Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting by Wenting Zhao, Haoran Xu, Peng Chen, Juan Zhang, Jing Li, Tingting Cai

“…Load forecasting plays a crucial role in power system planning and operational dispatch management. Accurate load prediction is essential for enhancing power system reliability and facilitating the local integration of renewable energy. …”
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Rapid in-air ultrasound holography measurement and camera-in-the-loop generation using thermography by Zak Morgan, Youngjun Cho, Sriram Subramanian

“…Finally, we integrate this with holography algorithms to propose a camera-in-the-loop algorithm that employs real-time measurement, enabling targeted data acquisition and on-line training of acoustic holography algorithms. …”
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Heisenberg-Limited Adaptive Gradient Estimation for Multiple Observables by Kaito Wada, Naoki Yamamoto, Nobuyuki Yoshioka

“…Our method paves a new way to precisely understand and predict various physical properties in complicated quantum systems using quantum computers.…”
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Quality assessment of chicken using machine learning and electronic nose by Hassan Anwar, Talha Anwar

“…This study investigates the use of an electronic nose system—a sensor array that detects odors and generates data, which is then analyzed by machine learning algorithms to predict chicken freshness. An electronic nose system was developed using six MQ gas sensors and one humidity temperature sensor. …”
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Application of Machine Learning for Target Selection and Acid Treatment Design by I. I. Mannanov, M. A. Varfolomeev, G. R. Ganieva, A. R. Gimaeva, R. R. Giniyatullin

“…These algorithms significantly simplify the tasks of acid treatment design.…”
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Computerized Adaptive Testing Framework Based on Excitation Block and Gumbel-Softmax by Chengsong Liu, Yan Wei

“…In addition, most algorithms primarily focus on accurately predicting students’ abilities, neglecting critical factors such as concept diversity and question exposure rate, which are essential for model effectiveness. …”
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Short-Term Electrical Load Forecasting in Power Systems Using Deep Learning Techniques by Nihat Pamuk

“…In the future, these new architectural methods can be applied to long- or short-term electric charge predictions and their results can be compared to LSTM, GRUs and their variations.…”
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Interpretable multiparametric MRI radiomics-based machine learning model for preoperative differentiation between benign and malignant prostate masses: a diagnostic, multicenter st... by Wenjun Zhou, Wenjun Zhou, Zhangcheng Liu, Zhangcheng Liu, Jindong Zhang, Shuai Su, Yu Luo, Lincen Jiang, Kun Han, Guohua Huang, Jue Wang, Jianhua Lan, Delin Wang

“…SHAP can disclose the underlying prediction process of the ML model, which may promote its clinical applications.…”
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Economic energy optimization in microgrid with PV/wind/battery integrated wireless electric vehicle battery charging system using improved Harris Hawk Optimization by Perne Mallikarjun, Sundar Rajan Giri Thulasiraman, Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri

“…Simulation results demonstrate that the IHHO algorithm achieves significant cost reductions and improves energy utilization efficiency compared to other state-of-the-art optimization algorithms such as Improved Quantum Particle Swarm Optimization (IQPSO), Honeybee Mating Optimization (HBMO), and Enhanced Exploratory Whale Optimization Algorithm (EEWOA). …”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. …”
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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network by Xuesen Xu, Shijia Luo, Xuchen Zhang, Weiming Xu, Rong Shu, Jianyu Wang, Xiangfeng Liu, Ping Li, Changheng Li, Luning Li

“…Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. …”
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人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceut... by 勾岚，姜明慧，姜勇，廖晓凌，李昊，张杰，程丝 (GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si)

“…By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. However, critical challenges, such as insufficient multi-omics resources, difficulties in multi modal data integration, and limited interpretability of algorithms, remain major bottlenecks in clinical translation. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.…”
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Evaluation of the elastic modulus of pavement layers using different types of neural networks models by M. M.M. Elshamy, A. N. Tiraturyan, E. V. Uglova

“…This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. …”
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Identification and validation of integrated stress-response-related genes as biomarkers for age-related macular degeneration by Jingyi Niu, Ling Jin, Yijun Hu, Yiting Wang, Xiaoning Hao, Wenwen Geng, Ruirui Ma

“…We obtained thirteen relevant miRNAs and 27 TFs by prediction, with three shared TFs, and seventeen potentially effective drugs were predicted. …”
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Fc-Binding Cyclopeptide Induces Allostery from Fc to Fab: Revealed Through in Silico Structural Analysis to Anti-Phenobarbital Antibody by Tao Zhou, Huiling Zhang, Xiaoting Yu, Kangliang Pan, Xiaojun Yao, Xing Shen, Hongtao Lei

“…The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. …”
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Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system by Junxiao Shen, Luyuan Lu, Zujie Chen, Wei Guo, Shuwen Wang, Ziqiao Liu, Xuke Gong, Yiming Qi, Ruyi Jin, Cheng Zhang

“…In clinical settings, the use of the NEP100 model can greatly improve the diagnostic and prognostic prediction of NEPC. Hierarchical clustering based on NEP100 revealed four distinct NEPC subtypes, designated VR_O, Prol_N, Prol_P, and EMT_Y, each of which presented unique biological characteristics. …”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…In spinal deformities, models such as support vector machines and convolutional neural networks achieved over 90% accuracy in classification and curve prediction. For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. …”
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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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Groundwater Pollution Concentration Estimation with Modified Kalman Filter Method by Nenik Estuningsih, Fatmawati Fatmawati

“…The modified Kalman filter method is a method that collaborates the Kalman filter estimation algorithm with the model order reduction method. The model order reduction method used in this research is the LMI (Linear Matrix Inequality) method because the model reduction error using the LMI method is the smallest error compared to the reduction error using the Balanced Truncation method or the Singular Pertrubation Approximation method. …”
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