Evaluating the Performance of Engineering’s Students in Mathematic Subject based on Academic Decision-Making Techniques

Data mining is characterized as a quest for useful knowledge via large quantities of data. Some basic and most common techniques for data extraction are association rules, grouping, clustering, estimation, sequence modeling. For a wide range of applications, data mining techniques are used. Techniques of data analysis are essential to the preparation and implementation of the administration of the learning system, including behavioral guidance and personal behavior appraisal. The article applies data analytical methods to the role of student classification. Several tests are used for the interpretation of the findings. In keeping with the methodology proposed in the paper, the classification using cognitive skills provides more detailed results than the findings of other study published. Five algorithms were used (J48, Naïve Bayes, Multilayer Perception, K Star and SMO). This essay discusses and measures the application of the various algorithms so that factors affecting the success and failure of students can be identified, student performance can be estimated, and the significant consequences of the mathematics system for the second university year can be identified. However the number of exams can be minimized using data mining techniques. In terms of time and consequences, this shortened analysis plays a key role.

Phylogeny Estimation Given Sequence Length Heterogeneity

Phylogeny estimation is a major step in many biological studies, and has many well known challenges. With the dropping cost of sequencing technologies, biologists now have increasingly large datasets available for use in phylogeny estimation. Here we address the challenge of estimating a tree given large datasets with a combination of full-length sequences and fragmentary sequences, which can arise due to a variety of reasons, including sample collection, sequencing technologies, and analytical pipelines. We compare two basic approaches: (1) computing an alignment on the full dataset and then computing a maximum likelihood tree on the alignment, or (2) constructing an alignment and tree on the full length sequences and then using phylogenetic placement to add the remaining sequences (which will generally be fragmentary) into the tree. We explore these two approaches on a range of simulated datasets, each with 1000 sequences and varying in rates of evolution, and two biological datasets. Our study shows some striking performance differences between methods, especially when there is substantial sequence length heterogeneity and high rates of evolution. We find in particular that using UPP to align sequences and RAxML to compute a tree on the alignment provides the best accuracy, substantially outperforming trees computed using phylogenetic placement methods. We also find that FastTree has poor accuracy on alignments containing fragmentary sequences. Overall, our study provides insights into the literature comparing different methods and pipelines for phylogenetic estimation, and suggests directions for future method development. [Phylogeny estimation, sequence length heterogeneity, phylogenetic placement.]

immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking

Summary B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection. Availability and implementation The package is available via https://github.com/GreiffLab/immuneSIM and on CRAN at https://cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https://immuneSIM.readthedocs.io. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking

SummaryB- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full length variable region immune receptor sequences. ImmuneSIM enables the tuning of the immune receptor features: (i) species and chain type (BCR, TCR, single, paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation, and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis, and machine learning methods for motif detection.AvailabilityThe package is available via https://github.com/GreiffLab/immuneSIM and will also be available at CRAN (submitted). The documentation is hosted at https://[email protected], [email protected]

Estimating components of propagated variance in growth simulation model projections

First-order Taylor series variance estimation equations were embedded in a growth simulation model to estimate propagated variances during growth and yield projections. Variance equations estimated three error components: covariances propagated through predictor variables, covariances from estimated regressor coefficients, and covariances between regressor coefficients and variables. A separate Monte Carlo process was used to estimate the total variance in projected variables caused by simultaneous perturbations in values of initialization variables and in regressor coefficients. Variances estimated by these two procedures were compared over five consecutive projection periods for six variables in a forest growth simulation model. While results agreed closely for the variance in mean stand diameter, disparities increased for other variables later in the model estimation sequence. Disparities were attributed to differences between the populations used in both variance estimation procedures and to possible violations of Taylor series assumptions in the variance estimation equations.

Time Estimation, Sequence Effects and Filling Activities

60 Ss were randomly and equally divided into three groups (2 experimental, 1 control) to investigate the effect of lack of feedback on sequential temporal estimates. The production method was employed, as was a “filling” task designed to be cognitively related to Ss' future expectations. As predicted, systematic changes occurred across trials for all groups, independent of differential expectations. These results were related to “judgment drift” and to the nature of the interpolated filling activity.