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processing algorithm » processing algorithms (Expand Search)
making algorithm » cosine algorithm (Expand Search)
systems making » systems using (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
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321
The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review
Published 2022“…The study selection process was carried out in three phases: study identification, study selection, and data extraction. …”
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322
From Collatz Conjecture to chaos and hash function
Published 2023“…This paper aims to introduce a novel approach to addressing current security challenges by utilizing our generalized Collatz process to create a chaos-based hash function. By leveraging the unpredictable behaviour of the Collatz sequence, the proposed hash function aims to enhance ergodicity and entropy properties, thereby making it well-suited for cryptographic applications. …”
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323
Topology and parameter estimation in power systems through inverter-based broadband stimulations
Published 2015“…Broadband stimulation signals are injected from distributed generators and their effects are measured at various locations in the grid. To process and evaluate this data, a novel aggregation method based on weighed least squares will be proposed in this study. …”
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324
Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Published 2019“…However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. …”
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325
An Improved Genghis Khan Optimizer based on Enhanced Solution Quality Strategy for Global Optimization and Feature Selection Problems
Published 2024“…Feature selection (FS) is the activity of defining the most contributing feature subset among all used features to improve the superiority of datasets with a large number of dimensions by selecting significant features and eliminating redundant and irrelevant ones. Therefore, this process can be seen as an optimization process. The primary goals of feature selection are to decrease the number of dimensions and enhance classification accuracy in many domains, such as text classification, large-scale data analysis, and pattern recognition. …”
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326
Recent advances on artificial intelligence and learning techniques in cognitive radio networks
Published 2015“…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …”
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327
A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass
Published 2025“…By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…”
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328
Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles
Published 2017“…The identified a priori knowledge is used to implement an optimal Kalman filter, a multi-hypothesis Kalman filter, and a variant of the full information estimator (moving horizon estimator) to the problem at hand. The proposed algorithms are initially deployed in a simulation environment, and then the experimental data sets are fed into the algorithms to validate their performance. …”
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329
Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea
Published 2018“…Lagrangian tracking of passive tracers in a stochastic velocity field within a sequential ensemble data assimilation framework is challenging due to the exponential growth in the number of particles. …”
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330
A MIMO Sampling-Rate-Dependent Controller
Published 2014“…Performance is compared to a PID controller with fuzzy gain scheduling, four multivariable PID controllers, an H ∞ optimal controller, and an iterative-learning-control algorithm.…”
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331
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332
Corrosion Monitoring Technologies for Reinforced Concrete Structures: A Review
Published 2023“…New technology, algorithms, data processing, and AI are new approaches to improving corrosion monitoring processes. …”
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333
Scheduling and allocation in high-level synthesis using stochastic techniques
Published 2020“…High-level synthesis is the process of automatically translating abstract behavioral models of digital systems to implementable hardware. …”
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334
Positive Unlabelled Learning to Recognize Dishes as Named Entity
Published 2019“…With the lack of labelled data, I try to overcome the cold start and avoid manual labelling by building a lookup table from a dictionary. …”
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335
Practical single node failure recovery using fractional repetition codes in data centers
Published 2016“…In addition, we account for new-comer blocks and allocate them to nodes with minimal computations and without changing the original optimal schema. This makes our work practical to apply. Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. …”
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336
Modelling of pollutant transport in compound open channels
Published 1998“…The numerical computation of open-channel flows requires preparing and processing larger volumes of boundary and bathymetry data for computer inputs and the development of numerical algorithms for treating complex boundary condition, channel properties, and free surface effects. …”
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masterThesis -
337
Modeling and Control of a Thermally Driven MEMS Actuator for RF Applications
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doctoralThesis -
338
Localizing-ground Transmitters Using Airborne Antenna Array
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doctoralThesis -
339
Modeling and Bias-Robust Estimation of the Acoustic Release of Chemotherapeutics from Liposomes
Published 2019“…Optimality is guaranteed by accurately identifying the underlying statistical noise characteristics in experimental data. The estimator also quantifies the bias in the release, exhibited by the experimental data. …”
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340
Anomaly Detection Based Framework for Profile Monitoring
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doctoralThesis