Cross-modal adaptive reconstruction of open education resources by Tang Shengju, Feng Li, Zhan Wang, Xie Zhaoyuan

“…To address this challenge, we proposed a Dynamic Knowledge Graph-enhanced Cross-Modal Recommendation model (DKG-CMR) to solve the problem. This model utilizes a dynamic knowledge graph—a structure organizing information and relationships—that continuously updates based on learner actions and course objectives. …”
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Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment by Sheng Hu, Junyu Liu, Jiayi Hong, Yuting Chen, Ziwen Wang, Jibo Hu, Shiying Gai, Xiaochao Yu, Jingjing Fu

“…An optimal machine learning (ML) algorithm was used for model development. Subsequently, models for clinical, radiomics, and clinical-radiomics were developed. …”
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High‐resolution mapping of cancer cell networks using co‐functional interactions by Evan A Boyle, Jonathan K Pritchard, William J Greenleaf

“…This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve complex cellular processes.…”
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Intelligent Evaluation Method for Scoliosis at Home Using Back Photos Captured by Mobile Phones by Yongsheng Li, Xiangwei Peng, Qingyou Mao, Mingjia Ma, Jiaqi Huang, Shuo Zhang, Shaojie Dong, Zhihui Zhou, Yue Lan, Yu Pan, Ruimou Xie, Peiwu Qin, Kehong Yuan

“…Therefore, based on computer vision technology, this paper puts forward an evaluation method of scoliosis with different photos of the back taken by mobile phones, which involves three aspects: first, based on the key point detection model of YOLOv8, an algorithm for judging the type of spinal coronal curvature is proposed; second, an algorithm for evaluating the coronal plane of the spine based on the key points of the human back is proposed, aiming at quantifying the deviation degree of the spine in the coronal plane; third, the measurement algorithm of trunk rotation (ATR angle) based on multi-scale automatic peak detection (AMPD) is proposed, aiming at quantifying the deviation degree of the spine in sagittal plane. …”
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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means by Bo Li, Guochao Qian, Lijun Tang, Peng Sun, Zhensheng Wu

“…The results show that, compared with the traditional fault section location and route selection strategy, this method can reduce the number of measurement devices optimally configured by 19–36% and significantly reduce the number of algorithm iterations. In addition, it can realize rapid fault location and precise line screening at a low equipment cost under multiple fault types and different fault locations, which significantly improves fault location accuracy while reducing economic investment.…”
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Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data by Tongtong Jin, Ayitijiang· Halili

“…This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. …”
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YOLO-RGDD: A Novel Method for the Online Detection of Tomato Surface Defects by Ziheng Liang, Tingting Zhu, Guang Teng, Yajun Zhang, Zhe Gu

“…Finally, dynamic convolution was used to replace the conventional convolution in the detection head in order to reduce the model parameter count. The experimental results show that the average precision, recall, and F1-score of the proposed YOLO-RGDD model for tomato defect detection reach 88.5%, 85.7%, and 87.0%, respectively, surpassing advanced object recognition detection algorithms. …”
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A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network by Owen He, Daoxing Chen, Yimei Li

“…In independent test sets, ReproTox-CMPNN achieved a mean AUC of 0.946, ACC of 0.857 and F1 score of 0.846, surpassing traditional algorithms to establish itself as a new state-of-the-art model in this field. …”
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Load identification method based on one class classification combined with fuzzy broad learning by Wang Yi, Wang Xiaoyang, Li Songnong, Chen Tao, Hou Xingzhe, Fu Xiuyuan

“…Considering the recognition rate and model complexity, the fuzzy broad learning system is used to classify and recognize the screened samples. …”
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Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054 by Yingzi Wu, Yuewen Zhang, Xiaoyu Zhong, Huiling Xia, Mingyang Zhou, Wenjin He, Yi Zheng

“…Further optimization of cultivation conditions using a hybrid backpropagation neural network–genetic algorithm (BP–GA) model identified optimal parameters as pH 5.5, a liquid volume of 96.6 mL (in a 250 mL shaker), and inoculum size of 2.4 $$\times$$ × $$10^{4}$$ 10 4 spores/mL, achieving a final enzyme activity of 3422.14 ± 36.86 U/L (1.1% deviation from the predicted 3460 U/L), representing a 4.2-fold increase over initial conditions. …”
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Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy by Guanmeng Gao, Zihan Wei, Fei Pei, Yajie Du, Beiying Liu

“…All stained slides were scanned for analysis by the Faster-R-CNN with ResNet 50 or VGG16, then the model performance was evaluated. Furthermore, the real-time microscopic field, smartphone and AI algorithm were connected through 5G networks and the AI results were sent back to the smartphone for confirmation by the pathologists. …”
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Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

“…Traditional invasive diagnostic methods are costly, inconvenient, and unsuitable for large-scale screening. Developing a non-invasive, accurate prediction model is clinically significant for early MetS detection and prevention. …”
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Structural strength optimization design of ultra-high-pressure and ultra-wear-resistant pneumatic ball valve opened and closed at large explosion instantaneously using finite eleme... by Xianmei Liu, Mingcun Zhang

“…By building an ultra-high pressure burst test bench, this paper combines strain gauges and high-speed cameras to verify the accuracy of the model and corrects the simulation boundary conditions based on the Kalman filter algorithm. …”
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Cross-validation of the safe supplement screener (S3) predicting consistent third-party-tested nutritional supplement use in NCAA Division I athletes by Kinta D. Schott, Avaani Bhalla, Emma Armstrong, Ryan G. N. Seltzer, Floris C. Wardenaar

“…IntroductionThis cross-sectional study aimed to cross-validate an earlier developed algorithm-based screener and explore additional potential predictors for whether athletes will use third-party-tested (TPT) supplements.MethodsTo justify the initial model behind the supplement safety screener (S3) algorithm which predicts whether athletes will use TPT supplements, a cross-validation was performed using this independent dataset based on responses of a large group of collegiate NCAA DI athletes. …”
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Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Research hotspots concentrated on random forest (RF) and deep learning models, showing transition from algorithm development to clinical applications (e.g., tumor immune microenvironment analysis). …”
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Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach by Debendra Muduli, Syed Irfan Yaqoob, Santosh Kumar Sharma, Anuradha S. Kanade, Mohammad Shameem, Harendra S. Jangwan, P.M. Ashok Kumar, Abu Taha Zamani

“…The model's performance was evaluated in a cloud-based environment using two datasets: ORIGA and G1020. …”
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Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints by Xiumin Liu, Xinyue Zhang, Ye Sheng, Zihe Zhang, Pan Xiong, Xuehai Ju, Junwu Zhu, Caichao Ye

“…The random forest (RF) algorithm exhibited the best predictive performance for material design and screening. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. …”
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Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk by Ji-Eun Song, Ji Young Jang, Kyung Nam Kang, Ji Soo Jung, Chul Woo Kim, Ah Sol Kim

“…Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…Experimental results show that PSO outperforms other algorithms in training the tool wear prediction model, with XGBoost feature selection reducing model construction time by 57.4% and increasing accuracy by 63.57%, demonstrating superior feature selection capabilities over Decision Tree, Random Fores, Adaboost and Extra Trees. …”
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