3 research outputs found
A Study on Comparative Analysis of Feature Selection Algorithms for Students Grades Prediction
No full textEducation data mining (EDM) applies data mining techniques to extract insights from educational data, enabling educators to evaluate their teaching methods and improve student outcomes. Feature selection algorithms play a crucial role in improving classifier accuracy by reducing redundant features. However, a detailed and diverse comparative analysis of feature selection algorithms on multiclass educational datasets is missing. This paper presents a study that compares ten different feature selection algorithms for predicting student grades. The goal is to identify the most effective feature selection technique for multi-class student grades prediction. Five classifiers, including Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting (GB), and k-Nearest Neighbors (KNN), are trained and tested on ten different feature selection algorithms. The results show that SelectFwe(SFWE-F) performed best, achieving an accuracy of 74.3% with Random Forests (RT) across all ten feature selection algorithms. This algorithm selects features based on their relationship with the target variable while controlling the family-wise error rate
Malware Images Visualization and Classification with Parameter Tunned Deep Learning Model
No full textMalwares can be termed as a malicious program that can gain unauthorized access to the computer. This unauthorized access can damage and harm computing world in many capacities. There are many malware detection approaches present in the world. These approaches include static and dynamic analysis, machine learning, semi -supervised and deep learning-based models. These approaches cannot be visualized, thus cyber security experts face difficulty in interpreting underlying patterns. Conversion of malware byte code into images exits. An improved approach that can not only visualize malware, but also predict malware with high accuracy can be beneficial. For this purpose, we have used existing malware visualization technique. A technique which converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. After conversion into images, we have applied parametrized tunned Convolutional Model to predict malware images. Comparing with existing our approach not only visualizes malware images but also outperforms previous approach by almost 2%, by achieving 98.27% accuracy.
 
Administration of prophylactic levetiracetam in patients with intracerebral hemorrhage: A systematic review and meta-analysis
No full textLevetiracetam (LEV) is not frequently recommended as a preventative medication for seizures after intracerebral hemorrhage (ICH). Although there are differing opinions among clinicians, current recommendations do not support its use. We aim to assess the effectiveness of LEV in seizure prophylaxis in patients with ICH. We systematically searched PUBMED, SCOPUS, and other databases. Clinical trials and observational studies that enrolled patients in Spontaneous ICH and provided independent data on LEV were included. The pooled proportions of reported findings were determined using the random-effects model and forest plots were created. We identified six studies with a total of 1,166 patients for the analyses of primary and secondary outcomes. There were no significant differences in the total frequency of seizures between LEV treatment and placebo (OR=0.52; 95% CI-0.21–1.31; P=0.17) and also LEV treatment did not lower the death rate. (OR=1.14, 95% CI-0.57–2.26, P- 0.71). In half of the investigations (n=3), the poor clinical outcomes were defined using the mRS (i.e. score >3). The results showed that taking the placebo resulted in worse outcomes (OR-6.24, 95% CI-3.97-9.81, P.00001). Overall, there were no appreciable differences between LEV and placebo regarding the change in NIHSS of less than 25 (MID, 1.98; 95%CI, 0.15–4.12; P=0.07). However, these two trials showed a significant amount of heterogeneity (I2=83%). LEV did not significantly reduce mortality and seizure occurrences on average than those on other anti-epileptic medications. Our study is the first to analyze the efficacy of this newer-generation anti-epileptic drug for seizure prophylaxis in patients with ICH
