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Gotowe bibliografie tematyczne / Explicable Machine Learning

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Artykuły w czasopismach

Gotowa bibliografia na temat „Explicable Machine Learning”

Autor: Grafiati

Data publikacji: 9 listopada 2024

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Artykuły w czasopismach na temat "Explicable Machine Learning"

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FOMICHEVA, S. G. "INFLUENCE OF ATTACK INDICATOR RANKING ON THE QUALITY OF MACHINE LEARNING MODELS IN AGENT-BASED CONTINUOUS AUTHENTICATION SYSTEMS." T-Comm 17, no. 8 (2023): 45–55. http://dx.doi.org/10.36724/2072-8735-2023-17-8-45-55.

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Security agents of authentication systems function in automatic mode and control the behavior of subjects, analyzing their dynamics using both traditional (statistical) methods and methods based on machine learning. The expansion of the cybersecurity fabric paradigm actualizes the improvement of adaptive explicable methods and machine learning models. Purpose: the purpose of the study was to assess the impact of ranking methods at compromise indicators, attacks indicators and other futures on the quality of detecting network traffic anomalies as part of the security fabric with continuous auth

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Abrahamsen, Nils-Gunnar Birkeland, Emil Nylén-Forthun, Mats Møller, Petter Eilif de Lange, and Morten Risstad. "Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models." Journal of Risk and Financial Management 17, no. 10 (2024): 432. http://dx.doi.org/10.3390/jrfm17100432.

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This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to li

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Fomicheva, Svetlana, and Sergey Bezzateev. "Modification of the Berlekamp-Massey algorithm for explicable knowledge extraction by SIEM-agents." Journal of Physics: Conference Series 2373, no. 5 (2022): 052033. http://dx.doi.org/10.1088/1742-6596/2373/5/052033.

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Abstract The article discusses the problems of applying self-explanatory machine learning models in Security Information Event Management systems. We prove the possibility of using information processing methods in finite fields for extracting knowledge from security event repositories by mobile agents. Based on the isomorphism of fuzzy production and fuzzy relational knowledge bases, a constructive method for identifying patterns based on the modified Berlekamp-Massey algorithm is proposed. This allows security agents, while solving their typical cryptanalysis tasks, to use the existing built

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Alharbi, Abdulrahman, Ivan Petrunin, and Dimitrios Panagiotakopoulos. "Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning." Drones 7, no. 5 (2023): 327. http://dx.doi.org/10.3390/drones7050327.

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The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution

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Fujii, Keisuke. "Understanding of social behaviour in human collective motions with non-trivial rule of control." Impact 2019, no. 10 (2019): 84–86. http://dx.doi.org/10.21820/23987073.2019.10.84.

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The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, d

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Wang, Chen, Lin Liu, Chengcheng Xu, and Weitao Lv. "Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework." International Journal of Environmental Research and Public Health 16, no. 3 (2019): 334. http://dx.doi.org/10.3390/ijerph16030334.

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The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subseq

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Valladares-Rodríguez, Sonia, Manuel J. Fernández-Iglesias, Luis E. Anido-Rifón, and Moisés Pacheco-Lorenzo. "Evaluation of the Predictive Ability and User Acceptance of Panoramix 2.0, an AI-Based E-Health Tool for the Detection of Cognitive Impairment." Electronics 11, no. 21 (2022): 3424. http://dx.doi.org/10.3390/electronics11213424.

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The high prevalence of Alzheimer-type dementia and the limitations of traditional neuropsychological tests motivate the introduction of new cognitive assessment methods. We discuss the validation of an all-digital, ecological and non-intrusive e-health application for the early detection of cognitive impairment, based on artificial intelligence for patient classification, and more specifically on machine learning algorithms. To evaluate the discrimination power of this application, a cross-sectional pilot study was carried out involving 30 subjects: 10 health control subjects (mean age: 75.62

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Hermitaño Castro, Juler Anderson. "Aplicación de Machine Learning en la Gestión de Riesgo de Crédito Financiero: Una revisión sistemática." Interfases, no. 015 (August 11, 2022): e5898. http://dx.doi.org/10.26439/interfases2022.n015.5898.

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La gestión de riesgos bancarios puede ser dividida en las siguientes tipologías: riesgo crediticio, riesgo de mercado, riesgo operativo y riesgo de liquidez, siendo el primero el tipo de riesgo más importante para el sector financiero. El presente artículo tiene como objetivo mostrar las ventajas y desventajas que posee la implementación de los algoritmos de machine learning en la gestión de riesgos de crédito y, a partir de esto, mostrar cuál tiene mejor rendimiento, mostrando también las desventajas que puedan presentar. Para lograr el objetivo se realizó una revisión sistemática de la liter

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Umar, Muhammad, Ashish Shiwlani, Fiza Saeed, Ahsan Ahmad, Masoomi Hifazat Ali Shah, and Anoosha Tahir. "Role of Deep Learning in Diagnosis, Treatment, and Prognosis of Oncological Conditions." International Journal of Membrane Science and Technology 10, no. 5 (2023): 1059–71. http://dx.doi.org/10.15379/ijmst.v10i5.3695.

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Deep learning, a branch of artificial intelligence, excavates massive data sets for patterns and predictions using a machine learning method known as artificial neural networks. Research on the potential applications of deep learning in understanding the intricate biology of cancer has intensified due to its increasing applications among healthcare domains and the accessibility of extensively characterized cancer datasets. Although preliminary findings are encouraging, this is a fast-moving sector where novel insights into deep learning and cancer biology are being discovered. We give a framew

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Valdivieso-Ros, Carmen, Francisco Alonso-Sarria, and Francisco Gomariz-Castillo. "Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms." Remote Sensing 15, no. 2 (2023): 312. http://dx.doi.org/10.3390/rs15020312.

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Land cover classification in semiarid areas is a difficult task that has been tackled using different strategies, such as the use of normalized indices, texture metrics, and the combination of images from different dates or different sensors. In this paper we present the results of an experiment using three sensors (Sentinel-1 SAR, Sentinel-2 MSI and LiDAR), four dates and different normalized indices and texture metrics to classify a semiarid area. Three machine learning algorithms were used: Random Forest, Support Vector Machines and Multilayer Perceptron; Maximum Likelihood was used as a ba

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