Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk by Annisa Damayanti, Anna Baita

“…One of the machine learning methods that can be used to predict GDM is the Support Vector Machine (SVM) algorithm and the Random Forest (RF) algorithm. This study aims to compare, and evaluate GDM disease prediction models using the SVM and RF algorithms by balancing the target data using the Random Undersampling Technique. …”
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Developing a novel approach for passive damped LCL filter and controller parameter design using PSO algorithm in VSC-based islanded microgrids by Yared Bekele Beyene, Getachew Biru Worku, Lina Bertling Tjernberg

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A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression by Tingkai Hou, Zonghong Zhou, Yonggang Zhang, Jing Zhang

“…Secondly, the DE-GWO, particle swarm optimization (PSO), genetic algorithm (GA), and SVR are integrated to identify the optimal superparameters, while the nonlinear mapping relationship between inversion parameters and displacements is established. …”
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Hybrid optimization technique for matrix chain multiplication using Strassen’s algorithm [version 2; peer review: 3 approved, 1 approved with reservations] by Srinivasarao Thota, Rakshitha T, Thulasi Bikku

“…Our approach consists of two key phases: (i) matrix chain order optimization, using a top-down memoized DP approach, we compute the best multiplication sequence, and (ii) hybrid multiplication strategy, we selectively apply Strassen’s algorithm for large matrices (n ≥ 128), reducing the complexity from O(n3) to O(n2.81), while using standard multiplication for smaller matrices to avoid recursive overhead. …”
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From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities. by Shreya Chappidi, Mason J Belue, Stephanie A Harmon, Sarisha Jagasia, Ying Zhuge, Erdal Tasci, Baris Turkbey, Jatinder Singh, Kevin Camphausen, Andra V Krauze

“…The prescription method identified progression an average of 5.2 months later than the clinical standard, while the CV and NLP algorithms identified progression earlier by 2.6 and 6.9 months, respectively. …”
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stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning by Xin Lu, Murong Zhou, Bo Gao, Fang Wang, Shuilin Jin, Qiaoming Liu, Guohua Wang

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Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. by Zhiheng Zhang, Zhenji Zhu, Yongjun Hua

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Autonomous UAV Path Optimization Using Genetic and Multiobjective Evolutionary Algorithms for Effective Data Retrieval in Cache-Enabled Mobile Ad-Hoc WSNs by Umair B. Chaudhry, Chris Ian Phillips

“…Unlike traditional methods, UAVs can collect data directly from mobile nodes, eliminating the need for routing. While most existing research focuses on static nodes, this paper introduces a multiple objective evolutionary approach “Strength Pareto Evolutionary Algorithm for Dynamic UAV Paths” (SPEA-DUP) for UAV data collection that predicts the future positions of caching-enabled mobile ad hoc wireless sensor network nodes. …”
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Utility of the FebriDx point-of-care assay in supporting a triage algorithm for medical admissions with possible COVID-19: an observational cohort study by Hamish Houston, John Ross, Ankur Gupta-Wright, Sarah Ann Filson, Nidhi Vaid, Guduru Gopal Rao, Laurence John, Kamal Shah, Gavin Deas, Shivam Naik, Shiras Patel, Maria Greca Dottori, Michael Tay, James Biggin-Lamming, Natalie Vaughan, Amit K Amin

“…Using a SARS-CoV-2 RT-PCR reference standard, clinical triage had sensitivity of 96% (95% CI 91% to 98%) and specificity of 61.5% (95% CI 59.8% to 63.1%), while the triage algorithm including FebriDx had sensitivity of 93% (95% CI 87% to 96%) and specificity of 86.4% (95% CI 85.2% to 87.5%). …”
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Hybrid Genetic Algorithm-Based Optimal Sizing of a PV–Wind–Diesel–Battery Microgrid: A Case Study for the ICT Center, Ethiopia by Adnan Kedir Jarso, Ganggyoo Jin, Jongkap Ahn

“…This optimization problem is solved using a hybrid genetic algorithm (HGA). Simulation results indicate that the HGA yielded the best solution, characterized by the levelized cost of energy of USD 0.2546/kWh, the loss of power supply probability of 0.58%, and a convergence time of 197.2889 s.…”
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Radiative heat and mass transfer significance through a permeable vertical plate with rotational effects: An artificial approach using the Levenberg–Marquardt algorithm by R. Kavitha, Kavikumar Jacob, Ahmad Haji Zadeh, Nagarajan Deivanayagampillai

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OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data. by Raga Krishnakumar, Anne M Ruffing

“…In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. …”
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Maximizing efficiency and performance of water distribution systems through the implementation of optimization algorithms: A comprehensive analysis of valve and chlorine booster pl... by Mohamadreza Najarzadegan, Mehrtash Eskandaripour

“…By employing advanced optimization algorithms, specifically the Genetic Algorithm (GA) and Slime Mould Algorithm (SMA), the research identifies optimal configurations across two benchmark networks, Jowitt and Xu and GoYang. …”
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Cost-Based Modeling for Optimal Energy Management of Smart Buildings with Renewable Energy Resources and Electric Vehicles Using a Scenario-Based Algorithm by Hanlie Cheng

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Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network by Noor Habib Khan, Yong Wang, Raheela Jamal, Sheeraz Iqbal, Mohamed Ebeed, Yazeed Yasin Ghadi, Z. M. S. Elbarbary

“…The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte‐Carlo simulation and reduction‐based approaches are utilized to generate the novel set of optimal scenarios. …”
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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…So, this study integrates the ANFIS (adaptive neuro-fuzzy inference system) and ELM (extreme learning machine) machine learning models with three optimization algorithms, i.e., WCA (water cycle algorithm), PSO (particle swarm optimization), and GWO (grey wolf optimizer) to precisely estimate the CS of fiber-reinforced concrete (FRC) containing SiO2. …”
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Derivation and validation of an algorithm to predict transitions from community to residential long-term care among persons with dementia-A retrospective cohort study. by Wenshan Li, Luke Turcotte, Amy T Hsu, Robert Talarico, Danial Qureshi, Colleen Webber, Steven Hawken, Peter Tanuseputro, Douglas G Manuel, Greg Huyer

“…<h4>Conclusions and implications</h4>We developed an algorithm to predict time to LTC entry among individuals living with dementia. …”
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Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance... by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Anchal Sharma, Surita Maini, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid, Rohit Bansal

“…Five ML algorithms-LR, RF, SVM, AB, and DT- were tested using two paradigms: (1) default classifiers and (2) hybrid GA-ML models- GA-RF, GA-LR, GA-SVM, GA-DT, and GA-AB. …”
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Techniques and Models for Addressing Occupational Risk Using Fuzzy Logic, Neural Networks, Machine Learning, and Genetic Algorithms: A Review and Meta-Analysis by Chris Mitrakas, Alexandros Xanthopoulos, Dimitrios Koulouriotis

“…This article aims to present a structured literature review that utilizes computational intelligence techniques, specifically fuzzy logic, neural networks, genetic algorithms, and machine learning, to assist in the assessment of workplace risk from human factors. …”
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Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network. by Yeping Shi, Yunbo Shi, Haodong Niu, Jinzhou Liu, Pengjiao Sun

“…Response data are classified and regressed using a sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). …”
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