Interval type-2 membership function for speed. حسب Seung-Min Ryu (21463891)

"…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …"

Interval type-2 membership function for distance. حسب Seung-Min Ryu (21463891)

"…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …"

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform حسب Zhehong Ai (17937324)

Metabolic tasks can be inferred from omics data to determine which tasks should be protected during the model extraction process. حسب Anne Richelle (6589178)

"…<p>(A) Metabolic functions are inferred from transcriptomic data using the genome-scale model and then protected during the implementation of the extraction algorithms. …"

Alignment of amino-acids sequence of nucleotide-binding domains of Ssa1 with DnaK from <i>E</i>. <i>coli</i> using the Needleman-Wunsch algorithm for global alignment. حسب Deepika Gaur (814560)

500 <i>ϕ</i> vectors learned from hard thresholding. حسب Ilias Rentzeperis (10215602)

"…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. In this work, we use biological vision as a test-bed and show that the soft thresholding operation associated to the use of the <i>ℓ</i>₁ norm is highly suboptimal compared to other functions suited to approximating <i>ℓ</i>_<i>p</i> with 0 ≤ <i>p</i> < 1 (including recently proposed continuous exact relaxations), in terms of performance. …"

500 <i>ϕ</i> vectors learned from CEL0. حسب Ilias Rentzeperis (10215602)

"…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. In this work, we use biological vision as a test-bed and show that the soft thresholding operation associated to the use of the <i>ℓ</i>₁ norm is highly suboptimal compared to other functions suited to approximating <i>ℓ</i>_<i>p</i> with 0 ≤ <i>p</i> < 1 (including recently proposed continuous exact relaxations), in terms of performance. …"

Simulation results by genetic algorithms (GAs). حسب Yuji Omachi (15307241)

Results of human and algorithmic categorization performance. حسب John K. Tsotsos (7435094)

"…<p>A,B: Box plots of human categorization performance plotted as a function of the percentage of the target within the parafovea. …"

Data imputation algorithm for the vector x. حسب Yu Yang Fredrik Liu (9023738)

Selected results as the standardized difference from baseline. حسب John N. Staffurth (20636565)

EM Algorithm for the Estimation of the RETAS Model حسب Tom Stindl (4109965)

"…Evaluating the log-likelihood function and directly maximizing it has been shown to be a viable approach to obtain the maximum likelihood estimator (MLE) of the RETAS model. …"

The run time for each algorithm in seconds. حسب Edward Antonian (21453161)

"…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …"

Test results of multimodal benchmark functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Fixed-dimensional multimodal reference functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Test results of multimodal benchmark functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Width of stripes as a function of time. حسب Pratik Mullick (12851011)

Fitness function over the 50 runs. حسب Mohammed Alqahtani (2049139)

"…Employing optimization techniques including the osprey optimization algorithm (OOA), zebra optimization algorithm (ZOA), and flying foxes optimization (FFO) algorithm, the study aims to determine the optimal sizing of solar PV, wind, biomass, and battery components. …"

Fitness function over the 50 runs. حسب Mohammed Alqahtani (2049139)

"…Employing optimization techniques including the osprey optimization algorithm (OOA), zebra optimization algorithm (ZOA), and flying foxes optimization (FFO) algorithm, the study aims to determine the optimal sizing of solar PV, wind, biomass, and battery components. …"

Fitness function over the 50 runs. حسب Mohammed Alqahtani (2049139)

"…Employing optimization techniques including the osprey optimization algorithm (OOA), zebra optimization algorithm (ZOA), and flying foxes optimization (FFO) algorithm, the study aims to determine the optimal sizing of solar PV, wind, biomass, and battery components. …"