Sparse intensity sampling for ultrafast full-field reconstruction in low-dimensional photonic systems by Egor Manuylovich

“…Abstract Phase-sensitive measurements usually utilize interferometric techniques to retrieve the optical phase. However, when the feature space of an electromagnetic field is inherently low dimensional, most field parameters can be extracted from intensity measurements only. …”
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A Rapid Concrete Crack Detection Method Based on Improved YOLOv8 by Yongzhen Wang, Jiacong He

“…Secondly, the DBB_Bottleneck is introduced into the C2f module, combining the lightweight GE_Conv with the structurally re-parameterized Diverse Branch Block, enhancing the model’s multi-scale feature extraction capability. Furthermore, the introduction of the GF_Detect detection head significantly reduces the number of model parameters while improving detection performance. …”
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EmotionNet-X: An Optimized CNN Architecture for Robust Facial Emotion Analysis by Syed Muhammad Aqleem Abbas, Qaisar Abbas, Syed Muhammad Naqi

“…We propose EmotionNet-X, a lightweight CNN architecture with 19.9M parameters and 18 ms/image inference time. Key innovations include a streamlined design (four convolutional layers, seven dropout layers) and batch normalization for robust feature learning. …”
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Sustainable energy: Advancing wind power forecasting with grey wolf optimization and GRU models by Zainab Al-Ibraheemi, Samaher Al-Janabi

“…Programming challenges include high computational demands and the trial-and-error nature of parameter determination in deep learning, mitigated by using GWO. …”
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Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study by Ali Koç, Serap Ulusam Seçkiner

“…The approach adopted for predicting hospital energy consumption involves five steps: data acquisition, data pre-processing, data prediction, hyper-parameter optimisation and feature analysis. Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. …”
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Advancing e-waste classification with customizable YOLO based deep learning models by P. Akhil Rajeev, Vivek Dharewa, D. Lakshmi, G. Vishnuvarthanan, Jayant Giri, T. Sathish, Mubarak Alrashoud

“…The ‘You Only Look Once’ (YOLO) methodology underpins our research, highlighting the distinctive architectural features of each model, including the CSPDarknet53 backbone, PANet, and advanced anchor-free detection. …”
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S₂Head: Small-Scale Human Head Detection Algorithm by Improved YOLOv8n Architecture by Yuteng Sui, Xinghua Shan, Linlin Dai, Hui Jing, Bo Li, Jianjun Ma

“…Additionally, a small object detection branch and a reparameterizable BiFPN (Rep-BiFPN) structure are incorporated into the neck to improve the model’s sensitivity to small-scale features. Finally, a lightweight MSBlock is also integrated into the head to reduce computational overhead and parameter count without sacrificing detection accuracy. …”
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Surface-based morphometry of the cerebral cortex in cognitive impairments of varying severity in patients with age-related cerebral small vessel disease by Elena I. Kremneva, Larisa A. Dobrynina, Kamila V. Shamtieva, Victoria V. Trubitsyna, Zukhra S. Gadzhieva, Angelina G. Makarova, Maria M. Tsypushtanova, Marina V. Krotenkova

“…The assessment included the analysis of signs of cerebral small vessel disease based on the results of magnetic resonance imaging with the computation of general cerebral small vessel disease index and processing T1 multiplanar reconstruction images by surface-based morphometry to quantify general and regional brain parameters, including the thickness of the cerebral cortex. …”
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A global object-oriented dynamic network for low-altitude remote sensing object detection by Daoze Tang, Shuyun Tang, Yalin Wang, Shaoyun Guan, Yining Jin

“…This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. …”
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Lightweight Domestic Pig Behavior Detection Based on YOLOv8 by Kaining Zhang, Yu Zhang, Hongli Xu

“…Ultimately, we implement BiFPN in the neck network to replace the original FPN, which streamlines the neck network and enhances its feature-processing capabilities. The test findings indicated that, in comparison to the original YOLOv8n model, the precision, recall, and mean average precision at 50% remain constant, while the parameters and floating-point computations are diminished by 59.80% and 39.50%, respectively. …”
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Accurately Estimate and Analyze Human Postures in Classroom Environments by Zhaoyu Shou, Yongbo Yu, Dongxu Li, Jianwen Mo, Huibing Zhang, Jingwei Zhang, Ziyong Wu

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Research on dimension measurement algorithm for parcel boxes in high-speed sorting system by Ning Dai, Jingchao Chen, Xudong Hu, Yanhong Yuan

“…The high-low layer feature fusion structure and C2f-GhostCondConv are designed on the neck of the model to achieve the selective fusion of input features at different levels with small parameter number and computational amount. …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…All models used a maximum of 24 features as input, spread across target species, needle configuration, pulsing parameters, and plasmid parameters. …”
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Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging by Chun Wang, Zejun Wang, Lijiao Chen, Weihao Liu, Xinghua Wang, Zhiyong Cao, Jinyan Zhao, Man Zou, Hongxu Li, Wenxia Yuan, Baijuan Wang

“…First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. …”
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Attention Enhanced InceptionNeXt-Based Hybrid Deep Learning Model for Lung Cancer Detection by Burhanettin Ozdemir, Emrah Aslan, Ishak Pacal

“…The use of InceptionNeXt blocks facilitates multi-scale feature processing, making the model particularly effective for complex and diverse lung nodule patterns. …”
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TransferBAN-Syn: a transfer learning-based algorithm for predicting synergistic drug combinations against echinococcosis by Haitao Li, Haitao Li, Yuanyuan Chu, Yuanyuan Chu, Liyuan Jiang, Liyuan Jiang, Lei Li, Lei Li, GuoDong Lv, Yuansheng Liu, Chunhou Zheng, Chunhou Zheng, Yansen Su, Yansen Su

“…TransferBAN-Syn is designed and initially trained on the abundant data from the 21 parasitic diseases, which serves as the source domain. The parameters in the feature representation modules of drug interactions and diseases are preserved from this source domain, and those in the prediction module are then fine-tuned to specifically identify the synergistic drug combinations for echinococcosis in the target domain. …”
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Concept and Design of Cutting Tools for Osseodensification in Implant Dentistry by Alexander Isaev, Maria Isaeva, Oleg Yanushevich, Natella Krikheli, Olga Kramar, Aleksandr Tsitsiashvili, Sergey Grigoriev, Catherine Sotova, Pavel Peretyagin

“…Results: The most important design features and parameters of osseodensification burs are identified. …”
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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

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Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. …”
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Bayesian Optimized Enhanced Ensemble Multi-Layer Perceptron for Rear-End Collision Prediction in IoV by Dilna Vijayan, Mohamed Saad, Ahmed M. Khedr

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