Research Articles (Computer Science)

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https://hdl.handle.net/10566/52
The research papers in this collection represent the work of several projects.

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Browsing by Author "Bagula, A"

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    Emotion and stress recognition related sensors and machine learning technologies
    (MPDI, 2021) Bagula, A; Kyamakya, K; Al-Machot, F
    Intelligent sociotechnical systems are gaining momentum in today’s informationrich society, where different technologies are used to collect data from such systems and mine this data to make useful insights about our daily activities. These systems range from driver-assistance systems, to medical-patient monitoring systems, to emotion-aware intelligent systems, to complex collaborative robotics systems. They are built around (i) intrusive technologies such as physiological sensors, used for example in EEG, ECG, electrodermal activity and skin conductance and (ii) nonintrusive technologies that use piezo-vibration sensors, facial images, chairborne differential vibration sensors and bedborne differential vibration sensors. However, despite their undisputable advantages in our daily lives, there are a number of issues relating to the design and development of such systems, as they rely on emotion and stress classification from physiological signals. These issues can be viewed from various perspectives including: (a) quality and reliability of sensor data; (b) classification performance in terms of accuracy, precision, specificity, recall and F1-measure; (c) robustness of subject-independent recognition; (d) portability of the classification systems to different environments and (e) the estimation of the emotional state for dynamic systems.
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    Intelligent transportation related complex systems and sensors
    (MPDI, 2021) Bagula, A; Kyamakya, K; Chedjou, J.C
    Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITSs) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas.
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    A Novel Epidemic Model for the Interference Spread in the Internet of Things
    (MDPI, 2022) Tuyishimire, E; Niyigena, J; Tubanambazi, F; Bagula, A
    : Due to the multi-technology advancements, internet of things (IoT) applications are in high demand to create smarter environments. Smart objects communicate by exchanging many messages, and this creates interference on receivers. Collection tree algorithms are applied to only reduce the nodes/paths’ interference but cannot fully handle the interference across the underlying IoT. This paper models and analyzes the interference spread in the IoT setting, where the collection tree routing algorithm is adopted. Node interference is treated as a real-life contamination of a disease, where individuals can migrate across compartments such as susceptible, attacked and replaced. The assumed typical collection tree routing model is the least interference beaconing algorithm (LIBA), and the dynamics of the interference spread is studied. The underlying network’s nodes are partitioned into groups of nodes which can affect each other and based on the partition property, the susceptible–attacked–replaced (SAR) model is proposed. To analyze the model, the system stability is studied, and the compartmental based trends are experimented in static, stochastic and predictive systems. The results shows that the dynamics of the system are dependent groups and all have points of convergence for static, stochastic and predictive systems.