Hybrid Chaotic Zebra Optimization Algorithm and Long Short-Term Memory for Cyber Threats Detection DOI Creative Commons

Reham Amin,

Ghada Eltaweel,

Ahmed F. Ali

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 93235 - 93260

Published: Jan. 1, 2024

Cyber Threat Detection (CTD) is subject to complicated and rapidly accelerating developments. Poor accuracy, high learning complexity, limited scalability, a false positive rate are problems that CTD encounters. Deep Learning defense mechanisms aim build effective models for threat detection protection allowing them adapt the complex ever-accelerating changes in field of CTD. Furthermore, swarm intelligence algorithms have been developed tackle optimization challenges. In this paper, Chaotic Zebra Optimization Long-Short Term Memory (CZOLSTM) algorithm proposed. The proposed hybrid between Algorithm (CZOA) feature selection LSTM cyber classification CSE-CIC-IDS2018 dataset. Invoking chaotic map CZOLSTM can improve diversity search avoid trapping local minimum. evaluating effectiveness newly CZOLSTM, binary multi-class classifications considered. acquired outcomes demonstrate efficiency implemented improvements across many other algorithms. When comparing performance detection, it outperforms six innovative deep five classification. Other evaluation criteria such as recall, F1 score, precision also used comparison. results showed best accuracy was achieved using 99.83%, with F1-score 99.82%, recall 99.82%. among compared

Language: Английский

Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia DOI Creative Commons
Sujan Ghimire, Binayak Bhandari, David Casillas-Pérez

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 112, P. 104860 - 104860

Published: April 13, 2022

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, CNN algorithm is used to extract local patterns as well common features that occur recurrently in time series data at different intervals. Then, SVR subsequently adopted replace fully connected layers predict daily GSR six solar farms Queensland, Australia. To develop CSVR we adopt most pertinent meteorological variables from Climate Model and Scientific Information Landowners database. From pool of Models ground-based observations, optimal are selected through metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters proposed optimized mean HyperOpt method, overall performance objective benchmarked against eight alternative DL methods, some other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML M5TREE) methods. results obtained shows model can offer several predictive advantages over models, conventional ML models. Specifically, note recorded root square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 m2 tested compared 2.514–3.879 m2/1.939–2.866 algorithms. Consistent this predicted error, correlation measured GSR, including Willmott's, Nash-Sutcliffe's coefficient Legates & McCabe's Index was relatively higher methods all sites. Accordingly, advocates merits provide viable accurately renewable energy exploitation, demand or forecasting-based applications.

Language: Английский

Citations

70
Advances in Manta Ray Foraging Optimization: A Comprehensive Survey DOI
Farhad Soleimanian Gharehchopogh,

Shafi Ghafouri,

Mohammad Hasan Namazi

et al.

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(2), P. 953 - 990

Published: Feb. 27, 2024

Language: Английский

Citations

57
A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction DOI Creative Commons
Sujan Ghimire, Thong Nguyen‐Huy, Mohanad S. AL‐Musaylh

et al.

Energy, Journal Year: 2023, Volume and Issue: 275, P. 127430 - 127430

Published: April 8, 2023

Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure the power generation network. To deliver a high-quality prediction, this paper proposes hybrid combination technique, based on deep learning model of Convolutional Neural Networks Echo State Networks, named as CESN. Daily from four sites (Roderick, Rocklea, Hemmant Carpendale), located Southeast Queensland, Australia, have been used to develop proposed prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, neural network, Light Gradient Boosting) compare evaluate outcomes approach. results obtained experimental showed that able obtain highest performance compared existing developed for daily forecasting. Based statistical approaches utilized study, approach presents accuracy among models. algorithm excellent accurate forecasting method, which outperformed state art algorithms are currently problem.

Language: Английский

Citations

43
Read-First LSTM model: A new variant of long short term memory neural network for predicting solar radiation data DOI

Mohammad Ehteram,

Mahdie Afshari Nia,

Fatemeh Panahi

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 305, P. 118267 - 118267

Published: March 7, 2024

Language: Английский

Citations

17
Multi-step solar ultraviolet index prediction: integrating convolutional neural networks with long short-term memory for a representative case study in Queensland, Australia DOI
Mohanad S. AL‐Musaylh, Kadhem Al‐Daffaie, Nathan Downs

et al.

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 15, 2025

Language: Английский

Citations

3
Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms DOI
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez

et al.

Applied Energy, Journal Year: 2022, Volume and Issue: 316, P. 119063 - 119063

Published: April 19, 2022

Language: Английский

Citations

69
Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model DOI
Sujan Ghimire, Thong Nguyen‐Huy, Ravinesh C. Deo

et al.

Sustainable materials and technologies, Journal Year: 2022, Volume and Issue: 32, P. e00429 - e00429

Published: May 20, 2022

Language: Английский

Citations

68
Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction DOI
Sujan Ghimire, Thong Nguyen‐Huy, Ramendra Prasad

et al.

Cognitive Computation, Journal Year: 2022, Volume and Issue: 15(2), P. 645 - 671

Published: Nov. 7, 2022

Language: Английский

Citations

49
Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions DOI Creative Commons

Abdallah Djaafari,

Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Nadjem Bailek

et al.

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 15548 - 15562

Published: Nov. 1, 2022

Although solar energy harnessing capacity varies considerably based on the employed technology and meteorological conditions, accurate direct normal irradiation (DNI) prediction remains crucial for better planning management of concentrating power systems. This work develops hybrid Long Short-Term Memory (LSTM) models assessing hourly DNI using datasets that include relative humidity, air temperature, global irradiation. The study proposes a unique model, combining balance-dynamic sine–cosine (BDSCA) algorithm with an LSTM predictor. Combining optimizers predictors, such are rarely developed to estimate DNI, especially in smaller intervals. Therefore, various commonly adopted algorithms relevant studies have been considered references evaluating new algorithm. results show errors proposed do not exceed 2.07%, minimum correlation coefficient 0.99. In addition, dimensionality inputs was reduced from four variables two most cost-effective prediction. these suggested reliable estimating arid desert areas Algeria other locations similar climatic features.

Language: Английский

Citations

44
Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications DOI Open Access
Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(24), P. 4925 - 4925

Published: Dec. 7, 2023

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. proliferation IoT resulted in an unprecedented surge data, presenting formidable challenges concerning efficient processing, meaningful analysis, informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent (RNNs), deep-belief (DBNs), have demonstrated significant efficacy mitigating these by furnishing robust tools for learning extraction insights from vast diverse IoT-generated data. This survey article offers comprehensive meticulous examination recent scholarly endeavors encompassing the amalgamation deep-learning techniques within landscape. Our scrutiny encompasses extensive exploration models, expounding on their architectures applications domains, including but not limited to smart cities, healthcare informatics, surveillance applications. We proffer into prospective research trajectories, discerning exigency innovative solutions that surmount extant limitations intricacies deploying methodologies effectively frameworks.

Language: Английский

Citations

31