Artificial Satellite Search: A New Metaheuristic Algorithm for Optimizing Truss Structure Design and Project Scheduling

Applied Mathematical Modelling, Journal Year: 2025, Volume and Issue: unknown, P. 116008 - 116008

Published: Feb. 1, 2025

Language: Английский

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Fault diagnosis method of mining vibrating screen mesh based on an improved algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110343 - 110343

Published: Feb. 26, 2025

Language: Английский

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A combination approach for optimization operation of multi-objective cascade reservoir systems (Case study: Karun reservoirs)

Journal of Hydroinformatics, Journal Year: 2024, Volume and Issue: 26(6), P. 1313 - 1332

Published: May 23, 2024

ABSTRACT Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due their uncertainties, non-linearities, dimensions conflicts. The operation of multi-reservoir is crucial increasing hydropower production. In this study, we investigated the application effectiveness new optimization algorithm MOAHA in multi-objective cascade reservoirs with objectives, it has been on a case-by-case basis Karun (Karun 3, 1, Masjed Soleyman Gotvand). suggested method (MOAHA) output other algorithms, MOALO, MOGWO NSGA-II, were compared evaluation criteria used select best performance. Additionally, employed powerful TOPSIS determine most suitable algorithm. considered restrictions also observed. results indicate MOAHA's proposed better than algorithms solving optimal reservoir utilization problems water resource systems. reduction evaporation (losses) from tank surface by 9% accompanied 15% increase energy MOAHA, scoring 0.90, deemed whereas MOGWO, score 0.10, regarded as least effective

Language: Английский

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Distributed denial of service attack detection and mitigation strategy in 5G-enabled internet of things networks with adaptive cascaded gated recurrent unit

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 28, 2025

Language: Английский

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ISO: An improved snake optimizer with multi-strategy enhancement for engineering optimization

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127660 - 127660

Published: April 1, 2025

Language: Английский

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Enhanced quantum long short-term memory neural network based multi-task learning for sentimental analysis and cyberbullying detection

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127555 - 127555

Published: April 1, 2025

Language: Английский

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An Intelligent Model of Segmentation and Classification Using Enhanced Optimization‐Based Attentive Mask RCNN and Recurrent MobileNet With LSTM for Multiple Sclerosis Types With Clinical Brain MRI

NMR in Biomedicine, Journal Year: 2025, Volume and Issue: 38(6)

Published: April 24, 2025

ABSTRACT In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires exceptional amount of skill because abnormalities on scans frequently inconsistent with clinical symptoms, making it difficult to convert the findings into effective treatment strategies. Furthermore, is expensive process, its frequent utilization monitor illness increases costs. To overcome these drawbacks, this research employs advanced technological approaches develop a deep learning system classifying types MS through brain scans. The major innovation model influence convolution network attention concept recurrent‐based disorder; also proposes optimization algorithm tuning parameter enhance performance. Initially, total as 3427 collected from database, in which samples categorized training testing phase. Here, segmentation carried out by adaptive attentive‐based mask regional neural (AA‐MRCNN). phase, MRCNN's parameters finely tuned enhanced pine cone (EPCOA) guarantee outstanding efficiency. Further, segmented image given recurrent MobileNet long short term memory (RM‐LSTM) getting classification outcomes. Through experimental analysis, acquired 95.4% accuracy, 95.3% sensitivity, specificity. Hence, results prove that has high potential appropriately disorder.

Language: Английский

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Random Walk‐Based GOOSE Algorithm for Solving Engineering Structural Design Problems

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(5)

Published: April 30, 2025

ABSTRACT The proposed Random Walk‐based Improved GOOSE (IGOOSE) search algorithm is a novel population‐based meta‐heuristic inspired by the collective movement patterns of geese and stochastic nature random walks. This includes inherent balance between exploration exploitation integrating walk behavior with local strategies. In this paper, IGOOSE has been rigorously tested across 23 benchmark functions where 13 benchmarks are varying dimensions (10, 30, 50, 100 dimensions). These provide diverse range optimization landscapes, enabling comprehensive evaluation performance under different problem complexities. various parameters such as convergence speed, magnitude solution, robustness for dimensions. Further, applied to optimize eight distinct engineering problems, showcasing its versatility effectiveness in real‐world scenarios. results these evaluations highlight competitive tool, offering promising both standard complex structural problems. Its ability effectively, combined deal positions valuable tool.

Language: Английский

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Enhancing Network Anomaly Intrusion Detection with IoT Data-Driven BOA-CNN-BiGRU-AAM -Net Classification

Journal of Machine and Computing, Journal Year: 2024, Volume and Issue: unknown, P. 785 - 803

Published: July 5, 2024

Network security is one of the key components cybersecurity anomaly intrusion detection, which responsible for identifying unusual behaviours or activities within a network that might indicate possible breaches threats. In this suggested detection system (IDS), traffic data continuously monitored via detection. The study makes utilising most recent datasets to spot behaviour in networks connected Internet Things, IoTID20 dataset, facilitate process. preprocessing stage involves painstaking steps smoothing, filtering, and cleaning data. Pine Cone Optimisation algorithm (PCOA), novel optimizer inspired by nature, introduced feature selection PCOA seeks increase effectiveness while drawing inspiration from various ways pine trees reproduce, such as pollination movement cones animals gravity. Moreover, IDS classified using Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Based on Convolutional Neural Networks (CNN-BiGRU-AAM), use deep learning's capabilities efficient classification tasks. addition, work presents Botox Algorithm (BOA) hyperparameter tuning, modelled after way functions human anatomy. BOA uses human-based method adjust hyperparameters model attain best accuracy. results experiments show methodologies are effective improving systems, with maximum accuracy 99.45%.

Language: Английский

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A generative adversarial network to Reinhard stain normalization for histopathology image analysis

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 15(10), P. 102955 - 102955

Published: July 14, 2024

Histopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues. This research work presents a new method that merges deep learning with Reinhardstain normalization, aiming to revolutionize histopathology analysis. multi-data stream attention-based generative adversarial network an innovative architecture designed enhance histopathological by integrating multiple data streams, attention mechanisms, networks improved feature extraction quality. Multi-data capitalizes on mechanisms process multi-modal efficiently, enhancing ensuring robust performance even in the presence variations. approach excels exact disease detection classification, emerging as invaluable tool both clinical diagnoses endeavors across diverse datasets. obtained accuracy proposed SCAN dataset 97.75%, BACH 99.50% Break His 99.66%. significantly advances analysis, offering diagnostic deeper insights networks. enhances extraction, quality, overall effectiveness medical

Language: Английский

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