The Role of Data Science in Enhancing Web Security DOI Creative Commons
Ahmad Sanmorino

JEECS (Journal of Electrical Engineering and Computer Sciences), Год журнала: 2024, Номер 9(2), С. 119 - 116

Опубликована: Ноя. 24, 2024

With the rise of digital transformation, web security has become a critical concern for organizations, governments, and individuals. This study explores role data science in enhancing by leveraging machine learning algorithms advanced analytics to predict identify potential attacks real-time. The main objective is demonstrate how data-driven techniques, including predictive analytics, anomaly detection, behavioral analysis, can be integrated into existing frameworks reduce vulnerabilities strengthen defenses against cyber threats. research gap addressed this lies insufficient application comprehensive, methodologies threat detection classification security. problem absence that combine feature engineering, models, both known unknown bridges these gaps employing structured dataset interactions model, detect, threats using techniques. Using simulated traffic previous attack records, applies preprocessing, such as decision trees random forests, levels anomalies. Results show models effectively classify levels, with accuracy 80 percent. contributes field demonstrating improve practices, offering proactive approach detecting mitigating cyber-attacks.

Язык: Английский

The Use of Artificial Intelligence to Curb Deforestation in the Brazilian Rainforest DOI
Silvio Andrae

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 81 - 122

Опубликована: Фев. 7, 2025

Tropical rainforests like the Amazon are invaluable ecosystems for human society and biodiversity. However, they facing unprecedented threats, primarily from deforestation. This chapter explores use of machine learning (ML) deep (DL) to address this pressing environmental problem. By analyzing different ML/DL methods, we show how these tools can be used understand deforestation patterns in Brazilian better. Specifically, discuss help identify drivers deforestation, improve remote sensing-based monitoring, predict future trends. Our results, particularly role providing actionable insights, empower decision-makers policymakers with knowledge make informed choices. Ultimately, strategies contribute more effective forest conservation measures sustainable land use, reassuring audience about reliability our research.

Язык: Английский

Процитировано

0
Engineering a multi model fallback system for edge devices DOI Creative Commons

Gaurav Kadve,

Abishi Chowdhury, Vishal Krishna Singh

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105165 - 105165

Опубликована: Май 1, 2025

Процитировано

0
A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding DOI Creative Commons
Thaweesak Trongtirakul, Karen Panetta, Artyom M. Grigoryan

и другие.

Entropy, Год журнала: 2025, Номер 27(5), С. 526 - 526

Опубликована: Май 14, 2025

Image segmentation is a fundamental challenge in computer vision, transforming complex image representations into meaningful, analyzable components. While entropy-based multilevel thresholding techniques, including Otsu, Shannon, fuzzy, Tsallis, Renyi, and Kapur approaches, have shown potential segmentation, they encounter significant limitations when processing thermal images, such as poor spatial resolution, low contrast, lack of color texture information, susceptibility to noise background clutter. This paper introduces novel adaptive unsupervised entropy algorithm (A-Entropy) enhance for segmentation. Our key contributions include (i) an image-dependent enhancement technique specifically designed images improve visibility contrast regions interest, (ii) so-called A-Entropy concept thresholding, (iii) comprehensive evaluation using the Benchmarking IR Dataset Surveillance with Aerial Intelligence (BIRDSAI). Experimental results demonstrate superiority our proposal compared other state-of-the-art methods on BIRDSAI dataset, which comprises both real synthetic substantial variations scale, clutter, noise. Comparative analysis indicates improved accuracy robustness traditional methods. The framework's versatility suggests promising applications brain tumor detection, optical character recognition, energy leakage face recognition.

Язык: Английский

Процитировано

0
YOLO-EFM: Efficient Traffic Flow Monitoring Algorithm with Enhanced Multi-level Information Fusion DOI Creative Commons
Shizhou Xu, Kaiming Cui

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105545 - 105545

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Blockchain-secured IoT-federated learning for industrial air pollution monitoring: A mechanistic approach to exposure prediction and environmental safety DOI
Montaser N.A. Ramadan, Mohammed A. H. Ali, Hadi Jaber

и другие.

Ecotoxicology and Environmental Safety, Год журнала: 2025, Номер 300, С. 118442 - 118442

Опубликована: Июнь 2, 2025

Язык: Английский

Процитировано

0
SecureIoT-FL: A Federated Learning Framework for Privacy-Preserving Real-Time Environmental Monitoring in Industrial IoT Applications DOI Creative Commons
Montaser N.A. Ramadan, Mohammed A. H. Ali,

Shin Yee Khoo

и другие.

Alexandria Engineering Journal, Год журнала: 2024, Номер 114, С. 681 - 701

Опубликована: Дек. 12, 2024

Язык: Английский

Процитировано

2
Travel route and formation optimization for flocks of drones in package delivery by using an ACO based V-Shape algorithm DOI Creative Commons
E. De Kuyffer, Wout Joseph, Luc Martens

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103627 - 103627

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
The Role of Data Science in Enhancing Web Security DOI Creative Commons
Ahmad Sanmorino

JEECS (Journal of Electrical Engineering and Computer Sciences), Год журнала: 2024, Номер 9(2), С. 119 - 116

Опубликована: Ноя. 24, 2024

With the rise of digital transformation, web security has become a critical concern for organizations, governments, and individuals. This study explores role data science in enhancing by leveraging machine learning algorithms advanced analytics to predict identify potential attacks real-time. The main objective is demonstrate how data-driven techniques, including predictive analytics, anomaly detection, behavioral analysis, can be integrated into existing frameworks reduce vulnerabilities strengthen defenses against cyber threats. research gap addressed this lies insufficient application comprehensive, methodologies threat detection classification security. problem absence that combine feature engineering, models, both known unknown bridges these gaps employing structured dataset interactions model, detect, threats using techniques. Using simulated traffic previous attack records, applies preprocessing, such as decision trees random forests, levels anomalies. Results show models effectively classify levels, with accuracy 80 percent. contributes field demonstrating improve practices, offering proactive approach detecting mitigating cyber-attacks.

Язык: Английский

Процитировано

0