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REINFORCEMENT LEARNING BASED APPROACHES FOR RESOURCE ALLOCATION IN SMART HEALTH SYSTEMS.
(Computing, 2022 , Master Thesis)
With the emergence of smart health (s-health) applications and services, several requirements for quality have arisen to foresee and react instantaneously to emergency circumstances. Such conditions demand adaptive fast-acting ...
An efficient PPG Compression Technique for Wearable Health Devices
(Computing, 2021 , Master Thesis)
Photoplethysmography is a simple, widely used, low-cost technique to acquire important diagnostic information for assessing significant physiological parameters of the human body based on the amount of light reflected from ...
Encoder-decoder architecture for ultrasound IMC segmentation and CIMT prediction
(Computing, 2021 , Master Thesis)
Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, Common Carotid Artery (CCA) segmentation and Intima-Media Thickness (IMT) measurement have been significantly implemented ...
ENERGY-EFFICIENT USER-EDGE ASSOCIATION AND RESOURCE ALLOCATION IN IOT-BASED HIERARCHICAL FEDERATED LEARNING
(Computing, 06-2 , Master Thesis)
The proliferation of data as part of the Internet of Things (IoT) systems needs to be efficiently utilized while respecting data privacy and scalability. Edge computing is an emerging paradigm that mandates efficient ...
A Deep Learning Based Approach To Detect Covert Channels Attacks and Anomaly In New Generation Internet Protocol IPv6
(Computing, 2020 , Master Thesis)
The increased dependence of internet-based technologies in all facets of life
challenges the government and policymakers with the need for effective shield mechanism
against passive and active violations. Following up ...
Bagged Randomized Conceptual Machine Learning Method
(Computing, 2018 , Master Thesis)
Formal concept analysis (FCA) is a scientific approach aiming to investigate, analyze and represent the conceptual knowledge deduced from the data in conceptual structures (lattice). Recently many researchers are counting ...
Time-Aware Workload Charactrization And Prediction For Proactive Auto-Scaling Of Web Applications
(Computing, 2019 , Master Thesis)
Proactive auto-scaling techniques aim to predict the future workload of web
applications to provision the required resources, such as virtual machines (VMs), ahead
of time. Nevertheless, deciding the optimal number of ...
ADVANCED MACHINE LEARNING TECHNIQUES FOR ARRHYTHMIA CLASSIFICATION
(Computing, 06-2 , Master Thesis)
With the development of Internet-of-Things (IoT) applications, the concept of smart healthcare applications has gradually emerged to be the main factor in medicine. In fact, this raises the need to have a secure system ...
SMART HARDWARE TROJAN DETECTION SYSTEM
(Computing, 06-2 , Master Thesis)
The IoT has become an indispensable part of our lives at work and in our home applications. Due to the need for many IoT devices, IoT manufacturers are least concerned about security vulnerabilities during designing and ...
MOBILE APP FOR HIDDEN DATA ANALYTICS OF ONLINE MARKETPLACE SYSTEMS
(Computer Science, 2016 , Master Thesis)
In this project, an extensive analysis and evaluation of the existing e-marketplaces is performed. The aim of this analysis is to improve the experience of end-users through an Android application that is capable of ...