3 research outputs found
Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
Predicting crime using machine learning and deep learning techniques has
gained considerable attention from researchers in recent years, focusing on
identifying patterns and trends in crime occurrences. This review paper
examines over 150 articles to explore the various machine learning and deep
learning algorithms applied to predict crime. The study provides access to the
datasets used for crime prediction by researchers and analyzes prominent
approaches applied in machine learning and deep learning algorithms to predict
crime, offering insights into different trends and factors related to criminal
activities. Additionally, the paper highlights potential gaps and future
directions that can enhance the accuracy of crime prediction. Finally, the
comprehensive overview of research discussed in this paper on crime prediction
using machine learning and deep learning approaches serves as a valuable
reference for researchers in this field. By gaining a deeper understanding of
crime prediction techniques, law enforcement agencies can develop strategies to
prevent and respond to criminal activities more effectively.Comment: 35 Pages, 6 tables and 11 figures. Consists of Dataset links used for
crime prediction. Review Pape
Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques
Cybercrime is a growing threat to organizations and individuals worldwide,
with criminals using increasingly sophisticated techniques to breach security
systems and steal sensitive data. In recent years, machine learning, deep
learning, and transfer learning techniques have emerged as promising tools for
predicting cybercrime and preventing it before it occurs. This paper aims to
provide a comprehensive survey of the latest advancements in cybercrime
prediction using above mentioned techniques, highlighting the latest research
related to each approach. For this purpose, we reviewed more than 150 research
articles and discussed around 50 most recent and relevant research articles. We
start the review by discussing some common methods used by cyber criminals and
then focus on the latest machine learning techniques and deep learning
techniques, such as recurrent and convolutional neural networks, which were
effective in detecting anomalous behavior and identifying potential threats. We
also discuss transfer learning, which allows models trained on one dataset to
be adapted for use on another dataset, and then focus on active and
reinforcement Learning as part of early-stage algorithmic research in
cybercrime prediction. Finally, we discuss critical innovations, research gaps,
and future research opportunities in Cybercrime prediction. Overall, this paper
presents a holistic view of cutting-edge developments in cybercrime prediction,
shedding light on the strengths and limitations of each method and equipping
researchers and practitioners with essential insights, publicly available
datasets, and resources necessary to develop efficient cybercrime prediction
systems.Comment: 27 Pages, 6 Figures, 4 Table
Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation
In recent years, there is a lot of interest in modeling students' digital
traces in Learning Management System (LMS) to understand students' learning
behavior patterns including aspects of meta-cognition and self-regulation, with
the ultimate goal to turn those insights into actionable information to support
students to improve their learning outcomes. In achieving this goal, however,
there are two main issues that need to be addressed given the existing
literature. Firstly, most of the current work is course-centered (i.e. models
are built from data for a specific course) rather than student-centered;
secondly, a vast majority of the models are correlational rather than causal.
Those issues make it challenging to identify the most promising actionable
factors for intervention at the student level where most of the campus-wide
academic support is designed for. In this paper, we explored a student-centric
analytical framework for LMS activity data that can provide not only
correlational but causal insights mined from observational data. We
demonstrated this approach using a dataset of 1651 computing major students at
a public university in the US during one semester in the Fall of 2019. This
dataset includes students' fine-grained LMS interaction logs and administrative
data, e.g. demographics and academic performance. In addition, we expand the
repository of LMS behavior indicators to include those that can characterize
the time-of-the-day of login (e.g. chronotype). Our analysis showed that
student login volume, compared with other login behavior indicators, is both
strongly correlated and causally linked to student academic performance,
especially among students with low academic performance. We envision that those
insights will provide convincing evidence for college student support groups to
launch student-centered and targeted interventions that are effective and
scalable.Comment: 43 pages, 9 figures, 18 tables, Journal of Educational Data Mining
(Initial Submission