Comparison of the feature selection algorithm in educational data mining

Abstract

Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely educational data mining (EDM). How the feature selection (FS) algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the FS algorithm from the student dataset. The results received from other FS algorithms and classifiers will help other researchers to gain some best combination regarding FS algorithms and the classification. Selecting features that are relevant for student forecast models is a sensitive problem to stakeholders in education because they must make decisions based on the results of the prediction models. For the future, our paper seeks to play a decisive part while developing quality concerning education, as well as guiding different researchers in conducting educational interventions.

Comparative evaluation for detection of brain tumor using machine learning algorithms

“…Results from an empirical research survey show that automatic brain tumor diagnosis is critical where there is a human existence at stake [21], [28]. Figure 1 shows the usable method for detecting brain tumors, starting with image acquisition, preprocessing, extraction and segmentation.…”

“…The patient controlled analgesia (PCA) is used for function extraction and the derived features of MRI. And the typical recognition rate is around 88.2%, whereas the best recognition rate is about 96.7% In their attempt to use picture the brain tumors, Rajesh and others suggested a segmentation strategy, which employs the probiotic neural network (PNN) to classify the MRI scans [19]- [21]. The PNN recommended handling the naming of brain tumors with greater finesse Suchita and Lalit, developed an unsupervised learning procedure for photographs of the human brain through the use of the positron emission tomography (PET) scanner [22].…”

“…A tumor is generated by a rise in the number of cells without being balanced out by a decrease in the number of neuron deaths a tumor in the human brain can exert a strong influence over the whole body, including the skull, and can lead to impairment in the human health. The numbers of brain tumors increase daily [21]. To improve treatment, detection early on is vital for all brain tumors, as it is for all diseases.…”

“…It's a tumor that occupies the brain, but is still able to shape itself in the membrane of the dura, and arises from the membrane. One of the most often occurring brain tumors in adults is of this kind is found to include between 15-20% of brain tumors [21]. Finally, a pituitary tumor grows in the pituitary gland, which establishes hormonal equilibrium, which in turn regulates the activity of other glands, which includes a portion of the menstrual cycle.…”

Superior Class to Improve Student Achievement Using the K-Means Algorithm

“…In many cases, this can described as the preprocessing stage of pattern recognition (Riana et al, 2022). Data mining is the best option used by researchers in data processing to fully recognize problems and solve them by adopting data mining techniques (Triayudi & Fitri, 2021).…”