Enhancing Real-Time Detection Transformer (RT-DETR) for Handgun Detection on Nvidia Jetson by Luis Bustamante, Juan C. Gutiérrez

“…We also compare the model with state-of-the-art alternatives such as YOLOv10, demonstrating the superiority of Transformer models in terms of accu- racy and performance. …”
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Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction by Yeung Manho

“…Its ensemble approach allowed for the effective capture of complex feature interactions, making it the most accurate model for predicting whether a team would qualify for the playoffs based on team performance. …”
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vFerryman: An Artificial Intelligence-Driven Personalized Companion Providing Calming Visuals and Social Interaction for Emotional Well-Being by Wei-Ji Wang

“…By utilizing large language model operations (LLMOps), vFerryman optimizes the performance of large language models to dynamically adapt to users’ emotional feedback. …”
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Classification of retinopathy of prematurity based on mixed attention by CHEN Shaobin, LEI Baiying, XIE Hai, ZHANG Guoming, DU Yueshanyi, ZHAO Xinyu

“…The enhanced data obtained by the weak enhancer is used to generate pseudo labels of the unlabeled data, which will be further used as the supervised training labels of enhanced data obtained by the strong enhancer. In the devised model, we consider the self-attention, spatial attention and channel attention together to enhance the capabilities of feature extraction. …”
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Survey of Mechatronic Techniques in Modern Machine Design by Devdas Shetty, Lou Manzione, Ahad Ali

“…This paper shows the simulation based mechatronic model of a complex system with a better understanding of the dynamic behavior and interactions of the components. …”
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Enhancing Predictive Capabilities for Identifying At-Risk Stocks Using Multivariate Time-Series Classification: A Case Study of the Thai Stock Market by Katsamapol Petchpol, Laor Boongasame

“…The study identifies models that effectively balance predictive accuracy with computational efficiency, with performance varying based on forecasting horizons. …”
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Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation by Lei Wang, Qun Ai

“…Using the naive Bayesian machine learning method and the feature vector set extracted and constructed by the Dice coefficient method, a semantic word disambiguation model based on semantics is realized. …”
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Prediction of drug target interaction based on under sampling strategy and random forest algorithm. by Feng Chen, Zhigang Zhao, Zheng Ren, Kun Lu, Yang Yu, Wenyan Wang

“…Here, the random projection method is adopted to reduce the feature dimension such that simplify the model calculation. …”
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Triplet Multi-Kernel CNN for Detection of Pulmonary Diseases From Lung Sound Signals by Pumin Duangmanee, Khomdet Phapatanaburi, Wongsathon Pathonsuwan, Talit Jumphoo, Atcharawan Rattanasak, Khwanjit Orkweha, Patikorn Anchuen, Monthippa Uthansakul, Peerapong Uthansakul

“…We also present a Triplet MK-CNN (TMK-CNN) model that combines the benefits of multi-kernel feature extraction with a triplet-based architecture to enhance detection performance. …”
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GDText-VM: an arbitrary-shaped scene text detector based on globally deformable VMamba by Yingnan Zhao, Zheng Hu, Fangqi Ding, Jielin Jiang, Xiaolong Xu

“…This module facilitates the fusion of global channel and spatial features, effectively mitigating the impact of feature imbalance on fitting performance and significantly enhancing text detection accuracy. …”
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PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data by Mousa Moradi, Saber Kazeminasab Hashemabad, Daniel M. Vu, Allison R. Soneru, Asahi Fujita, Mengyu Wang, Tobias Elze, Mohammad Eslami, Nazlee Zebardast

“…Permutation importance analysis revealed that the superior temporal sector was the most influential VF feature, with importance scores of 0.08 in Kang’s and 0.04 in HAP2_part1 models. …”
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Functionalization of biochar using SDS/SAP nanomicelles enhanced its immobilization capacity for dyes and heavy metals in water by Kun Tian, Chunping Li, Huiming Liu, Lianchun Wang

“…Finding Extreme Gradient Boosting (XGBoost) exhibited best performance, the order of feature importance as time> Ratio> pH> concentration> temperature. …”
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Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection by Xiuhao Liang, Jun Xiang, Sheng Qin, Yundan Xiao, Lifen Chen, Dongxia Zou, Honglun Ma, Dong Huang, Yongxin Huang, Wei Wei

“…In practical applications, the SAHI-Improved-YOLOv8 model performs excellently in tree pit detection in UAV imagery, significantly reducing false detections and missed detections, and providing reliable technology support for large-scale forest management.…”
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Multiomics integration analysis identifies tumor cell-derived MIF as a therapeutic target and potentiates anti-PD-1 therapy in osteosarcoma by Yan Liao, Hao Yao, Weidong Chen, Yutong Zou, Ji Fang, Jiongfeng Zhang, Zehao Guo, Jian Tu, Junkai Chen, Zijun Huo, Lili Wen, Xianbiao Xie

“…This study aimed to leverage multiomics integration to develop a new risk model, characterizing the immune features of osteosarcoma to uncover novel therapeutic targets.Methods Metabolomics profiling was conducted to identify key metabolites in osteosarcoma. …”
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CASSNet: Cross-Attention Enhanced Spectral–Spatial Interaction Network for Hyperspectral Image Super-Resolution by Zhanxu Zhang, Linzi Yang, Guanglian Zhang, Jiangwei Deng, Lifeng Bian, Chen Yang

“…Deep-learning-based super-resolution (SR) methods for a single hyperspectral image have made significant progress in recent years and become an important research direction in remote sensing. Existing methods perform well in extracting spatial features, but challenges remain in integrating spectral and spatial features when modeling global relationships. …”
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Application of XAI to the prediction of CTCF binding sites by Thomas Vanhaeren, Angela del Robledo Troncoso-García, José Francisco Torres Maldonado, Federico Divina, Pedro Manuel Martínez-García

“…In the last years, Explainable Artificial Intelligence (XAI) techniques have proven to be effective not only in prediction itself but also in the extraction of meaningful knowledge from deep learning models by means of feature interpretation. In this study, Local Interpretable Model-agnostic Explanations are applied to the prediction of CTCF binding sites, a common task in the field of genomics. …”
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Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context by Limeng Qu, Jinfeng Zhu, Xilong Mei, Zixi Yi, Na Luo, Songlin Yuan, Xuan Liu, Mingwen Liu, Haiqing Xie, Xiongqiang Hu, Liangrui Pan, Qingchun Liang, Yanhui Li, Qiongyan Zou, Qin Zhou, Danhua Zhang, Meirong Zhou, Lei Pei, Ke Qian, Qian Long, Qitong Chen, Xi Chen, Jennifer K. Plichta, Qingyao Shang, Meishuo Ouyang, Jiachi Xu, Wenjun Yi

“…Regions of interest (ROIs) were segmented, and CT radiomic features were extracted. 11 supervised learning models were built on the basis of labeled ALNs, and pseudolabels (low-risk, high-risk groups) were assigned to unlabeled ALNs. …”
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Multi-spatial-attention U-Net: a novel framework for automated gallbladder segmentation on CT images by Henan Lou, Xiaobo Wen, Fanxia Lin, Zhan Peng, Qiuxiao Wang, Ruimei Ren, Junlin Xu, Jinfei Fan, Hao Song, Xiaomeng Ji, Huiyu Wang, Xiangyin Sun, Yinying Dong

“…Our proposed MSAU-Net model was transformed into two versions V1(with one Multi-Scale Feature Extraction and Fusion (MSFEF) module in each MSA block) and V2 (with two parallel MSEFE modules in each MSA blcok). …”
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Renal Biopsy Pathological Tissue Segmentation: A Comprehensive Review and Experimental Analysis by Yan Liu, Taiping Wang, Xinxin He, Wei Wang, Qiang He, Juan Jin

“…A complete pipeline is proposed, encompassing data preprocessing, model training, and validation, including multi-scale feature extraction and optimized loss function configurations. …”
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Weakly Supervised Semantic Segmentation of Mangrove Ecosystem Using Sentinel-1 SAR and Deep Convolutional Neural Networks by Arsalan Ghorbanian, Ardalan Ghorbanian, Seyed Ali Ahmadi, Ali Mohammadzadeh, Amin Naboureh

“…The temporal transferability of the model, examined in 2017 and 2023 using the baseline model with no modification, confirmed its applicability by generating the ecosystem maps meticulously, though the predictive uncertainty value increased. …”
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