Basics of Algorithms DAA - Introduction to Algorithms DAA - Analysis of Algorithms DAA - Methodology of Analysis DAA - Asymptotic Notations & Apriori Analysis DAA - Time Complexity DAA - Master's Theorem DAA - Space Complexities Divide & Conquer DAA - Divide & Conquer Algorithm DAA - Max-Min...
In this paper, DAA Well Clear (DWC) standards suitable for the terminal area are analyzed using Automatic Dependent Surveillance-Broadcast (ADS-B) data received in the Republic of Korea. A route-finding algorithm was developed to remove the controller intervention portion from the recorded ...
Big O notation describes the upper bound on the growth rate of an algorithm’s runtime. It provides a way to classify algorithms based on their worst-case performance. For example, an algorithm with a time complexity of O(n) has a linear growth rate, meaning its runtime increases linearly...
To see the convergence characteristics more precisely, we calculate the updated amount of conductivity in a simple model. We introduce a new algorithm to recover conductivity using intermediate transversal current and magnetic flux density. We call this as B-substitution algorithm. This algorithm can ...
However, the current criticality score algorithm suffers from inconsistent and redundant features. In this work, we examine the weaknesses of these features through statistical analysis and identify the most important features to create a more consistent, precise criticality score. First, we randomly ...
Differential expression analysis and enrichment analysis were done by DESeq2 V. 1.22.2 [27] and the hypergeometric distribution algorithm and gene set enrichment analysis (GSEA) [28] based on GO [29] and KEGG [30] databases. Transcriptomics analysis...
Current methods of analyzing Affymetrix GeneChip® microarray data require the estimation of probe set expression summaries, followed by application of statistical tests to determine which genes are differentially expressed. The S-Score algorithm descr
This review highlights the efficacy of combining federated learning (FL) and transfer learning (TL) for cancer detection via image analysis. By integrating
Large changes in transcript abundance were observed which were categorised into distinct phases of differentiation (6–10 daa), grain fill (12–21 daa) and desiccation/maturation (28–42 daa) and were associated with specific tissues and processes. A similar experiment on developing caryopses ...
Soil loss estimation and susceptibility analysis using RUSLE and random forest algorithm: a case study of Nainital district, India doi:10.1007/s41324-025-00620-5Soil lossRUSLE modelRandom forestNainitalSoil erosion susceptibilityThe paper makes an attempt to estimate soil erosion and identify soil ...