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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Integrating Multi-Variable Driving Factors to Improve Land Use & Land Cover Classification Accuracy using Machine Learning Approaches: A Case Study from Lombok Island by Miftahul Irsyadi Purnama, Hüseyin Oğuz Çoban

“…In field validation assessments, comparing the predictions of these machine learning models with ground truth data, Random Forest was the most reliable, achieving an overall accuracy of 88%. …”
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A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles by Chenghao Lyu, Nuo Lei, Chaoyi Chen, Hao Zhang

“…This framework employs lightweight manifold dimensionality reduction to compress the decision space, enabling Bayesian optimization (BO) on low-dimensional manifolds for a global coarse search. …”
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Deep Reinforcement Learning for Dynamic Pricing and Ordering Policies in Perishable Inventory Management by Yusuke Nomura, Ziang Liu, Tatsushi Nishi

“…The results show that dynamic programming with action reduction achieved an average of 63.1% reduction in computation time compared to vanilla dynamic programming. …”
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Patient Controlled Analgesia for Adults with Sickle Cell Disease Awaiting Admission from the Emergency Department by Josue Santos, Sasia Jones, Daniel Wakefield, James Grady, Biree Andemariam

“…Mean pain intensity (MPI) reduction did not differ between groups. Among visits where PCA was begun in the ED, low utilizers demonstrated greater MPI reduction than high utilizers (2.8 versus 2.0, p=0.04). …”
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MUSCLE MRI SEGMENTATION USING RANDOM WALKER METHOD by A. V. Shukelovich, E. V. Snezhko, V. A. Kovalev, A. V. Tuzikov

“…The possibility of clinician’s manual labor amount reduction and random walker algorithm optimization is studied.…”
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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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ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis by Wenqian Jiang, Xiaohua Huang, Qun Zhao, Sheng Liu

“…Furthermore, to enhance crowd movement predictions in real-world applications, these methods must demonstrate temporal coherence. …”
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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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