodsThe clinical features and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 75 patients with post-stroke motor dysfunction (PSMD), 33 patients with PSCMD, and 35 healthy controls (HCs). …”
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PolSAR-MPIformer: A Vision Transformer Based on Mixed Patch Interaction for Dual-Frequency PolSAR Image Adaptive Fusion Classification by Xinyue Xin, Ming Li, Yan Wu, Xiang Li, Peng Zhang, Dazhi Xu

“…Besides the global-spatial information learning within samples by ViT, the MPI module adds the learning of local-spatial information within samples and correlation information among samples, thereby obtaining more discriminative features through a low-complexity network. Subsequently, a dual-frequency adaptive fusion (DAF) module is constructed as the classifier of PolSAR-MPIformer. …”
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Integration of Spatiotemporal Dynamics and Structural Connectivity for Automated Epileptogenic Zone Localization in Temporal Lobe Epilepsy by Linxia Xiao, Qingqing Zheng, Sixian Li, Yanjie Wei, Weixin Si, Yi Pan

“…By retrospectively analyzing SEEG, post-implant Computed Tomography (CT) and MRI (T1 & Diffusion Tensor Imaging (DTI)) data from 15 patients, we reconstructed SEEG electrode positions and obtained the SEEG and structural connectivity fusion features. We then proposed a spatiotemporal co-attention deep neural network (ST-CANet) to identify the fusion features, categorizing electrodes into seizure onset zone (SOZ), propagation zone (PZ), and non-involved zone (NIZ). …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…Previous OSSEs combined low-resolution L4 satellite equivalent ADTs with high-resolution “perfectly known” SSTs to derive high-resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high-resolution SST and ADT from synthetic, satellite equivalent L4 products. …”
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Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study by Andrew Johnson, Luke Oakden-Rayner, Catherine M Jones, Jarrel Seah, Cyril Tang, Quinlan D Buchlak, Nazanin Esmaili, Luke Danaher, Michael R Milne

“…The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists.Design This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting.Setting The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020.Participants Eleven consultant diagnostic radiologists of varying levels of experience participated in this study.Primary and secondary outcome measures Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. …”
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D³-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario by Ao Li, Chunrui Wang, Tongtong Ji, Qiyang Wang, Tianxue Zhang

“…Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive convolution kernels are aggregated to extract local effective features for fruit size adaption. …”
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Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models – LightGBM, XGBoost, and CatBoost – and three deep learning (DL) models – Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…Detection results are demonstrated in various conditions, including sparse and dense distributions and day and night scenarios. Five images featuring dense targets, minimal targets, dark scenes, occluded targets, and complex backgrounds are randomly selected from the Visdrone2021 test challenge set to evaluate detection performance in UAV aerial images. …”
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A Comparative Study of Statistical and Machine Learning Methods for Solar Irradiance Forecasting Using the Folsom PLC Dataset by Oscar Trull, Juan Carlos García-Díaz, Angel Peiró-Signes

“…The analysis includes an evaluation of a range of models, including statistical regressions (OLS, LASSO, ridge), regression trees, neural networks, LSTM, and random forests, which are applied to physical modelling and time series approaches. …”
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Integrated multi-omics analysis identifies TPX2 as a potential prognostic and immunological biomarker in hepatocellular carcinoma by Hui Peng, Yong-peng Wei, Xin-bo Liu, Yu Wang, Jian-yong Yuan

“…The correlations between TPX2 expression levels and immune and genomic features were examined, and drug sensitivity associations were evaluated. …”
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Pedagogical management in the digital educational environment: Theoretical aspect by S. S. Kulikova, O. V. Yakovleva

“…The essence of the concept of “pedagogical management in digital educational environment” was clarified. The features and nature of pedagogical management in digital educational environment were determined, its structural components were highlighted: motivational-target, information-content, organisational-activity and control-evaluative. …”
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Expression characteristics, molecular mechanisms, and clinical significance of DICER1 in breast cancer by Xi Zhang, Long Yu, Cuizhi Geng

“…Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with the breast cancer phenotype, and gene set enrichment analysis (GSEA) was performed to explore their biological functions. …”
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Advanced Analysis of OCT/OCTA Images for Accurately Differentiating Between Glaucoma and Healthy Eyes Using Deep Learning Techniques by Pourjavan S, Gouverneur F, Macq B, Van Drooghenbroeck T, De Potter P, Boschi A, El Maftouhi A

“…Sayeh Pourjavan,1 François Gouverneur,2 Benoit Macq,2 Thomas Van Drooghenbroeck,2 Patrick De Potter,1 Antonella Boschi,1 Adil El Maftouhi3 1Department of Ophthalmology, Cliniques Universitaires Saint Luc, UCL, Brussels, Belgium; 2Institute for Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Louvain School of Engineering, UCLouvain, Louvain-la-Neuve, Belgium; 3Centre Ophtalmologique de RIVE, Geneve, SwitzerlandCorrespondence: Sayeh Pourjavan, Email Sayeh.pourjavan@saintluc.uclouvain.bePurpose: To evaluate the discriminative power of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, identifying the best image combination for differentiating glaucoma from healthy eyes using deep learning (DL) with a convolutional neural network (CNN).Methods: This cross-sectional study included 157 subjects contributing 1,106 eye scans. …”
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Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework by Omid Halimi Milani, Lauren Mills, Amanda Nikho, Marouane Tliba, Veerasathpurush Allareddy, Rashid Ansari, Ahmet Enis Cetin, Mohammed H. Elnagar

“…Our preprocessing steps include region-of-interest extraction, followed by high-pass and Sobel filtering for emphasis of low-level features. The feature extraction integrates Convolutional Neural Networks (CNN) architectures, such as EfficientNet and ResNet18, alongside our novel Multi-Filter Convolutional Residual Attention Network (MFCRAN) enhanced with Discrete Cosine Transform (DCT) layers. …”
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GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition by Hongyun Sheng

“…Previous gait recognition methods mostly focused on constructing a sophisticated model structure for better model performance during evaluation. Moreover, these methods are primarily based on traditional convolutional neural networks (CNNs) due to the dominance of CNNs in computer vision. …”
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Quantum magnetization exchange through transient hydrogen bond matrix defines magnetic resonance signal relaxation and anisotropy in central nervous system by Dmitriy A. Yablonskiy, Alexander L. Sukstanskii

“…Abstract The integrity of cellular membranes (lipid bilayers) and myelin sheaths covering axons is a crucial feature controlling normal brain structural and functional networks. …”
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Effects of internal tides on GNSS-A seafloor crustal deformation observation by Yusuke Yokota, Tadashi Ishikawa, Shun-ichi Watanabe, Koya Nagae, Yuto Nakamura, Eiji Masunaga

“…In this study, we quantitatively evaluated the impact of internal tides on GNSS-A observations by numerical reproduction and used data from the GNSS-A observation network around Japan to quantitatively identify the effect of internal tides on GNSS-A observations for the first time. …”
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Global and regional perspectives on optimizing thermo-responsive dynamic windows for energy-efficient buildings by Yuan Gao, Jacob C. Jonsson, D. Charlie Curcija, Simon Vidanovic, Tianzhen Hong

“…World heatmap results, derived from well-trained artificial neural network models, reveal that thermo-responsive windows are especially useful in climates where buildings demand both heating and cooling energy, whereas thermo-responsive windows with optimal transition temperatures show no dynamic features in most of low-latitude tropical regions. …”
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SMaRT: Stick via Motion and Recognition Tracker by Fatih Emre Simsek, Cevahir Cigla, Koray Kayabol

“…Inspired by leading MOT methods like CenterTrack and FairMOT, SMaRT enhances tracking robustness by fusing re-identification features from an advanced teacher-student model. This integration enables the simultaneous regression of object locations and extraction of re-identification vectors within a single neural network. …”
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