Search Results - "Neural networks"

The paper presents a technology based on an ensemble of neural networks that solves the problem of predicting a multidimensional time series of telemetry data. A distinctive feature of this is the possibility of additional training in the case of analyzing parameters in a non-stationary environment,...

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In infectious disease outbreak modeling, there remains a gap in addressing spatiotemporal challenges present in established models. This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing ti...

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Batch normalization (BN) is crucial for achieving state-of-the-art binary neural networks (BNNs). Unlike full-precision neural networks, BNNs restrict activations to discrete values <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><...

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Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand ar...

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Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analy...

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We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) survey, we ev...

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The purpose of this study is to determine the feasibility and features of training a recurrent neural network consisting of FitzHugh-Nagumo systems with delayed feedback to predict an impulse (spike signal). Methods. The network under consideration consisted of N=60 FitzHugh&ndash;Nagumo system...

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The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures. While machine learning (ML) has proven effective in predicting the...

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Object is a neural networks in the field of computer vision and data analysis. The article explores the fundamental principles and aspects underlying the functioning of neural networks. Among the identified limitations are the complexity of tuning hyperparameters and computational costs associated...

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This study investigates the use of Recurrent Neural Networks (RNNs) and Long ShortTerm Memory (LSTM) networks to predict crime patterns in Bucaramanga, Colombia. A temporal approach is presented, which starts by splitting the city into 17 communes. Using a dataset of robbery incidents from 2016 to...

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The automatic modulation recognition of the received signal is very attractive in both military and civilian applications. In the recent years, deep learning techniques have received much attention due to their excellent performance in signal, audio, image and video processing. This paper examines t...

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Depression is a significant mental health problem and presents a challenge for the machine learning field in the detection of this illness. This study explores automated depression classification, leveraging computational techniques to address this issue. The proposed approach performs spectrogram a...

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Multi-Version Concurrency Control (MVCC) is a critical mechanism in blockchain systems such as Hyperledger Fabric. MVCC is crucial for managing concurrent transactions ensuring integrity through versioning and timestamp techniques. Enhancing MVCC is essential due to the significant overhead involved...

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Malware remains a persistent and evolving cybersecurity threat, necessitating advanced detection techniques to counter increasingly sophisticated attacks. This survey provides a comprehensive review of deep learning-based approaches for malware detection, synthesizing 109 publications published betw...

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In the Kanter’s and Kinsella’s works is proposes the use of two synchronized artificial neural networks (SANN) connected by opening communication channel to confidential formation of a common cryptographic key. At the same time, there are few questions of practical suitability of this technology for...

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Accurate prediction of reservoir temperature is critical for optimizing geothermal energy systems, yet the complexity of geothermal data poses significant challenges for traditional modeling approaches. This study conducts a comprehensive comparative analysis of advanced machine learning models, inc...

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