Search alternatives:
training algorithms » learning algorithms (Expand Search)
encoding algorithm » cosine algorithm (Expand Search)
based training » based learning (Expand Search)
training algorithms » learning algorithms (Expand Search)
encoding algorithm » cosine algorithm (Expand Search)
based training » based learning (Expand Search)
-
1
Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021Subjects: “…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…”
Get full text
Get full text
Get full text
-
2
-
3
The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs
Published 2023“…We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. …”
-
4
-
5
-
6
Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy
Published 2023“…The complex gas hydrate prevention unit is simulated using the MLPNN model trained by 20 different optimization algorithms. This study investigates the gradient-based, evolutionary, and Bayesian-based optimization algorithms. …”
-
7
Web Based Online Hybrid Teaching Method of Network Music Course
Published 2022“…The experimental results show that the average accuracy of the improved algorithm is 79.63% in the limited training times, and has better adaptability. …”
Get full text
-
8
Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems
Published 2022“…The proposed improved ABO method consists in reducing the number of samples in the training data set using the Euclidean distance and extracting the most significant features from the reduced data using ABO algorithm. …”
-
9
An image processing and genetic algorithm-based approach for the detection of melanoma in patients
Published 2018“…The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.…”
Get full text
Get full text
Get full text
Get full text
article -
10
-
11
On the Optimization of Band Gaps in Periodic Waveguides
Published 2025Subjects: “…Nature-inspired optimization algorithms…”
-
12
-
13
AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques
Published 2023“…This paper proposes a novel automated algorithm for the health monitoring of concrete column base cover degradation based on IoT and the state-of-the-art deep learning framework, Convolutional Neural Network (CNN). …”
Get full text
Get full text
Get full text
article -
14
Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
Published 2021“…We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. …”
-
15
Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices
Published 2016Subjects: Get full text
doctoralThesis -
16
-
17
A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Published 2025“…<p dir="ltr">Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. …”
-
18
Design of adaptive arrays based on element position perturbations
Published 1993“…The authors report on the design of a digital feedback control system to provide null steering by controlling the array element positions automatically. The array comprises a signal processor, digital control algorithm (PID), stepper motors, shaft encoders, actuators and multiplexers. …”
Get full text
Get full text
article -
19
TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Published 2020“…TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. Initially, the features are grouped randomly to increase the probability of making them participating in the generation of different groups, and sorted based on their accuracy scores. …”
-
20