Open system of text processing for scientometric and bibliometric purposes by L. S. Lomakina, A. S. Surkova, D. V. Zhevnerchuk, O. S. Rassadin

“…The key features of the proposed solution are the ability to implement basic algorithms for text data analyzing and processing in the form of RESTful services, executing of these processes in a distributed processing environment, creating combined algorithms based on them, and also the ability to build an applied programming interface for creating client-server systems with mobile and fixed client subsystems in the field of science and education.…”
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Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning by Yuqi Li, Tao Zhou, Zhiyu Liu, Xinyao Zhu, Qilong Wu, Chunyang Meng, Qingfu Deng

“…Subsequently, the best predictive model was selected through a combination of 108 machine learning algorithms. …”
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A Bionic Goal-Oriented Path Planning Method Based on an Experience Map by Qiang Zou, Yiwei Chen

“…Our experimental validation demonstrates the method’s effectiveness in experience sequence prediction and goal-oriented global path planning. The comparative results highlight its superior performance over traditional Dijkstra’s algorithm, particularly in terms of adaptability to environmental changes and computational efficiency in optimal global path generation.…”
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Hybrid Hunger Games Search optimization using a neural networks approach applied to UAVs by Nadia Samantha Zuñiga-Peña, Salatiel Garcia-Nava, Norberto Hernandez-Romero, Juan Carlos Seck-Touh-Mora

“…Optimization methods like population-based algorithms are valuable when applied to multidimensional and nonlinear problems. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform by Luca Gerevini, Gianni Cerro, Alessandro Bria, Claudio Marrocco, Luigi Ferrigno, Michele Vitelli, Andrea Ria, Mario Molinara

“…The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. …”
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A data-driven approach to solve the RT scheduling problem by Mruga Gurjar, Jesper Lindberg, Thomas Björk-Eriksson, Caroline Olsson

“…The comparison between manual and algorithm allocations showed that the number of delayed patients reduced by 10 % in the algorithm suggestion with an average delay reduction of 2 weeks. …”
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Unbiased High-Precision Cloud Detection for Advanced Himawari Imager Using Automatic Machine Learning by Bochun Liu, Jinming Ge, Qingyu Mu, Chi Zhang, Xiaoyu Hu, Jiajing Du, Yanyan Wu, Bo Wang, Xiang Li, Jianping Huang, Qiang Fu

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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.…”
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Maximum Power Exploitation of Photovoltaic System under Fast-Varying Solar Irradiation and Local Shading by Yi-Jui Chiu, Bi Li, Chin-Ling Chen, Shui-Yang Lien, Ding Chen, Ji-Ming Yi, Yung-Hui Shih

“…Finally, an improved MPPT system with the PF-MPPT algorithm was proposed to solve the problem of the significant reduction of the output power of a PV system under fast-varying solar irradiation and local shading. …”
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Lightweight coal mine conveyor belt foreign object detection based on improved Yolov8n by Jierui Ling, Zhibo Fu, Xinpeng Yuan

“…Fourthly, the original channel attention mechanism in the Head part is replaced with C2f_MLCA, which not only speeds up the processing speed but also successfully avoids the problems of accuracy degradation caused by channel dimensionality reduction. Fifthly, the lightweight detection head Detect-LSDECD is added, which uses Detail Enhancement Convolution (DEConv) for improvement and optimization, enhancing the stability of the algorithm’s recognition under various environmental factors. …”
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A Study on the Susceptibility of Wildfire Disasters in Wuzhou City Based on Interpretability Model by Lei Zou, Hongjuan Shao, Yintao Liu, Chao Ren, Qinyi Chen, Haoming Bai, Zhengzhong Huang, Yao Gu

“…The Extreme Gradient Boosting (XGBoost) algorithm was adopted to construct the prediction model, aiming to provide precise wildfire susceptibility assessment. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. …”
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Embedding With Preservation of Semantics of the Original Data by M. E. Vatkin, D. A. Vorobey, M. V. Yakovlev, M. G. Krivova

“…Working with them can be computationally inefficient, and often leads to overfitting; therefore, the data dimension reduction algorithms are used, one of which is auto encoders. …”
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Thermal Conductivity Characteristics of Thermal Insulation Materials Immersed in Water for Cold-Region Tunnels by Youyun Li, Yongmei Sun, Yuan Zhuang, LiMin Duan, Ke Xie

“…Related experimental research can predict the thermal conductivity of thermal insulation materials by using a new perspective and shows the influence of pore water distribution on the thermal conductivity of materials.…”
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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…Effectiveness of models is validated with eight deep learning algorithms trained for two forecasting horizons. Results demonstrate a 93.74% improvement on an average in convergence compared to conventional mini-batch sizes, achieving one among top three accuracy across various prediction horizons and user categories. …”
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Graph semantic similarity-based automatic assessment for programming exercises by Chengguan Xiang, Ying Wang, Qiyun Zhou, Zhen Yu

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Analysis of Factors Affecting the Success of Sustainable Development Projects with the Help of Machine Learning Tools by Zhi-Jun Chen, Tsung-Shun Hsieh, Seyed Mehdi Mousavi Davoudi

“…The study’s results reveal that employing machine learning tools and algorithms to create a link between project success factors and criteria and prediction can bring multiple advantages, including high accuracy, ease of use, and inference for decision-making. …”
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On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data. by Barbara Witulla, Nicole Goerig, Florian Putz, Benjamin Frey, Tobias Engelhorn, Arnd Dörfler, Michael Uder, Rainer Fietkau, Christoph Bert, Frederik Bernd Laun

“…It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.…”
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