Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function DOI Creative Commons
Hardi Sabah Talabani, Hezha M.Tareq Abdulhadi,

Muhammand Intizar Ali

et al.

passer, Journal Year: 2024, Volume and Issue: 6(1), P. 130 - 137

Published: Feb. 17, 2024

Malware is a severe threat to the network and host system security. It frequently primary cause of many events, such as Distributed Denial-of-Service attacks (DDoS), spam emails, etc. The detection elimination malware are hence subjects intensive study. As result, antivirus programs have been created help identify remove malware. issue with this software that it uses an obsolete method detecting malware, signature-matching approach, which forms code obfuscation may deceive. Since then, has resulted in creation new generation metamorphic polymorphic In paper, we investigated using Instance-Based Learner (IBK) algorithm for obfuscated given dataset. Utilizing Lazy IBK technique beneficial because can accurately detect classify dataset Manhattan Distance function, one most well-known distance metric functions measuring between points. We analysed 58,596 records selected from 3 categories. was illustrated on utilizing 10-fold cross-validation. results demonstrate proposed quickly accuracy 99.99%, precision 100%, recall respectively.

Language: Английский

Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach DOI
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 6, 2024

Language: Английский

Citations

13
Harmony search: Current studies and uses on healthcare systems DOI

Maryam T. Abdulkhaleq,

Tarik A. Rashid, Abeer Alsadoon

et al.

Artificial Intelligence in Medicine, Journal Year: 2022, Volume and Issue: 131, P. 102348 - 102348

Published: July 5, 2022

Language: Английский

Citations

29
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer DOI
Masoud Ahmadipour, Muhammad Murtadha Othman, Rui Bo

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 235, P. 121212 - 121212

Published: Aug. 18, 2023

Language: Английский

Citations

20
A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges DOI Creative Commons

Binhe Chen,

Li Cao,

Changzu Chen

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(7)

Published: June 11, 2024

Abstract The application of optimization theory and the algorithms that are generated from it has increased along with science technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial issues. Swarm intelligence have been successful machine learning, process control, engineering prediction throughout years shown to efficient handling An intelligent system called chicken swarm algorithm (CSO) mimics organic behavior flocks chickens. In benchmark problem's objective function, outperforms several popular methods like PSO. concept advancement flock algorithm, comparison other meta-heuristic algorithms, development trend reviewed order further enhance search performance quicken research algorithm. fundamental model is first described, enhanced based on parameters, chaos quantum optimization, learning strategy, population diversity then summarized using both domestic international literature. use group areas feature extraction, image processing, robotic engineering, wireless sensor networks, power. Second, evaluated terms benefits, drawbacks, algorithms. Finally, direction anticipated.

Language: Английский

Citations

8
Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems DOI Creative Commons

Hazem Hassan Ellithy,

Hany M. Hasanien, Mohammed Alharbi

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(2), P. 66 - 66

Published: Jan. 23, 2024

Photovoltaic (PV) systems are becoming essential to our energy landscape as renewable sources become more widely integrated into power networks. Preserving grid stability, especially during voltage sags, is one of the significant difficulties confronting implementation these technologies. This attribute referred low-voltage ride-through (LVRT). To overcome this issue, adopting a Proportional-Integral (PI) controller, control system standard, proving be an efficient solution. paper provides unique algorithm-based approach Marine Predator Algorithm (MPA) for optimized tuning used PI mainly focusing on inverter control, improve LVRT grid, leading improvements in overshoot, undershoot, settling time, and steady-state response system. The fitness function using MPA determine settings controller. process helps optimally design controllers optimally, thus improving performance enhancing system’s capability. methodology tested case 3L-G fault. test its validity, proposed compared with rival standard optimization-based controllers, namely Grey Wolf Optimization Particle Swarm Optimization. comparison shows that algorithm better results higher convergence rate overshoot ranging from 14% 40% less DC-Link Voltage active also times being than PSO GWO by 0.76 0.95 s.

Language: Английский

Citations

7
Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review DOI Open Access
Jannatul Ferdous, Farid Bensebaa, Abbas S. Milani

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 2684 - 2684

Published: March 25, 2024

The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there been a growing interest in hybrid approaches that combine MCDM with MOO, aiming enhance the efficacy final decisions. However, critical gap exists terms providing clear methodological guidance, particularly when dealing data uncertainties. To address this gap, systematic review is designed develop generic decision tree serves as practical roadmap for practitioners seeking perform MOO an fashion, specific focus on accounting identified recent studies conducted both way. It important note does not aim identify superior or methods, but rather it delves into strategies integrating these two common methodologies. prevalent methods used reviewed articles were evolution-based metaheuristic methods. TOPSIS PROMETHEE II are ranking can occur either priori, posteriori, through combination both, each offering distinct advantages drawbacks. developed illustrated all three paths uncertainty considerations path. Finally, real-world case study pulse fractionation process Canada basis demonstrating pathways presented their application identifying optimized processing sustainably obtaining protein. This will help different use way most sustainable system.

Language: Английский

Citations

6
Modified-Improved Fitness Dependent Optimizer for Complex and Engineering Problems DOI

Hozan K. Hamarashid,

Bryar A. Hassan, Tarik A. Rashid

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 300, P. 112098 - 112098

Published: June 26, 2024

Language: Английский

Citations

5
A prescriptive analytics approach to solve the continuous berth allocation and yard assignment problem using integrated carbon emissions policies DOI
Sunil Kumar Jauhar, Saurabh Pratap, Sachin Kamble

et al.

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

Published: July 17, 2023

Language: Английский

Citations

11
Evolutionary and Nature-Inspired Algorithms for Disaster-Resilient Networks DOI Creative Commons
Bidyarani Langpoklakpam, Lithungo K. Murry

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 19, 2025

Disaster management system necessitates efficient and resilient communication networks to ensure effective emergency response recovery efforts. Disasters pose significant challenges infrastructures, often leading breakdowns in disrupting rescue relief In recent years, metaheuristic algorithms have emerged as a promising solution for optimizing various aspects of disaster scenarios. this paper, we investigate the use application addressing optimization problems that arise during operations. The key design, including victim localization, routing, coverage, resource allocation, are discussed. This study also discusses strengths limitations different Finally, it highlights recently developed models future research directions area network optimization.

Language: Английский

Citations

0
The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index DOI Creative Commons

Feridon Ghadimi,

Saeed Zolfaghari Moghaddam

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Abstract Groundwater is a vital resource for drinking water, agriculture, and industry worldwide. Effective groundwater quality management crucial safeguarding public health ensuring ecological sustainability. Hydrogeochemical data modeling widely utilized to predict using various approaches. The method proposed in this study leverages an intelligent model combined with chemical compositions. Sampling was conducted from 175 agricultural wells the Arak Plain. By utilizing hydrogeochemical performing correlation sensitivity analyses, key compositions were identified: Ca²⁺, Cl⁻, EC, HCO₃⁻, K⁺, Mg²⁺, Na⁺, pH, SO₄²⁻, TDS, NO₃⁻.The predicted Water Quality Index (WQI) values composition artificial neural network (ANN) model. of served as model’s input, while WQI treated output. To enhance ANN's accuracy, several optimization algorithms used, including: Simulated Annealing Algorithm (SAA), Firefly (FA), Invasive Weed Optimization (IWO), Shuffled Frog Leaping (SFLA).The comparison results indicated that ANN-SAA outperformed other models. R² MSE predicting training data: = 0.8275, 0.0303 test 0.7357, 0.0371.These demonstrate provides reliable accurate index values, offering valuable tool assessment management.

Language: Английский

Citations

0