Application of Machine Learning for Radiowave Propagation Modeling Below 6 GHz by Mohammud Z. Bocus, Afzal Lodhi

“…We present a simple hybrid approach in this paper that resolves the generalisation issues and provide far superior performance compared to pure physical propagation models or data driven models based on FCNN.…”
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MedAlmighty: enhancing disease diagnosis with large vision model distillation by Yajing Ren, Zheng Gu, Wen Liu

“…We adopt a hybrid loss function that combines cross-entropy loss (for classification accuracy) and Kullback-Leibler divergence (for distillation), enabling the student model to capture rich semantic features while remaining efficient and domain-aware.ResultsExperimental evaluations reveal that MedAlmighty significantly improves disease diagnosis performance across datasets characterized by sparse and diverse medical data. …”
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Machine learning-based radiomic features of perivascular adipose tissue in coronary computed tomography angiography predicting inflammation status around atherosclerotic plaque: a... by Kunlin Ye, Lingtao Zhang, Hao Zhou, Xukai Mo, Changzheng Shi

“…The RadScore achieved an Area Under the Curve (AUC) of 0.897 (95% CI: 0.88–0.92) in the training set and 0.717 (95% CI: 0.63–0.81) in the validation set. The combined model (RadScore + Clinic) demonstrated improved performance with an AUC of 0.783 (95% CI: 0.69–0.87) in the validation set and 0.903 (95% CI: 0.83–0.98) in an independent test set. …”
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Semiparametric Estimation and Application of Realized GARCH Model with Time-Varying Leverage Effect by Jinguan Lin, Yizhi Mao, Hongxia Hao, Guangying Liu

“…Simulation studies demonstrate that the proposed model yields better performances than traditional RG models under different situations. …”
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CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study by Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu

“…Results The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. …”
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DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation by Elizar Elizar, Rusdha Muharar, Mohd Asyraf Zulkifley

“…Its foundation follows encoder–decoder pair architecture that utilizes the Spatial Pyramid Pooling (SPP) layer to improve the performance of cardiac semantic segmentation. The SPP layer is designed to pool features from densely convolutional layers at different scales or sizes, which will be combined to maintain a set of spatial information. …”
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GUIDE: a prospective cohort study for blood-based early detection of gastrointestinal cancers using targeted DNA methylation and fragmentomics sequencing by Ao Huang, De-Zhen Guo, Zhi-Xi Su, Yun-Shi Zhong, Liang Liu, Zhi-Guo Xiong, Dong-Li He, Bin Yan, Quan-Lin Li, Zhen Feng, Wen-Quan Wang, Pin-Xiang Lu, Meng-Jiang He, Zhi-Peng Qi, Qi Guo, Jian-Wen Cheng, Shi-Yu Zhang, Wei Guo, Qing Li, Guo-Yong Lin, Hui-Chuan Sun, Shuang-Jian Qiu, Qi-Ye He, Jia Fan, Ajay Goel, Rui Liu, Gang Jin, Xin-Rong Yang, Jian Zhou

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Urban informal settlements interpretation via a novel multi-modal Kolmogorov–Arnold fusion network by exploring hierarchical features from remote sensing and street view images by Hongyang Niu, Runyu Fan, Jiajun Chen, Zijian Xu, Ruyi Feng

“…The proposed KANFusion model employs the Kolmogorov–Arnold Network (KAN) instead of the conventional MLP structure to enhance the model-fitting capability of heterogeneous modality-specific features and uses a novel Multi-level Feature Fusion Module with KAN block (MFF) to fuse the hierarchical modality-specific and modality-fusion features from remote sensing and street view images for better UIS interpretation performance. …”
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Effective workflow from multimodal MRI data to model-based prediction by Kyesam Jung, Kevin J. Wischnewski, Simon B. Eickhoff, Oleksandr V. Popovych

“…We in particular show that incorporating the simulated data into machine learning can significantly improve the prediction performance compared to using empirical features alone. …”
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Infrared and visible image fusion driven by multimodal large language models by Ke Wang, Dengshu Hu, Yuan Cheng, Yukui Che, Yuelin Li, Zhiwei Jiang, Fengxian Chen, Wenjuan Li

“…IntroductionExisting image fusion methods primarily focus on obtaining high-quality features from source images to enhance the quality of the fused image, often overlooking the impact of improved image quality on downstream task performance.MethodsTo address this issue, this paper proposes a novel infrared and visible image fusion approach driven by multimodal large language models, aiming to improve the performance of pedestrian detection tasks. …”
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A Hybrid K-Means++ and Particle Swarm Optimization Approach for Enhanced Document Clustering by Eisha Hassan, Fazila Malik, Qazi Waqas Khan, Nadeem Ahmad, Muhammad Sardaraz, Faten Khalid Karim, Hela Elmannai

“…The research also attempts to assess the results of the best-performing feature extraction techniques and create a combined approach using their average. …”
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Psychometric Evaluation of Large Language Model Embeddings for Personality Trait Prediction by Julina Maharjan, Ruoming Jin, Jianfeng Zhu, Deric Kenne

“…ObjectiveThis study evaluates LLM embeddings for personality trait prediction through four key analyses: (1) performance comparison with zero-shot methods on PANDORA Reddit data, (2) psychometric validation and correlation with LIWC (Linguistic Inquiry and Word Count) and emotion features, (3) benchmarking against traditional feature engineering approaches, and (4) assessment of model size effects (OpenAI vs BERT vs RoBERTa). …”
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Social Media Text Stance Detection Based on Large Language Models by LI Juhao, SHI Lei, DING Meng, LEI Yongsheng, ZHAO Dongyue, CHEN Long

“…This improves the representation of stance features and enhances the overall performance of the model in stance detection tasks. …”
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A Machine Learning Model for Prostate Cancer Prediction in Korean Men by Sukjung Choi, Beomgi So, Shane Oh, Hongzoo Park, Sang Wook Lee, Geehyun Song, Jong Min Lee, Jung Ki Jo, Seon Hyeok Kim, Si Eun Lee, Eun-Bi Cho, Jae Hung Jung, Jeong Hyun Kim

“…Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. …”
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Leveraging RAG With Transformer for Context-Based Personalized Recommendations by Faten S. Alamri, Amjad Rehman, Bayan Alghofaily, Adeel Ahmed, Khalid Saleem

“…Recent advancements in large language models (LLMs) have shown significant progress in addressing challenges related to data sparsity and the cold-start problem. …”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

“…Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. …”
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Citrus Disease Classification Model Based on Improved ConvNeXt by Jichi Yan, Yongbin Mo, Yannan Yu, Shiqing Dou, Rongfeng Yang

“…Secondly, the multi-scale feature fusion module is incorporated to improve the model’s adaptability to disease features at different scales and improve the network classification performance. …”
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Kans-Unet Model and Its Application in Image Patch-Shaped Detection by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Kexin Zhu, Bo Hao, Junjie Song, Yumeng Huo

“…Secondly, in the transition region between encoder and decoder, the Feature Pyramid Attention (FPA) module is introduced to enhance the ability of the model to capture and analyze multiple scale features. …”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…The explainable AI models identified the important features, and their performance was evaluated using cross-validation. …”
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Collision Risk Perception Models Using Physiological and Eye-Tracking Signals by Hyowon Lee, Ocktaeck Lim, Amandeep Singh, Siby Samuel

“…These findings suggest that EEG and pupil features are optimal for real-time risk perception models, with heart activity features serving as complementary factors in enhancing model accuracy and reliability. …”
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