204.674 hasil untuk machine learning
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Artikel Jurnal
Machine Learning for Online Transient Stability Assessment
Machine learning (ML) offers a computationally tractable alternative to time-domain simulation for online transient stability assessment (TSA), yet inconsistent evaluation practice limits reproducibility and cross-study comparison. We benchmark 17 ML classifiers on IEEE 9-bus and New England 39-bus…
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Ransomware Detection System Based on Machine Learning
…Although there is a lot of research on detecting malware using machine learning (ML), only a few focuses on ML-based ransomware detection. Especially attacks targeting smartphone operating systems (e.g., Android) and applications. In this research, a new system was proposed to protect smartphones…
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Loan Default Prediction Using Machine Learning Algorithms
…losses. The objective of this study is to identify the most suitable machine learning model to predict loan default by comparing four models which are Random Forest, Decision Tree, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, it also examines…
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Evaluating Machine Learning Classifiers in Detecting Cyberattacks
This study aims to develop a machine learning model that can accurately detect cyberattacks. We compare the performance of Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) in predicting cyberattacks. Furthermore, we investigate whether using Information Gain Attribute…
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Artikel Jurnal
Advanced Machine Learning Based Malware Detection Systems
In the area of machine learning (ML) training data optimization through the construction of compact data, the focus of this paper is presented. The concept of compact data design, aimed at creating an optimized dataset that maximizes benefits without the need to manage a vast amount of complex…
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Machine Learning for Nanomaterial Discovery and Design
Machine learning (ML) has become a transformative tool in nanomaterial research, driven by the rapid growth of data-intensive experimental techniques, multiscale simulations, and computational modeling. This study provides a bibliometric analysis to characterize how ML has been integrated into…
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Research Frontiers in Machine Learning & Knowledge Extraction
Machine Learning and Knowledge Extraction have evolved from algorithmic tools for pattern recognition into a unifying foundational scientific framework underpinning virtually all of today’s groundbreaking advances, enabling systematic discovery, interpretation and understanding across domains. This…
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A short review on quantum machine learning
Quantum Machine Learning (QML) integrates two prosperous scientific fields: quantum computing and artificial intelligence. We will review recent developments in QML-based methods, their theoretical foundations, algorithmic progress, practical applications, and remaining issues. By analyzing…
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Artikel Jurnal
Opportunities of machine learning algorithms for education
Abstract Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build…
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Artikel Jurnal
Accelerating Giant-impact Simulations with Machine Learning
…later collisions. To accelerate giant-impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 N -body simulations of three-planet systems, we develop an ML model that can accurately predict which two…
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Understanding Online Purchases with Explainable Machine Learning
…a website visitor will make a purchase, thereby generating revenue. Machine learning models are the most accurate means to achieve this objective. However, the opaque nature of these models may deter companies from adopting them. Instead, they may prefer simpler models that allow for a clear…
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Artikel Jurnal
Galaxy Cluster Characterization with Machine Learning Techniques
We present an analysis of the X-ray properties of the galaxy cluster population in the z = 0 snapshot of the IllustrisTNG simulations, utilizing machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central…
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Artikel Jurnal
Machine learning fusion for glioma tumor detection
…gliomas are extracted and classified as independent components. A deep learning model is then employed to categorize these gliomas. The proposed model classifies gliomas into three primary categories: meningioma, pituitary, and glioma. Performance evaluation demonstrates a high level of accuracy…
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Predicting Startup Success Using Machine Learning Approach
Predicting startup success is important because it helps investors, entrepreneurs, and stakeholders allocate resources more efficiently, minimize risks, and enhance decision-making in an uncertain and competitive environment. Therefore, investors need to predict whether a startup will succeed or…
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Machine Learning Applied to Optical Communication Systems
As the global demand for high-speed and high-capacity communication continues to surge, driven by cloud computing, artificial intelligence, 5G, virtual reality, and the Internet of Things (IoT), optical communication systems have emerged as the backbone of modern digital infrastructure [...]
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Artikel Jurnal
Modeling PROTAC degradation activity with machine learning
…and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC…
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Machine learning based crystal collimator alignment optimization
…of Tesla at LHC top energies, improving the cleaning performance of the machine. However, crystals must be in optimal alignment with respect to the circulating beam to maximize the efficiency of the channeling process. A newly developed machine learning model automatically classifies the channeling…
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Artikel Jurnal
Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces…
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Artikel Jurnal
Network modal innovation for distributed machine learning
Distributed machine learning, as a popular computing architecture for artificial intelligence, still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios…
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