CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

“…Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. …”
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A Lightweight Deep Learning Model for Profiled SCA Based on Random Convolution Kernels by Yu Ou, Yongzhuang Wei, René Rodríguez-Aldama, Fengrong Zhang

“…In this article, a DL-SCA model is proposed by introducing a non-trained DL technique called random convolutional kernels, which allows us to extract the features of leakage like using a transformer model. …”
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A dual branch model for predicting microseismic magnitude time series named DTFNet by Hao Luo, Zhongyi Liu, Yishan Pan, Liang Wang, Chao Kong, Huan Zhang

“…Subsequently, a dual branch time series prediction model is constructed, which effectively models the microseismic time series data and deeply extracts the features contained in the microseismic magnitude data. …”
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Individual differences in neurophysiological correlates of post-response adaptation: A model-based approach by Anna Grabowska, Filip Sondej, Julia Haaf, Michael D. Nunez, Magdalena Senderecka

“…The cognitive control system continuously monitors actions and adapts to enhance task performance and prevent errors. More pronounced amplitudes of early neurophysiological response monitoring signals, such as error-related negativity (ERN) and correct-related negativity (CRN), have been linked to increased response caution and heightened attention to task-relevant features in subsequent trials. …”
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Lightweight detection model for safe wear at worksites using GPD-YOLOv8 algorithm by Jian Xing, Chenglong Zhan, Jiaqiang Ma, Zibo Chao, Ying Liu

“…Abstract To address the significantly elevated safety risks associated with construction workers’ improper use of helmets and reflective clothing, we propose an enhanced YOLOv8 model tailored for safety wear detection. Firstly, this study introduces the P2 detection layer within the YOLOv8 architecture, which substantially enriches semantic feature representation. …”
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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

“…We used random forest algorithms to select important features from potential predictors. The features identified using the random forest model were subjected to logistic regression analysis to develop a cognitive impairment prediction model. …”
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A sleep staging model based on adversarial domain generalized residual attention network by Pengwei Zhang, Sijia Xiang, Kailei Hu, Jialing He, Jingxia Chen

“…The model is divided into three parts: feature extractor, domain discriminator and label classifier. …”
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Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data by Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Kazem Zendehdel, Zahra Zangeneh Madar, Sepideh Hajivalizadeh, Zeinab Hasani, Ali Sarmadi, Behzad Amanpour‐Gharaei, Mohammad Amin Barati, Mozafar Saadat, Seyed‐Ali Sadegh‐Zadeh, Saeid Amanpour

“…While the Gail model, a traditional logistic regression‐based model, has been broadly used, its predictive performance may be limited by its linear assumptions. …”
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Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models by Enrico Giarnieri, Elisabetta Carico, Stefania Scarpino, Alberto Ricci, Pierdonato Bruno, Simone Scardapane, Daniele Giansanti

“…<b>Results:</b> The models achieved an Intersection over Union (IoU) score of 0.72, demonstrating robust performance in class prediction for both categories, with YOLOv11 showing performance improvements over YOLOv8 in different metrics. …”
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Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar, Punit Fulzele

“…The stroke pre-diction using optimized boosting machine learning algorithms is supported with explainable AI using LIME and SHAP. This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Incorporating stool miR-92a detection into the model further improved diagnostic performance. Shapley additive explanations (SHAP) plots indicated that FOBT, CEA, lymphocyte percentage (LYMPH%), and hematocrit (HCT) were the most significant features contributing to CRC diagnosis. …”
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A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification by Amoakoh Gyasi-Agyei

“…The performance differences between the learned ML models were then validated using repeated stratified 10-fold cross-validation and 5x2 cross-validation paired t-tests to select the best model for our task. …”
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A novel hybrid model for brain tumor analysis with CNN and Moth Flame Optimization by Mohit Prakram, Kirti Rawal, Arun Singh, Ankur Goyal, Shiv Kant, Shakeel Ahmed, Saiprasad Potharaju

“…A hybrid segmentation approach is employed, combining K-means clustering with MFO and a custom fitness function to extract tumor regions. Feature extraction is followed by MFO-based feature selection to reduce dimensionality and enhance classification performance. …”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…These advancements underscore the model’s superior performance in real-time forest fire detection, outperforming other mainstream lightweight YOLO models in both accuracy and efficiency.…”
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A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules by Hyunho Kim, Bongsung Bae, Minsu Park, Yewon Shin, Trey Ideker, Hojung Nam

“…The model outperforms existing methods in diversity, feasibility, and condition fitness. …”
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Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors by Juan Carrillo-Ríos, Ignacio González-Prieto, Ángel González-Prieto, Mario J. Durán, Juan José Aciego

“…To take advantage of the multi-phase benefits, the design of high-performance control techniques is a crucial task. In this regard, the inherent features of model predictive control (MPC) have aroused the interest of the scientific community in the last decade. …”
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A method of urban high-precision DEM construction based on point cloud model by Weibo Zeng, Xiaoxiao Zhang, Shuangxi Gu, Ran Zhang, Lele Yu, Yijun Zhou, Yuqing Zhuo

“…The experimental results demonstrate that the proposed method in this paper exhibits superior performance in terms of point cloud filtering accuracy, DEM construction error, efficiency, and method adaptability when compared to traditional elevation grid, ordinary Kriging model, and other methods.…”
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Impact of Normalization Techniques on Synthetic Load Profile Generation Using Deep Generative Models by Luis H. T. Bandoria, Walquiria N. Silva, Madson C. De Almeida

“…Additionally, this study introduces DDIM for synthetic load profile generation, providing a deterministic and faster sampling approach compared to traditional probabilistic denoising models. Results based on statistical and temporal metrics indicate that Max-Abs normalization consistently yields more accurate and stable synthetic profiles across all models and datasets, while Robust and Quantile methods often degrade essential distributional features. …”
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Pathological omics prediction of early and advanced colon cancer based on artificial intelligence model by Zhe Wang, Yang Wu, Yingjie Li, Qingwen Wang, Hongfei Yi, Hongyan Shi, Xinyue Sun, Chengxiang Liu, Kuanyu Wang

“…Compared with machine learning, deep learning has higher recognition and accuracy of images, and the performance of the model is better.…”
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A Data-Driven Intelligent Methodology for Developing Explainable Diagnostic Model for Febrile Diseases by Constance Amannah, Kingsley Friday Attai, Faith-Michael Uzoka

“…To ensure transparency, XAI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Large Language Models (LLMs) were integrated, enabling clinicians to understand model predictions. …”
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