The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy by Jiaqi Yin, Feilong Li, Ruidan Luo, Xiao Chen, Linhui Zhao, Hong Yuan, Guang Yang

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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

“…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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Development of an algorithm to improve on the National Early Warning Score 2 (NEWS2) system's accuracy in predicting critical outcomes using additional patient data and amendments... by Lynsey Threlfall, Chris Plummer, Edward Meinert, Cen Cong, Madison Milne-Ives

“…Intended outputs: By June 2025, an algorithm based on 8 years of historical patient data will have been trained and tested on NUTH datasets to create a new tool that is of national and international importance. …”
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Evolutionary approach for composing a thoroughly optimized ensemble of regression neural networks by Lazar Krstic, Milos Ivanovic, Visnja Simic, Boban Stojanovic

“…The paper presents the GeNNsem (Genetic algorithm ANNs ensemble) software framework for the simultaneous optimization of individual neural networks and building their optimal ensemble. …”
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Application of machine learning in the determination of rock brittleness for CO2 geosequestration by Efenwengbe Nicholas Aminaho, Mamdud Hossain, Nadimul Haque Faisal, Reza Sanaee

“…Therefore, it is important to monitor the brittleness of reservoir and cap rocks, to ascertain their integrity for CO2 storage. In this study, an algorithm was developed to generate numerical simulation datasets for a more reliable machine learning model development, and an artificial neural network (ANN) model was developed to evaluate the brittleness index of rocks using data from numerical simulations of CO2 geosequestration in sandstone and carbonate reservoirs, overlain by shale caprock. …”
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Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China by Luyao Zhou, Weimin Zhang, Min Shao, Cui Wang, Yu Wang

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The Technical Development of a Prototype Lower-Limb Therapy Device for Bed-Resting Users by Juan Fang, Adrien Cerrito, Simón Gamero Schertenleib, Patrick von Raumer, Kai-Uwe Schmitt

“…The aim of this work was to develop and evaluate the usability of a prototype in-bed lower-limb therapy device that offers various training patterns for the feet and legs, featuring an intuitive user interface and interactive exergames. …”
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Effective Facial Expression Recognition System Using Artificial Intelligence Technique by Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah

“…ANNS are inspired by the neural architecture of human brain capable of learning and recognizing patterns in unchartered data after trained examples, on the other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, and mutation. …”
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AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural net... by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…It was further evaluated on unseen data from diverse UK cities without retraining, confirming its predictive power across varying climatic conditions. …”
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Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation by Pouya Rajaee, Faramarz Ashenai Ghasemi, Amir Hossein Rabiee, Mohammad Fasihi, Behnam Kakeh, Alireza Sadeghi

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Preparation for Entering a Higher Educational Institution as an Investment Project by V. A. Kataeva, V. R. Masalkina, A. A. Ponomareva

“…By applying the method of evaluating the effectiveness of an investment project and the approach to managing it, based on the created model, an algorithm is described for choosing the best trajectory for preparing for admission to a higher educational institution, which allows applicants to independently evaluate investments in training as an investment project and choose the most cost-effective way of training. …”
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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

“…In addition, the study area's hydro-chemical facies were examined, and machine learning models’ hyperparameters such as Random Forest (RF), Borda Scoring Algorithm (BSA), Decision Tree (DT), Multilayer Perception (MLP), and Naïve Bayes (NB), were executed before, to training and testing the samples of surface water. …”
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Near Real-Time Flood Monitoring Using Multi-Sensor Optical Imagery and Machine Learning by GEE: An Automatic Feature-Based Multi-Class Classification Approach by Hadi Farhadi, Hamid Ebadi, Abbas Kiani, Ali Asgary

“…Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. …”
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Autonomous Robot Goal Seeking and Collision Avoidance in the Physical World: An Automated Learning and Evaluation Framework Based on the PPO Method by Wen-Chung Cheng, Zhen Ni, Xiangnan Zhong, Minghan Wei

“…However, application of RL methods to real-world tasks such as mobile robot navigation, and evaluating their performance under various training–testing settings has not been sufficiently researched. …”
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Implementation of Machine Learning in Flat Die Extrusion of Polymers by Nickolas D. Polychronopoulos, Ioannis Sarris, John Vlachopoulos

“…The dataset was used to train and evaluate the following three powerful machine learning (ML) algorithms: Random Forest (RF), XGBoost, and Support Vector Regression (SVR). …”
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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image by Jianhong Zhao, Huamin Yang, Yi Sui

“…This approach not only enhances the training process but also simplifies loss function design, ultimately leading to a proper evaluation of surface modifications in steel materials. …”
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Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP... by Sana Qaiyum, Kashif Irshad, Mohamed E. Zayed, Salem Algarni, Talal Alqahtani, Asif Irshad Khan

“…Consequently, Emperor Penguins Colony (EPC) optimization algorithm was also employed for selecting the essential features, which reduces the data dimensionality and assists the predictive algorithm to focus on important features in its training phase. …”
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Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients by Gaimei Wang, Cendi Lu, Owusu Mensah Solomon, Yujia Gu, Yijing Ling, Fanchi Xu, Yumin Tao, Yehong Wei

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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…In conclusion, integrating IMU technology into horse performance evaluation offers valuable insights, with implications for breeding and training.…”
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The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma by Qian Shen, Cong Xiang, Yongliang Han, Yongmei Li, Kui Huang

“…This study aimed to establish and validate CT-based intratumoral and peritumoral radiomics models to clarify the characteristics between parotid PA with and without complete capsule.MethodsIn total, data of 129 patients with PA were randomly assigned to a training and test set at a ratio of 7:3. Quantitative radiomics features of the intratumoral and peritumoral regions of 2 mm and 5 mm on CT images were extracted, and radiomics models of Tumor, External2, External5, Tumor+ External2, and Tumor+External5 were constructed and used to train six different machine learning algorithms. …”
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