Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine learning algorithms (MLAs) in patients with type 2 DM (T2DM).Methods The electronic health records of 12,190 T2DM patients with 3-year follow-ups were extracted, and the dataset was divided into a training and testing dataset in a 4:1 ratio. …”
Get full text

TCN-LSTM-MHSA model optimized by improved slime mould algorithm for stress prediction of roadway anchor bolts (cables) by QI Junyan, CHE Yuhao, WANG Lei, YUAN Ruifu

Subjects: Get full text

Chemometric and meta-heuristic algorithms to find optimal wavelengths and predict ‘Red Delicious’ apples traits using Vis-NIR by Mahsa S. Razavi, Vali Rasouli Sharabiani, Mohammad Tahmasebi, Silvia Grassi, Mariusz Szymanek

Get full text

Prediction of Liquefaction-Induced Lateral Displacements Using Hybrid GBRT and EOA by Arash Ziaie, Bahareh Mehdizadeh, Farzad Safi Jahanshahi, Nazanin Ahmadi, Ali Reza Ghanizadeh

“…This research employs the Gradient Boosting Regression Tree (GBRT) approach, tuned through the Equilibrium Optimization Algorithm (EOA), to estimate LILD. A dataset containing 247 data points was used to build the predictive model. …”
Get full text

Research on the Stability Prediction and Optimization of CNC Milling Based on Bagging–NSGAⅡ Under the Influence of Multiple Factors by Congying DENG, Qian YOU, Yang ZHAO, Lijun LIN, Guofu YIN

“…Considering these multiple influencing factors, herein, a method is proposed to predict the milling stability and determine optimal machining parameters based on a bootstrap aggregating (bagging) procedure and the non-dominated sorting genetic algorithm–Ⅱ (NSGA–Ⅱ). …”
Get full text

Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

Get full text

Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm by Hua Xu, Zongkai Guo, Yu Cao, Xu Cheng, Qiong Zhang, Dan Chen

Get full text

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms by Jiayi Zhang, Zheng Zhang, Chenqing Yang, Qingguang Liu, Tao Song

“…Additionally, we affirmed the predictive precision and superiority of our model through a meta-analysis against existing HCC models. …”
Get full text

Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa by Agmasie Damtew Walle, Shimels Derso Kebede, Jibril Bashir Adem, Daniel Niguse Mamo

“…The random forest (RF) model, selected as the best-performing algorithm, was used to predict home delivery after ANC visits. …”
Get full text

Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

Subjects: “…slime mold algorithm (SMA)…”
Get full text

Artificial intelligence technologies in predicting preeclampsia by A. A. Ivshin, T. Z. Bagaudin, A. V. Gusev

Subjects: Get full text

Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends by Vivek Singh, Gaurav Tiwari, Amarendra Singh, Rajeeb Samanta, Atul Kumar Srivastava, Deewan Singh Bisht, Ashish Routray, Sushil Kumar, Shivaji Singh Patel, Abhishek Lodh

“…Notably, research since 2019 highlights a shift toward machine‐based algorithms aimed at improving intensity predictions. …”
Get full text

TIRDH: A Novel Three-Shadow-Image Reversible Data Hiding Algorithm Using Weight and Modulo by Li-Chian Chin, Yu-Chen Li, Hung-Mo Hsieh, Chung-Ming Wang

Get full text

Improving IR drop effect and power noise using digital OLED power in AMOLED display by Sanguk Lee, Byungmun Kang, DaeEun Kim

“…We employed a predictive control and IR drop compensation algorithm to supply power to the AMOLED, thus improving panel uniformity. …”
Get full text

Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks by Hu Cao, Lingling Ma, Guoying Liu, Zhijian Liu, Hang Dong

Get full text

An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm by Zhiqing Zhang, Zicheng He, Yuguo Wang, Feng Jiang, Weihuang Zhong, Bin Zhang, Yanshuai Ye, Zibin Yin, Dongli Tan

“…In the era of industry 4.0, artificial intelligence (AI) offers new perspectives for researching the complex sustainable chemical reactions in selective catalytic reduction (SCR). This aims to further improve the utilization and efficiency of SCR. …”
Get full text

Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

“…The IDBO algorithm was then utilized to optimize the hyperparameters of the KELM model for predicting pile bearing capacity. …”
Get full text

Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm by PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin

Subjects: “…base baijiu; support vector machine; bootstrap aggregating; genetic algorithm; classification prediction…”
Get full text

Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity by Jin-Xia Yang, Jin-Xia Yang, Yue Liu, Yue Liu, Rong Huang, Hai-ying Wu, Ya-yun Wang, Su-ying Cao, Guo-ying Wang, Jian-Min Zhang, Zi-Sheng Ai, Hui-min Zhou

“…Eight ML algorithms (Decision Tree, Logistic Regression, Support Vector Machine (SVM), Multilayer Perceptron, Adaptive Boosting, Random Forest, Gradient Boosting Decision Tree, and Extreme Gradient Boosting) were compared for their capacity to identify key clinical and laboratory characteristics of T2DM in children and to create a risk prediction model.ResultsForty-nine children were diagnosed with T2DM during the follow-up period. …”
Get full text

Machine learning opportunities to predict obstetric haemorrhages by Yu. S. Boldina, A. A. Ivshin

Subjects: Get full text