IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN DOI Open Access
Yanhua Liu, Yuting Han, Hui Chen

et al.

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2024, Volume and Issue: 80(2), P. 1851 - 1866

Published: Jan. 1, 2024

As the scale of networks continually expands, detection distributed denial service (DDoS) attacks has become increasingly vital. We propose an intelligent model named IGED by using improved generalized entropy and deep neural network (DNN). The initial is based on to filter out as much normal traffic possible, thereby reducing data volume. Then fine DNN perform precise DDoS filtered suspicious traffic, enhancing network's generalization capabilities. Experimental results show that proposed method can efficiently distinguish from traffic. Compared with benchmark methods, our reaches 99.9% low-rate (LDDoS), flooded CICDDoS2019 datasets in terms both accuracy efficiency identifying attack flows while time 17%, 31% 8%.

Language: Английский

Application of machine learning in adsorption energy storage using metal organic frameworks: A review DOI

Nokubonga P. Makhanya,

Michael Kumi, Charles Mbohwa

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115363 - 115363

Published: Jan. 13, 2025

Language: Английский

Citations

1
Research on Image Recognition and Classification Algorithms in Cloud Computing Environment Based on Deep Neural Networks DOI Creative Commons

Zihang Jia

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 19728 - 19754

Published: Jan. 1, 2025

Language: Английский

Citations

1
Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models DOI Creative Commons

Hongkun Fu,

Jian Lü,

Jian Li

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 205 - 205

Published: Jan. 16, 2025

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding management, and optimizing resource allocation. This study proposes a method predicting yields in China’s major winter wheat-producing regions using MOD13A1 data deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of Convolutional Neural Network (CNN) with IGWO, accuracy significantly enhanced. Additionally, explores potential Green Normalized Difference Vegetation Index (GNDVI) prediction. The research utilizes collected from March to May between 2001 2010, encompassing vegetation indices, environmental variables, statistics. results indicate that IGWO-CNN outperforms traditional machine approaches standalone CNN models terms accuracy, achieving highest performance R2 0.7587, RMSE 593.6 kg/ha, MAE 486.5577 MAPE 11.39%. finds April optimal period early wheat. validates effectiveness combining remote sensing prediction, providing technical support precision agriculture contributing global food security sustainable development.

Language: Английский

Citations

1
Optuna Tabanlı Hiper Parametre Optimizasyonu ile Konut Fiyat Tahminlemede Makine Öğrenmesi Tekniklerinin Karşılaştırmalı Analizi DOI Creative Commons
Vahid Sinap

Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 1

Published: Feb. 12, 2025

Konut fiyatlarının etkili bir şekilde tahmin edilmesi, ekonominin şekillenmesinde kritik rol oynamaktadır. Bu çalışmanın amacı, konut fiyatlarını tahminlemede en iyi performans gösteren makine öğrenmesi modelini belirlemektir. amaçla, 10 farklı denetimli regresyon algoritması kullanılarak çeşitli modeller eğitilmiştir. Modellerin performansını optimize etmek amacıyla Grid Search, Random Search ve Optuna gibi hiper parametre ayarlama yöntemleri uygulanmıştır. Eğitim test setlerinde elde edilen metrik değerler, modellerin genel değerlendirmek için kullanılmıştır. Araştırma sonuçları, yöntemlerinin başarısını etkileyen faktör olduğunu göstermiştir. ile Gradyan Artırma Regresyonu modeli, veri setinde ettiği yüksek R2 değeri (0.6558) düşük RMSE (4469.48) başarılı model olarak belirlenmiştir. Optuna, optimizasyonunda sağladığı hassasiyet etkinlik diğer yöntemlere kıyasla belirgin üstünlük sunmuştur.

Citations

0
New Frontiers in Machine Learning Optimization DOI

Pooja Dehankar,

Susanta Das

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 427 - 454

Published: Jan. 10, 2025

Machine learning (ML) optimization techniques serve as essential for training models to achieve high performance in a diverse areas. This chapter offers thorough summary of machine techniques. analysis the development over time. A number common constraints are also discussed. Developing model that works effectively and provides accurate predictions certain set instances is main objective ML. We require ML accomplish that. The practice modifying hyper parameters with an technique minimize cost function called optimization. Because indicates difference between actual value estimated parameter predicted by model, it crucial reduce it. will provide general explanation workings drawbacks strategies. Numerous advancements have been put forth this chapter.

Language: Английский

Citations

0
A Review of Ant Colony Optimization for Solving 0-1 Knapsack and Traveling Salesman Problems DOI Creative Commons

Isamadeen A. Khalifa,

Sagvan Ali Saleh

Deleted Journal, Journal Year: 2025, Volume and Issue: 3(2), P. 87 - 99

Published: March 10, 2025

Ant Colony Optimization (ACO) represents a widespread nature-based metaheuristic algorithm which solves combinatorial optimization problems effectively [1]. This research study examines ACO-based solutions for Traveling Salesman Problem (TSP) and 0-1 Knapsack (0-1 KP) are both identified as NP-hard problems. ACO successfully achieves near-optimal because it duplicates real ants' pheromone-based foraging approach operates between exploration exploitation modes effectively. review discusses methods solving complex through discussion of modern solution their evaluation results performance benefits over basic approaches. section presents challenges include computational complexity two additional hybrid models while exploring adaptive parameter adjustments well quantum-inspired optimizations [2]. The development aims at combining this with deep learning reinforcement approaches to boost its operational speed practical across dynamic contexts. findings suggest that remains promising technique vast potential large-scale in various domains [3].

Language: Английский

Citations

0
Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change DOI Creative Commons
Hoa Thi Nguyen, Md. Arifur Rahman Khan,

Thuong Thi Nguyen

et al.

Plants, Journal Year: 2025, Volume and Issue: 14(6), P. 907 - 907

Published: March 14, 2025

Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses environmental offering new opportunities for both stress resilience breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, machine learning, enhance our ability assess traits under various including drought, salinity, extreme temperatures, pest disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency programs. HTP can also play vital role by accelerating genetic gain through precise trait evaluation hybridization enhancement. However, challenges data standardization, management, high costs equipment, complexity linking phenotypic observations improvements limit its broader application. Additionally, variability genotype-by-environment interactions complicate reliable selection. Despite these challenges, advancements in robotics, artificial intelligence, automation are precision scalability analyses. This review critically examines dual assessment tolerance performance, highlighting transformative potential existing limitations. By addressing key leveraging technological advancements, significantly research, discovery, parental selection, scheme optimization. While current methodologies still face constraints fully translating insights into practical applications, continuous innovation high-throughput holds promise revolutionizing ensuring sustainable agricultural production changing climate.

Language: Английский

Citations

0
Evolution of Swarm Intelligence: A Systematic Review of Particle Swarm and Ant Colony Optimization Approaches in Modern Research DOI
Rahul Priyadarshi, Ravi Kumar

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Language: Английский

Citations

0
Application of Machine Learning in LC-MS-Based Non-Targeted Analysis DOI
Jin Zhang, Lu Chen, Yu Wang

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118243 - 118243

Published: March 1, 2025

Language: Английский

Citations

0
How did we get there? AI applications to biological networks and sequences DOI Creative Commons
Marco Anteghini, Francesco Gualdi,

Baldo Oliva

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 190, P. 110064 - 110064

Published: April 5, 2025

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume data is available for analysis. This paper provides comprehensive overview the current state AI-driven methodologies in genomics, proteomics, systems biology. We discuss how machine learning algorithms, particularly deep models, have enhanced accuracy efficiency embedding sequences, motif discovery, prediction gene expression protein structure. Additionally, we explore integration AI analysis biological networks, protein-protein interaction networks multi-layered networks. By leveraging large-scale data, techniques enabled unprecedented insights into processes disease mechanisms. work underlines potential applying to highlighting applications suggesting directions future research further this evolving field.

Language: Английский

Citations

0