Real-Time Coronary Artery Dominance Classification from Angiographic Images Using Advanced Deep Video Architectures by Hasan Ali Akyürek

“…These models were trained and evaluated on a large dataset derived from a publicly available source. …”
Get full text

Beef Carcass Grading with EfficientViT: A Lightweight Vision Transformer Approach by Hyunwoo Lim, Eungyeol Song

“…Unlike conventional two-stage methods that require prior segmentation of the loin region, our model directly predicts beef quality grades from raw RGB images, significantly simplifying the pipeline and reducing computational overhead. We evaluate the proposed model against representative convolutional neural networks (VGG-16, ResNeXt-50, DenseNet-121) as well as two-stage combinations of segmentation and classification models. …”
Get full text

A Graph Representation Learning-Based Method for Event Prediction by Xi Zeng, Guangchun Luo, Ke Qin, Pengyi Zheng

“…By constructing causal graphs and introducing the script event simulation method, the architecture combines graph neural networks (GNNs) with BERT to simplify the event prediction process. …”
Get full text

Handwritten Geez Digit Recognition Using Deep Learning by Mukerem Ali Nur, Mesfin Abebe, Rajesh Sharma Rajendran

“…Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. …”
Get full text

Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

“…Each technique contributes specific strengths to the modeling of spatiotemporal series: CNNs are effective at capturing local spatial patterns, while LSTM networks excel at modeling long-term temporal dependencies. …”
Get full text

Enhanced visibility graph for EEG classification by Asma Belhadi, Pedro G. Lind, Pedro G. Lind, Pedro G. Lind, Youcef Djenouri, Youcef Djenouri, Anis Yazidi, Anis Yazidi

“…Our framework offers a holistic approach for capturing both frequency-domain characteristics and temporal dynamics of EEG signals. We evaluate four DL architectures, namely multilayer perceptron (MLP), long short-term memory (LSTM) networks, InceptionTime and ChronoNet, applied to several datasets and in different experimental conditions. …”
Get full text

Leaf Development and Its Interaction with Phyllospheric Microorganisms: Impacts on Plant Stress Responses by Huanhuan Yang, Jing Liu, Mei Ma, Zilong Tan, Kaiyue Zhang, Ruiqi Sun, Xinxin Zhan, Dayong Cui

“…Leaf development is regulated by intricately genetic and hormonal networks, which are further modulated by environmental inputs. …”
Get full text

Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). …”
Get full text

RelayGAN: Sequential knowledge propagation for sustainable multi-generation by Namkyung Yoon, Hwangnam Kim

“…To evaluate the effectiveness of RelayGAN, we conducted extensive experiments using quantitative methods. …”
Get full text

Vertiport Infrastructure Location Optimization for Equitable Access to Urban Air Mobility by Vasileios Volakakis, Hani S. Mahmassani

“…The proposed framework is illustrated through applications in the Chicago Metropolitan Area, with the demand estimated on the basis of existing taxi and Transportation Network Company (TNC) data.…”
Get full text

Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback by Hafiz Muhammad Qadir, M. Taseer Suleman, Rafaqat Alam Khan, Muhammad Sohaib, Md Junayed Hasan, Syed Abid Hussain

“…In this paper, we implement several ensemble models-AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost-and deep learning architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). …”
Get full text

Europe's adaptation to the energy crisis: reshaped gas supply–transmission–consumption structures and driving factors from 2022 to 2024 by C. Zhou, B. Zhu, A. Halff, A. Halff, S. J. Davis, Z. Liu, S. Bowring, S. B. Arous, P. Ciais

“…Total gas consumption fell by 19.0 %, which was driven by (1) consumer behavioral changes in household heating (contributed to 29 % of the total reduction; the following numbers are also percentage contributions to the total reduction); (2) drops in industrial production (25 %); (3) heating drops due to the warmer winter temperatures (11 %); (4) shifts towards renewable electricity including wind, solar, and hydro (10 %); (5) a decline in gas-powered electricity generation (9 %); (6) adoption of energy-efficient heat pumps for industrial gas heating (4 %); (7) shifts towards non-renewable electricity including coal, oil, and nuclear (1 %); and (8) other unmodeled factors (11 %). We evaluated the benefits and costs associated with these pattern changes and discussed whether these changes would potentially lead to long-term structural changes in the EU energy dynamics. …”
Get full text

A Multi-Epiphysiological Indicator Dog Emotion Classification System Integrating Skin and Muscle Potential Signals by Wenqi Jia, Yanzhi Hu, Zimeng Wang, Kai Song, Boyan Huang

“…Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. …”
Get full text

A deep learning-driven multi-layered steganographic approach for enhanced data security by Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Mosleh Abualhaj, Emran Alzubi

Get full text

Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models by Ningchang Kang, Zhaocai Wang, Anbin Zhang, Hang Chen

“…These components are then modeled independently: long short-term memory (LSTM) and convolutional neural networks (CNN) handle trend and seasonal patterns, while gated recurrent units (GRU) and Transformer process residual fluctuations. …”
Get full text

Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model complex spatial-temporal dependencies, especially in dynamic or subtle actions. …”
Get full text

Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…Following this, the cervical cells are segmented and classified using a combination of machine learning algorithms, such as shallow machine learning models and Convolutional Neural Networks (CNN). We selected publicly available datasets, including Herlev and SIPaKMeD, to evaluate the performance of the proposed models, which are a very respectful base in the state-of-the-art analysis. …”
Get full text

Examining the role of semiotics in social media-driven information campaigns by Mayor Inna Gurung, Nitin Agarwal

“…Engagement was measured by likes, while diffusion patterns followed the SEIZ epidemiological model. Findings show that posts featuring SCP symbols consistently received more interaction, even when follower counts were equal. …”
Get full text

Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Ramiro Saltos, Ignacio Carvajal, Fernando Crespo, Richard Weber

“…Dynamic clustering algorithms play a crucial role in numerous real-world applications by continuously adapting to evolving data patterns and identifying changes within the underlying cluster structure. …”
Get full text

Predictive Models for Educational Purposes: A Systematic Review by Ahlam Almalawi, Ben Soh, Alice Li, Halima Samra

“…This systematic literature review evaluates predictive models in education, focusing on their role in forecasting student performance, identifying at-risk students, and personalising learning experiences. …”
Get full text