A Coupling Error Compensation Approach Concerning Constrained Space Coordinate Precision of a Heavy-Load Longitudinal and Transversal Swing Table by Manxian Liu, Rui Bao, Shuo Li, Liang Ji, Suozhuang Li, Xiaoqiang Yan, Wei Li

“…Numerical simulation is employed to examine the distribution law, sensitivity, and volatility of independent error and coupling error. This aids in optimizing the design of the table’s machining process by balancing machining accuracy and economy. …”
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Resource Allocation in 5G Cellular IoT Systems with Early Transmissions at the Random Access Phase by Anastasia Daraseliya, Eduard Sopin, Vyacheslav Begishev, Yevgeni Koucheryavy, Konstantin Samouylov

“…The aim of this paper is to propose a model for CIoT systems with and without early transmission functionality and assess the optimal distribution of resources at the random access and data transmission phases. …”
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Innovative assessment scheme of navigation risk based on improved multi-source information fusion techniques by Bo Li, Fuwen Pang

“…Different from the existing studies, we first deduce the nonlinear support vector machine classification model for the general scenario. …”
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Blockchain based solid waste classification with AI powered tracking and IoT integration by Aliaa M. Alabdali

“…AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. …”
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Absorbent material composition prediction based on multi-objective regression with value stacking and selection by Shi He, Jiaying Chen, Kai Huang, Jian Mao, Kexun Li, Taikang Liu

“…Performance metrics identify the optimal predictor variables for material composition, aiding in the classification of carbon nanotubes based on required performance and predicted values.Result and discussionExperimental results indicate that the model achieves better R2 and mean squared error for carbon nanotubes, carbon black, and carbon fiber than other methods, with optimal Accuracy and Matthews Correlation Coefficient in classifying carbon nanotubes, validating the method for material composition design.…”
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Contribution of artificial intelligence for understanding animal rabies epidemiology in Morocco: What are the perspectives of an innovative and predictive approaches? by Ilham Ahamjik, Ayman Agbani, Mounia Abik, Mounir Khayli, Naima Galzim, Jaouad Berrada, Mohammed Bouslikhane

“…The expected outcome was the construction of probabilistic diagrams that can guide actions for the integrated control of this disease, involving all stakeholders, in the country. Such modeling is an essential step in operational epidemiology to optimize expenditure of public funds allocated to the integrated strategy for fighting this disease.The methodology employed combined the use of geospatial analysis tools (kriging) and artificial intelligence models (Machine Learning). …”
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CO2 Gas Layer Recognition Method Based on Long Short-Term Memory Network by HE Lina, WU Wensheng, WANG Xiannan, ZHANG Wei, ZHANG Chuanju, SONG Xiaoyu

“…CO2 gas layer recognition model based on Long Short-Term Memory Network (LSTM) is constructed, and CO2 sensitive logging parameters are optimized through m × 2 regularized cross validation to train the model. …”
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A Predictive Method for Unplanned Postoperative Readmission Risk Based on Heterogeneous Data by Kai YU, Xiaofeng DONG, Zhenming YUAN, Zhaojian CUI, Weibin LUO

“…To address these issues, it is proposed to use machine learning technology combined with patient clinical heterogeneous data to develop a predictive model for unplanned postoperative readmission. …”
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BTFBS: Binding Prediction of Bacterial Transcription Factors and Binding Sites Based on Deep Learning by Bingbing Jin, Song Liang, Xiaoqian Liu, Rui Zhang, Yun Zhu, Yuanyuan Chen, Guangjin Liu, Tao Yang

“…For the model inputs, we use two negative sample sampling methods: RS and EE. …”
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Construction of a High-Resolution Temperature Dataset at 40–110 KM over China Utilizing TIMED/SABER and FY-4A Satellite Data by Qian Ye, Mohan Liu, Dan Du, Xiaoxin Zhang

“…The model demonstrates the highest accuracy below 80 km with RMSE < 12 K, while its performance decreases above 100 km, where RMSE can exceed 20 K, indicating optimal performance in the upper stratosphere and lower mesosphere regions.…”
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MEASURING THE IMPACT OF DIGITAL TECHNOLOGY ON ENHANCING E-COMMERCE IN ALGERIA COMPARED TO A GROUP OF ARAB COUNTRIES (2010-2023) by Abdelfettah Daoudi, Mohamed Balouz, Mustapha Bouakel, Abderraouf Abada

“…Using an econometric approach, the study employs panel models and analyzes the outputs generated by the EVIEWS 12 software. …”
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A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study by Xiaomeng Han, Jing Guan, Li Guo, Qiyan Jiao, Kexin Wang, Feng Hou, Shunli Liu, Shifeng Yang, Chencui Huang, Wenbin Cong, Hexiang Wang

“…We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. …”
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Intestinal permeability of N-acetylcysteine is driven by gut microbiota-dependent cysteine palmitoylation by Yu-Hang Zhang, Chen-Shu Dai, Ya-Jie Wang, Wen-Yu Wang, Tian-Tian Qi, Man-Cheng Xia, Gan Zhou, Yi-Min Cui

“…By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. …”
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The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data by Simona Stojanova, Mojca Volk, Gregor Balkovec, Andrej Kos, Emilija Stojmenova Duh

“…We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. …”
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Predictive factors of adolescents’ happiness: a random forest analysis of the 2023 Korea Youth Risk Behavior Survey by Eun Joo Kim, Seong Kwang Kim, Seung Hye Jung, Yo Seop Ryu

“…Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. …”
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Hierarchical Clustering of Residential Appliances Considering the Characteristics of the Appliances by Shima Simsar, Mahmood Alborzi, Ali Rajabzadeh Ghatari, Ali Yazdian

“…Considering the sensor technologies, clarification on electricity consumption details helps to optimally monitor how the appliances are used. In this research, an unsupervised machine learning model was proposed for the clustering of home appliances to manage the bills of customers based on their inherent characteristics. …”
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Heterogeneity-aware device selection for efficient federated edge learning by Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li

“…Federated learning (FL) combined with mobile edge computing (FEEL) provides an end-to-edge synergetic learning approach to allow end devices to participate in machine learning model training parallelly while ensuring user privacy is maintained. …”
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Predicting the Tensile Strength of Plant Leaves Based on GA-SVM by Wei Chang, Meihong Liu, Yayu Huang, Junjie Lei, Kai Wu

“…A genetic algorithm (GA) is then applied to optimize the structural parameters of the support vector machine (SVM), establishing a GA-SVM-based predictive model for the tensile strength of plant leaves. …”
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Reinforcement learning in artificial intelligence and neurobiology by Tursun Alkam, Andrew H Van Benschoten, Ebrahim Tarshizi

“…We also highlight clinical applications, including how RL frameworks are used to model cognitive decline and psychiatric disorders, while acknowledging limitations in scaling RL to biological complexity.Looking ahead, RL offers powerful tools for understanding brain function, guiding brain–machine interfaces, and personalizing psychiatric treatment. …”
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Preliminary investigation of the design space of geared magnetorheological actuators for safer robotic manipulators by Samuel Gingras, Alexandre St-Jean, Jean-Sébastien Plante

“…Geared magnetorheological (MR) actuators have the potential to provide safe and fast physical interactions between human and machine due to their low inertia and high bandwidth. …”
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