204.674 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Thefittest: evolutionary machine learning in Python
Thefittest is a new Python library specializing in evolutionary optimization methods and machine learning methods that use evolutionary optimization methods. Thefittest provides both classical evolutionary algorithms and efficient modifications of these algorithms that do not have implementations…
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Machine learning-based infant crying interpretation
…plots, and RGB GAF. Then, these features are provided to different machine learning classifiers, such as decision tree, random forest, K nearest neighbors, and bagging. The use of MFCCs, ZCR, and RMS as features achieved high performance, outperforming state of the art (SOTA). Optimal parameters…
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Predicting Landslide Using Machine Learning Techniques
…are employed. The analysis highlights the effectiveness of employing machine learning models, Random Forest (RF), Stacking, Bagging, and Vote methods. The results demonstrate the efficiency of the models in accurately predicting landslide susceptibility. The study suggests that similar hybrid…
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Quantum Machine Learning for Credit Scoring
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the…
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Advances in machine learning-enhanced nanozymes
…in their design and applications, particularly through the integration of machine learning (ML). Machine learning (ML) has optimized nanozyme efficiency by predicting ideal size, shape, and surface chemistry, reducing experimental time and resources. This review explores the rapid advancements in…
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CME Arrival Modeling with Machine Learning
…Liu et al. developed a tool, called CME Arrival Time Prediction Using Machine Learning Algorithms (CAT-PUMA), to obtain fast and accurate predictions of CME transit time. This present work aims at the expansion of the CAT-PUMA concept, employing supervised learning to obtain vital information about…
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A survey of multimodal machine learning
…are characteristic of multimodes. As a result, based on classical machine learning strategies, multi-modal learning has become a valuable research topic, enabling computers to process and understand “big data”. The cognitive processes of humans involve perception through different sense organs…
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Crop recommendation system using machine learning
…will help farmers by reducing the losses they face and improving yield. Machine learning plays an important role in the area of crop cultivation. This work proposes a crop recommendation system using machine learning techniques such as k-nearest neighbor (KNN), artificial neural network (ANN)…
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Machine Learning Techniques for Disease Prediction
…diseases is through the analysis of social media data, such as Twitter. Machine learning (ML) techniques can be applied to Twitter data to identify patterns and trends that may be indicative of an emerging epidemic. For example, natural language processing (NLP) techniques can be used to analyze…
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Machine-Learning Methods for Complex Flows
We are delighted to introduce this Special Issue focused on novel machine-learning (ML) methods aimed at predicting, modeling, and controlling a variety of complex fluid flow scenarios [...]
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Quantum machine learning for image classification
…data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even…
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Machine Learning-assisted spatiotemporal chaos forecasting
Long-term forecasting of extreme events such as oceanic rogue waves, heat waves, floods, earthquakes, has always been a challenge due to their highly complex dynamics. Recently, machine learning methods have been used for model-free forecasting of physical systems. In this work, we investigated the…
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Neurocognition-inspired design with machine learning
Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training…
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Machine Learning-Based Radon Monitoring System
…costs). Therefore, a system for predicting radon concentrations based on Machine Learning has been developed. Its output activates the fan control system when certain thresholds are reached.
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Hardening quantum machine learning against adversaries
Security of machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help or hinder the security of machine learning. Here we discuss a number of ways that quantum information can be used to help…
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Diagnosing Diabetes with Machine Learning Techiques
…diagnostic tools for the early diagnosis of diabetes in this context. Machine learning algorithms can help diagnose diseases early. Numerous studies are being conducted to improve the speed, performance, reliability, and accuracy of diagnosing with these methods for a particular disease. This study…
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Voice Profile Authentication Using Machine Learning
In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication. Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented…
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Data compression for quantum machine learning
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of achieving quantum speedups in machine learning tasks. These devices, however, are composed of a small number of qubits and can faithfully run only short circuits. This puts many proposed approaches…
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Requirements Engineering in Machine Learning Projects
Over the last decade, machine learning methods have revolutionized a large number of domains and provided solutions to many problems that people could hardly solve in the past. The availability of large amounts of data, powerful processing architectures, and easy-to-use software frameworks have…
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On Machine-Learning Morphological Image Operators
…trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of…
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