An improved lightweight tiny-person detection network based on YOLOv8: IYFVMNet by Fan Yang, Lihu Pan, Hongyan Cui, Linliang Zhang

“…This operation also reduces the computational cost by decreasing the amount of required feature map channels, while maintaining the effectiveness of the feature representation. (3) he Minimum Point Distance Intersection over Union loss function is employed to optimize bounding box detection during model training. (4) to construct the overall network structure, the Layer-wise Adaptive Momentum Pruning algorithm is used for thinning.ResultsExperiments on the TinyPerson dataset demonstrate that IYFVMNet achieves a 46.3% precision, 30% recall, 29.3% mAP50, and 11.8% mAP50-95.DiscussionThe model exhibits higher performance in terms of accuracy and efficiency when compared to other benchmark models, which demonstrates the effectiveness of the improved algorithm (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) in small-object detection and provides a reference for future research.…”
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Identification and Validation of NK Marker Genes in Ovarian Cancer by scRNA-seq Combined with WGCNA Algorithm by Xin He, Weiwei Feng

“…The predictive performance of this model has been well-verified in different external cohorts. …”
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A nomogram for predicting the risk of tigecycline-associated drug-induced liver injury in a Chinese population by Xin Zhang, Lanfang Li, Chuanpeng Zhang, Huixian Zhang, Haining Huang

“…Nomogram model performance was evaluated via the area under the receiver operating characteristic curve (AUC). …”
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Generative Adversarial and Transformer Network Synergy for Robust Intrusion Detection in IoT Environments by Pardis Sadatian Moghaddam, Ali Vaziri, Sarvenaz Sadat Khatami, Francisco Hernando-Gallego, Diego Martín

“…Additionally, an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm is employed to fine-tune the hyper-parameters of the hybrid model, further enhancing learning stability and detection performance. …”
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A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning by Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber

“…For this, we trained a Machine Learning (ML) LightGBM model to predict 13 phenological stages for eight major crops across Germany at 20 m scale. …”
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BuildWin-SAM: An Improved SAM-Based Method for Extracting Building Windows From Street View Images by Zhengnan Li, Yizhen Yan, Bo Huang

“…However, accurately identifying windows in diverse urban environments poses significant challenges due to dataset limitations and variability in model performance. This study presents two primary contributions: first, we develop the Street View Building Window (SVBW) segmentation dataset, comprising 1,172 images that represent diverse urban contexts and window types, with a total of 50,321 meticulously annotated window instances. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…For the feature extraction process, the ResNet50 model is utilized. …”
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Detectron2-enhanced mask R-CNN for precise instance segmentation of rice blast disease in Tanzania: Supporting timely intervention and data-driven severity assessment by Reuben Alfred, Judith Leo, Shubi F. Kaijage

“…The model exhibited consistent performance across object scales, achieving an AP of 81.31 % for small objects and 86.06 % for large objects. …”
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Artificial Neural Networks for Residual Capacity Estimation of Cycle-Aged Cylindric LFP Batteries by Pasquale Franzese, Diego Iannuzzi, Roberta Merolla, Mattia Ribera, Ivan Spina

“…Despite the constraints imposed by the dataset size, the ANN demonstrates remarkable performance, underscoring the model’s capability to operate effectively with minimal data. …”
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Discrimination Analysis of Conventional Baijiu-making Sorghum from Southwest China and Hybrid Sorghum Based on Physicochemical Properties and Metabolomics by Hongmei WANG, Zhe LI, Songtao WANG, Qingliang ZHANG, Anjing CHEN, Ling LI, Rongkun TU

“…The model was based on the basic physicochemical composition and all metabolites. …”
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Sistem Pakar Penentuan Penggunaan Bahan Tambahan Pangan untuk Produk Pangan by Melva Linda Aritonang, Kudang Boro Seminar, Nugraha Edhi Suyatma, Irman Hermadi

“…The performance evaluation of the decision tree model uses k-fold cross validation with k ten and has the highest accuracy in the tenth iteration of 72.7%. …”
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Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach by Tomas G Neilan, Judith Mueller, Samuel Peter Heilbroner, Reed Few, Jitesh Chalwa, Francois Charest, Somasekhar Suryadevara, Christine Kratt, Andres Gomez-Caminero, Brian Dreyfus

“…All curated data from patients with non-small cell lung cancer, melanoma, and renal cell carcinoma, and who were prescribed PD-1/PD-L1 therapy between 2013 and 2019, were used for training, feature interpretation, and model performance evaluation. …”
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Classification of Nitrogen-Efficient Wheat Varieties Based on UAV Hyperspectral Remote Sensing by Yumeng Li, Chunying Wang, Junke Zhu, Qinglong Wang, Ping Liu

“…This approach aimed to mitigate the impact of high collinearity and noise in high-dimensional hyperspectral data on model construction. Furthermore, the SVM-XGBoost method integrated the extracted feature bands with the support vectors and decision function outputs from the preliminary SVM classification. …”
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Correlation between liver fibrosis in non-alcoholic fatty liver disease and insulin resistance indicators: a cross-sectional study from NHANES 2017–2020 by Bo Yang, Mingsu Gong, Xiaojie Zhu, Yang Luo, Ruiqiu Li, Hai Meng, Yuhan Wang

“…Subsequently, we used multivariate logistic regression to develop and validate a predictive model for LF, and evaluated the model’s performance using the area under the curve (AUC) and calibration curve.ResultsA total of 904 patients were included in the final analysis. …”
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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…The feature fusion CNN model based on spectra and images can achieve good classification of the four strains, which outperforms the other three traditional machine learning algorithm models. …”
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High-Speed and Enhanced Motion Control for a Wheeled-Legged Humanoid Robot Using a Two-Wheeled Inverted Pendulum With Roll Joint by Jaewoo An, Jun Young Kim, Myo-Taeg Lim, Yonghwan Oh

“…This framework generates optimized torque commands, enabling feasible and stable motion even in dynamic scenarios. Simulations featuring challenging maneuvers, such as slalom, demonstrate its ability to enhance stability and dynamic performance compared to traditional two-wheeled inverted pendulum (TWIP)-based methods by leveraging a dynamics model with roll motion. …”
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MoSViT: a lightweight vision transformer framework for efficient disease detection via precision attention mechanism by Yuanqi Chen, Aiping Wang, Ziyang Liu, Jie Yue, Enxu Zhang, Fei Li, Ning Zhang

“…This study introduces MoSViT, an innovative classification model leveraging advanced machine learning and computer vision technologies. …”
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Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence by Paolo Sorino, Angela Lombardi, Domenico Lofù, Tommaso Colafiglio, Antonio Ferrara, Fedelucio Narducci, Eugenio Di Sciascio, Tommaso Di Noia

“…However, inconsistencies in diagnostic labeling, arising from subjective assessments, evolving clinical criteria, and measurement variability, introduce noise that can impact machine learning (ML) model performance. This study explores the potential of explainable artificial intelligence to detect and characterize noisy labels in longitudinal datasets. …”
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Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease by Qi Zhou, Mohammed Goryawala, Mercedes Cabrerizo, Warren Barker, Ranjan Duara, Malek Adjouadi

“…Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. …”
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Nanoengineering of Chitosan Sponges Via Atomic Layer Deposition of ZnO for Water Remediation Technologies by Sara Lotito, Domenico Cignolo, Jennifer Gubitosa, Gianni Barucca, Paolo Mengucci, Marinella Striccoli, Fabio Palumbo, Pinalysa Cosma, Paola Fini, Sapia Murgolo, Vito Rizzi, Francesco Fracassi, Antonella Milella, Alberto Perrotta

“…A reaction‐limited growth model is found and strong interactions between ZnO and chitosan amino groups significantly enhance the stability in water and impart photocatalytic features. …”
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