Wavelet transform and scaling function. by Menaa Nawaz (14304920)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…<p dir="ltr">The first algorithm for segmentation and localization (see PathOlOgics_script_1; segment & localize using a pen) relied on manually tracing the borders of each cell using a digital pen tool on a big touchscreen display showing source images/patches. …”

Simulation results for average reward rate function using the UCB algorithm , where <i>A</i> = 100, <i>ℓ</i> = 20, <i>μ</i> = [0.75, …(×50), 0.5, …(×50)], and <i>T</i> = 1000, and . by Justin Zobel (241587)

“…<p>Simulation results for average reward rate function using the UCB algorithm , where <i>A</i> = 100, <i>ℓ</i> = 20, <i>μ</i> = [0.75, …(×50), 0.5, …(×50)], and <i>T</i> = 1000, and .…”

Summary of steps in iterative point set registration for scRNA-seq data. by Amir Alavi (9569337)

“…The number of source and/or target cells matched can vary for different matching strategies. 3) Based on the selected pairs, a global transformation function is learned so that source cells in <i>A</i> become closer to their paired cell in <i>B</i>. 4) The learned transformation is next applied to all points in <i>A</i>. 5) This process (steps 2–4) is repeated, iteratively aligning set <i>A</i> onto <i>B</i> until the mean distance between the assigned pairs of cells no longer improves. 6) The final global transformation function is the composition of the functions learned in each iteration at step 3.…”

Penalty functions used in different methods. by Souvik Seal (12437166)

Algorithm parameters. by Ruyi Dong (9038174)

“…In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. …”

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…The “level” denotes the number of perceptually equal units of brightness, while the scale is an array storing brightness vs. luminous intensity function values.</p>…”

Stepping motor driving circuit. by Shaohua Fu (10573209)

wIRBMO feature selection algorithm improving rate. by Chenyi Zhu (9383370)

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Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Summary of classical and CEC-based benchmark test functions used in this study. by Mahmood A. Jumaah (22272371)

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Training accuracy/loss as a function of epochs for different datasets. by Hiam Alquran (17636906)

A combined algorithm to distinguish between IB and NC models based on the differences in binding kinetics and DynR temporal evolution. by Federico Sevlever (8934533)

“…This Example illustrates the only group of situations where the algorithm fails. A global analysis indicates that only the 5% of the cases fall in this group (this number corresponds to R₀ = 1000, which is the value used in the seven examples analyzed in this figure). …”

The number of inactive nodes as a function of time steps at higher values of <i>P</i> and <i>α</i> in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

Algorithm parameter setting. by Tianrui Zhang (2294542)

“…Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.…”

Algorithm parameter setting. by Tianrui Zhang (2294542)

“…Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.…”

Functional pattern of the proposed method for depression detection. by Umme Marzia Haque (11846815)