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[HTML][HTML] Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection
Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves
and brain activity. Despite its precision in capturing brain electrical activity, certain factors …
and brain activity. Despite its precision in capturing brain electrical activity, certain factors …
Forecasting bitcoin price by tuned long short term memory model
The interest for cryptocurrencies is high and hence this work focuses on providing a practical
real-world application of the swarm metaheuristics and long short term memory model …
real-world application of the swarm metaheuristics and long short term memory model …
Anomaly detection in ecg using recurrent networks optimized by modified metaheuristic algorithm
Cardiovascular disorders, a leading cause of death, demand urgent research attention.
Early detection systems hold the potential to improve patient outcomes by enabling timely …
Early detection systems hold the potential to improve patient outcomes by enabling timely …
The xgboost approach tuned by tlb metaheuristics for fraud detection
The recent pandemic had a major impact on online transactions. With this trend, credit card
fraud increased. For the solution to this problem the authors explore existing solutions and …
fraud increased. For the solution to this problem the authors explore existing solutions and …
Seizure detection via time series classification using modified metaheuristic optimized recurrent networks
Epilepsy, colloquially termed seizure disorder, constitutes a neurological condi-tion
characterized by unpredictable and sudden episodes of heightened electrical activity in the …
characterized by unpredictable and sudden episodes of heightened electrical activity in the …
Exploring the potential of generative adversarial networks for synthetic medical data generation
Proper artificial intelligence (AI) and machine learning (ML) model implementation and
training are highly reliant on quality input data. However, the high costs of clinical studies …
training are highly reliant on quality input data. However, the high costs of clinical studies …
Artificial neural network tuning by improved sine cosine algorithm for healthcare 4.0
M Gajevic, N Milutinovic, J Krstovic… - Proceedings of the …, 2023 - books.google.com
This paper explores classification of datasets for Healthcare 4.0 using artificial neural
networks which are tuned by improved sine cosine algorithm (SCA). Healthcare 4.0 themes …
networks which are tuned by improved sine cosine algorithm (SCA). Healthcare 4.0 themes …
Intrusion detection by xgboost model tuned by improved social network search algorithm
The industry 4.0 flourished recently due to the advances in a number of contemporary fields,
alike artificial intelligence and internet of things. It significantly improved the industrial …
alike artificial intelligence and internet of things. It significantly improved the industrial …
[HTML][HTML] Cloud spot instance price forecasting multi-headed models tuned using modified PSO
The increasing dependence and demands on cloud infrastructure have brought to light
challenges associated with cloud instance pricing. The often unpredictable nature of …
challenges associated with cloud instance pricing. The often unpredictable nature of …
Optimizing machine learning for space weather forecasting and event classification using modified metaheuristics
Abstract Space weather profoundly impacts Earth and its surrounding space environment,
necessitating improved prediction to safeguard critical infrastructure such as communication …
necessitating improved prediction to safeguard critical infrastructure such as communication …