CUFID-query: accurate network querying through random walk based network flow estimation by Hyundoo Jeong, Xiaoning Qian, Byung-Jun Yoon

“…First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. …”
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Cluster-Fault Tolerant Routing in a Torus by Antoine Bossard, Keiichi Kaneko

“…Not only is this algorithm tolerant to faulty nodes, it also tolerates faulty node clusters. The described algorithm selects a fault-free path of length at most <inline-formula> <math display="inline"> <semantics> <mrow> <mi>n</mi> <mo>(</mo> <mn>2</mn> <mi>k</mi> <mo>+</mo> <mo>⌊</mo> <mi>k</mi> <mo>/</mo> <mn>2</mn> <mo>⌋</mo> <mo>−</mo> <mn>2</mn> <mo>)</mo> </mrow> </semantics> </math> </inline-formula> with an <inline-formula> <math display="inline"> <semantics> <mrow> <mi>O</mi> <mo>(</mo> <msup> <mi>n</mi> <mn>2</mn> </msup> <msup> <mi>k</mi> <mn>2</mn> </msup> <mo>|</mo> <mi>F</mi> <mo>|</mo> <mo>)</mo> </mrow> </semantics> </math> </inline-formula> worst-case time complexity with <inline-formula> <math display="inline"> <semantics> <mi>F</mi> </semantics> </math> </inline-formula> the set of faulty nodes induced by the faulty clusters.…”
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Reference genes for real-time PCR quantification of messenger RNAs and microRNAs in mouse model of obesity. by Petra Matoušková, Hana Bártíková, Iva Boušová, Veronika Hanušová, Barbora Szotáková, Lenka Skálová

“…The expression stability of these reference genes were tested upon treatment of mice with catechins using geNorm, NormFinder and BestKeeper algorithms. Selected normalizers for mRNA quantification were tested and validated on expression of<h4>Nad(p)h</h4>quinone oxidoreductase, biotransformation enzyme known to be modified by catechins. …”
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A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures by Fei Xing, Rong Luo, Ming Liu, Zongke Zhou, Zhou Xiang, Xin Duan

“…The random forest algorithm selected or excluded variables according to the feature importance. …”
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Quality guided reversible data hiding with contrast enhancement by Zixuan Bian, Heng Yao, Yanfen Le, Chuan Qin

“…Specifically, instead of simply selecting only one pair of peaks at each peak selection stage, our algorithm selects several pairs of peaks that meet the requirements as candidate peak pairs. …”
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The Algorithm That Maximizes the Accuracy of <i>k</i>-Classification on the Set of Representatives of the <i>k</i> Equivalence Classes by Alexandra Bernadotte

“…The Maximal Algorithm provides <i>k</i>-partite cliques with the maximum total weight (the problem belongs to the <i>NP</i>-hard class). The presented algorithms select a set of representatives optimally in terms of classification accuracy for the certain classifier and runtime. …”
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Numerical Analysis and Validation of an Optimized B-Series Marine Propeller Based on NSGA-II Constrained by Cavitation by Alejandra Vázquez-Santos, Nahum Camacho-Zamora, José Hernández-Hernández, Agustín L. Herrera-May, Lorena del Carmen Santos-Cortes, María Elena Tejeda-del-Cueto

“…Optimization was applied to Wageningen B-series propellers and conducted using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The algorithm selected optimum parameters to create the propeller model, which was then evaluated numerically through computational fluid dynamics (CFD) with a multiple reference frame (MRF) and under the SST k-ω turbulence model, to obtain the open water hydrodynamic characteristics. …”
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System of license plate recognition considering large camera shooting angles by Heorhii Kuchuk, Andrii Podorozhniak, Nataliia Liubchenko, Daniil Onischenko

“…As the main segment-search algorithm, selective search is chosen. The combined loss function is used to fasten the process of training and classification of the network. …”
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Active Sampling-Based Binary Verification of Dynamical Systems by Quindlen, John F., Topcu, Ufuk, Chowdhary, Girish, How, Jonathan P.

“…As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction er- ror. …”
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Adherence monitoring of rehabilitation exercise with inertial sensors: A clinical validation study by Bavan, L, Surmacz, K, Beard, D, Mellon, S, Rees, J

“…Time and frequency domain features were calculated from labelled data segments and ranked in terms of their predictive importance using the ReliefF algorithm. Selected features were used to train four supervised learning algorithms: decision tree, k-nearest neighbour, support vector machine and random forests. …”

Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki by S Kataraki, Pramodkumar

“…The generated ODV is validated by percentage error (%) and machining of parts. The algorithm selects the type of machining operation to be performed and auto-allocates each SDV-VF to the face it belongs to. …”
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Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III by Boon, K. H., Khalil Hani, M., Malarvili, M. B.

“…The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. …”
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A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model by Nur Aqilah Paskhal Rostam, Nurul Hashimah Ahamed Hassain Malim, Rosni Abdullah, Abdul Latif Ahmad, Boon Seng Ooi, Derek Juinn Chieh Chan

“…In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chl-a) as a strong indicator of algal presence for coastal studies.…”
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Testing the Validity of a Spatiotemporal Gait Model Using Inertial Measurement Units in Early Parkinson&#x2019;s Patients by Shuai Tao, Haoye Wang, Liwen Kong, Zeping Lv, Zumin Wang

“…The aim of this study is to measure EPD gait using the IMU algorithm, select gait features using Recursive Feature Elimination (RFE), and classify EPD patients with healthy (HT) older adults using ML on the selected features. …”
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Community-based active-case finding for tuberculosis: navigating a complex minefield by Peter MacPherson, Kwame Shanaube, Mphatso D. Phiri, Hannah M. Rickman, Katherine C. Horton, Helena R. A. Feasey, Elizabeth L. Corbett, Rachael M. Burke, Molebogeng X. Rangaka

“…Here we synthesise evidence and experience from implementing ACF programmes to provide practical guidance, focusing on the selection of populations, screening algorithms, selecting outcomes, and monitoring and evaluation. …”
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A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases by Que N. N. Tran, Minh-Khang Le, Tetsuo Kondo, Takeshi Moriguchi

“…We developed a static nomogram, a web app, and a risk table based on a logistic regression model using algorithm-selected variables. <b>Conclusions:</b> Our model can stratify lung cancer patients into high- and low-risk of in-hospital mortality to assist clinical further planning.…”
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Dwell Time Estimation of Import Containers as an Ordinal Regression Problem by Laidy De Armas Jacomino, Miguel Angel Medina-Pérez, Raúl Monroy, Danilo Valdes-Ramirez, Carlos Morell-Pérez, Rafael Bello

“…Thus, we have compared an ordinal regression algorithm (selected from 15) against two supervised classifiers (selected from 30). …”
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Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data by Raftery Adrian E, Bumgarner Roger E, Annest Amalia, Yeung Ka Yee

“…On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. …”
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Security-aware dynamic VM consolidation by Mohamed A. Elshabka, Hanan A. Hassan, Walaa M. Sheta, Hany M. Harb

“…The proposed MRI with RITH VM placement algorithm selects the host that leads to minimum risk increase to the overall security risk while maintaining the risk increase for each VM does not exceed the value of the proposed RITH constraint; which is set according to the aims of the cloud provider. …”
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RESAMPLING METHODS FOR A RELIABLE VALIDATION SET IN DEEP LEARNING BASED POINT CLOUD CLASSIFICATION by A. Nurunnabi, F. N. Teferle

“…A validation data set plays a pivotal role in tweaking a machine learning model trained in a supervised manner. Many existing algorithms select a part of available data by using random sampling to produce a validation set. …”
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