Numerical computing in engineering mathematics

Kamalov, Firuz; Leung, Ho-Hon

doi:10.1109/aset53988.2022.9734960

Cited by 3 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Learning outcomes can also be improved by automating often time-consuming administrative tasks like assessment, grading, and teaching and learning activity planning, allowing educators to focus on direct student interaction and effective teaching strategies. Existing technology-driven instruction tools [77,78] can also be further enhanced with the power of AI.…”

Section: Enhanced Learning Outcomesmentioning

confidence: 99%

New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution

2023

Self Cite

View full text Add to dashboard Cite

The recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sustainable development and deployment of AI-driven technologies at schools and universities. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges. Our review focuses on the use of artificial intelligence in collaborative teacher–student learning, intelligent tutoring systems, automated assessment, and personalized learning. We also report on the potential negative aspects, ethical issues, and possible future routes for AI implementation in education. Ultimately, we find that the only way forward is to embrace the new technology, while implementing guardrails to prevent its abuse.

show abstract

Section: Enhanced Learning Outcomesmentioning

confidence: 99%

New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Random Over-sampling involves the random duplication of cases from the minority class [4]. SMOTE [5], which stands for Synthetic Minority Over-sampling Technique, is a method used to create synthetic samples comparable to the minority data cluster. The second sampling approach is by under-sampling majority classes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Addressing the imbalance often involves sampling solutions. Techniques such as Random Oversampling [4] or Synthetic Minority Oversampling Technique (SMOTE) [5] augment infrequent cases, while methods like Random Undersampling [6] or Tomek links [7] reduce redundancies in the dataset by decreasing majority samples. Hybrid techniques like SMOTEENN [8], which combine oversampling and undersampling, and ROSE (Random OverSampling Examples) [9], which create synthetic spaces between classes, are also utilized.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Tree-based Ensemble Strategies for Imbalanced Network Attack Classification

Soon,

Amir,

Nishizaki

et al. 2024

IJACSA

View full text Add to dashboard Cite

With the continual evolution of cybersecurity threats, the development of effective intrusion detection systems is increasingly crucial and challenging. This study tackles these challenges by exploring imbalanced multiclass classification, a common situation in network intrusion datasets mirroring realworld scenarios. The paper aims to empirically assess the performance of diverse classification algorithms in managing imbalanced class distributions. Experiments were conducted using the UNSW-NB15 network intrusion detection benchmark dataset, comprising ten highly imbalanced classes. The evaluation includes basic, traditional algorithms like the Decision Tree, K-Nearest Neighbor, and Gaussian Naive Bayes, as well as advanced ensemble methods such as Gradient Boosted Decision Trees (GraBoost) and AdaBoost. Our findings reveal that the Decision Tree surpassed the Multi-Layer Perceptron, K-Nearest Neighbor, and Naive Bayes in terms of overall F1-score. Furthermore, thorough evaluations of nine tree-based ensemble algorithms were performed, showcasing their varying efficacy. Bagging, Random Forest, ExtraTrees, and XGBoost achieved the highest F1-scores. However, in individual class analysis, XGBoost demonstrated exceptional performance relative to the other algorithms. This is confirmed by achieving the highest F1-scores in eight out of the ten classes within the dataset. These results establish XGBoost as a predominant method for handling multiclass imbalance classification with Bagging being the closest feasible alternative, as Bagging gains an almost similar accuracy and F1-score as XGBoost.

show abstract