Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Notations and definitions used in the algorithm. by Nazym Alimbekova (22766158)

Summary of the feature selection algorithms. by Martin Kostadinov (20410120)

Present an algorithm to prove P = NP by Sang-un Lee (22242148)

“…<p dir="ltr">Aiming to prove the P vs. NP problem, a long-standing challenge in computer science, we present a novel deterministic algorithm that solves the Hamiltonian path problem, a representative NP-complete problem, in polynomial time. …”

Present an algorithm to prove P = NP by Sang-un Lee (22242910)

“…<p dir="ltr">Aiming to prove the P vs. NP problem, a long-standing challenge in computer science, we present a novel deterministic algorithm that solves the Hamiltonian path problem, a representative NP-complete problem, in polynomial time. …”

Present an algorithm to prove P = NP by Sang-un Lee (22242055)

“…<p dir="ltr">Aiming to prove the P vs. NP problem, a long-standing challenge in computer science, we present a novel deterministic algorithm that solves the Hamiltonian path problem, a representative NP-complete problem, in polynomial time. …”

Present an algorithm to prove P = NP by Sang-un Lee (22239061)

Cost of implementing different screening algorithm. by Tushar Garg (9283574)

Choices on individual test tasks were not explained by model-free perseveration. by Sam Hall-McMaster (10343795)

Subjects:

Aggregated actual vs. predicted power output across all ML models. by Montaser Abdelsattar (20606102)

Subjects:

Aggregated actual vs. predicted power output across all DL models. by Montaser Abdelsattar (20606102)

Subjects:

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…<div><p>Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. …”

Actual vs. forecasted solar irradiance. by Muhammad Sadiq (9449915)

Subjects:

MEA-BP neural network algorithm flowchart. by Dongling Ma (1269888)

Subjects:

Licking behaviors and neural firings of the model. by Huu Hoang (9188747)

Subjects: “…supervised learning algorithms…”

t-Test results for SLADRO vs. baseline models. by Yousef Sanjalawe (22216626)

“…To bridge this gap, the paper proposes a hybrid load-balancing methodology that integrates feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, <i>SLADRO</i>, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. …”

Data and code resources. by Sam Hall-McMaster (10343795)

Subjects:

Experimental design. by Sam Hall-McMaster (10343795)

Subjects:

Behavioral results. by Sam Hall-McMaster (10343795)

Subjects:

Choice behavior on individual test tasks. by Sam Hall-McMaster (10343795)

Subjects: