Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection DOI Creative Commons
Enow Takang Achuo Albert,

Ngalle Hermine Bille,

Bell Joseph Martin

et al.

Journal of Scientific Agriculture, Journal Year: 2023, Volume and Issue: unknown, P. 63 - 76

Published: Sept. 11, 2023

The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using combination genetic markers advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, algorithms high-performance computing employed. First, highly associated were identified next-generation sequencing data statistical analysis. Then, an adiabatic quantum algorithm developed integrate these into single predictor susceptibility. results demonstrate that the integrative use significantly improved accuracy efficiency resistance-based marker-assisted plant selection. By leveraging power markers, effective strategies selection can be developed.

Language: Английский

Web-based phishing URL detection model using deep learning optimization techniques DOI Creative Commons
Kousik Barik, Sanjay Misra,

R.S. Mohan

et al.

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

Citations

2
Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization DOI Creative Commons
Nastaran Mehrabi Hashjin, Mohammad Hussein Amiri, Ardashir Mohammadzadeh

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(7), P. 10197 - 10234

Published: May 5, 2024

Abstract This paper presents a unique hybrid classifier that combines deep neural networks with type-III fuzzy system for decision-making. The ensemble incorporates ResNet-18, Efficient Capsule network, ResNet-50, the Histogram of Oriented Gradients (HOG) feature extraction, neighborhood component analysis (NCA) selection, and Support Vector Machine (SVM) classification. innovative inputs fed into come from outputs mentioned networks. system’s rule parameters are fine-tuned using Improved Chaos Game Optimization algorithm (ICGO). conventional CGO’s simple random mutation is substituted wavelet to enhance CGO while preserving non-parametricity computational complexity. ICGO was evaluated 126 benchmark functions 5 engineering problems, comparing its performance well-known algorithms. It achieved best results across all except 2 functions. introduced applied seven malware datasets consistently outperforms notable like AlexNet, GoogleNet, network in 35 separate runs, achieving over 96% accuracy. Additionally, classifier’s tested on MNIST Fashion-MNIST 10 runs. show new excels accuracy, precision, sensitivity, specificity, F1-score compared other recent classifiers. Based statistical analysis, it has been concluded propose method exhibit significant superiority examined algorithms methods. source code available publicly at https://nimakhodadadi.com/algorithms-%2B-codes . Graphical abstract

Language: Английский

Citations

17
Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey DOI
Qasem Abu Al‐Haija,

Shahad Altamimi,

Mazen Alwadi

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 253, P. 124317 - 124317

Published: May 27, 2024

Language: Английский

Citations

17
Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics DOI Creative Commons
Luka Jovanovic, Robertas Damaševičius,

Rade Matić

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2031 - e2031

Published: May 13, 2024

Neurodegenerative conditions significantly impact patient quality of life. Many do not have a cure, but with appropriate and timely treatment the advance disease could be diminished. However, many patients only seek diagnosis once condition progresses to point at which life is impacted. Effective non-invasive readily accessible methods for early can considerably enhance affected by neurodegenerative conditions. This work explores potential convolutional neural networks (CNNs) gain freezing associated Parkinson’s disease. Sensor data collected from wearable gyroscopes located sole patient’s shoe record walking patterns. These patterns are further analyzed using accurately detect abnormal The suggested method assessed on public real-world dataset parents as well individuals control group. To improve accuracy classification, an altered variant recent crayfish optimization algorithm introduced compared contemporary metaheuristics. Our findings reveal that modified (MSCHO) outperforms other in accuracy, demonstrated low error rates high Cohen’s Kappa, precision, sensitivity, F1-measures across three datasets. results suggest CNNs, combined advanced techniques, early, conditions, offering path

Language: Английский

Citations

12
An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis DOI Creative Commons
Ahmed I. Saleh, Asmaa H. Rabie, Shimaa E. ElSayyad

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

As the world recovered from coronavirus, emergence of monkeypox virus signaled a potential new pandemic, highlighting need for faster and more efficient diagnostic methods. This study introduces hybrid architecture automatic diagnosis by leveraging modified grey wolf optimization model effective feature selection weighting. Additionally, system uses an ensemble classifiers, incorporating confusion based voting scheme to combine salient data features. Evaluation on public sets, at various training samples percentages, showed that proposed strategy achieves promising performance. Namely, yielded overall accuracy 98.91% with testing run time 5.5 seconds, while using machine classifiers small number hyper-parameters. Additional experimental comparison reveals superior performance over literature approaches metrics. Statistical analysis also confirmed AMDS outperformed other models after running 50 times. Finally, generalizability is evaluated its external sets COVID-19. Our achieved 98.00% 99.00% COVID respectively.

Language: Английский

Citations

1
Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm DOI
Nebojša Bačanin, Vladimir Šimić, Miodrag Živković

et al.

Annals of Operations Research, Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 15, 2023

Language: Английский

Citations

22
Classification of Fritillaria using a portable near-infrared spectrometer and fuzzy generalized singular value decomposition DOI
Xiaohong Wu, Y Wang, Bin Wu

et al.

Industrial Crops and Products, Journal Year: 2024, Volume and Issue: 218, P. 119032 - 119032

Published: June 20, 2024

Language: Английский

Citations

6
Using BERT with Modified Metaheuristic Optimized XGBoost for Phishing Email Identification DOI
Miloš Antonijević, Luka Jovanovic, Nebojša Bačanin

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

5
Tuning Natural Language Processing by Altered Metaheuristics Algorithm for Phishing Email Identification DOI
Luka Jovanovic, Nebojša Bačanin, Rejitha Ravikumar

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 265 - 282

Published: Jan. 1, 2025

Language: Английский

Citations

0
Parkinsons Detection from Gait Time Series Classification Using Modified Metaheuristic Optimized Long Short Term Memory DOI Creative Commons
Filip Marković, Luka Jovanovic, Petar Spalević

et al.

Neural Processing Letters, Journal Year: 2025, Volume and Issue: 57(1)

Published: Feb. 7, 2025

Language: Английский

Citations

0