A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images by Francesca Arezzo, Annarita Fanizzi, Rosanna Mancari, Emiliano Cocco, Samantha Bove, Maria Colomba Comes, Mariangela Gianciotta, Giorgia Lanza, Salvatore Lopez, Gerardo Cazzato, Erica Silvestris, Elsa Vitale, Enrico Vizza, Gennaro Cormio, Raffaella Massafra, Vera Loizzi

“…After a pre-processing phase of ultrasound images, a pre-trained Inception-V3 convolutional neural network (CNN) was used as features extractor. Then, a binary classification model and a multiclass model were trained, after a double step of feature selection; the first selection stage performed feature filtering based on a nonparametric test, the second stage selected features by evaluating not only the relationship with the outcome of interest, but also the relationship with other predictive features. …”
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Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration by Yi Shi, Hong Wang, Fei Wang, Yingkuan Wang, Jianjun Liu, Long Zhao, Hui Wang, Feng Zhang, Qiongmin Cheng, Shunhao Qing

“…This technique facilitates a rapid and non-invasive assessment of leaf maturity, significantly elevating the accuracy and efficiency of tobacco leaf quality evaluation. In our study, we have advanced the YOLOv10 framework by integrating DCNv3 with C2f to construct an enhanced neck network, designated as C2f-DCNv3. …”
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Emotion Modeling in Speech Signals: Discrete Wavelet Transform and Machine Learning Tools for Emotion Recognition System by K. Daqrouq, A. Balamesh, O. Alrusaini, A. Alkhateeb, A. S. Balamash

“…The performance of various classifiers, including support vector machine (SVM), K-Nearest Neighbors (KNN), Efficient Logistic Regression, Naive Bayes, Ensemble, and Neural Network, was evaluated for emotion classification using the EMO-DB dataset. …”
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Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. by Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan, Faisal S Alsubaei, Eesa Alsolami, Muhammad D Zakaria

“…For the real-time zero-day vulnerabilities detection, we bring out a novel reinforcement learning (RL) methodology with the help of Deep Q-Networks (DQN). It works by learning the vulnerabilities without any prior knowledge of vulnerabilities, and it is evaluated using rigorous statistical metrics. …”
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Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities by Yang Chen, Xiaolin Wang, Chang Zhang

“…Then, LSTM and RBF-BP neural networks in deep learning are used in the feature layer for adaptive learning of multiple feature signals, and finally, fuzzy logic is used to control the system decision output to improve the efficiency of 3D landscape design. …”
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Bio-Inspired Object Detection and Tracking in Aerial Images: Harnessing Northern Goshawk Optimization by Agnivesh Pandey, Rohit Raja, Sumit Srivastava, Krishna Kumar, Manoj Gupta, Chanyanan Somthawinpongsai, Aziz Nanthaamornphong

“…This study presents a novel approach for object detection and tracking in aerial images using a multi-scale Northern Goshawk Pyramid Generative Adversarial Network (NGPGAN). The research evaluates different algorithms and features to identify people, trees, cars, and buildings in real-world drone videos, addressing challenges in pinpointing specific objects among multiple entities. …”
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Integrated single-cell and transcriptomic analysis of bone marrow-derived metastatic neuroblastoma reveals molecular mechanisms of metabolic reprogramming by Jing Chu, Rong Qin, Shu-Jing Wang, Qiang Wang, Qiang Wu

“…Furthermore, co-expression network analysis was used to evaluate the relationships between candidate genes and known NB regulatory factors. …”
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Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation by Thai la, Linh Tao, Dai Watanabe

“…This framework delivers high-performance stereo matching suitable for devices with limited computational capabilities. Through evaluation on benchmark datasets, the proposed network achieves 1.37 px EPE on Scene Flow and 4.22 % and 4.09 % D1-all on KITTI 2012 and KITTI 2015, respectively, yet runs in 13 ms with only 27 GFLOPs and 0.41 M parameters.…”
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Improving 3D deep learning segmentation with biophysically motivated cell synthesis by Roman Bruch, Mario Vitacolonna, Elina Nürnberg, Simeon Sauer, Rüdiger Rudolf, Markus Reischl

“…Furthermore, we present a generative adversarial network (GAN) training scheme that generates not only image data but also matching labels. …”
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Identification Method of Dynamic Propagation Process of Rock Fracture Based on Ground Penetrating Radar by CHEN Jun, ZHANG Bo, ZHUANG Xingyue, SONG Zhishu, ZHENG Jun

“…The integration of GPR with borehole imaging and acoustic emission data can provide a multi-scale monitoring system, offering a more comprehensive view of the fracture network from micro-crack initiation to macro-scale propagation. …”
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A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems by Md Shahab Uddin, Ahsan Ahmed, Md Aktarujjaman, Mohammad Moniruzzaman, Mumtahina Ahmed, M. F. Mridha, Md. Jakir Hossen

“…Experimental results on real and synthetic healthcare datasets demonstrate that the proposed model outperforms traditional regressors, deep neural networks, and standalone RL agents across multiple evaluation metrics, including cost prediction error, diagnostic classification accuracy, cumulative reward, and average billing reduction. …”
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Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun

“…At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. …”
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Using U-Net models in deep learning for brain tumor detection from MRI scans by Minh Khiem Nguyen, Phuoc Huy Tran, Tan Tai Phan

“…We propose a method employing two U-Net models: ResNeXt-50 and EfficientNet architectures, integrated with a Feature Pyramid Network (FPN) for segmenting brain tumor. …”
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Crafting a grassroots introduction to food policy course by William Schanbacher, Joe Bohn, Erica Hall

“…We include participant comments from a post-course survey and an outside evaluation from the North American Food Systems Network. …”
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Artificial Sensing: AI-Driven Electronic Nose for Real-Time Gas Leak Detection and Food Spoilage Monitoring by Lubna Aziz, Hassan Adil, Raheel Sarwar

“…Sensor data underwent preprocessing, feature extraction, and exploratory data analysis before training and evaluation. …”
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Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms by Manny Villa, Eduardo Casilari

“…This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. …”
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Formation of Low-Centered Ice-Wedge Polygons and Their Orthogonal Systems: A Review by Yuri Shur, Benjamin M. Jones, M. Torre Jorgenson, Mikhail Z. Kanevskiy, Anna Liljedahl, Donald A. Walker, Melissa K. Ward Jones, Daniel Fortier, Alexander Vasiliev

“…Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. …”
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Long Short-Term Memory-Based Fall Detection by Frequency-Modulated Continuous Wave Millimeter-Wave Radar Sensor for Seniors Living Alone by Yun Seop Yu, Seongjo Wie, Hojin Lee, Jeongwoo Lee, Nam Ho Kim

“…The histogram of oriented gradient (HOG) method was used for feature extraction, while LSTM networks captured temporal dependencies. …”
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Cross-View Geo-Localization: A Survey by Abhilash Durgam, Sidike Paheding, Vikas Dhiman, Vijay Devabhaktuni

“…This paper provides a thorough survey of cutting-edge methodologies, techniques, and associated challenges that are integral to this domain, with a focus on feature-based and deep learning strategies. Feature-based methods capitalize on unique features to establish correspondences across disparate viewpoints, whereas deep learning-based methodologies deploy neural networks (convolutional or transformer-based) to embed view-invariant attributes. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Use LASSO regression to screen clinical features based on the training set. Nine machine learning algorithms, Logistic Regression (LR), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forest (RF), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting Machines (GBM), and Neural Network (NNet), were used to construct models. …”
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