Bootstrap-based Aggregations and their Stability in Feature Selection حسب Salman, Reem Elfatih

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Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem حسب Nezir Aydin (8355378)

"…Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). …"

Novel Peak Detection Algorithms for Pileup Minimization in Gamma Ray Spectroscopy حسب Raad, M.W.

"…Gamma pulses from a 3" Na(TI) scintillation detector were captured as single and double pulses for the purpose of testing the peak detection algorithms. The pulse classification technique was tested successfully on a TMS320C6000 high performance floating-point processor yielding a reduction of the execution time to 2 msec…"
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks حسب Najam Us Sahar Riyaz (22927843)

"…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R² = 0.05 to 0.99 in a representative thiophene set. …"

Application of updated joint detection algorithm for the analysis of drilling parameters of roof bolters in multiple joints conditions حسب Liu, Wenpeng

"…This paper reviews testing procedures, data analysis, updated algorithms used for joint detection, and discusses the latest round of testing in samples with simulated joints at various angles along the borehole.…"
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Heuristic Algorithm for Obtaining Approximate Optimum Stratification With Mixture of Ratio and Product Estimators حسب S. E. H. Rizvi (22052105)

A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method حسب Amit Kumar Balyan (18288964)

"…In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. …"

Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems حسب Khaled Dhibi (16891524)

"…Therefore, an enhanced particle swarm optimization (PSO), data reduction, and interval-valued representation are proposed. First, a feature selection tool using PSO Algorithm is developed. Then, in order to maximize the diversity between data samples and improve the effectiveness of using PSO algorithm for feature selection, the Euclidean distance metric is used in order to reduce the data and maximize the diversity between data samples. …"

Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems حسب Majdi Mansouri (16869885)

"…The proposed improved ABO method consists in reducing the number of samples in the training data set using the Euclidean distance and extracting the most significant features from the reduced data using ABO algorithm. …"

Data mining approach to predict student's selection of program majors حسب SIDDARTHA, SHARMILA

"…The purpose of this study is to develop a data mining approach for predicting student's selection of program majors. The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. …"
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The effects of data balancing approaches: A case study حسب Paul Mooijman (4453189)

Parallel Algorithm for Hardware Implementation of Inverse Halftoning حسب Siddiqi, Umair F.

"…The 15-pixel parallel version of the algorithm was tested on sample images and a simple and effective method has been used to overcome quality degradation due to pixel loss in the proposed algorithm. …"
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Parallel algorithm for hardware implementation of inverse halftoning حسب Siddiqi, U.F.

"…The 15-pixel parallel version of the algorithm was tested on sample images and a simple and effective method has been used to overcome quality degradation due to pixel loss in the proposed algorithm. …"
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Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators حسب Abu Zitar, Raed

"…Pattern recognition algorithms are widely applied to gene expression data to differentiate between health and cancerous patient samples. …"
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VEGAWES: variational segmentation on whole exome sequencing for copy number detection حسب Samreen Anjum (19651882)

"…We tested this algorithm on synthetic data and 100 Glioblastoma Multiforme primary tumor samples. …"

Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology حسب Senyuk, Mihail

"…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …"
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Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems حسب Tarek Berghout (16905132)

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice حسب Turker Tuncer (16677966)

"…A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. …"

Diagnostic performance of artificial intelligence in detecting and subtyping pediatric medulloblastoma from histopathological images: A systematic review حسب Hiba Alzoubi (18001609)

"…</p><h3>Conclusion</h3><p dir="ltr">AI algorithms show promise in detecting and subtyping medulloblastomas, but the findings are limited by overreliance on one dataset, small sample sizes, limited study numbers, and lack of meta-analysis Future research should develop larger, more diverse datasets and explore advanced approaches like deep learning and foundation models. …"

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data حسب Rajesh Kumar Dhanaraj (19646269)

"…The results revealed that the RFBXSQLQC technique outperforms the existing algorithms by 19% with pattern detection accuracy, 34% minimized time complexity, 64% false-positive rate, and 31% in terms of computational overhead.…"