Search results for "decision tree"
showing 10 items of 170 documents
Machine learning for land use change analysis and modelling.
2019
Urban development can take different forms or features, depending on its geographical location and its socioeconomic, political and cultural context. Nevertheless, the overall action relies on one fundamental principle: building construction in order to give people housing. Therefore, the main objective of this research is to determine whether an underlying universal aspect of the urban development process can be distinguished from a specific one, being the reflect of local specificities. Specifically, this research analyzes the land use change on the French-German cross-border area. Indeed, the border context enhances the difference within this territory. Nonetheless, the internal European…
WiHAR : From Wi-Fi Channel State Information to Unobtrusive Human Activity Recognition
2020
A robust and unobtrusive human activity recognition system is essential to a multitude of applications, such as health care, active assisted living, robotics, sports, and tele-immersion. Existing well-performing activity recognition methods are either vision- or wearable sensor-based. However, they are not fully passive. In this paper, we develop WiHAR—an unobtrusive Wi-Fi-based activity recognition system. WiHAR uses the Wi-Fi network interface card to capture the channel state information (CSI) data. These CSI data are effectively processed, and then amplitude and phase information is used to obtain the spectrogram. In the subsequent step, the time-variant mean Doppler shift (MDS) caused …
Deep Learning for Resource-Limited Devices
2020
In recent years, deep neural networks have revolutionized the development of intelligent systems and applications in many areas. Despite their numerous advantages and potentials, these intelligent models still suffer from several issues. Among them, the fact that they became very complex with millions of parameters. That is, requiring more resources and time, and being unsuitable for small restricted devices. To contribute in this direction, this paper presents (1) some state-of-the-art lightweight architectures that were specifically designed for small-sized devices, and (2) some recent solutions that have been proposed to optimize/compress classical deep neural networks to allow their dep…
An Agents and Artifacts Approach to Distributed Data Mining
2013
This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision trees built with J48, but the approach is flexible enough to allow different kinds of learning models. The twofold contribution of this work includes: i) JaCA-DDM: an extensible tool implemented in the agent oriented programming language Jason [2] and CArtAgO [10,9] to experiment DDM agent-based approaches on different, well known training sets. A…
Analysing urban development with decision tree based cellular automata. Toward an automatic transition rule creation process.
2016
International audience
DeTreex Inductive Knowledge Acquisition System in Project Risk Assessment
2016
The aim of this paper is to present the possibility of employing DeTreex inductive knowledge acquisition system in project risk assessment. DeTreex from AITECH Artificial Intelligence Laboratory is a system for knowledge acquisition implementing the inductive machine learning method. Induction in the knowledge acquisition system draws on the induction of decision trees proposed by Quinlan. DeTreex was used to carry out the analysis of data elaborated for the purposes of project risk assessment.The problem of acquisition of knowledge of potential project risk sources, the structuring and processing of such knowledge is, in the case of project management, a relatively new and not fully resear…
Operations Management GADE Decision Trees Technique Lesson 4
2023
El document forma part dels materials docents programats mitjançant l'ajut del Servei de Política Lingüística de la Universitat de València. A refresher on the use of decision trees in Operations Management
Machine learning for mortality analysis in patients with COVID-19
2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…
Application of the threshold of toxicological concern (TTC) to the safety evaluation of cosmetic ingredients.
2007
The threshold of toxicological concern (TTC) has been used for the safety assessment of packaging migrants and flavouring agents that occur in food. The approach compares the estimated oral intake with a TTC value derived from chronic oral toxicity data for structurally-related compounds. Application of the TTC approach to cosmetic ingredients and impurities requires consideration of whether route-dependent differences in first-pass metabolism could affect the applicability of TTC values derived from oral data to the topical route. The physicochemical characteristics of the chemical and the pattern of cosmetic use would affect the long-term average internal dose that is compared with the re…
Reliability of a decision-tree model in predicting occupational lead poisoning in a group of highly exposed workers
2016
Objective This study aimed to provide the toxicological profile of some lead-exposed workers and obtain a predictive model for lead poisoning. Methods Data regarding external and absorbed exposure were collected from 585 subjects employed in ten metallurgical production departments. Airborne lead concentration, blood lead level (BLL), cumulative blood lead index (CBLI), urine delta-aminolevulinic acid (DALA), age, workplace/section, exposure period, and whether reported lead poisoning as occupational disease were examined using ANOVA, and, post-ANOVA, Pearson correlation matrix, PCA (principal component analysis), decision-tree modeling, and logistic modeling. Results BLL was less sensitive…