School of IT and Computer Sciences
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/1163
2024-03-29T06:10:18ZHigher Education in Low and Middle-Income Countries: How is AI likely to disrupt the sector?
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/8429
Higher Education in Low and Middle-Income Countries: How is AI likely to disrupt the sector?
Maina, Anthony; Kuria, Jane
Artificial Intelligence (AI) has the potential to revolutionize higher education by
enhancing teaching and learning processes, improving access to education, and
facilitating administrative tasks. However, the implementation of AI in low and middleincome
countries
(LMICs)
comes
with
its
own
set
of
opportunities
and
challenges.
This
study
aims
to
explore
the
opportunities
and
challenges
of
AI
in
higher
education
among
LMICs,
taking
into
consideration
the
socioeconomic
context
and
specific
needs
of
these
countries.
The findings suggest that AI can address the educational inequalities and
improve access to quality education in LMICs. However, challenges such as limited
resources, lack of infrastructure, and cultural barriers need to be overcome for successful
implementation. To fully leverage the potential of AI in higher education, it is crucial
for institutions to invest in faculty development, research, and collaboration to ensure
responsible and effective implementation.
2023-11-01T00:00:00ZEnhanced OpenCV for Text Detection Using Multi-Scale Attention Mechanism
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/8428
Enhanced OpenCV for Text Detection Using Multi-Scale Attention Mechanism
Njoroge, Gedion; Kamau, Gabriel; Maina, Anthony
This research introduces an innovative strategy by merging a multi-scale attention
mechanism with the OpenCV framework to enhance text detection. OpenCV, a
foundational computer vision library, excels in image preprocessing and feature
extraction [1]. Despite emerging deep learning frameworks, OpenCV's prowess in
addressing complex text detection scenarios remains limited. To address this, a multiscale
attention mechanism is proposed, enabling the model to decode text features
across diverse scales and contexts [2]. This approach improves text detection and
recognition, particularly in complex scenes, demonstrated through comprehensive
experiments on benchmark datasets [3]. Results highlight its superiority over
conventional OpenCV methods, enhancing text-related tasks and bolstering real-time
applications [4]. This integration advances text detection by combining OpenCV's
processing abilities with a multi-scale attention mechanism, aligning with OCR
frameworks such as Tesseract OCR for recognition [5]. The method's potential is
underscored in a text-focused technological landscape.
2023-11-01T00:00:00ZAnalysing Student Engagement in an Online Course in the Context of Hybrid Learning Environment. An Empirical Study
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/4850
Analysing Student Engagement in an Online Course in the Context of Hybrid Learning Environment. An Empirical Study
Wahiu, Michael; Fahima Djelil; Laurent Brisson; Jean-Marie Gilliot; Antoine Beugnard
This paper aims to understand student learning engagement in an online course. We describe an empirical
study we conducted to investigate learner profiles when interacting with learning content. This study is based
on data records about student online navigation and took place in the context of a hybrid environment. The
obtained results showed that students mostly select assessment activities and visit the online course content
without engaging deeply in the learning activities. This leads us to conclude on the role of assessment to
motivate and engage students and on the importance of thinking out the design of the hybrid course. Finally,
future work is motivated to study how to provide effective interactions with course content and how this can
impact learning engagement and course design.
2021-04-01T00:00:00ZAnomaly Detection on Roads Using C-ITS Messages
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/4734
Anomaly Detection on Roads Using C-ITS Messages
Moso, Juliet Chebet; Ramzi, Boutahala; Brice Leblanc; Hacène Fouchal; Cyril de Runz; Stephane Cormier; Wandeto, John Mwangi
Cooperative Intelligent Transport Network is one of the most challenging issue in networking and computer science. In this area, huge amount of data are exchanged. Smart analysis of this collected data could be achieved for many purposes: traffic prediction, driver profile detection, anomaly detection, etc. Anomaly detection is an important issue for road operators. An anomaly on roads could be caused by various reasons: potholes, obstacles, weather conditions, etc. An early detection of such anomalies will reduce incident risks such as traffic jams, accidents. The aim of this paper is to collect message exchanges between vehicles and analyze trajectories. This analysis becomes difficult since a privacy principle is applied in the case of C-ITS. Indeed, each message sent is generated with an identifier of the sender. This identifier is kept only over a specified time interval thus one vehicle will have multiple identifiers. We first have to solve Trajectory-User Linking problem by chaining anonymous trajectories to potential vehicles by considering similarity in movement patterns. After that we apply various methods to check variations of trajectories from normal ones. When we observe some differences, we can raise an alarm about a potential anomaly. In order to check the validity of this work, we generated a large amount of messages exchanges by many vehicles using the Omnet simulator together with the Artery, Sumo plug-in. We applied various variations on some obtained trajectories. Finally, we ran our detection algorithm on the obtained trajectories using different parameters (angles, speed, acceleration) and obtained very interesting results in terms of detection rate.
2020-12-01T00:00:00Z