Breast cancer classification using breast ultrasound images with a hybrid of transfer learning and Bayesian-optimized fast learning network by Emmanuel Ahishakiye, Fredrick Kanobe

“…DenseNet201 was employed to obtain robust, high-quality features from pre-trained weights. FLN is finely tuned with Bayesian optimization to select optimal hyperparameters such as learning rate, hidden neurons, and dropout rate. …”
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Associations between characteristics of large national regions and individual smoking: A scoping review by Sergey A. Maksimov, Darya P. Tsygankova, Yana V. Danilchenko, Svetlana A. Shalnova, Oxana M. Drapkina

“…Conclusion — We examined the features of research on the topic under consideration and highlighted the best evidence-based predictors of the regional environment in relation to smoking. …”
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Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application by Eko Abdul Goffar, Rosa Eliviani, Lili Ayu Wulandhari

“…This study addresses the challenge of stunting by developing a predictive model using machine learning techniques to forecast stunting risks based on public health data. The literature review section discusses the factors that influence stunting, and these factors are used as features to build a stunting prediction model. …”
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Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat by Meng Guanglei, Zhao Runnan, Wang Biao, Zhou Mingzhe, Wang Yu, Liang Xiao

“…Specifically, we employ support vector machine (SVM) to forecast the attack intention based on 19 low correlation features. The purpose of the employment of SVM is to avoid local optimization and reduce data dimension. …”
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A method to retrieve mixed-phase cloud vertical structure from airborne lidar by E. Crosbie, E. Crosbie, J. W. Hair, A. R. Nehrir, R. A. Ferrare, C. Hostetler, T. Shingler, D. Harper, M. Fenn, M. Fenn, J. Collins, J. Collins, R. Barton-Grimley, B. Collister, K. L. Thornhill, K. L. Thornhill, C. Voigt, C. Voigt, S. Kirschler, S. Kirschler, A. Sorooshian, A. Sorooshian

“…Clouds known to be liquid-only based on ambient temperature were used to train an empirical model of the multiple-scattering depolarization that results at different ranges from the lidar. …”
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OriLoc: Unlimited-FoV and Orientation-Free Cross-View Geolocalization by Boni Hu, Haowei Li, Shuhui Bu, Lin Chen, Pengcheng Han

“…Our approach employs a dual-weighted soft-margin triplet loss with hard sample mining to extract discriminative features. Additionally, we develop an orientation estimation module using convolution-based sliding windows to assess similarity between satellite-view and query embeddings. …”
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Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction by Shuqi Liu, Yufeng Zhuang, Chenxu Zhang, Qifei Li, Jiayu Hou

“…This system constructs a more accurate 3D environmental model by fusing semantic, shape, and geometric orientation features. Meanwhile, to deeply excavate the semantic information in images, a pre-trained frozen 2D segmentation algorithm is employed to extract semantic features, providing a powerful support for 3D environmental reconstruction. …”
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Deep Spatio-Temporal Illuminant Estimation Under Time-Varying AC Lights by Jun-Sang Yoo, Kang-Kyu Lee, Chan-Ho Lee, Ji-Min Seo, Jong-Ok Kim

“…We propose a network composed of spatial and temporal branches to train the model with both spatial and temporal features. …”
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Quantifying CineECG Output for Enhancing Electrocardiography Signals Classification by MHD Jafar Mortada, Agnese Sbrollini, Ilaria Marcantoni, Erica Iammarino, Laura Burattini, Peter Van Dam

“…Our research aims to quantify CineECG outputs by defining 54 features that describe the route, shape, and direction of electrical activation. …”
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Action-Oriented Deep Reinforcement Learning Method for Precast Concrete Component Production Scheduling by Rongzheng Yang, Shuangshuang Xu, Hao Li, Hao Zhu, Hongyu Zhao, Xiangyu Wang

“…The experimental results demonstrate that AO-DRL outperforms traditional rule-based methods and heuristic algorithms while exhibiting strong generalization capabilities.…”
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Dual stream transformer for medication state classification in Parkinson’s disease patients using facial videos by Vasileios Skaramagkas, Iro Boura, Georgios Karamanis, Ioannis Kyprakis, Dimitrios I. Fotiadis, Zinovia Kefalopoulou, Cleanthe Spanaki, Manolis Tsiknakis

“…Our approach integrated two streams of data: facial frame features and optical flow, processed through a transformer-based architecture. …”
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A smart walking stick with voice guidance in an African language for visually impaired persons by Abisola Olayiwola, Wasilat Olayode, Temiloluwa Akintayo, Ajibola Oyedeji, Dare Olayiwola, Martins Osifeko, Olatilewa Abolade

“…A Convolutional Neural Network is then trained using this dataset to achieve precise obstacle detection and classification. …”
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Development and validation of a machine learning model for predicting vulnerable carotid plaques using routine blood biomarkers and derived indicators: insights into sex-related ri... by Yimin E, Zhichao Yao, Maolin Ge, Guijun Huo, Jian Huang, Yao Tang, Zhanao Liu, Ziyi Tan, Yuqi Zeng, Junjie Cao, Dayong Zhou

“…A total of 30 laboratory variables—including blood count, coagulation, and biochemistry—were extracted, alongside derived indices such as triglyceride-glucose index (TyG), atherogenic index of plasma (AIP), neutrophil-to-lymphocyte ratio (NLR) and others. Features were standardized and selected based on statistical and clinical relevance. …”
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24 Hours Ahead Forecasting of the Power Consumption in an Industrial Pig Farm Using Deep Learning by Boris Evstatiev, Nikolay Valov, Katerina Gabrovska-Evstatieva, Irena Valova, Tsvetelina Kaneva, Nicolay Mihailov

“…The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. …”
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Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Results WES variant calls from Genome in a Bottle (GIAB) cell lines and their associated quality features were used to train five different machine learning models to predict whether a variant was a true positive or false positive based on quality metrics. …”
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Detection of Violent Scenes in Cartoon Movies Using a Deep Learning Approach by Noreen Fayyaz Khan, Sareer Ul Amin, Zahoor Jan, Changhui Yan

“…Subsequently, a Convolutional Neural Network (CNN) was utilized to extract prominent features from these selected key frames. In the third phase, the acquired features were utilized to train a sequential model using sequence-based learning. …”
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Neural models for detection and classification of brain states and transitions by Arnau Marin-Llobet, Arnau Manasanch, Leonardo Dalla Porta, Melody Torao-Angosto, Maria V. Sanchez-Vives

“…For the transitions, we report an average accuracy of 74%. Our models were trained using a leave-one-out methodology, allowing for broad applicability across subjects and pre-trained models for deployments. …”
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Laser interferometry for high-speed railway health inspection using telecom fiber along the line by Guan Wang, Dongqi Song, Zhongwang Pang, Fangmin Wang, Hongfei Dai, Wenlin Li, Bo Wang

“…Based on the distributed vibration sensing of daily passing trains, an average power spectrum density indicator is used to assess the health of high-speed railway infrastructures. …”
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Automatic description of rock thin sections: A web application by Stalyn Paucar, Christian Mejia-Escobar, Victor Collaguazo

“…A dataset of images and corresponding textual descriptions is used to train a hybrid deep learning model. Features extracted from the images using EfficientNetB7 are processed by a Transformer network to generate textual descriptions, which are then converted into verbal responses using a speech synthesis service. …”
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Multitask Adaptation for Unlabeled Domain Using Multiple Single-Task Domains by Youngwook Kang, Hawook Jeong, Junsup Shin, Jongwon Choi

“…In addition, we design a task correlation network that trains multiple tasks through attentional correlation, increasing the synergies between various tasks. …”
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