Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. …”
Get full text

AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols by Umesh Kumar Lilhore, R. Sunder, Sarita Simaiya, Majed Alsafyani, M. D. Monish Khan, Roobaea Alroobaea, Hamed Alsufyani, Abdullah M. Baqasah

“…Traditional methods of tumor segmentation, often manual and labour-intensive, are prone to inconsistencies and inter-observer variability. Recently, deep learning models, particularly Convolutional Neural Networks (CNNs), have shown great promise in automating this process. …”
Get full text

Advanced Defect Detection in Wrap Film Products: A Hybrid Approach with Convolutional Neural Networks and One-Class Support Vector Machines with Variational Autoencoder-Derived Cov... by Tatsuki Shimizu, Fusaomi Nagata, Maki K. Habib, Koki Arima, Akimasa Otsuka, Keigo Watanabe

“…We introduce a pioneering methodology centered on covariance vectors extracted from latent variables, a product of a Variational Autoencoder (VAE). …”
Get full text

YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

Get full text

Predicting renal function using fundus photography: role of confounders by Hyun-Woong Park, Hae Ri Kim, Ki Yup Nam, Bum Jun Kim, Taeseen Kang

Get full text

Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy by Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes, Songlin Fei

“…We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. …”
Get full text

Estimating PM_2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Dat... by Muhammad Jawad Hussain, Myeongsu Seong, Behjat Shahid, Heming Bai

“…This study introduced a spatiotemporal convolutional approach to fill sampling gaps in TOAR and AOD data from the Himawari-8 geostationary satellite over the Yangtze River Delta (YRD) in 2016. …”
Get full text

Handwritten Words Image Character Extraction Adaptive Algorithm Based on the Multi-branch Structure by GUO Xiaojing, ZHAO Xiaoyuan, ZOU Songlin

“…First, the enhanced re-parameterized structure across multiple stages and branches achieves an effect equivalent to variable convolution. Second, the refined classifier with fully convolutional layers combines features from specific intermediate layers with the output layer, resulting in improved precision for complex and similar words. …”
Get full text

Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

Get full text

Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…It can simultaneously capture short-term local features and long-term global trends in power data, help to deeply mine spatial correlations and local patterns in data, effectively extract fine relationships between variables and optimize information flow. In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
Get full text

Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The effects of various process variables, including, transmembrane pressure (TMP), Reynolds number, feed pH, and temperature, on the rejection of the minerals were surveyed. …”
Get full text

A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting by Zain Ahmed, Mohsin Jamil, Ashraf Ali Khan

“…These optimal inputs are fed to D-TCNet (Deep – Temporal Convolution Network). This network uses multi-layer perceptrons (MLP) to encode the spatial relationship among exogenous variables which is fed to a Temporal Convolutional Network (TCN). …”
Get full text

Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network by Ming Zheng, Tong Li, Rui Zhu, Jing Chen, Zifei Ma, Mingjing Tang, Zhongqiang Cui, Zhan Wang

“…Based on the weights of traffic accident’s features, the feature matrix to gray image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident’s data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident’s severity prediction has a better performance.…”
Get full text

Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data by Jaechan Park, Sungsoo Park, Kwang-Sig Lee, Yeongkeun Kwon

“…They were followed for one year after surgery. The dependent variable consisted of three categories: minimal, moderate, and significant change groups, classified based on postoperative percentage total weight loss (%TWL) in body mass index. …”
Get full text

Machine vision-based recognition of safety signs in work environments by Jesús-Ángel Román-Gallego, María-Luisa Pérez-Delgado, Miguel A. Conde, Marcos Luengo Viñuela

“…However, to improve classification capabilities, especially for highly degraded or complex images, a larger and more diverse data set might be needed, including real-world images that introduce greater entropy and variability. Implementing such a system would provide workers and companies with a proactive measure against workplace accidents, thereby enhancing overall safety in occupational environments.…”
Get full text

Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps by Somayeh Khakpash, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, Charles Keeton

Get full text

Hybrid Neural Network Models to Estimate Vital Signs from Facial Videos by Yufeng Zheng

“…Given the temporal variability of HR and BP, emphasis is placed on temporal resolution during feature extraction. …”
Get full text

Visual design element recognition of garment based on multi-view image fusion by Meng Fanyu

“…The image texture characteristic variables can be utilized to classify the defects. …”
Get full text

MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition by Khan Abrar Shams, Md. Rafid Reaz, Mohammad Ryan Ur Rafi, Sanjida Islam, Md. Shahriar Rahman, Rafeed Rahman, Md. Tanzim Reza, Mohammad Zavid Parvez, Subrata Chakraborty, Biswajeet Pradhan, Abdullah Alamri

Get full text

Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation by Zhang J, Jiang Q, Chen Q, Hu B, Chen L

“…Existing assessment methods like the Melasma Area and Severity Index (MASI) are subjective and prone to inter-observer variability.Objective: This study aimed to develop an AI-assisted, real-time melasma severity classification framework based on deep learning and clinical facial images.Methods: A total of 1368 anonymized facial images were collected from clinically diagnosed melasma patients. …”
Get full text