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A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity
Published 2025“…We examine cutting-edge AI methodologies and principal models across many domains, including machine learning algorithms, deep learning architectures, natural language processing techniques, and anomaly detection algorithms, emphasizing their distinct contributions to enhancing security. …”
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Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey
Published 2024“…As new <u>malware</u> gets introduced frequently by <u>malware developers</u>, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. …”
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A comparative analysis to forecast carbon dioxide emissions
Published 2022“…After evaluating those deep learning models, a multivariate polynomial regression has also been employed to forecast CO<sub>2 </sub>emissions. …”
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NOVEL STACKING CLASSIFICATION AND PREDICTION ALGORITHM BASED AMBIENT ASSISTED LIVING FOR ELDERLY
Published 2022“…Therefore, this thesis proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based on AAL for the older people with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. …”
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186
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
Published 2023“…Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. …”
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Various Faults Classification of Industrial Application of Induction Motors Using Supervised Machine Learning: A Comprehensive Review
Published 2025“…Machine learning algorithms are a set of data-driven rules that are able to classify specific faults in induction motors, which will be explained further in this review paper. …”
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Using artificial intelligence to improve body iron quantification: A scoping review
Published 2023“…The search revealed a wide range of machine learning algorithms used by different studies. …”
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Random vector functional link network: Recent developments, applications, and future directions
Published 2023“…Finally, we present potential future research directions/opportunities that can inspire the researchers to improve the RVFL’s architecture and learning algorithm further. </p> <h2>Other Information</h2> <p>Published in: Applied Soft Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/ </a> <br> See article on publisher's website: <a href="http://dx.doi.org/10.1016/j.asoc.2023.110377" target="_blank">http://dx.doi.org/10.1016/j.asoc.2023.110377</a> </p>…”
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191
Robustness and Convergence of P-type Learning Control
Published 1993“…The robustness and convergence of P-type learning control algorithms for a class of time-varying, nonlinear systems to state disturbances, measurement noise at the output, and reinitialization errors at each iteration is studied extensively. …”
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Identification of phantom movements with an ensemble learning approach
Published 2022“…In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. …”
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A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation
Published 2021“…<p>This paper presents a comprehensive evaluation of the effect of quasi oppositional - based learning method utilization in output tracking control through a swarm-based multivariable Proportional-Integral-Derivative (SMPID) controller, which is tuned by a novel performance index based on the step response characteristics in multi-input multi-output (MIMO) system. …”
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Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization
Published 2002“…To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. …”
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A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks
Published 2025“…<p>The energy internet (EI) is evolving toward decentralized, data-rich, and time-critical operation, where legacy optimization often fails to meet complexity, scalability, and real-time constraints. Deep reinforcement learning (DRL) offers a data-driven alternative that couples perception with sequential decision-making. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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Using machine learning for disease detection. (c2013)
Published 2016“…Classification accuracy is a measure of how well a classification algorithm classifies the un-classified data. …”
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