Search Results - research algorithm

Prospects of anti-inflammatory and urate-lowering therapy of gout: A vector from the past to the future by A. A. Garanin, N. L. Novichkova, N. L. Novichkova

“…The article provides an overview of new and promising drugs aimed at anti-inflammatory and urate-lowering therapy of gout, both already registered and used in clinical practice, and at the stages of implementation or clinical research and demonstrating their high efficacy and safety. …”
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Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach by Xinwei Zhao, Eberhard Korsching

“…It is adaptable to various research needs and can incorporate different tools and parameters for in-depth analysis, especially in low read rate scenarios. …”
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Opportunistic Diagnostics of Dental Implants in Routine Clinical Photon-Counting CT Acquisitions by Maurice Ruetters, Holger Gehrig, Christian Mertens, Sinan Sen, Ti-Sun Kim, Heinz-Peter Schlemmer, Christian H. Ziener, Stefan Schoenberg, Matthias Froelich, Marc Kachelrieß, Stefan Sawall

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Methodological Aspects of Predictive Mineragenic Studies Using Earth Remote Sensing Data by Petrov Vladislav, Ustinov Stepan, Minaev Vasilii

“…Of the entire range of areas of fundamental and exploratory scientific research, the main attention within the framework of predictive and mineragenic studies is paid to solving the following problems: 1) allocation of lineaments (fault zones) based on processing of digital elevation models; 2) determination of hydraulically active fault structures for the period of ore formation based on tectonophysical reconstructions; 3) analysis of multispectral characteristics of pre-ore, ore-accompanying and post-ore metasomatites based on statistical processing of Landsat-8 satellite data; 4) assessment of fluid-dynamic settings of deposit formation based on data on the composition, properties and genesis of mineral-forming fluids. 5) creation of weight of evidence models based on statistical algorithms for processing data on the dynamics of ore-genetic processes. …”
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Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges by Chenzuo Ye, Hao Wu, Takashi Oguchi, Yuting Tang, Xiangjun Pei, Yufeng Wu

“…In contrast, data-driven models, primarily developed using machine learning and statistical algorithms, often provide acceptable predictive accuracy in assessing landslide susceptibility. …”
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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

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Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM by Fanyu ZENG, Yaning HAN, Hongyuan YANG, Dapeng YANG, Fan ZHENG

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Optimizing audit processes through open innovation: Leveraging emerging technologies for enhanced accuracy and efficiency by Anu Sayal, Amar Johri, N. Chaithra, Hamad Alhumoudi, Zuhur Alatawi

“…Using Random Forest and K-Means algorithms, the analysis processes over 14 million records to classify filing risks and detect anomalies across 399 industries. …”
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Possibilities of using artificial intelligence technologies in the morphological diagnosis of inflammatory bowel diseases (literature review) by E. G. Churilova, A. B. Kazumova, Kh. M. Akhrieva, N. V. Pachuasvili, A. S. Tertychnyy

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Optimal Allocation of Gas Supply Reliability in Natural Gas Pipeline System Based on Exterior Penalty Function Method by Yueqi LIU, Lei HOU, Shuaishuai TANG, Huai SU, Xingtao LI

“…Optimal allocation of gas supply reliability is an important part of gas supply reliability of natural gas pipeline system.In order to study the optimal allocation scheme of gas supply reliability with the lowest cost,a cost function model based on the gas supply capacity of the pipeline system was constructed.To address the limitation of traditional intelligent optimization algorithms (e.g.,Particle Swarm Optimization) that overlook constraints during iterative updates,this research proposed a novel Exterior Penalty Function Method for optimizing gas supply reliability.This method transformed constraints in the allocation model into penalty function terms,established a revised objective function,and converted the constrained allocation problem into an unconstrained extremum problem.Applying this method to a practical pipeline system,optimal gas supply reliability allocation values were derived.The results demonstrate that the Exterior Penalty Function Method significantly reduces computational time without compromising accuracy.The allocation outcomes exhibit robust convergence and align with engineering practicality.By clarifying the optimized allocation values of unit gas supply reliability and comparing them with the current reliability,the weak units in the gas supply system can be identified,providing a scientific basis for improving the gas supply reliability of pipeline systems.…”
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Exploring machine learning techniques for open stope stability prediction: A comparative study and feature importance analysis by Alicja Szmigiel, Derek B. Apel, Yashar Pourrahimian, Hassan Dehghanpour, Yuanyuan Pu

“…However, modern advancements in machine learning present new opportunities for enhancing predictive capabilities and understanding complex relationships influencing stope stability. Building upon research demonstrating the feasibility of using machine learning for stability prediction, our study investigates and compares several machine learning algorithms. …”
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The Advanced Actuarial Data Science Based AI-Driven Solutions for Automated Loss Reserving Under IFRS 17 in Non-Life Insurance by Brighton Mahohoho, Charles Chimedza, Florance Matarise, Sheunesu Munyira

“…A unique aspect of this research is the integration of bancassurance services, enabling automated management for both microfinance and car insurance on a unified platform. …”
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Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study by Marina Galanina, Anna Rekiel, Anna BaCzyk, Bozena Kostek

“…Such results underscore the necessity of integrating more personalized methods into the creation of machine learning algorithms for mental diagnostics.…”
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Rapid Classification and Quantitative Prediction of Aflatoxin B₁ Content and Colony Counts in Nutmeg Based on Electronic Nose by Ruiqi Yang, Keyao Zhu, Yuanyu Zhao, Xingyu Guo, Yushi Wang, Jiayu Wang, Huiqin Zou, Yonghong Yan

“…Subsequently, electronic nose (E-nose) was employed to analyze the odor of nutmeg and was combined with six machine learning algorithms to establish a classification model for samples with different degrees of mold. …”
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A Generic Modeling Method of Multi-Modal/Multi-Layer Digital Twins for the Remote Monitoring and Intelligent Maintenance of Industrial Equipment by Maolin Yang, Yifan Cao, Siwei Shangguan, Xin Chen, Pingyu Jiang

“…Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. …”
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Marker Loci in Brucella Genome for Differential PCR Indication of Pathogenic strains by N. I. Khammadov, K. A. Osyanin, K. V. Usol’tsev, T. Kh. Faizov, A. V. Khammadova, E. A. Shuralev

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Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event by Francisca Santana-Robles, Eva Selene Hernández-Gress, Ricardo Martínez-López, Isidro Jesús González-Hernández

“…Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. …”
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A Custom Reinforcement Learning Environment for Hybrid Renewable Energy Systems: Design and Implementation by Dalton F. Guedes Filho, Marcelo A. Moret, Erick G. Sperandio Nascimento

“…We present HybridEnergyEnv, an open-source, Gym-style simulation environment designed for reinforcement learning (RL) research in hybrid renewable energy systems (HRES) combining wind, solar, and battery storage. …”
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Identification and analysis of driving factors for product evolution: A text data mining approach by Shifeng Liu, Jianning Su, Shutao Zhang, Kai Qiu, Shijie Wang

“…Traditional studies primarily rely on inductive summarization, which often faces issues of subjectivity, uncertainty, and low reliability. This research presents a method combining the Bidirectional Encoder Representations from Transformers (BERT) model and Dynamic Topic Model (DTM) to analyze the driving factors of product evolution. …”
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Scientific Machine Learning for Elastic and Acoustic Wave Propagation: Neural Operator and Physics-Guided Neural Network by Nafisa Mehtaj, Sourav Banerjee

“…Lastly, this article identifies current limitations and suggests promising directions for future research on NO-based methods within computational wave mechanics.…”
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