Development of an AI-Based Image Analysis Model for Verifying Partial Defects in Nuclear Fuel Assemblies by Seulah Kim, Dayun Park, Hyung-Joo Choi, Chulhee Min, Jaejoon Ahn

“…This study aims to increase the accuracy of SNF defect detection by optimizing artificial intelligence (AI)-based classification algorithms. …”
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Energy optimization through morphing blade design under structural constraints: a case study on the NREL 1.5 MW wind turbine by Najafian Arezoo, Jahangirian Alireza

“…The morphing process is modeled using an m-degree shape function and optimized through a Genetic Algorithm (GA) to maximize power generation while minimizing structural displacement and thrust forces. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…This research proposes deep residual transfer ensemble model for mRNA gene-expression based breast cancer detection. …”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

“…This article introduces a drone detection model, MEP-YOLOv5s, which optimizes the Backbone, Neck layer, and C3 module based on YOLOv5s, and combines effective attention mechanisms to improve the training efficiency of the model by replacing the traditional CIoU loss (Complete Intersection over Union) with MPDIoU (Minimum Point Distance-based Intersection over Union) loss. …”
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Exploring the Current Status of Risk Stratification in Hypertrophic Cardiomyopathy: From Risk Models to Promising Techniques by Alexandros Kasiakogias, Christos Kaskoutis, Christos-Konstantinos Antoniou, Stavros Georgopoulos, Dimitrios Tsiachris, Petros Arsenos, Alexandrina Kouroutzoglou, Dimitrios Klettas, Charalambos Vlachopoulos, Konstantinos Tsioufis, Konstantinos Gatzoulis

“…Some research suggests that integrating electrophysiological studies into traditional risk assessment models may further optimize risk prediction and significantly improve accuracy in detecting high risk patients. …”
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A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning by Bo Zhang, Yumei Qin, Liandi Jiu, Chunming Qin, Jiangbo Wang, Haiqing Zhao

“…Feature analysis and data mining were conducted, and eight different machine learning algorithms were used to build the prediction model. …”
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Two-Stage Dispatch of CCHP Microgrid Based on NNC and DMC by Suhao CHEN, Yue WU, Wei ZENG, Xiaohui YANG, Xiaopeng WANG, Yunfei WU

“…The combination of combined cooling, heating and power (CCHP) and microgrid promotes the consumption of renewable energy. In order to improve the economy, environmental protection and stability of CCHP microgrid, a two-stage optimal dispatching model is proposed. …”
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Developing an Interpretable Machine Learning Model for Early Prediction of Cardiovascular Involvement in Systemic Lupus Erythematosus by Deng Z, Liu H, Chen F, Liu Q, Wang X, Wang C, Lyu C, Li J, Li T

“…Notably, the GBM model showed optimal performance, and its interpretability allowed clinicians to visualize decision-making processes, facilitating early identification of high-risk patients.Keywords: systemic lupus erythematosus, cardiovascular involvement, machine learning, prediction model, interpretability…”
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Research on Maneuvering Motion Prediction for Intelligent Ships Based on LSTM-Multi-Head Attention Model by Dongyu Liu, Xiaopeng Gao, Cong Huo, Wentao Su

“…The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance.…”
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Dynamic Multibody Modeling of Spherical Roller Bearings with Localized Defects for Large-Scale Rotating Machinery by Luca Giraudo, Luigi Gianpio Di Maggio, Lorenzo Giorio, Cristiana Delprete

“…In conclusion, the developed model represents a promising tool for generating useful datasets for training diagnostic and prognostic algorithms, thereby contributing to the improvement of predictive maintenance strategies in industrial settings. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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Game algorithm based on link quality: Wireless sensor network routing game algorithm based on link quality by Zhanjun Hao, Jiaojiao Hou, Jianwu Dang, Xiaochao Dang, Nanjiang Qu

“…In the simulation experiment, the influence of the change of link quality parameters on the performance of the algorithm is analyzed, and the proposed algorithm is compared with non-linear weight particle swarm optimization (NWPSO) algorithm and Low Energy Adaptive Clustering Hierarchy-Improvement (LEACH-IMPT) algorithm in three aspects: the number of surviving nodes, network lifetime, and network energy consumption. …”
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Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine by Qinqin Zhang, Zihui Zhang, Xiaofei Wang, Zhonglin Xu, Yao Wang

“…Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. …”
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Development platform for artificial pancreas algorithms. by Mohamed Raef Smaoui, Remi Rabasa-Lhoret, Ahmad Haidar

“…<h4>Results</h4>The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. …”
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A hybrid Bayesian network-based deep learning approach combining climatic and reliability factors to forecast electric vehicle charging capacity by David Chunhu Li

“…This architecture uses extensive transaction data and climate analysis to build a detailed model of EV charging pile reliability. Additionally, two algorithms are designed to assess the usage and reliability of charging stations. …”
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Research on monthly runoff prediction model considering secondary decomposition of multiple fitness functions and deep learning by Zhongfeng Zhao, Xueni Wang, Hua Jin, Jie Zheng

“…The issue of high-frequency noise in the decomposed components of runoff sequences and the difficulty in ensuring the rationality of algorithm parameters, this study proposes a monthly runoff prediction model for that considers multiple fitness function optimization. …”
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Connected Transit Bus Dynamic Priority Weight Modeling and Conflicting Request Resolution Control at the Signalized Intersection by Shuxian He, Haihang Han, Huan Zhang, Shanzhi Sun, Tony Z. Qiu

“…Moreover, for the transit signal priority system, how to establish a model to measure traffic demand for conflicting priority request resolution and improve system response time has been widely researched for the last few decades. …”
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Optimized Dual-Battery System with Intelligent Auto-Switching for Reliable Soil Nutrient Monitoring in Remote IoT Applications by Doan Perdana, Pascal Lorenz, Bagus Aditya

“…To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). …”
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The open-closed mod-minimizer algorithm by Ragnar Groot Koerkamp, Daniel Liu, Giulio Ermanno Pibiri

“…Abstract Sampling algorithms that deterministically select a subset of $$k$$ k -mers are an important building block in bioinformatics applications. …”
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