214,423 results for machine learning
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Machine Learning in E-Commerce
…functionality and advanced implementation based on the integration of machine learning algorithms. The adequacy and advantages / disadvantages of different methods, such as general recommendation systems and content-based recommendations, were analyzed and presented. This type of implementation is…
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Machine Learning Lifecycle: A Survey
The operationalization of machine learning (ML) introduces distinct engineering and lifecycle management challenges—such as extreme data dependence, silent model degradation (concept drift), and inherent non-determinism—which traditional software engineering workflows fail to adequately address…
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Machine Learning in E-commerce
With the explosive growth of data, it is one of the most important challenges for modern enterprises to develop data-based infrastructures. The scientific discovery of Artificial Intelligence (IA) is an open opening for a wide range of applications, which allows large quantities of real-time…
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Machine Learning Using Approximate Computing
…quality. This work analyzes the application of approximate computing for machine learning, specifically focusing on k-means clustering, one of the more widely used unsupervised machine learning algorithms. The k-means algorithm partitions data into k clusters, where k also denotes the number of…
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Machine Learning G-Code Optimization
G-codes are essential in CNC systems, providing crucial instructions for controlling machine parameters and operations in manufacturing, including 3D printing. They may contain errors affecting product quality and increasing resource consumption. This research applies the K-means machine learning…
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Machine Learning for Biomedical Applications
Machine learning (ML) is a field of artificial intelligence that uses algorithms capable of extracting knowledge directly from data that could support decisions in multiple fields of engineering [...]
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Quantum Machine Learning—Quo Vadis?
The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the…
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Machine Learning on Mainstream Microcontrollers
This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning…
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Machine Learning in Renewable Energy
The volume of energy produced and consumed from renewable sources increases by a significant rate both in absolute value and also as a proportion of the total energy produced and consumed [...]
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Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the…
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Machine Learning for Biomedical Application
The tremendous development of technology also affects medical science, including imaging diagnostics [...]
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Simulating Hadronization with Machine Learning
…there remain phenomenological areas where they do not match data well. A machine-learning-based alternative called MLhad, intended ultimately to be data-trainable, can simulate hadronization by encoding latentspace vectors, trained to be distributed according to a user-defined distribution using…
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Machine Learning for Dissimulating Reality
In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, sometimes better than those produced by a human being…
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Machine Learning Applications in Seismology
The comprehension of earthquakes and natural hazards, including volcanic eruptions and landslides, as well as explosions, through observational data is a pivotal activity within the field of seismology [...]
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Deep learning—Using machine learning to study biological vision
Many vision science studies employ machine learning, especially the version called “deep learning.” Neuroscientists use machine learning to decode neural responses. Perception scientists try to understand how living organisms recognize objects. To them, deep neural networks offer benchmark…
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A Machine Learning approach to predict surgical learning curves
…given the trainees’ intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model utilizing information from the initial learning stage to predict…
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Machine learning and deep learning applications in microbiome research
…interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive…
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A machine learning enabled affective E-learning system model
The purpose of this study is to propose an e-learning system model for learning content personalisation based on students’ emotions. The proposed system collects learners’ brainwaves using a portable Electroencephalogram and processes them via a supervised machine learning algorithm, named…
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Maximizing reusability of learning objects through machine learning techniques
Maximizing the reusability of learning objects through machine learning techniques has significantly transformed the landscape of e-learning systems. This progress has fostered authentic resource sharing and expanded opportunities for learners to explore these materials with ease. Consequently, a…
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