Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus by D. Yu. Sorokin, E. S. Trufanova, O. Yu. Rebrova, O. B. Bezlepkina, D. N. Laptev

“…We constructed recurrent neural network (RNN) to predict glucose concentration for 30-120 minutes, an algorithm for optimizing IP settings using prediction results. …”
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Online Estimation of the State of Health for the Lithium-Ion Battery Based on Open Circuit Model by Pan Yajia, Junda Li, Wang Tian'An, Yang Shengxun, Zhang Yunxuan, Li Zigang, Hu Yanwen

“…The accurate prediction of the state of health (SOH) for lithium-ion batteries is of significant importance in proactively avoiding unsafe behavior. …”
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Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology by Chen Bin, Lei Dong, Huang Liangjun, Liu Qingdong, Zhao Xuan, Shi Yuanping

“…Combining the deep learning model with the multi model hierarchical classification algorithm, the remote sensing prediction map based on deep learning is used as the classification base map, and the normalized difference vegetation index threshold is used to classify the uncovered area. …”
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Coordinated Interaction Strategy of User-Side EV Charging Piles for Distribution Network Power Stability by Juan Zhan, Mei Huang, Xiaojia Sun, Zuowei Chen, Zhihan Zhang, Yang Li, Yubo Zhang, Qian Ai

“…Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. …”
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Optimization of Adaptive I²H ∞ Control Method Based on Multiple Input Sensors by Yu Gu, Hanyang Li, Zeting Mei, Hao Wen, Yuanxiong Jin, Wenxuan Dong

“…The core contributions of this study include: 1) Designing a multi-sensor current reference estimator to dynamically generate the optimal electromagnetic torque through state variables such as wheel speed, acceleration, slope, and human factor database (heart rate, subjective score, fatigue index) to achieve real-time prediction of rider demand; 2) Proposing an adaptive current reference value estimation algorithm that integrates feedforward compensation and error feedback to ensure smooth switching of assistance modes and suppress sensor noise; 3) Developing an intention-induced H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> robust current tracking controller that significantly enhances the system&#x2019;s robustness to parameter fluctuations and external disturbances by optimizing the H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> norm of the closed-loop transfer function, while supporting personalized riding assistance.…”
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Depth Integrated Multi-Task Prototypical Learning With Self Refinement for Unsupervised Domain Adaptation by Antonio Dauphin Fernando, Thumma Anirudh, Selvaraj Palanisamy, Karthika Prasad, Katia Alexander, Pandiyarasan Veluswamy, Rohini Palanisamy

“…The task-specific classifiers capture the semantic and depth features, and the task-integrated classifier captures the hidden semantic features from depth prediction. This framework ensures that the prototypes from respective heads learn better class-representative features using semantic information and depth cues. …”
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A hybrid model based on the photovoltaic conversion model and artificial neural network model for short-term photovoltaic power forecasting by Ran Chen, Shaowei Gao, Yao Zhao, Dongdong Li, Shunfu Lin

“…Backpropagation, gradient descent, and L2 regularization methods are applied in the structure of the ANN model to achieve the best weights, improve the prediction accuracy, and alleviate the effect of overfitting. …”
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Enhancing Process Control in Agriculture: Leveraging Machine Learning for Soil Fertility Assessment by Ashutosh Sarangi, Sailesh Kumar Raula, Sohamdev Ghoshal, Swadhin Kumar, Chinta Sai Kumar, Neelamadhab Padhy

“…<b>Result</b>: The results demonstrated that the machine learning classifier significantly improves prediction accuracy. We used LR, KNN, NB, and DT classifiers to increase the accuracy, as well as to increase the efficiency of the soil fertility assessment. …”
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Mental health of university students twenty months after the beginning of the full-scale Russian-Ukrainian war by Marina Polyvianaia, Yulia Yachnik, Jörg M. Fegert, Emily Sitarski, Nataliia Stepanova, Irina Pinchuk

“…Correlation between variables was calculated with Pearson correlation, adjusted with Benjamini-Hochberg procedure. To develop the predictive model the XGBoost algorithm was employed, additionally, the SHAP algorithm was utilized. …”
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Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts: Progress and perspectives by Fan Cheng, Lei Yutian, Mo Jinhan, Wang Huilong, Wu Qiuting, Cai Jiena

“…This review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks, i.e., building energy predictions, fault detection and diagnosis, and control optimizations. …”
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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

“…A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. …”
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Exploring machine learning classification for community based health insurance enrollment in Ethiopia by Seyifemickael Amare Yilema, Seyifemickael Amare Yilema, Yegnanew A. Shiferaw, Yikeber Abebaw Moyehodie, Setegn Muche Fenta, Denekew Bitew Belay, Denekew Bitew Belay, Haile Mekonnen Fenta, Haile Mekonnen Fenta, Teshager Zerihun Nigussie, Ding-Geng Chen, Ding-Geng Chen

“…Therefore, this study aimed to identify the ML algorithm with the best predictive accuracy for CBHI enrollment and to determine the most influential predictors among the dataset.MethodsThe 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. …”
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A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture by Rui Lu, Lei Shi, Yinlong Liu, Zhongkai Dang

“…Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. …”
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Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance by Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK

“…Conclusion: After performing the complete process, we conclude that the extra tree classifier out of 17 models is the most suitable machine learning algorithm for liver cancer prediction. …”
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Securing and optimizing optical transmission in quantum wells using OAM and advanced modulation techniques by Muhammad Ahmad, Zhiping Wang, Ming Fang, Zhixiang Huang, Guoda Xie

“…After the signal generation process is completed, a hybrid method called Traffic Prediction Assisted with a Spotted Hyena Optimizer (TPAR-SHO) is proposed to analyze traffic in the optical transmission system. …”
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Mechanistic role of miR-375 in regulating PDPK1 to promote progression of small bowel neuroendocrine tumors: a silico analysis by Tao Ren, Lu Zhou, Zhenlong Li, Mingmei Pan, Xueqiong Han

“…Drug targeting prediction and immune environment evaluation were identified. …”
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Energy saving and low carbon oriented renovation framework for educational buildings with Tianjin University case study by Xinge Du, Xiang Liu, Feng Gao, Zhihua Zhou

“…The XGBoost model based on Bayesian optimization performed well in performance prediction with an accuracy of 0.86, precision of 0.77, recall of 0.86, and F1 score of 0.816, which is a significant advantage over LGBM, AdaBoost, and Random Forest models. …”
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Improvement of physics-based and data-driven model simulations based on multi-source soil moisture datasets by Xiao Liang, Haiting Gu, Yue-Ping Xu, Lu Wang, Yuxue Guo, Li Liu

“…Study focus: Accurate streamflow simulation is essential for effective water resource management in watersheds, necessitating the use of reliable models and precise data for the simulation and prediction of streamflow. This study integrates multi-source soil moisture datasets into both a process-based model and a data-driven model to explore the potential benefits of calibration and streamflow simulation within a mesoscale watershed in eastern China. …”
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Analysis of approaches to identification of trend in the structure of the time series by U. S. Mokhnatkina, D. V. Parfenov, D. A. Petrusevich

“…A combination of polynomial model for trend description and ARIMA for seasonally description and combination of ACD algorithm for trend and ETS for seasonal model obtained good forecasts in case of seasonal time series, while Fourier time series as a trend model also achieved close quality of prediction.Conclusions. …”
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models by Ayub Mohammadi, Khalil Valizadeh Kamran, Sadra Karimzadeh, Himan Shahabi, Nadhir Al-Ansari

“…Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. …”
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