CEC2017 basic functions. by Tengfei Ma (597633)

“…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …”

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Classification thresholds and prediction algorithms for novel PGx variants. by Ewa Goljan (3463487)

The GOR4 and SOPMA algorithm findings. by Ahmad Abdullah Mahdeen (19704401)

Test function results. by Ruyi Dong (9038174)

“…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”

Benchmark test functions. by Ruyi Dong (9038174)

“…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”

Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems by Dongge Lei (6836915)

“…The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.…”

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

Subjects:

Hyper parameter settings for ML Algorithms. by R. S. Jayaram (22139155)

Flowchart of proposed algorithm. by Tibebu Legesse (21030014)

“…Moreover, the proposed algorithm significantly extends network lifetime, with a <b>3.5%</b> and <b>7.5%</b> improvement over EAPS-AODV and AODV. …”

(a-c) Sampling event times with an intensity function . by Gerrit Großmann (9585147)

Membership function. by Tuan Anh Nguyen (121944)

Confusion matrices depict the performance of various ML algorithms. by Caroline L. Alves (14271413)

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

Subjects:

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Data_Sheet_1_SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.docx by Ekaterina Mikhaylets (17865407)

“…<p>The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). …”

Control parameters of the SOMA algorithm. by Quoc Bao Diep (18452207)

“…To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. …”

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)