POSSIBILITIES OF CARIES PROGNOSIS IN CHILDREN OF SCHOOL-AGE ACCORDING TO DATA GAINED FROM THEM AND THEIR PARENTS QUESTIONNAIRE by L.F. Kaskova, T.B. Mandziuk, L.P. Ulasevych, L.D. Korovina, M.A. Sadovski

“…Therefore, the purpose of our study was to identify the possibility of predicting caries in preschool children according to questionnaires of surveyed children and their parents. …”
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Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer by Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher

“…With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. …”
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Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems by Yang Xu, Pengfei Liu, Xiang Li, Wei Ren

“…Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes.…”
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Single cell analysis of host response to helminth infection reveals the clonal breadth, heterogeneity, and tissue-specific programming of the responding CD4+ T cell repertoire. by Ivy K Brown, Nathan Dyjack, Mindy M Miller, Harsha Krovi, Cydney Rios, Rachel Woolaver, Laura Harmacek, Ting-Hui Tu, Brian P O'Connor, Thomas Danhorn, Brian Vestal, Laurent Gapin, Clemencia Pinilla, Max A Seibold, James Scott-Browne, Radleigh G Santos, R Lee Reinhardt

“…Antigen-reactivity of the broader repertoires was predicted to be shared across both tissues and individual mice. …”
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ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT by Ghardapaty Ghaly Ghiffary, Nabila Tri Amanda, Rizky Ardhani, Bagus Sartono, Aulia Rizki Firdawanti

“…The stacking method is an ensemble technique in machine learning that combines predictions from several base models to improve classification accuracy. …”
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Vibration Diagnostic Methods from Methodsof Obtaining Data to Processing It Using Modern Means by Anton O. Zhuravlev, Alexey O. Polyakov, Denis A. Andrikov

“…Thus, self-diagnosis, combined with a high level of automated analytics, makes it possible to predict a malfunction with a high degree of probability, warn about the timing of its occurrence and methods of preventive elimination. …”
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Integrating feedback control for improved human-structure interaction analysis by Santiago A. Lopez, Daniel Gomez, Albert R. Ortiz, Sandra Villamizar

“…The results of this study indicate that feedback controllers accurately predict the experimental structural response for different subjects. …”
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SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads by Ramesh Rajaby, Wing-Kin Sung

“…We also show that SurVIndel2 is able to complement small indels predicted by Google DeepVariant, and the two software used in tandem produce a remarkably complete catalogue of variants in an individual. …”
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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…Among these, green hydrogen—particularly via water electrolysis and biomass gasification—received the most attention, reflecting its central role in decarbonization strategies. ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Using the proposed system, it was demonstrated that predictions of diseases like powdery mildew and blight are improved compared to traditional methods both in terms of accuracy as well as in the speed of response. …”
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A classification modeling approach for determining metabolite signatures in osteoarthritis. by Jason S Rockel, Weidong Zhang, Konstantin Shestopaloff, Sergei Likhodii, Guang Sun, Andrew Furey, Edward Randell, Kala Sundararajan, Rajiv Gandhi, Guangju Zhai, Mohit Kapoor, Mohit Kapoor

“…Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. …”
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Smart Farming: AI and IoT-Based Solutions for Real-Time Agriculture Monitoring by Kadao Anjali Krushna, Shivaji Ghorpade Bipin

“…The machine learning models are used to predict possible points of problems like disease outbreaks or nutrient deficiencies so that appropriate steps can be taken preemptively. …”
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Data-driven decoding of quantum error correcting codes using graph neural networks by Moritz Lange, Pontus Havström, Basudha Srivastava, Isak Bengtsson, Valdemar Bergentall, Karl Hammar, Olivia Heuts, Evert van Nieuwenburg, Mats Granath

“…The decoding problem is formulated as a graph classification task in which a set of stabilizer measurements is mapped to an annotated detector graph for which the neural network predicts the most likely logical error class. We show that the GNN-based decoder can outperform a matching decoder for circuit level noise on the surface code given only the simulated data, while the matching decoder is given full information of the underlying error model. …”
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Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data. by David L Corcoran, Kusum V Pandit, Ben Gordon, Arindam Bhattacharjee, Naftali Kaminski, Panayiotis V Benos

“…<h4>Background</h4>MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. …”
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IMPROVEMENT OF THE RUSSIAN CITIES’ TRANSPORT INFRASTRUCTURE by E. A. Safronov, K. E. Safronov

“…The problems of influence of the city form on the formation of the transport frame, of the density of settlement and of the efficiency of urban development are considered. New methods of predicting the transport demand of the population, the level of the transport systems’ development and assessing accessibility are proposed. …”
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Learning-based parallel acceleration for HaplotypeCaller by Xiangxing Lai, Minguang Xiao, Lingling Weng, Zhiguang Chen

“…This paper introduces a learning-based framework LPA (learning-based parallel acceleration), leveraging model to accurately predict the computational complexity of data. By employing adaptive data segmentation algorithms and Multi-Knapsack Problem (MKP) based task scheduling, LPA significantly alleviates computational skew. …”
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PENERAPAN PARTICLE SWARM OPTIMIZATION PADA ALGORITMA C 4.5 UNTUK SELEKSI PENERIMAAN KARYAWAN by Agus Wiyatno

“…Many studies using the method of decision tree and classification tree in predicting the employees selection but results the accuracy of the resulting value is less accurate. …”
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Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease by Xia Wang, Lin Zhang

“…Current therapies face challenges in targeting specific immune pathways and predicting treatment responses. Methods Multi-cohort transcriptomic data were integrated to identify inflammation-related genes (IRGs). …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…A feedforward neural network is employed to predict the PCA-reduced S-parameters, serving as a surrogate model that enables faster decision-making within the Q-learning framework. …”
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Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT by Hafiyyan Putra Pratama, Dewi Indriati Hadi Putri, Hafiziani Eka Putri, Elysa Nensy Irawan, Makna A’raaf Kautsar

“…The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. …”
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