Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model by Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

“…Images of waste were captured in many different settings and analyzed with a YOLOv12-based model. …”
Get full text

A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction by Ping Tang, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou, Jifeng Song

“…The experimental results indicate that the proposed framework demonstrates competitive predictive performance and generalization capability across different time horizons and weather conditions compared with benchmark frameworks.…”
Get full text

CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach by Yunidar Yunidar, Y Yusni, N Nasaruddin, Fitri Arnia

“…Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. …”
Get full text

Combining Region-Guided Attention and Attribute Prediction for Thangka Image Captioning Method by Fujun Zhang, Wendong Kang, Wenjin Hu

“…This predictor leverages feature maps from four different convolutional blocks within the region-guided module to incorporate more detailed information into the model. …”
Get full text

Medical Image Hybrid Watermark Algorithm Based on Frequency Domain Processing and Inception v3 by Yu Fan, Jingbing Li, Uzair Aslam Bhatti, Saqib Ali Nawaz, Yenwei Chen

“…Existing research has mostly focused on optimizing individual techniques, lacking comprehensive solutions that integrate the strengths of different methods. This article proposes a hybrid digital watermarking algorithm for medical images based on frequency domain transformation and deep learning convolutional neural networks. …”
Get full text

A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…The CNN model is adjusted for different convolutional layers and fully connected layers to identify patterns and features in brain tumor pictures using an enhanced salp swarm algorithm (SSA) with kernel extreme learning machine (KELM). …”
Get full text

Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules by Yihao Chen, Ling Guo, Yue Pan, Hui Cai, Zhitong Bing

“…However, most of the existing network inference methods are based on large-scale gene collections, which ignore the characteristics of different tumors. Methods: In this work, weighted gene coexpression network analysis was deployed to screen key genes and gene modules. …”
Get full text

Predicting road quality using high resolution satellite imagery: A transfer learning approach. by Ethan Brewer, Jason Lin, Peter Kemper, John Hennin, Dan Runfola

“…We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as "low", "middle", or "high" quality using an open, cellphone-based measuring platform. …”
Get full text

Deep Learning Forecasting Model for Market Demand of Electric Vehicles by Ahmed Ihsan Simsek, Erdinç Koç, Beste Desticioglu Tasdemir, Ahmet Aksöz, Muammer Turkoglu, Abdulkadir Sengur

“…This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. …”
Get full text

Cross modal recipe retrieval with fine grained modal interaction by Fan Zhao, Yuqing Lu, Zhuo Yao, Fangying Qu

“…Preceding a hierarchical recipe Transformer for encoding individual recipe components, we introduce the cross-component multiscale recipe enriching (CCMRE) module, which enhances the components of the recipe through fully convolutional operations with convolutional kernels of different lengths. …”
Get full text

Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

“…Secondly, to enhance the feature extraction capabilities of the grasping network, the parallel use of different-size convolutional kernels in the Inception-ResNet module was utilized to broaden the network's receptive field. …”
Get full text

YOLO-GML: An object edge enhancement detection model for UAV aerial images in complex environments. by Zhihao Zheng, Jianguang Zhao, Jingjing Fan

“…Finally, we propose a Lightweight layered Shared Convolutional BN(LLSCB) Detection Head based on LSCD, so that the detection heads share the convolutional layer, and the BN is calculated independently, which improves the detection accuracy and reduces the number of parameters. …”
Get full text

HUMAN EMOTION RECOGNITION SYSTEM USING DEEP LEARNING ALGORITHMS by Kateryna Yuvchenko, Valentyn Yesilevskyi, Olena Sereda

“…They can be expressed in different ways: facial expressions, posture, motor reactions, voice. …”
Get full text

Small-Target Detection Algorithm Based on STDA-YOLOv8 by Cun Li, Shuhai Jiang, Xunan Cao

“…The CAM introduces multi-scale dilated convolutions, where convolutional kernels with different dilation rates capture contextual information from various receptive fields, enabling more accurate extraction of small-target features. …”
Get full text

EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement by Hu Haoyan, Tong Jinwu, Wang Haibin, Lu Xinyun

“…Finally, the designed C2f_EMSCP method is integrated into the backbone and neck networks, effectively merging multi-scale convolutional networks and position-aware modules to enhance the model’s sensitivity and detection capability for multi-scale targets by fusing feature maps of different scales. …”
Get full text

Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning by G. Mastella, F. Corbi, J. Bedford, F. Funiciello, M. Rosenau

“…The onset, magnitude, and propagation of analog earthquakes can thus be predicted at different prediction horizons. From all architectures tested in this study, convolutional recurrent neural networks (CNN‐LSTM and CONVLSTM) provide the best predictions although their performances depend on experiment characteristics and hyperparameters tuning. …”
Get full text

Intelligent Sports Weights by Olga dos Santos Duarte, Gustavo Jacinto, Mário Véstias, Rui Policarpo Duarte

“…To counter this, this work proposes a different approach to automatic weightlifting supervision off-the-person. …”
Get full text

Geological Stratification Technology Based on Artificial Intelligence Algorithms and Its Application Effects by GAO Yuan, YAO Weihua, CAI Shaofeng, LI Liang, XUE Yuan, ZHAO Peipei, WANG Xiaoyang, WEI Wei

“…Moreover, there are significant differences in stratification among different geological researchers, and the stratification results are unstable. …”
Get full text

Needle detection and localisation for robot‐assisted subretinal injection using deep learning by Mingchuan Zhou, Xiangyu Guo, Matthias Grimm, Elias Lochner, Zhongliang Jiang, Abouzar Eslami, Juan Ye, Nassir Navab, Alois Knoll, Mohammad Ali Nasseri

“…In this paper we demonstrate a robust framework for needle detection and localisation in robot‐assisted subretinal injection using microscope‐integrated Optical Coherence Tomography with deep learning. Five convolutional neural networks with different architectures were evaluated. …”
Get full text

Contrasted Trends in Chlorophyll‐a Satellite Products by Etienne Pauthenet, Elodie Martinez, Thomas Gorgues, Joana Roussillon, Lucas Drumetz, Ronan Fablet, Maïlys Roux

“…Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. …”
Get full text