Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…Among the primary challenges in automated skin cancer classification are addressing differences in lesion appearance, occlusions, and data class imbalance that impact model performance and reliability. …”
Get full text

DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…With the rapid development of remote sensing technology, the application scope of high-resolution remote sensing images (HR-RSIs) has been continuously expanding. The emergence of convolutional neural networks and Transformer models has significantly enhanced the accuracy of semantic segmentation. …”
Get full text

P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…Conflict of interest: No Introduction and Objectives: Differents ultrasound (USD) signs have been described for the diagnosis of cirrhosis. …”
Get full text

Modeling Temperature in the Ecuadorian Paramo Through Deep Learning by Marco Javier Castelo Cabay, Jose Antonio Piedra-Fernandez, Rosa Maria Ayala

“…Six neural network architectures were evaluated: Long short-term memory (LSTM), bidirectional LSTM, LSTM with attention mechanism, gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM. At the Airport Ambato station, the LSTM with attention mechanism was the most effective, achieving an RMSE of 0.82 and an R-squared of 0.66. …”
Get full text

Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery by Jiawei Li, Huihui Zhang, David Barnard

“…While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. …”
Get full text

AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…Classical ML models outperformed most DL architectures, including Transformer and Convolutional Neural Network (CNN)-LSTM, which underperformed despite their complexity. …”
Get full text

Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network by Yuxin Cong, Roxana Dev Omar Dev, Shamsulariffin Bin Samsudin, Kaihao Yu

“…The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). A prediction model is established based on the characteristics of sports behavior and psychological indices of well-being, such as psychological resilience, self-efficacy, and subjective well-being. …”
Get full text

Tomato detection in natural environment based on improved YOLOv8 network by Wancheng Dong, Yipeng Zhao, Jiaxing Pei, Zuolong Feng, Zhikai Ma, Leilei Wang, Simon Shemin Wang

“… In this paper, an improved lightweight YOLOv8 method is proposed to detect the ripeness of tomato fruits, given the problems of subtle differences between neighboring stages of ripening and mutual occlusion of branches, leaves, and fruits. …”
Get full text

Methodology for Occupant Head-Neck Injury Testing in Under-Body Blast Impact Based on Virtual-Real Fusion by Xinge Si, Changan Di, Peng Peng, Cong Xu

“…To address the limitations of low-cost, simplified dummy head–neck structures, which exhibit significant differences in mechanical properties compared to high-biofidelity dummies, a virtual–real fusion-based test method for assessing occupant head–neck injury in under-body blast impacts is proposed. …”
Get full text

Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products by Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang

“…We develop eight models for forecasting ≥M-class flares within 24 hr, including the image-based convolutional neural network (CNN), CNN-BiLSTM, CNN-BiLSTM-Attention, and Vision Transformer models, as well as the knowledge-informed neural network, BiLSTM, BiLSTM-Attention, and iTransformer models. …”
Get full text

Fine-Grained Aircraft Recognition Based on Dynamic Feature Synthesis and Contrastive Learning by Huiyao Wan, Pazlat Nurmamat, Jie Chen, Yice Cao, Shuai Wang, Yan Zhang, Zhixiang Huang

“…However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation functions and downsampling operations in convolutional networks lead to frequency deviations and loss of local detail information, affecting fine-grained object recognition; (2) class imbalance and long-tail distributions further degrade the performance of minority categories; (3) large intra-class variations and small inter-class differences make it difficult for traditional deep learning methods to effectively extract fine-grained discriminative features. …”
Get full text

Assessment of Vegetation Indices Derived from UAV Imagery for Weed Detection in Vineyards by Fabrício Lopes Macedo, Humberto Nóbrega, José G. R. de Freitas, Miguel A. A. Pinheiro de Carvalho

“…Despite the lack of statistically significant differences, visual analysis favored NGRDI and SVM for generating cleaner classification outputs. …”
Get full text

Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features by Youxin Yu, Wenbo Zhu, Xiaoli Ma, Jialei Du, Yu Liu, Linhui Gan, Xiaoping An, Honghui Li, Buyu Wang, Xueliang Fu

“…The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. …”
Get full text

Small-Sample Authenticity Identification and Variety Classification of <i>Anoectochilus roxburghii</i> (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning by Yiqing Xu, Haoyuan Ding, Tingsong Zhang, Zhangting Wang, Hongzhen Wang, Lu Zhou, Yujia Dai, Ziyuan Liu

“…Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two counterfeit species (Bloodleaf and Spotted-leaf), followed by classification using a variety of machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN). The experimental results demonstrated that the SVM model achieved 100% classification accuracy for distinguishing Goldthread from its counterfeit species, effectively capturing the spectral differences between the front and back leaves. …”
Get full text

Predictive analytics in education- enhancing student achievement through machine learning by Sunawar khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir khan, Wajahat Waheed, Ahsen Waheed, Habib Hamam

“…Additionally, key predictive factors such as studied credits, entrance results, and regional differences were identified, offering a comprehensive understanding of student performance. …”
Get full text

A deep learning approach for automated scoring of the Rey–Osterrieth complex figure by Nicolas Langer, Maurice Weber, Bruno Hebling Vieira, Dawid Strzelczyk, Lukas Wolf, Andreas Pedroni, Jonathan Heitz, Stephan Müller, Christoph Schultheiss, Marius Troendle, Juan Carlos Arango Lasprilla, Diego Rivera, Federica Scarpina, Qianhua Zhao, Rico Leuthold, Flavia Wehrle, Oskar Jenni, Peter Brugger, Tino Zaehle, Romy Lorenz, Ce Zhang

“…Memory deficits are a hallmark of many different neurological and psychiatric conditions. …”
Get full text

On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…However, a systematic evaluation of the skill and robustness of different ML approaches is an active field of research that requires more investigation. …”
Get full text

PatchOut: A novel patch-free approach based on a transformer-CNN hybrid framework for fine-grained land-cover classification on large-scale airborne hyperspectral images by Renjie Ji, Kun Tan, Xue Wang, Shuwei Tang, Jin Sun, Chao Niu, Chen Pan

“…A multi-scale spatial-spectral feature fusion (MSSSFF) module is also proposed to amalgamate the characteristics of different levels from the encoder, which enhances the overall feature representation. …”
Get full text

Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…A machine learning study developed a parallel Convolutional Neural Network algorithm with 96.4% recall and 99.1% precision for study screening. …”
Get full text

Choroidal neovascularization activity and structure by optical coherence tomography angiography in age related macular degeneration by M. A. Kovalevskaya, O. A. Pererva

“…CVS is a quantitative biomarker for determining the activity of type 1 CNV in patients with AMD and can serve as a parameter for convolutional neural networks training for automated analysis of OCT angiography images based on the “Key to Diagnosis II” platform…”
Get full text