Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization by Jing-Jing Liu, Jian-Chao Liu

“…The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. …”
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Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection by V. K. U. Ahamed Gani, N. Shanmugasundaram

“…The proposed CO-CNN approach shows superior performance compared to that of state-of-the-art methods, offering potential applications in telemedicine, treatment planning, early detection, screening, and patient education. Integrating fuzzy logic enhances the model’s interpretability and robustness, paving the way for improved healthcare outcomes in diabetic retinopathy management.…”
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A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection by Dong Kyun Park, Eui Joo Kim, Jong Pil Im, Hyun Lim, Yun Jeong Lim, Jeong-Sik Byeon, Kyoung Oh Kim, Jun-Won Chung, Yoon Jae Kim

“…Abstract Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. …”
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A statistical method for high-throughput emergence rate calculation for soybean breeding plots based on field phenotypic characteristics by Yan Sun, Mengqi Li, Meiling Liu, Jingyi Zhang, Yingli Cao, Xue Ao

“…Then, a soybean seedling counting algorithm was constructed: by establishing a soybean seedling growth model, the idea of “growth normalization” was proposed, and the expansion-compression factor was defined to eliminate the influence of soybean seedling growth inconsistency on counting. …”
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Research on predicting the risk level of coal mine roof accident based on machine learning by Zhao-Yang Guan, Jin-Ling Xie, Shen-Kuang Wu, Chao Liang

“…Finally, KNN, SVM and DT algorithms are used to evaluate the model performance. …”
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Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data. …”
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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…This two-stage approach provides human interpretable information between stages, which helps clinicians gain insights into the screening process copiloting with the DL model.…”
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Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer by Quan Yuan, Rongjie Ye, Yao Qian, Hao Yu, Yuexin Zhou, Xiaoqiao Cui, Feng Liu, Ming Niu

“…The Least Absolute Shrinkage and Selection Operator (LASSO) Cox algorithm, combined with XGBoost and Random Forest (RF) models, identified 9 overlapping prognostic features, enhancing the nomogram’s predictive accuracy for overall survival (OS). …”
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Module Partition of Mechatronic Products Based on Core Part Hierarchical Clustering and Non-Core Part Association Analysis by Shuai Wang, Yi-Fei Song, Guang-Yu Zou, Jia-Xiang Man

“…Firstly, the core part screening method is used to simplify the structural model of mechatronic products and reduce the difficulty of modeling. …”
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Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs. by Andrew McDonald, Anurag Agarwal, Ben Williams, Nai-Chieh Liu, Jane Ladlow

“…Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.…”
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Examination of Teacher Candidates’ Intercultural Sensitivity Levels by CART Analysis by Nesrin Hark Söylemez

“…The study was conducted on a voluntary basis. A relational screening model was employed to assess the intercultural sensitivity levels. …”
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Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging by Xiaoyu Xue, Haiqing Tian, Kai Zhao, Yang Yu, Ziqing Xiao, Chunxiang Zhuo, Jianying Sun

“…The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. …”
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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. <i>Results</i>: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). …”
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Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy by Hua Chen, Kehui Mei, Yuan Zhou, Nan Wang, Guangxing Cai

“…In this paper, we take breast cancer as the research object, and pioneer a hybrid strategy to process the data, and combine the machine learning method to build a more accurate and efficient breast cancer auxiliary diagnosis model. …”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

“…Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. …”
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Diagnostic Value of Glycosylated Extracellular Vesicle microRNAs in Gastric Cancer by Wang S, Ma C, Ren Z, Zhang Y, Hao K, Liu C, Xu L, He S, Zhang J

“…The signatures were screened in a discovery cohort of GC patients (n=55) and non-disease controls (n=46) using an integrated process, including high-throughput sequencing technology, screening using a complete bioinformatics algorithm, validation using RT-qPCR, and evaluation by constructing a diagnostic model. …”
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The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data by Yue Cui, Yi Qiao, Rongming An, Xuan Pan, Jing Tu

“…Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.…”
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Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults: a retrospective study by Qiaoli Wang, Tao Liang, Yuexi Li, Peng Zhou, Xiaoqin Liu

“…After feature screening, 10 factors were selected for the prediction model. …”
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High-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery by Qi Ou, Hongshuai Wang, Minyang Zhuang, Shangqian Chen, Lele Liu, Ning Wang, Zhifeng Gao

“…We employed a 3D transformer-based molecular representation learning algorithm to create the Org-Mol pre-trained model, using 60 million semi-empirically optimized small organic molecule structures. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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