9 citations · 18 across the 11 of their papers we have counts for
13 papers
Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation
Justin M. Cheney, Clement N. Nyirenda
Deep Reinforcement Learning (DRL) offers learned job selection for High-Performance Computing (HPC), but performance against heuristics alone does not establish value beyond action…
Beyond Group Splits: Specimen-Level Cross-Validation and Visual Attribution for Remaining-Shelf-Life Regression in Climacteric Fruit
Rovhona Mudau, Jean Frederic Isingizwe Nturambirwe, Clement Nthambazale Nyirenda
Estimating remaining shelf life (RSL) from images could provide affordable decision support for perishable produce, but evaluation protocols can substantially affect reported perfo…
Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning
Ubayd Ali Bapoo, Clement N Nyirenda
Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization. Prior work establ…
Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Ubayd Bapoo, Clement N Nyirenda
This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using ful…
Real-time Pipe Burst Localization in Water Distribution Networks Using Change Point Detection Algorithms
Takudzwa Mzembegwa, Clement N Nyirenda
Change point detection (CPD) has proved to be an effective tool for detecting drifts in data and its use over the years has become more pronounced due to the vast amount of data an…
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets
Taahir Aiyoob Patel, Clement N. Nyirenda
In South Africa, there is an ever-growing issue of vehicle hijackings. This leads to travellers constantly being in fear of becoming a victim to such an incident. This work present…
Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks
Luke Borchjes, Clement Nyirenda, Louise Leenen
This paper focuses on the impact of leveraging autonomous offensive approaches in Deep Reinforcement Learning (DRL) to train more robust agents by exploring the impact of applying…
Model-Free Deep Reinforcement Learning in Software-Defined Networks
Luke Borchjes, Clement Nyirenda, Louise Leenen
This paper compares two deep reinforcement learning approaches for cyber security in software defined networking. Neural Episodic Control to Deep Q-Network has been implemented and…
A Twitter-Driven Deep Learning Mechanism for the Determination of Vehicle Hijacking Spots in Cities
Taahir Aiyoob Patel, Clement N. Nyirenda
Vehicle hijacking is one of the leading crimes in many cities. For instance, in South Africa, drivers must constantly remain vigilant on the road in order to ensure that they do no…
A Modified UDP for Federated Learning Packet Transmissions
Bright Kudzaishe Mahembe, Clement Nyirenda
This paper introduces a Modified User Datagram Protocol (UDP) for Federated Learning to ensure efficiency and reliability in the model parameter transport process, maximizing the p…
The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques
Ezra Fielding, Clement N. Nyirenda, Mattia Vaccari
In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-…
A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification
Ezra Fielding, Clement N. Nyirenda, Mattia Vaccari
The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep le…
A Comparative Evaluation of Population-based Optimization Algorithms for Workflow Scheduling in Cloud-Fog Environments
Dineshan Subramoney, Clement N. Nyirenda
This work presents a comparative evaluation of four population-based optimization algorithms for workflow scheduling in cloud-fog environments. These algorithms are as follows: Par…