A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems DOI Creative Commons
Jia Guo, Wenhao Ye, Dong Wang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7161 - 7161

Published: Nov. 7, 2024

To address the limitations of traditional optimization methods in achieving high accuracy high-dimensional problems, this paper introduces snow leopard (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by territorial behaviors leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, achieves balance between exploration exploitation, to navigate vast complex search spaces. The algorithm's performance was evaluated using CEC2017 benchmark genetic data feature selection tasks, demonstrating SLO's competitive advantage solving problems. In experiments, ranked first Friedman test, outperforming several well-known algorithms, including ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, HHO. effective application further highlights its adaptability practical utility, marking significant progress field selection.

Language: Английский

IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection DOI Creative Commons

Jinpeng Huang,

Yi Chen, Ali Asghar Heidari

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(8), P. 110561 - 110561

Published: July 22, 2024

Rime optimization algorithm (RIME) encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, low convergence accuracy when handling problems. This paper introduces a variant of RIME called IRIME address these drawbacks. integrates the soft besiege (SB) composite mutation strategy (CMS) restart (RS). To comprehensively validate IRIME's performance, IEEE CEC 2017 benchmark tests were conducted, comparing it against many advanced algorithms. The results indicate that performance is best. In addition, applying in four engineering problems reflects solving practical Finally, proposes binary version, bIRIME, can be applied feature selection bIRIMR performs well on 12 low-dimensional datasets 24 high-dimensional datasets. It outperforms other algorithms terms number subsets classification accuracy. conclusion, bIRIME has great potential selection.

Language: Английский

Citations

4
Special Issue “Algorithms for Feature Selection (2nd Edition)” DOI Creative Commons
Muhammad Adnan Khan

Algorithms, Journal Year: 2025, Volume and Issue: 18(1), P. 16 - 16

Published: Jan. 3, 2025

This Special Issue focuses on advancing research algorithms, with a particular emphasis feature selection techniques [...]

Language: Английский

Citations

0
Analysis of electronic product design schemes based on embedded systems DOI
B.H. Liu

International Journal of Emerging Electric Power Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 6, 2025

Abstract This paper explores electronic product design with a focus on embedded systems, highlighting the strategic approaches used by engineers to balance innovation, efficiency, and competitiveness. It examines practices across firms introduces framework that portrays as “administrative men,” adept at navigating project complexities choosing among four distinct paths: “Repeat Order, Variant Design, Innovative Strategic Design.” The Order” path focuses reproducing existing designs minimal changes, while “Variant Design” adapts specific requirements. “Innovative aims for novel solutions, “Strategic integrates long-term innovation business strategies. Each reflects unique goals, resource demands, methodologies. To support selection of most suitable path, this study metric structured methodology evaluating yield cost. approach includes analyzing complexity, estimating yield, calculating total costs, culminating in an objective function designed enhance efficiency accuracy. By offering actionable insights comprehensive decision-making framework, optimize process, addressing evolving challenges opportunities development.

Language: Английский

Citations

0
Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model DOI Creative Commons
Rayed AlGhamdi

Mathematics, Journal Year: 2023, Volume and Issue: 11(22), P. 4607 - 4607

Published: Nov. 10, 2023

In the domain of network security, intrusion detection systems (IDSs) play a vital role in data security. While utilization internet amongst consumers is increasing on daily basis, significance security and privacy preservation system alerts, due to malicious actions, also increasing. IDS widely executed that protects computer networks from attacks. For identification unknown attacks anomalies, several Machine Learning (ML) approaches such as Neural Networks (NNs) are explored. However, real-world applications, classification performances these fluctuant with distinct databases. The major reason for this drawback presence some ineffective or redundant features. So, current study proposes Network Intrusion Detection System using Lion Optimization Feature Selection Deep (NIDS-LOFSDL) approach remedy aforementioned issue. NIDS-LOFSDL technique follows concept FS hyperparameter-tuned DL model recognition intrusions. purpose FS, method uses LOFS technique, which helps improving results. Furthermore, attention-based bi-directional long short-term memory (ABiLSTM) applied detection. order enhance performance ABiLSTM algorithm, gorilla troops optimizer (GTO) deployed so perform hyperparameter tuning. Since trial-and-error manual tuning tedious process, GTO-based process performed, demonstrates novelty work. validate enhanced solution terms detection, comprehensive range experiments was performed. simulation values confirm promising results compared existing methodologies, maximum accuracy 96.88% 96.92% UNSW-NB15 AWID datasets, respectively.

Language: Английский

Citations

7
ArSa-Tweets: A Novel Arabic Sarcasm Detection System Based on Deep Learning Model DOI Creative Commons
Qusai Abuein, Ra’ed M. Al-Khatib,

Aya Migdady

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e36892 - e36892

Published: Aug. 28, 2024

Sarcasm in Sentiment Analysis (SA) is important due to the sense of sarcasm sentences that differs from their literal meaning. Arabic still has many challenges like implicit indirect idioms express opinion, and lack corpus. In this paper, we proposed a new detecting model for tweets called ArSa-Tweet model. It based on implementing developing Deep Learning (DL) models classify as sarcastic or not. The development our consists adding main improvements by applying robust preprocessing steps before feeding data adapted DL models. are LSTM, Multi-headed CNN-LSTM-GRU, BERT, AraBert-V01, AraBert-V02. addition, ArSa-data golden corpus tweets. A comparative process shows method most impact accuracy rate deploying AraBert-V02 model, which obtains best performance results all metrics when compared with other methods.

Language: Английский

Citations

2
A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer DOI Creative Commons
Mohammed Azmi Al‐Betar, Ammar Kamal Abasi, Zaid Abdi Alkareem Alyasseri

et al.

Algorithms, Journal Year: 2024, Volume and Issue: 17(4), P. 160 - 160

Published: April 15, 2024

The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource constraints geographically dispersed settings. Federated (FL) emerges as a transformative paradigm by decentralizing ML training to edge devices. However, communication bottlenecks hinder its scalability sustainability. This paper introduces an FL framework that enhances efficiency. proposed addresses the bottleneck harnessing power of Lemurs optimizer (LO), nature-inspired metaheuristic algorithm. Inspired cooperative foraging behavior lemurs, LO strategically selects most relevant model updates communication, significantly reducing overhead. was rigorously evaluated on CIFAR-10, MNIST, rice leaf disease, waste recycling plant datasets representing various areas development. Experimental results demonstrate reduces overhead over 15% average compared baseline while maintaining high accuracy. breakthrough extends applicability resource-constrained environments, paving way more scalable real-world initiatives.

Language: Английский

Citations

1
Gyro fireworks algorithm: A new metaheuristic algorithm DOI Creative Commons
Xiaowei Wang

AIP Advances, Journal Year: 2024, Volume and Issue: 14(8)

Published: Aug. 1, 2024

In this paper, a novel Gyro Fireworks Algorithm (GFA) is proposed by simulating the behaviors of gyro fireworks during display process, which adopts framework multi-stage and multiple search strategies. At beginning iteration, are full gunpowder; they move via Lévy flight spiral rotation, sprayed sparks widely distributed more balanced, an effective global exploration method. later iteration stages, due to consumption gunpowder, gradually undergo aggregation contraction conducive group exploit local area near optimal position. The GFA divides iterative process into four phases, each phase different strategy, in order enhance diversity population balance capability space exploitation space. verify performance GFA, it compared with latest algorithms, such as dandelion optimizer, Harris Hawks Optimization (HHO) algorithm, gray wolf slime mold whale optimization artificial rabbits optimization, 33 test functions. experimental results show that obtains solution for all algorithms on 76% functions, while second-placed HHO algorithm only 21% Meanwhile, has average ranking 1.8 CEC2014 benchmark set 1.4 CEC2019 set. It verifies paper better convergence robustness than competing algorithms. Moreover, experiments challenging engineering problems confirm superior over alternative

Language: Английский

Citations

1
Tashaphyne0.4: a new arabic light stemmer based on rhyzome modeling approach DOI
Ra’ed M. Al-Khatib, Taha Zerrouki,

Mohammed M. Abu Shquier

et al.

Information Retrieval, Journal Year: 2023, Volume and Issue: 26(1-2)

Published: Dec. 1, 2023

Language: Английский

Citations

2
A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems DOI Creative Commons
Jia Guo, Wenhao Ye, Dong Wang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7161 - 7161

Published: Nov. 7, 2024

To address the limitations of traditional optimization methods in achieving high accuracy high-dimensional problems, this paper introduces snow leopard (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by territorial behaviors leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, achieves balance between exploration exploitation, to navigate vast complex search spaces. The algorithm's performance was evaluated using CEC2017 benchmark genetic data feature selection tasks, demonstrating SLO's competitive advantage solving problems. In experiments, ranked first Friedman test, outperforming several well-known algorithms, including ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, HHO. effective application further highlights its adaptability practical utility, marking significant progress field selection.

Language: Английский

Citations

0