Research on Optimization Control Method of Laminar Cooling Process Based on Improved Black Widow Algorithm DOI
Jing Hou,

Siyao Luan,

Jinxiang Pian

et al.

Published: Dec. 29, 2023

Precision Temperature Control of Hot Rolled Steel Strips through Laminar Cooling Process Using an Improved Black Widow Algorithm-Based Model relying solely on the output rollers a steel plant to dissipate most heat in hot-rolled strips is fundamentally insufficient meet requirements strips. Therefore, laminar cooling necessary for precise temperature control In this paper, we propose model based improved algorithm address complex industrial characteristics process and overcome limitations establishing models. This enables water spray quantities actual operating conditions. Experimental results demonstrate that exhibits higher stability superior optimization capabilities, enhancing performance practical processes.

Language: Английский

Employing CNN and black widow optimization for sustainable wastewater management in an environmental engineering context DOI
Rabah Ismail, Jamal Alsadi,

Randa I. Hatamleh

et al.

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: 25(5), P. 3973 - 3988

Published: April 8, 2024

Language: Английский

Citations

12
An enhanced hybrid scheme for ship roll prediction using support vector regression and TVF-EMD DOI
Dongxing Xu, Jianchuan Yin

Ocean Engineering, Journal Year: 2024, Volume and Issue: 307, P. 117951 - 117951

Published: May 14, 2024

Language: Английский

Citations

12
Maximizing Renewable Energy Integration Using High-Step up Modified Boost Converter with Optimized Approach for Enhancing Efficiency DOI

K. Kumarasamy,

M. Devesh Raj,

M. Sivasubramanian

et al.

Iranian Journal of Science and Technology Transactions of Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Language: Английский

Citations

0
Chaos-enhanced metaheuristics: classification, comparison, and convergence analysis DOI Creative Commons
Abdelhadi Limane, Farouq Zitouni, Saad Harous

et al.

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(3)

Published: Feb. 19, 2025

Language: Английский

Citations

0
SBTC-Net: Secured Brain Tumor Segmentation and Classification Using Black Widow With Genetic Optimization in IoMT DOI Creative Commons

M. V. S. Ramprasad,

Md. Zıa Ur Rahman,

Masreshaw Bayleyegn

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 88193 - 88208

Published: Jan. 1, 2023

The people around the globe are suffering from different types of brain tumors. So, early prediction tumors can save human lives. This work focused on implementation secured tumor classification network (SBTC-Net) using transfer learning methods. Initially, security is achieved by performing medical image watermarking (MIW) operation translation invariant wavelet transform (TIWT). Here, process covers source MRI patient with unknown (cover image). Then, this watermarked transmitted over Internet Medical Things (IoMT) environment. attackers unable to visualize image. a At receiver IoMT, segmentation performed learning-based Recurrent U-Net (RU-Net) model, which localizes exact area tumor. In addition, multilevel features extracted black widow optimization-genetic algorithm (BWO-GA), selects best natural inspired properties. Further, based AlexNet used train optimal features, classifies benign and malignant Finally, simulation results show that proposed SBTC-Net resulted in superior watermarking, segmentation, performance terms subjective visualization objective metrics as compared state art approaches. 99.97% accuracy, 99.98% accuracy BraTS-2020 dataset.

Language: Английский

Citations

9
Exploring nitrogen gas-assisted ECDµM of glass by modified blackwidow optimization DOI
Vijay Manoharan,

T. Sekar,

Prasanth Ponnusamy

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108295 - 108295

Published: Feb. 3, 2024

Language: Английский

Citations

1
Adaptive Weighted Feature Fusion for Multiscale Atrous Convolution based 1DCNN with Dilated LSTM-aided Fake News Detection using Regional DOI Creative Commons

Rathinapriya Vasu,

J. Kalaivani

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 26, 2023

Abstract The people present in the world rely on social media for gathering news, and it is mainly because of development technologies. approaches employed natural language processing are still deficient judgment factors, these techniques frequently upon political or circumstances. Numerous low-level communities area curious after experiencing negative effects caused by spread false information different sectors. Low-resource languages distracted, considering fact that extensively English language. This work aims to provide an analysis regional fake news develop a referral system with advanced identify Hindi Tamil. proposed model includes (a) Regional Language Text Collection, (b) pre-processing, (c) Feature Extraction, (d) Weighted Stacked Fusion, (e) Fake News Detection. text data collected from standard datasets. pre-processed given into feature extraction using Bidirectional Encoder Representations Transformers (BERT), Transformer networks, seq2seq network extracting three sets features. These extracted inserted weighted stacked fusion model, where features integrated optimized weights acquired through Enhanced Osprey Optimization Algorithm (EOOA). Here, fused accomplished passed toward detection phase. performed Multi-scale Atrous Convolution-based One-Dimensional Convolutional Neural Network Dilated Long Short Term Memory (MACNN-DLSTM). detected finally. experimental carried out comparing conventional algorithms showcase efficiency developed language-based model.

Language: Английский

Citations

1
An MTBWO Algorithm Based on BiGRU Model DOI Open Access
Yongjie Yang,

Liumeng Sun,

Ningtao Zhang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(7), P. 1195 - 1195

Published: March 25, 2024

To address the challenge of distinguishing health status bearings, in this paper, a index (HI) is developed through utilization multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and Bray–Curtis distance. This offers visual representation enabling more intuitive monitoring prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from current bearing vibration data. Additionally, characteristics initial time window data serve features. Next, HI constructed by computing distance between bearing’s cloud platform constantly tracks employs MTBWO-BiGRU anticipate forthcoming state health. generates an immediate alert when overtakes alteration rate threshold foresees condition bearing. We compare with bidirectional long short-term memory (BiLSTM) BiGRU models. results indicate accuracy level 92.57%, which evidently higher than that obtained using other two Moreover, lighter, demonstrating practicality proposed approach.

Language: Английский

Citations

0
An efficient safest route prediction-based route discovery mechanism for drivers using improved golden tortoise beetle optimizer DOI

A Lakshmi,

A. Parthiban,

K. Suresh Joseph

et al.

Journal of Experimental & Theoretical Artificial Intelligence, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: April 25, 2024

Several commercialised route recommendation systems only consider the metrics like cost, time, and distance. The essential metric 'safety' is neglected by existent systems. It suggests short way doesn't include any safety information, such as crime awareness, road availability. This paper describes an inventive ideology to discover safest with minimal risk score for security of travellers. Hence, a new navigation mechanism developed solve challenges in traditional discovery approaches using deep learning. In mechanism, examination roads done learning network, where network trained inputs obtained from roads, surface conditions, Road users, weather traffic accidental cases, areas. availability will be determined 'Long Short-Term Memory Attention Mechanism' (LSTM-AM). help 'Fitness-based Golden Tortoise Beetle Optimizer' (FGTBO) multi-objective constraints distance, implementation outcome scheme validated concerning various measures.

Language: Английский

Citations

0
Adaptive weighted feature fusion for multiscale atrous convolution‐based 1DCNN with dilated LSTM‐aided fake news detection using regional language text information DOI

V. Rathinapriya,

J. Kalaivani

Expert Systems, Journal Year: 2024, Volume and Issue: 41(11)

Published: July 4, 2024

Abstract The people in the world rely on social media for gathering news, and it is mainly because of development technology. approaches employed natural language processing are still deficient judgement factors, these techniques frequently upon political or circumstances. Numerous low‐level communities area curious after experiencing negative effects caused by spread false information different sectors. Low‐resource languages distracted, extensively English language. This work aims to provide an analysis regional fake news develop a referral system with advanced identify Hindi Tamil. proposed model includes (a) Regional Language Text Collection; (b) preprocessing; (c) Feature Extraction; (d) Weighted Stacked Fusion; (e) Fake News Detection. text data collected from standard datasets. preprocessed given into feature extraction, which done using bidirectional encoder representations transformers (BERT), transformer networks, seq2seq network extracting three sets features. These extracted inserted weighted stacked fusion model, where features integrated optimized weights that acquired through enhanced osprey optimization algorithm (EOOA). Finally, resultant multi‐scale atrous convolution‐based one‐dimensional convolutional neural dilated long short‐term memory (MACNN‐DLSTM) detecting news. Throughout result analysis, experimentation conducted based Tamil Moreover, developed shows 92% datasets 96% effective performance regarding accuracy measures. experimental carried out comparing conventional algorithms detection showcase efficiency language‐based model.

Language: Английский

Citations

0