Optimizing ADWIN for Steady Streams by Moharram, Hassan

“…However, online machine learning comes with many challenges for the different aspects of the learning process, starting from the algorithm design to the evaluation method. …”
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Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea by Issa, Leila

“…To this end, an efficient algorithm for forward and backward tracking of passive particles in stochastic flow-fields is presented. …”
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Structural similarity evaluation between XML documents and DTDs by Tekli, J.

“…We consider the various DTD operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes. Our approach is based on the concept of tree edit distance, as an effective and efficient means for comparing tree structures, XML documents and DTDs being modeled as ordered labeled trees. …”
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Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization by Alhadhrami, Esra Ebrahim

“…These outcomes were compared with those of a feed-forward neural network learning, the Castella innovation/ Q values mapping, and fixed Q values. The PPO algorithm yielded promising results. We employed the Stone Soup library to simulate ground truths, measurements, and the Kalman filter tracking process.…”
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An effective design system for dynamically reconfigurable architectures by Ouaiss, I.

“…The results, obtained on a commercial RC architecture, show that the multiply-reconfigured version of the JPEG compression algorithm achieves reasonable improvement in execution times compared to the one-time configured version.…”
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FPGA-Based Network Traffic Classification Using Machine Learning by Elnawawy, Mohammed

“…The performed experiments show that 60% of flow packets are a good compromise that ensures high performance in the least processing time. The results of the conducted experiments indicate that random forest outperforms other algorithms achieving a maximum accuracy of 98.5% and an F-score of 0.932. …”
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Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying by Tekli, Joe

“…Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. …”
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Development of multivariable PID controller gains in presence of measurement noise by Saab, Samer S.

“…It turns out that such convergence requires time-varying gains. Subsequently, novel recursive algorithms providing optimal and sub-optimal time-varying PID gains are proposed for discrete-time varying linear multiple-input multiple-output (MIMO) systems. …”
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Energy-Efficient Computation Offloading in Vehicular Edge Cloud Computing by Xin Li (51274)

“…Numerical results corroborate that our designed algorithm can gain above 80% execution time conservation and 62% conservation on energy consumption, and it exhibits fast convergence and superior performance compared to benchmark solutions.…”

Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. …”

Adaptive PID Controller for a Non Linear Pendulum System Using Recurrent Neural Networks by Jafari, Reza

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Extreme Early Image Recognition Using Event-Based Vision by Abubakar Abubakar (18278998)

“…It is demonstrated that, on average for all the datasets, the proposed technique recognizes an image 38.7 ms before the first perfect event and 603.4 ms before the last event is received, which is a reduction of 34% and 69% of the time needed, respectively. Further, less processing is required as the image is recognized 9460 events earlier, which is 37% less than waiting for the first perfectly recognized image. …”

Exploring the System Dynamics of Covid-19 in Emergency Medical Services by Ali, Muhammad

“…The predictive analysis yielded a model of response times for emergency missions through machine learning, specifically using a random forest algorithm. …”
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MoveSchedule by Zouein, Pierette

“…This research develops an integrated scheduler and space planner, MoveSchedule, that adjusts an initial project schedule to comply with site space limitations in the process of accommodating resource space needs as they change over time. …”
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Improving Rule Set Based Software Quality Prediction by Azar, Danielle

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Virtualizing and Scheduling FPGA Resources in Cloud Computing Datacenters by Farhan, Abid

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Tuning of AGC of interconnected reheat thermal systems with geneticalgorithms by Abdel-Magid, Y.L.

“…A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. …”
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An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System by Hamza Djelouat (14603379)

“…Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process. …”

Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller by Abualigah, Laith

“…This combination aims to enhance the stability and performance of the aircraft pitch control system by effectively mitigating the kick effect through the incorporation of a filter coefficient in the derivative gain. The study employs a time-domain-based objective function to guide the optimization process. …”
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Fragment based protein structure prediction. (c2013) by Terzian, Meghrig Ohanes

“…In recent years, the protein structure prediction problem has come under extensive investigation, and computational structure prediction methods are circumventing the time-consuming experimental methods by accelerating the prediction process. …”
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