3.327 hasil untuk machine learning
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Applications of machine learning to water resources management: A review of present status and future opportunities
…and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in…
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Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide…
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Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach
In recent years, ground-level ozone has become a severe ambient pollutant in major urban areas of China, which has adverse impacts on population health. However, in-situ measurements of the ozone concentration before 2013 in China are quite scarce, which cannot facilitate the assessment of the…
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Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms
…Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and…
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Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management
…need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational…
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Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran
This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to…
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Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
Abstract Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial…
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Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods
Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low…
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From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare
…and shaped by the applications of data-driven, robust, and efficient machine learning (ML) to deep learning (DL) technologies. ML in the medical sector is developing quickly, causing rapid progress, reshaping medicine, and improving clinician and patient experiences. ML technologies evolved into…
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Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques
Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate…
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Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications
The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low-cost HMI can expediently track the motions of human fingers, resulting in a…
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Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems
The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine…
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A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
…of the battery and ensure the safe and reliable operation of the system. Machine learning (ML) technology has attracted increasing attention due to its competitiveness in studying the behavior of complex nonlinear systems. With the development of big data and cloud computing, ML technology has a…
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Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
…relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29…
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A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
…and no treatment that can successfully change the course of the disease. Machine learning models that can predict the time until a patient develops dementia are important tools in helping understand dementia risks and can give more accurate results than traditional statistical methods when…
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Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow
…of mitophagy modulators. Here we report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new…
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A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry
…In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug…
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