The good and the bad: using C reactive protein to distinguish bacterial from non-bacterial infection among febrile patients in low-resource settings by Camille Escadafal, Sabine Dittrich, Sandra Incardona, B Leticia Fernandez-Carballo

“…CRP testing may be best used as part of a panel of diagnostic tests and algorithms. Further studies in low-resource settings, particularly with regard to impact on antibiotic prescribing and cost-effectiveness of CRP testing, are warranted.…”
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False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions by Maxim Edelson, Anh Pham, Tsung-Ting Kuo

“…Abstract As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inherent security risks must be addressed. …”
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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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Use of Data Mining for Intelligent Evaluation of Imputation Methods by David Red, Carlos R. Primorac

“…Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. …”
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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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Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach by Mohamed Koubàa, Abdullah S. Karar, Faouzi Bahloul

“…To leverage this opportunity, we propose an advanced VM placement algorithm designed to maximize the number of hosted SVMs in cloud data centers. …”
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Explainable data mining model for hyperinsulinemia diagnostics by Nevena Rankovic, Dragica Rankovic, Mirjana Ivanovic, Igor Lukic

“…Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. …”
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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

“…Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. …”
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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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Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica by Roberto Vargas-Masís, Diego Quesada

“…Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. …”
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IMPROVEMENT OF THE RUSSIAN CITIES’ TRANSPORT INFRASTRUCTURE by E. A. Safronov, K. E. Safronov

“…Therefore, the developed algorithms and recommendations for reducing the volume of transport infrastructure helps to improve its performance and availability. …”
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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

“…In this study created a C 4.5 Algorithm model and C 4.5 Algorithm model based on particle swarm optimization to get the rule in employees selection and provide a more accurate value of accuracy. …”
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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

“…Additionally, while intelligent algorithms can assist in tuning, they often require large volumes of simulated data, leading to high computational costs. …”
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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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Towards solving NLP tasks with optimal transport loss by Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

“…Loss functions are essential to computing the divergence of a model’s predicted distribution from the ground truth. Such functions play a vital role in machine learning algorithms as they steer the learning process. …”
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