1,261 research outputs found
Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata
Recent research techniques, such as genetic algorithm (GA), Petri net (PN), and cellular automata (CA) have been applied in a number of studies. However, their capability and performance in land-cover land-use (LCLU) classification, change detection, and predictive modeling have not been well understood. This study seeks to address the following questions: 1) How do genetic parameters impact the accuracy of GA-based LCLU classification; 2) How do image parameters impact the accuracy of GA-based LCLU classification; 3) Is GA-based LCLU classification more accurate than the maximum likelihood classifier (MLC), iterative self-organizing data analysis technique (ISODATA), and the hybrid approach; 4) How do genetic parameters impact Petri Net-based LCLU change detection; and 5) How do cellular automata components impact the accuracy of LCLU predictive modeling. The study area, namely the Tickfaw River watershed (711mi²), is located in southeast Louisiana and southwest Mississippi. The major datasets include time-series Landsat TM / ETM images and Digital Orthophoto Quarter Quadrangles (DOQQ’s). LCLU classification was conducted by using the GA, MLC, ISODATA, and Hybrid approach. The LCLU change was modeled by using genetic PN-based process mining technique. The process models were interpreted and input to a CA for predicting future LCLU. The major findings include: 1) GA-based LCLU classification is more accurate than the traditional approaches; 2) When genetic parameters, image parameters, or CA components are configured improperly, the accuracy of LCLU classification, the coverage of LCLU change process model, and/or the accuracy of LCLU predictive modeling will be low; 3) For GA-based LCLU classification, the recommended configuration of genetic / image parameters is generation 2000-5000, population 1000, crossover rate 69%-99%, mutation rate 0.1%-0.5%, generation gap 25%-50%, data layers 16-20, training / testing data size 10000-20000 / 5000-10000, and spatial resolution 30m-60m; 4) For genetic Petri nets-based LCLU change detection, the recommended configuration of genetic parameters is generation 500, population 300, crossover rate 59%, mutation rate 5%, and elitism rate 4%; and 5) For CA-based LCLU predictive modeling, the recommended configuration of CA components is space 6025 * 12993, state 2, von Neumann neighborhood 3 * 3, time step 2-3 years, and optimized transition rules
Learning About Meetings
Most people participate in meetings almost every day, multiple times a day.
The study of meetings is important, but also challenging, as it requires an
understanding of social signals and complex interpersonal dynamics. Our aim
this work is to use a data-driven approach to the science of meetings. We
provide tentative evidence that: i) it is possible to automatically detect when
during the meeting a key decision is taking place, from analyzing only the
local dialogue acts, ii) there are common patterns in the way social dialogue
acts are interspersed throughout a meeting, iii) at the time key decisions are
made, the amount of time left in the meeting can be predicted from the amount
of time that has passed, iv) it is often possible to predict whether a proposal
during a meeting will be accepted or rejected based entirely on the language
(the set of persuasive words) used by the speaker
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting
Contextual Recommendations using Intention Mining on Process Traces
International audienceNowadays, digital traces are omnipresent in Information System (IS). Companies track IS interactions to retrieve and compile information about actors. Researchers of various streams, within IT and beyond, focused on recording actor interactions with systems and the technical possibilities to identify record and store these interactions. Tracing functionality has appeared in almost all common computer applications. This PhD project will focus on the establishment of a trace-based system and propose recommendations to actors regarding to their context. The objective of this thesis is to study process traces to propose recommendations to the actors by identifying a set of generic processes adaptable to the current actors' context. Thus, any actor, expert or novice, will be able to use this knowledge that gives contextual clues to identify the potential steps he could perform
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