Screenshot of our visualization tool MGDrawVis. by Fadi K. Dib (5204807)

“…<div><p>Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. …”

DataSheet_1_Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.docx by Haoyu Wang (429641)

“…The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. …”

Using BART to Perform Pareto Optimization and Quantify its Uncertainties by Akira Horiguchi (11768593)

“…The performance of our BART-based method is compared to a GP-based method using analytic test functions, demonstrating convincing advantages. …”

Testing results for classifying AD, MCI and NC. by Nicodemus Songose Awarayi (18414494)

Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

Warning dialog box of proposed NIDS. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Feature extraction of proposed NIDS. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Performance comparison analysis. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Trained dataset after preprocessing. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Environmental setup. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Data repository. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Proposed architecture of fast R–CNN. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Test dataset after preprocessing. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Accuracy comparison with various datasets. by Parthiban Aravamudhan (15338781)

“…After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. …”

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”

Fig 5 - by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

DataSheet1_An optimized three-dimensional time-space domain staggered-grid finite-difference method.docx by Wei Liu (20030)

“…Examining the numerical dispersion, algorithm stability and computational cost, we compare our optimized time-space domain LS-based 3D SFD method with three conventional TE-based and LS-based 3D SFD methods to illustrate and demonstrate its effectiveness and feasibility. …”

Cross Validation mechanism for an RL case. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Monte carlo test ranking from elitism phase. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Individual #5’s action ratio, position states. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”