MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images by Angel Luis Perales Gomez, Juan Jesus Losada del Olmo, Pedro E. Lopez-de-Teruel, Alberto Ruiz, Felix J. Garcia Clemente, Andres Conesa Bueno

“…In particular, the best-performing model was Random Forest that achieved a Mean Square Error of 1.6111 and a corresponding error rate of 7.9944% on the test set.…”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. …”
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Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8 by Yanxing Liu, Xudong Li, Ruyu Qiao, Yu Chen, Xueliang Han, Agyemang Paul, Zhefu Wu

“…Secondly, to address the issue of the network model’s complexity hindering its application on UAV equipment, depthwise separable convolution is incorporated for lightweight enhancement within the YOLOv8 algorithm framework. …”
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Development of GUI-Driven AI Deep Learning Platform for Predicting Warpage Behavior of Fan-Out Wafer-Level Packaging by Ching-Feng Yu, Jr-Wei Peng, Chih-Cheng Hsiao, Chin-Hung Wang, Wei-Chung Lo

“…The platform efficiently processes large datasets, automating feature extraction, data cleansing, and model training, ensuring accurate and reliable predictions. …”
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A Hybrid Security Framework for Train-to-Ground (T2G) Communication Using DOA-Optimized BPNN Detection, Bayesian Risk Scoring, and RL-Based Response by Chaoyuan Sun, Weijiao Zhang, Peng Sun, Hui Wang, Chunhui Yang

“…This paper presents an intelligent security defense framework that integrates intrusion detection, risk scoring, and response mechanisms to enhance the security and responsiveness of the T2G communication system. First, feature selection is performed on the TON_IoT dataset to develop a Dream Optimization Algorithm (DOA)-optimized backpropagation neural network (DOA-BPNN) model for efficient anomaly detection. …”
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Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances by Chaofan Lan, Qingquan Luo, Tao Yu, Minhang Liang, Wenlong Guo, Zhenning Pan

“…The method employs a lightweight model that is constructed with dual output branches using depthwise separable convolution for load identification and novelty detection. …”
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Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks by Xingyi Li, Junming Li, Jun Hao, Xingyu Liao, Min Li, Xuequn Shang

“…Moreover, samples with imbalanced classes in driver gene identification hamper the performance of models. To address this, we propose a novel deep learning framework MMGN, which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes, which not only enhances gene feature learning based on the mutual information and the consensus regularizer, but also achieves balanced class of positive and negative samples for model training. …”
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Tracing the evolutionary pathway of SARS-CoV-2 through RNA sequencing analysis by Mostafa Rezapour, Sean V. Murphy, David A. Ornelles, Patrick M. McNutt, Anthony Atala

“…Our first objective was to examine differences in gene expression dynamics using Generalized Linear Models with Quasi-Likelihood F-tests and the Magnitude-Altitude Scoring (GLMQL-MAS) technique, followed by Gene Ontology (GO) and pathway analyses. …”
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Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions by Kirsten Zantvoort, Barbara Nacke, Dennis Görlich, Silvan Hornstein, Corinna Jacobi, Burkhardt Funk

“…The results substantiate the concern that small datasets (N ≤ 300) overestimate predictive power. For uninformative feature groups, in-sample prediction performance was negatively correlated with dataset size. …”
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Generalized cross-entropy for learning from crowds based on correlated chained Gaussian processes by J. Gil-González, G. Daza-Santacoloma, D. Cárdenas-Peña, A. Orozco-Gutiérrez, A. Álvarez-Meza

“…Through extensive simulated and real-world experiments, CCGP-GCE demonstrated superior classification performance, outperforming state-of-the-art multilabeler models in terms of both accuracy and expert reliability estimation. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Empirical experiments on the CSI 500 and S&P 500 datasets demonstrate that STGAT outperforms other deep learning models in both prediction accuracy and portfolio performance. …”
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Finite Element Analysis of a Novel Temporary External Fixator (TEF) in Mandibular Reconstruction by Rungsan Chaiyachet, Surasith Piyasin, Weerayut Jina, Teerawat Paipongna, Apichart Boonma

“…Structural analysis was performed in ANSYS using literature-based mechanical properties. …”
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Ship Contour: A Novel Ship Instance Segmentation Method Using Deep Snake and Attention Mechanism by Chen Chen, Songtao Hu, Feng Ma, Jie Sun, Tao Lu, Bing Wu

“…These improvements to CenterNet enhance model robustness and effectively reduce missed and false detection. …”
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Design of Small-Sized Spiral Slot PIFA Antenna Used Conformally in Laminated Body Tissues by Rong Li, Jian Liu, Cuizhen Sun, Wang Yao, Ying Tian, Xiaojun Huang

“…When implanted in a layered biological tissue model (skin, fat, muscle), the antenna exhibits a near-omni directional radiation pattern in the azimuthal plane, with a peak gain of 2.94 dBi and consistent performance across the target band. …”
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YOLO-SCNet: A Framework for Enhanced Detection of Small Lunar Craters by Wei Zuo, Xingye Gao, Di Wu, Jiaqian Liu, Xingguo Zeng, Chunlai Li

“…Key challenges in lunar crater detection, such as varying lighting conditions and complex terrains, are addressed through the innovative model architecture, which incorporates a small object detection head, dynamic anchor boxes, and multi-scale feature fusion. …”
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Interfacial behavior prediction and regulation on inclined superhydrophobic surfaces with transfer learning-assisted method by Yibin Liu, Konghua Yang, Yuchao Luo, Jiawei Xiong, Yunhong Liang, Zhihui Zhang, Chunbao Liu

“…The proposed method leverages knowledge learned from a source SHS to assist the interfacial behavior prediction for target SHSs, even when historical measurements are limited, without model performance degradation caused by data distribution discrepancies and insufficient target samples. …”
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Photodiagnosis with deep learning: A GAN and autoencoder-based approach for diabetic retinopathy detection by Kerem GENCER, Gülcan GENCER, Tuğçe Horozoğlu CERAN, Aynur ER BİLİR, Mustafa DOĞAN

“…This study proposes a novel framework integrating Generative Adversarial Networks (GANs) for data augmentation, denoising autoencoders for noise reduction, and transfer learning with EfficientNetB0 to enhance the performance of DR classification models. Methods: GANs were employed to generate high-quality synthetic retinal images, effectively addressing class imbalance and enriching the training dataset. …”
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Radiomics in thymic epithelial tumors: a scoping review of current status and advances by Xiaolian Wang, Pei Huang, Zonghuo Wang, Yangchun Liu, Bing Fan, Wentao Dong

“…The studies were assessed for their methodologies, including imaging protocols, feature extraction techniques, and model performance metrics. …”
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Heterogeneous Graph Neural Network Framework for Session-Based Cyberbullying Detection by Munkhbuyan Buyankhishig, Thanda Shwe, Israel Mendonca, Masayoshi Aritsugi

“…Our approach leverages a Heterogeneous Graph Neural Network to capture complex high-order semantic relationships from a learned heterogeneous graph. The model comprises three components: a feature encoder, a heterogeneous graph encoder, and a classifier that collaboratively classifies social media sessions as bullying or not. …”
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Electrophysical Microwave Installation for Heat Treatment of Animal Slaughter Waste by Evgeny V. Voronov

“…The authors investigated the dynamics of heating and used the data obtained to develop a digital installation model and test the electrodynamics in the resonator. …”
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