Cyber threats are becoming more frequent and complex, making decision systems crucial to cybersecurity. The review paper uses machinelearning to examine decision systems and cybersecurity, highlighting its fundamenta...
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ISBN:
(纸本)9789819601424
Cyber threats are becoming more frequent and complex, making decision systems crucial to cybersecurity. The review paper uses machinelearning to examine decision systems and cybersecurity, highlighting its fundamentals, applications, and challenges. The paper begins with a detailed cybersecurity overview, emphasizing the need for strong decision-making systems to address changing threats. We define and discuss decision systems in the fundamentals section, emphasizing their importance in cybersecurity. An analysis of cybersecurity decision system development highlights significant achievements and technological progress. This paper focuses on machinelearning in cybersecurity decision systems. Analyzing cybersecurity machinelearning methods allows for a thorough examination of their use in decision-making. We discuss the pros and cons of integrating machinelearning into decision-making systems and its impact on cybersecurity. Intrusion Detection systems (IDS), Threat Intelligence Platforms (TIP), Incident Response and Management, and Adaptive Security Architectures are examined in the methods and processes section. Each system will be examined for its function, machinelearning algorithms, and case studies to demonstrate their practicality. Assessing decision system effectiveness requires metrics and benchmarks. This paper analyzes decision system performance metrics and cybersecurity machinelearning model benchmarks and datasets. Using these metrics to compare decision systems reveals their strengths and weaknesses. The review concludes with future machinelearning-based decision system challenges and new trends and technologies. To maintain cybersecurity resilience, recommendations are made to address future challenges and improve decision-making systems. In-depth machinelearning analysis in this review paper expands cybersecurity decision system knowledge. This resource helps researchers, practitioners, and policymakers understand, implement, and improve cybe
Stroke, the second leading cause of mortality globally, demands timely and accurate prediction for effective intervention. This study explores advanced machinelearning techniques to enhance stroke prediction models. ...
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In this paper, machinelearning (ML) Algorithms are used to implement the proposed approach to identify social distance, face masks, drowsiness detection, age-gender detection, and emotion detection. While dealing wit...
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Dyslexia is identified as a typical learning disorder which affects children due to which they develop difficulty in pronouncing words, writing with the correct spellings, etc. The numerical dataset includes results f...
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Aerodynamic optics has important research value in many fields such as aerospace optical sensors, remote sensing detection and so on. The traditional gas radiation calculation models are insufficient in terms of accur...
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The proceedings contain 112 papers. The topics discussed include: protecting vehicle location privacy with contextually-driven synthetic location generation;enhancing spatio-temporal quantile forecasting with curricul...
ISBN:
(纸本)9798400711077
The proceedings contain 112 papers. The topics discussed include: protecting vehicle location privacy with contextually-driven synthetic location generation;enhancing spatio-temporal quantile forecasting with curriculum learning: lessons learned;critical features tracking on triangulated irregular networks by a scale-space method;revisiting the bus stop problem in road networks;augmentation techniques for balancing spatial datasets in machine and deep learning applications;discretized random walk models for efficient movement interpolation;prompt mining for language models-based mobility flow forecasting;privacy preserved taxi demand prediction system for distributed data;and deep reinforcement learning for multi-period facility location: pk-median dynamic location problem.
Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especi...
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ISBN:
(纸本)9789811965814;9789811965807
Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especially those based on artificial intelligence, have been developed for meteorological modeling. Indeed, in recent years, machinelearning has enabled fundamental advances in the modeling of random systems. In this context, we will show the contribution of techniques based on artificial intelligence in the estimation of precipitation. Based on the expertise of our research laboratories in this field, the objective of this paper is to present our recent results and developments using machinelearning, such as ANN, SVM, and RF. For the classification and estimation of rainfall intensities, satellite images were used for the implementation of these techniques. The training and validation was carried out by comparing the satellite images to the corresponding radar images. The results of these artificial intelligence-based techniques indicate very interesting performance.
作者：
Costa, Samuel SampaioPato, MatildeDatia, Nuno
Politécnico de Lisboa ISEL Lisbon Lisbon Portugal LASIGE
Faculdade de Ciencias Universidade de Lisboa LASIGE Lisbon Lisbon Portugal NOVA LINCS
NOVA School of Science and Technology NOVA LINCS Setubal Caparica Portugal
Kolmogorov-Arnold Networks (KANs) represent a breakthrough in deep learning, diverging from Multi-Layer Perceptrons (MLPs) by generalizing the Kolmogorov-Arnold representation theorem (KAT) to networks of arbitrary de...
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In this paper, we modeled a system to recognize human activities using integrated sensors like gyroscopes and accelerometers in smartphones. To perform human activity recognition (HAR) accurately, appropriate machine ...
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Bridges are the essential part of our transportation in daily life. Infrastructure safety and integrity are crucial to prevent accidents and ensure smooth traffic flow. Regular inspections require careful observation ...
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