Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers by Devotha G. Nyambo, Edith T. Luhanga, Zaipuna O. Yonah, Fidalis D. N. Mujibi

“…Results indicated that, for the Ethiopian dataset, clusters derived from the fuzzy algorithm had the highest predictive power (77% for milk yield and 48% for milk sales), while for the Tanzania data, clusters derived from Self-Organizing Maps were the best performing. …”
Get full text

MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation by Dejie Chen, Xiangping Chen, Hao Gu, Su Zhao, Hao Jiang

“…First, the backbone employs uniform-sized convolutional kernels to process feature maps across all channels within the same layer, resulting in homogeneous receptive fields and a single-scale bottleneck that impedes global contextual feature extraction. …”
Get full text

Energy-Accuracy Trade-Offs for Resistive In-Memory Computing Architectures by Saion K. Roy, Naresh R. Shanbhag

“…This result clearly implies the need for other approaches, e.g., algorithmic- and learning-based methods, to improve the inference accuracy of resistive IMC architectures.…”
Get full text

Deciphering Socio-Spatial Integration Governance of Community Regeneration: A Multi-Dimensional Evaluation Using GBDT and MGWR to Address Non-Linear Dynamics and Spatial Heterogene... by Hong Ni, Jiana Liu, Haoran Li, Jinliu Chen, Pengcheng Li, Nan Li

“…By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. …”
Get full text

AdaSpline-Net for Improved Short-Term Solar Irradiance Forecasting by Ngiap Tiam Koh, Anurag Sharma, Jianfang Xiao, Cheng Siong Chin, Chin Jun Xing, Wai Lok Woo

“…Furthermore, its robustness across various neural network architectures demonstrates its adaptability and compatibility with backpropagation. …”
Get full text

Ensemble modelling reveals spiny monkey orange (Strychnos spinosa Lam.) as a vulnerable wild edible fruit tree in West Africa by Hospice Gérard Gracias Avakoudjo, Mahunan Eric José Vodounnon, Rodrigue Idohou, Aly Coulibaly, Achille Ephrem Assogbadjo

“…The Models’ performance was evaluated using the Area under the Curve (AUC), True Skill Statistic (TSS), Correlation coefficient (COR), and the Deviance. The most efficient algorithm (RF) was employed for the modelling, and the resulting maps were overlaid on the existing protected area network. …”
Get full text

Enhanced Defect Detection in Additive Manufacturing via Virtual Polarization Filtering and Deep Learning Optimization by Xu Su, Xing Peng, Xingyu Zhou, Hongbing Cao, Chong Shan, Shiqing Li, Shuo Qiao, Feng Shi

“…The YOLO V5-W model, integrated with CBAM attention, DenseNet connections, and an EIoU loss function, demonstrates superior performance in defect identification across various lighting conditions. Experiments show a 40.3% reduction in loss, a 10.8% improvement in precision, a 10.3% improvement in recall, and a 13.7% improvement in mAP compared to the original YOLO V5 model. …”
Get full text

The Coronal Plasma Density Structure over a Solar Cycle: A Comparison of Tomography and Magnetohydrodynamic Simulations by Liam T. Edwards, Huw Morgan, Pete Riley, Jon A. Linker

“…This study compares densities from tomography with the Magnetohydrodynamic Algorithm outside a Sphere (MAS) model across the whole corona over a solar cycle from 2007 to late 2023. …”
Get full text

Defect intelligent recognition of membrane product based on deep learning by Maonian Wu, Ling Li, Wei Peng, Tao Wu, Jinwei Yu, Bo Zheng, Shaojun Zhu

“…The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.…”
Get full text

Effect of Droplet Contamination on Camera Lens Surfaces: Degradation of Image Quality and Object Detection Performance by Hyunwoo Kim, Yoseph Yang, Youngkwang Kim, Dong-Won Jang, Dongil Choi, Kang Park, Sangkug Chung, Daegeun Kim

“…To assess the effect on object detection, performance changes were evaluated across different detection algorithms. The findings reveal that droplet contamination can reduce the detection performance of small objects by up to 90%, as measured by the mean average precision (mAP) metric. …”
Get full text

Matching and Control Optimisation of Variable-Geometry Turbochargers for Hydrogen Fuel Cell Systems by Matt L. Smith, Alexander Fritot, Davide Di Blasio, Richard Burke, Tom Fletcher

“…A MATLAB-Simulink reduced-order model is used first to identify the most efficient components that match the fuel cell air path requirements. Maps representing the compressor and turbines are then evaluated in a 1D flow model to optimise cathode pressure and stoichiometry operating targets for net system efficiency, using an accelerated genetic algorithm (A-GA). …”
Get full text

Neural Network-Based Coordinated Virtual Inertia Allocation Method for Multi-Region Distribution Systems by Heng Liu, Jingtao Zhao, Zhi Wu, Shu Zheng

“…Following this, a genetic algorithm-based solving strategy is designed to achieve the global optimal allocation of virtual inertia. …”
Get full text

Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. …”
Get full text

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators by Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen

“…Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. …”
Get full text

The China Coastal Front from <i>Himawari-8</i> AHI SST Data—Part 2: South China Sea by Igor M. Belkin, Shang-Shang Lou, Yi-Tao Zang, Wen-Bin Yin

“…The SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate monthly maps of the CCF’s intensity (defined as SST gradient magnitude GM) and frontal frequency (FF). …”
Get full text

A Lightweight Model for Small-Target Pig Eye Detection in Automated Estrus Recognition by Min Zhao, Yongpeng Duan, Tian Gao, Xue Gao, Guangying Hu, Riliang Cao, Zhenyu Liu

“…The model utilizes ocular appearance features—eye’s spirit, color, shape, and morphology—across different estrus stages as key indicators. …”
Get full text

Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

Get full text

Palette-based image recoloring based on moving least squares(基于移动最小二乘的图像重着色) by 周家伟(ZHOU Jiawei), 黄子康(HUANG Zikang), 彭俊豪(PENG Junhao), 李盛善(LI Shengshan), 刘晓静(LIU Xiaojing), 杜正君(DU Zhengjun)

“…In recent years, palette-based image recoloring techniques have garnered widespread attention and application across various domains such as graphic design, photo editing, and advertising production. …”
Get full text

Innovative Lightweight Detection for Airborne Remote Sensing: Integrating G-Shuffle and Dynamic Multiscale Pyramid Networks by Ruofei Liang, Yigang Cen, Linna Zhang, Fugui Zhang, Yansen Huang, Fei Gan

“…Experimental results demonstrate that the proposed method achieves a mAP&#x0040;0.5 of 0.552 and mAP&#x0040;0.5:0.95 of 0.265 on the AI-TOD dataset, with a 2.7 ms improvement in inference speed and a 14&#x0025; reduction in model parameters. …”
Get full text

HAD-YOLO: An Accurate and Effective Weed Detection Model Based on Improved YOLOV5 Network by Long Deng, Zhonghua Miao, Xueguan Zhao, Shuo Yang, Yuanyuan Gao, Changyuan Zhai, Chunjiang Zhao

“…With the purpose of improving the model’s capacity to extract features and making it more lightweight, this algorithm employs the HGNetV2 as its backbone network. …”
Get full text