Experiment with the X-band radar at the Nizhny Novgorod cable car: First Results by Ponur Kirill Aleksandrovich, Titchenko Yuriy Andreevich, Karaev Vladimir, Meshkov Evgeniy Mihaylovich, Panfilova Mariya Andreevna, Krylov Andrey Vladimirovich, Lebedev I., Khakin E.

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Study of the interval width of features for improving forecasting efficiency of the transport flow intensity by I. N. Pugachev, N. G. Sheshera, D. Yе. Grigorov

“…For a comprehensive characterization, additional analysis was necessary to conduct of the remaining independent features. New algorithmic structures were created using the Python programming language, in which the established feature interval ranges were sequentially compared in such a way as to process all possible combinations. …”
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Non-invasive ML methods for diagnosis of congenital heart disease associated with pulmonary arterial hypertension by Yuyang Gao, Pengyue Ma, Jiahua Pan, Hongbo Yang, Tao Guo, Weilian Wang

“…In the classification phase, considering the lack of CHD-PAH data, ensemble learning was widely used.Main resultsAn accuracy of 88.61% was achieved with direct three-divided model and 90.9% with two-stage classification model.SignificanceBy analyzing and discussing these algorithms, future research directions of CHD-PAH assisted diagnosis were discussed. …”
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Intelligent Gamification Mechanics Using Fuzzy-AHP and K-Means to Provide Matched Partner Reference by Fitri Marisa, Sharifah Sakinah Syed Ahmad, Nasreen Kausar, Sajida Kousar, Dragan Pamucar, Nasr Al Din Ide

“…The following steps are carried out sequentially in carrying out this research. First, analyze needs for a recommendation model that supports partner reference. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Approach: Standard machine learning models were applied sequentially as feature selectors and filters. CatBoost demonstrated superior performance as a classification method compared to other approaches. …”
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning by Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Jianhua Zhang, Sen Yang

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Dataset of apples for grading by sweetness, ripeness and varietyMendeley Data by Shilpa Gaikwad, Sonali Kothari, Ignisha Rajathi G

“…This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. …”
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