A METHOD OF COMPUTER SIMULATION MODELING OF USER AND BOT BEHAVIOR IN A RECOMMENDATION SYSTEM USING THE GRAPH DATABASE NEO4J by Yelyzaveta Meleshko, Mykola Yakymenko, Viktor Bosko

“…Based on the working datasets, the preferences of users by the method of collaborative filtering were predicted. Based on testing datasets, the accuracy of prediction predictions was checked. …”
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Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field by Fan Jiang, Shikun Dai, Ying Zhang, Hongjun Tian

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Integration of DDPM and ILUES for Simultaneous Identification of Contaminant Source Parameters and Non‐Gaussian Channelized Hydraulic Conductivity Field by Xun Zhang, Simin Jiang, Na Zheng, Xuemin Xia, Zhi Li, Ruicheng Zhang, Jiangjiang Zhang, Xinshu Wang

“…To reduce the computational burden, an AR‐Net‐WL (ARNW) surrogate model was introduced, resulting in an efficient inversion framework (AEdiffusion‐ILUES‐ARNW) with similar prediction accuracy and predictive uncertainty estimation as the AEdiffusion‐ILUES but at a lower computational cost.…”
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Enhancing wheat yellow rust detection through modified deep learning approach by Shant Kumar, Rohit Singh, Sudheer Kumar, Sandeep Gupta

“…Our proposed modified CNN method attained prediction accuracy of about 98 % for detection of yellow rust of wheat. …”
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Comprehensive protein datasets and benchmarking for liquid–liquid phase separation studies by Carlos Pintado-Grima, Oriol Bárcenas, Eva Arribas-Ruiz, Valentín Iglesias, Michał Burdukiewicz, Salvador Ventura

“…Moreover, we describe limitations in classical and state-of-the-art predictive algorithms by providing the most comprehensive benchmark to date. …”
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Regenerative Braking Systems in Electric Vehicles: A Comprehensive Review of Design, Control Strategies, and Efficiency Challenges by Emilia M. Szumska

“…Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. …”
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Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
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Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM by Nicholas Niako, Jesus D. Melgarejo, Gladys E. Maestre, Kristina P. Vatcheva

“…Results All imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. …”
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A multi-label classification method for disposing incomplete labeled data and label relevance by Lina ZHANG, Lingpeng DAI, Tai KUANG

“…Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously.By integrating out the missing information，it also provides a disciplined approach to handle missing labels.Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms.…”
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AI-Driven Gamification and Biotechnical Systems in Cardiovascular Monitoring of Cyclic Sport Athletes by Shukurova Sayyora, Pulatova Mavlyuda, Adilbekov Taxir

“…The paper attempts to move forward research in biotechnical systems and gamification from traditional sports medicine, athlete conditioning, and exercise physiology contexts to emerging AI-enhanced frameworks that address current cardiovascular health challenges.In proposing such a novel approach, the authors reason why AHP-regression-based studies may be particularly suited for the iterative assessment, validation, and optimization of findings in the form of decision-making models such as personalized AI-driven training algorithms. Additionally, the analytical hierarchy process (AHP) framework is used to organize a systematic evaluation of predictive modeling techniques to identify some best practices related to specific biométrie parameters and athlete performance metrics.This methodological application then furthers the examination of the physiological and computational implications related to the use of AI-driven cardiovascular monitoring in terms of accuracy, efficiency, and adaptability. …”
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Simulation of incremental update of electronic document information based on big data technology by Zhiyuan Jin, Qi Zhang, Tiejun Pan

“…Abstract Experimental results show that the algorithm converges faster and has a smaller average error compared to the BP algorithm and the attribute reduction algorithm. …”
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Dynamic hierarchy resource management for heterogeneous cognitive network by Juan WEN, Min SHENG, Yan ZHANG

“…A dynamic hierarchy resource management approach-DHRM based on intelligent prediction was proposed for heterogeneous cognitive network.In DHRM,according to different time scale,the method of wavelet neural network,wiener prediction and reinforcement learning were brought to get the variation of traffic d ion,the resource requirement of the handover calls,and the information of users’preferences,and available hierarchical resources of all networks were allocated flexibly.Multi-attribute decision making method,based on network status and user preference was used to make decision to dynamically assign network traffic flow to the most appropriate network.Simulation results show that,the system capacity is improved about 20% by DHRM compared with the other joint radio resource management algorithms.…”
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A strategy for network multi-layer information fusion based on multimodel in user emotional polarity analysis by Ronghua Wang, Peng Zhuang

“…Despite the large number of negative users, the frequency of connections between active users was significantly higher than between negative users. Multiple algorithms were utilized for emotional analysis and prediction. …”
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Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…Machine learning is a technique that uses algorithms to find useful patterns in data. Based on previous NST data, it can be designed as a school readiness prediction model that will facilitate teachers and parents in knowing the readiness of children to enter elementary school. …”
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Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines by Jianghao Zhu, Tingting Pei, Le Su, Bin Lan, Wei Chen

“…By integrating random kitchen sinks (RKS) algorithm with KPCA, we achieved a 25.21% reduction in computational time while maintaining model accuracy. …”
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A Hybrid Analytical Framework for Cracking and Some Fruit Quality Features in Sweet Cherries by Erol Aydın, Mehmet Ali Cengiz, Leyla Demirsoy, Hüsnü Demirsoy

“…A hybrid analytical pipeline was developed by integrating Principal Component Analysis (PCA) for dimensionality reduction, Random Forest regression for nonlinear prediction and Shapley Additive Explanations (SHAP) for interpretability. …”
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A Decade of Computational Mass Spectrometry from Reference Spectra to Deep Learning by Michael A. Stravs

“…Herein, advances in CompMS for small molecule chemistry are discussed in the areas of spectral libraries, spectrum prediction, and tentative structure identification (annotation): Automatic spectrum curation is facilitating the expansion of openly available spectral libraries, a crucial resource both for compound annotation directly and as a resource for machine learning algorithms. …”
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Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis by Xiaoyu Hu, Lanting Guo, Jiyuan Wang, Yang Liu

“…The methodology addresses the fundamental challenge of balancing computational efficiency with prediction accuracy in thermal system design. A validated CFD model generated 935 numerical cases across diverse operational and design parameters, which were used to train and evaluate three machine learning algorithms: linear regression (LR), support vector regression (SVR), and artificial neural networks (ANN). …”
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Cable Force Optimization of Circular Ring Pylon Cable-Stayed Bridges Based on Response Surface Methodology and Multi-Objective Particle Swarm Optimization by Shengdong Liu, Fei Chen, Qingfu Li, Xiyu Ma

“…The response surface model demonstrates prediction errors of 0.35% for strain energy and 5.1% for maximum vertical mid-span deflection. …”
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Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review by Arief Wibowo, Ikhwan Amri, Asep Surahmat, Rusdah Rusdah

“…Six research clusters were identified through keyword network mapping: (1) disaster monitoring and prediction using IoT networks, (2) AI-based geospatial technology for risk management, (3) decision support systems for disaster emergency management, (4) social media analysis for emergency response, (5) machine learning algorithms for disaster risk reduction, and (6) big data and deep learning for disaster management. …”
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