Revealing Depression Through Social Media via Adaptive Gated Cross-Modal Fusion Augmented With Insights From Personality Traits by Gede Aditra Pradnyana, Wiwik Anggraeni, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

“…Leveraging domain-specific transformer-based language models and a convolutional neural networks-based model, we extract modality-specific features and fuse them via a novel adaptive gating mechanism. …”
Get full text

Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…Through comprehensive experiments on the LC25000 dataset, DC-CAD achieves 99.52% accuracy, significantly outperforming baseline models such as standard Capsule Networks and Convolutional Neural Networks. The model also reduces the error rate to 0.48%, demonstrating substantial improvements in diagnostic performance, including increased accuracy, sensitivity, and specificity. …”
Get full text

Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models by Heyang Li, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li, Yucheng Zhang

“…The optimized XGBOOST model achieved the highest performance with an AUC-ROC of 0.9909, and through SHAP analysis, we identified roughness as the most significant factor affecting local gully erosion, with a relative importance of 0.277195. …”
Get full text

Global Aerosol Climatology from ICESat-2 Lidar Observations by Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman, Jackson Begolka

“…This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). …”
Get full text

Identifying native grasslands and key phenological stages using time series Sentinel-2 data and deep learning models by Yihan Pu, Amy Nixon, Beatriz Prieto, Xulin Guo

“…Canadian prairies are among the world’s most endangered ecosystems, and the identification of native grasslands is crucial for supporting grassland management and wildlife conservation. …”
Get full text

Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. …”
Get full text

Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

“…Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and a deep learning method named Convolutional Neural Networks (CNN). To evaluate the performance of proposed models, two gauging river stations situated in United States (i.e. …”
Get full text

An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body r... by Yi Chen, David Pasquier, Damon Verstappen, Henry C. Woodruff, Philippe Lambin

“…Deep learning models, leveraging various convolutional neural networks (CNNs), were employed to effectively integrate both image and clinical data. …”
Get full text

A Systematic Review of Reimagining Fashion and Textiles Sustainability with AI: A Circular Economy Approach by Hiqmat Nisa, Rebecca Van Amber, Julia English, Saniyat Islam, Georgia McCorkill, Azadeh Alavi

“…By analyzing the effectiveness and challenges of related peer-reviewed articles, conference papers, and technical reports, this study highlights state-of-the-art methodologies such as convolutional neural networks (CNNs), hybrid models, and other machine vision systems. …”
Get full text

Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. …”
Get full text

BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. …”
Get full text

A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…The evaluated models include both self-supervised and supervised approaches, employing different network structures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). …”
Get full text

Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…Abstract There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. …”
Get full text

Img2Neuro: brain-trained neural activity encoders for enhanced object recognition by Mona A Aboelnaga, Mohamed W El-Kharashi, Seif Eldawlatly

“…Therefore, rather than using the brain as an inspiration, in this paper, we introduce Img2Neuro; a convolutional neural network model feature extractor that predicts the visual brain’s response to images by encoding neural activity. …”
Get full text

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer by Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao, Tim Q. Duong

“…The model integrates 3D convolutional neural networks and self-attention to capture spatial and cross-modal interactions. …”
Get full text

SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory by Yanzhao Ren, Jinyuan Ye, Xiaochuan Wang, Fengjin Xiao, Ruijun Liu

“…In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. …”
Get full text

Spectral estimation of the aboveground biomass of cotton under water–nitrogen coupling conditions by Shunyu Qiao, Jiaqiang Wang, Fuqing Li, Jing Shi, Chongfa Cai

“…Support vector machine (SVM), regression tree (RT), and convolutional neural network (CNN) were employed to verify the accuracy. …”
Get full text

Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device by Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park

“…Compared to a standard convolutional neural network-based model which exhibited 81.5% overall accuracy, the proposed lightweight model achieved more precise arrhythmia classification with achieving 87.1% overall accuracy. …”
Get full text

Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…We compare the baseline Super-Resolution Convolutional Neural Network (SRCNN) model with two advanced models: Very Deep Super-Resolution (VDSR) and Enhanced Deep Super-Resolution (EDSR) to assess the impact of residual networks and architectural improvements. …”
Get full text

Continuous Arabic Sign Language Recognition Models by Nahlah Algethami, Raghad Farhud, Manal Alghamdi, Huda Almutairi, Maha Sorani, Noura Aleisa

“…This study is the first to use the Temporal Convolutional Network (TCN) model for Arabic Sign Language (ArSL) recognition. …”
Get full text