MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…First, the model uses an attention scale sequence fusion mode to achieve more efficient multiscale feature fusion. Meanwhile, a tiny prediction head is incorporated to make the model focus on the low-level features, thus improving its ability to detect small objects. …”
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Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking by Yao Yang, Xu Sun, Bin Liu, Yunshu Zhang, Tong Xie, Junchen Li, Jifeng Liu, Qingkai Zhang

“…Through machine learning algorithms, the diagnostic model was established. Further elucidating the mechanisms and regulatory network of the model gene in UC were GSVA, immunological correlation analysis, transcription factor prediction, immunofluorescence, and single-cell analysis. …”
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OMAL: A Multi-Label Active Learning Approach from Data Streams by Qiao Fang, Chen Xiang, Jicong Duan, Benallal Soufiyan, Changbin Shao, Xibei Yang, Sen Xu, Hualong Yu

“…To solve these two issues, we propose a novel online multi-label active learning (OMAL) algorithm that considers simultaneously adopting uncertainty (using the average entropy of prediction probabilities) and diversity (using the average cosine distance between feature vectors) as an active query strategy. …”
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Downscaling Satellite Night-Time Light Imagery While Addressing the Blooming Effect by Nikolaos Tziokas, Ce Zhang, Alexandros Tziokas, Qunming Wang, Peter M. Atkinson

“…We compared several image fusion algorithms for downscaling while reducing the blooming effect. …”
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Mammographic Screening as a Tool for Cardiovascular Risk Assessing. Part 2. Association of Breast Arterial Calcification and Cardiovascular Diseases by E. V. Bochkareva, I. V. Kim, E. K. Butina, I. D. Stulin, S. A. Trukhanov, B. A. Rudenko, S. A. Boytsov, O. M. Drapkina

“…It is shown that the addition of BAC to the generally accepted (standard) vascular risk assessment algorithms Framingham Risk Score and Pooled Cohort Equation significantly increases the accuracy of prediction of CHD (p=0.02 and p=0.010, respectively). …”
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Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency by Jonathan Tarquino, Jhonathan Rodríguez, David Becerra, Lucia Roa-Peña, Eduardo Romero

“…This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation. …”
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A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing by Guiwen Jiang, Rongxi Huang, Zhiming Bao, Gaocai Wang

“…Finally, an offloading decision algorithm TOMAC-PPO is proposed. The algorithm applies the proximal policy optimization to the multi-agent system and combines the Transformer neural network model to realize the memory and prediction of network state information. …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…These improvements highlight the model’s ability to assimilate diverse climatic and topographical inputs for more accurate rainfall prediction, particularly in areas where conventional methods fall short due to sparse or irregular precipitation. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…The significance of this research lies in its ability to automate and accelerate complex structural analysis using Adaptive Neuro-Fuzzy Inference Systems (ANFISs), achieving an average error of less than 2% in multi-variable prediction scenarios. The results were compared against reference calculations and ETABS simulations to validate its effectiveness, demonstrating deviations of less than 3%. …”
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Outpatient diagnosis of endogenous intoxication in surgery by A. A. Solomakha, A. P. Vlasov, V. I. Gorbachenko

“…The problems of diagnosis, treatment, prevention and prediction of purulent diseases in surgery can be solved thanks to advanced digital technologies.Aim of the study. …”
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Molecular insights into ulcerative colitis and orbital inflammation by Kang Tan, Pei Liu, Zixuan Wu, Xi Long, Yunfeng Yu, Pengfei Jiang, Qinghua Peng

“…GO enrichment, PPI networks, and transcription factor prediction were performed using Cytoscape plugins (cytoHubba and iRegulon). …”
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Computational Intelligence-Based Structural Health Monitoring of Corroded and Eccentrically Loaded Reinforced Concrete Columns by Somain Sharma, Harish Chandra Arora, Aman Kumar, Denise-Penelope N. Kontoni, Nishant Raj Kapoor, Krishna Kumar, Arshdeep Singh

“…Machine learning (ML)-based prediction models are beneficial in dealing with such complex databases. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Despite its practical importance, the direct impact of parallel shaft misalignment on bearing fault prediction remains unaddressed, mainly in the existing literature. …”
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Genomic profiling, implications for genotype-based treatment of 131 patients with phenylketonuria and characterization of novel p.Pro416Leu PAH variant by K. Klaassen, B. Kecman, S. Stankovic, J. Komazec, S. Pavlovic, Maja Stojiljkovic, M. Djordjevic

“…We detected one novel variant, p.Pro416Leu, which was classified as pathogenic, based on computational algorithms prediction, with destabilization as the mechanism of the effect upon PAH protein. …”
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Integrating Machine Learning Workflow into Numerical Simulation for Optimizing Oil Recovery in Sand-Shale Sequences and Highly Heterogeneous Reservoir by Dung Bui, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Adewale Amosu, William Ampomah

“…A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. …”
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Tactical Coordination-Based Decision Making for Unmanned Combat Aerial Vehicles Maneuvering in Within-Visual-Range Air Combat by Yidong Liu, Dali Ding, Mulai Tan, Yuequn Luo, Ning Li, Huan Zhou

“…The former combines missile attack zone calculation and trajectory prediction to optimize the control quantity of a single aircraft, while the latter uses fuzzy logic to analyze the overall situation of the three aircraft to drive tactical selection. …”
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Liquid biopsy-derived extracellular vesicle protein biomarkers for diagnosis and prognostic assessment of lung squamous cell carcinoma by Sheng Ma, Na Zhao, Xin Dong, Yaru Wang, Lei Song, Ruiqi Zheng, Xiaochen Zhi, Congcong Ma, Shujun Cheng, Jie Li, Yutao Liu, Ting Xiao

“…Additionally, a prognostic prediction model for LUSC was developed using a combination of 101 machine learning algorithms. …”
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Discovering biomarkers associated with infiltration of CD8+ T cells and tumor-associated fibrosis in colon adenocarcinoma using single-cell RNA sequencing and gene co-expression ne... by Jinning Zhang, Jinning Zhang, Ziquan Sun, Ziquan Sun, Guodong Li, Guodong Li, Lixian Ding, Zitong Wang, Ming Liu, Ming Liu

“…We utilized univariate Cox regression and LASSO analysis to create a prognostic model. The predictive model was assessed through Kaplan-Meier analysis, and a survival prediction nomogram was created. …”
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Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems by Andrea Pozzi, Alessandro Incremona, Daniele Toti

“…While stochastic predictive control policies can address these uncertainties by incorporating them directly into the optimization process, they typically introduce considerable computational complexity. …”
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Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days by Yilun XU, Binqiao ZHANG, Jing HUANG, Xiao XIE, Ruoxin WANG, Danqing SHEN, Lina HE, Kaifan YANG

“…In order to improve the prediction accuracy of photovoltaic power, the input of the photovoltaic power prediction model is determined according to the characteristics of photovoltaic output power under different weather types. …”
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