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All Journal International Journal of Engineering, Science and Information Technology Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Karthikeyan, S.
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Wrapper Feature Selection Method for Predicting Student Dropout in Higher Education Singh, Anuradha; Karthikeyan, S.
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i1.441

Abstract

Background of Study: Student dropout in higher education is influenced by a variety of factors including demographic, socioeconomic, macroeconomic, admission-related, and academic performance data. Accurately identifying students at risk of dropping out is a significant challenge within educational data mining (EDM), especially when working with large, complex datasets.Aims and Scope of Paper: This study aims to identify an optimal subset of features that can improve the accuracy of student dropout prediction. The scope includes comparing the effectiveness of different machine learning algorithms combined with a heuristic-based feature selection method to find the best-performing model.Methods: A Wrapper-based feature selection approach was employed using Ant Colony Optimization (ACO) as the search strategy. ACO was integrated with five classifiers—Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN)—to select the most relevant feature subsets. The performance of each combination was evaluated and compared.Result: The study found that ACO combined with Random Forest (ACO-RF) outperformed the other combinations in feature selection effectiveness. The selected features were then validated using various machine learning algorithms and a neural network. Among them, the neural network achieved the highest accuracy of 93%.Conclusion: The proposed ACO-RF wrapper method is an effective feature selection strategy for predicting student dropout in higher education. The method enhances model performance, especially when used with neural networks, and offers a promising approach for early identification of at-risk students.
Beyond 5G: Exploring AI-Driven Network Optimisation for 6G Communications Meher, Kunal; Karthikeyan, S.; Ranjan Sahu, Bharat Jyoti; Sunil, M.P.; Mishra, Smita; Singh, Amanveer; Tejesh, Kukatla
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1305

Abstract

This research consists of various features of 5G networks; the vision for 6G networks promises significant advancements, including ultra-high data rates, sub-millisecond latency, highly intelligent network operations, and exceptional device interconnectivity, among others.  Artificial Intelligence (AI) meets these requirements, which act as a fundamental base in self-organising and proactive adaptive network management. In the scope of this paper, AI integration with core 6G network functions is considered, including AI techniques such as machine learning, deep learning, federated learning, and reinforcement learning. Focus is on the AI-driven optimisation of spectrum utilisation, user experience, traffic pattern prediction, dynamic network slicing, robust QoS, and responsive QoS retention. Advancing edge computing, reconfigurable intelligent surfaces (RIS), and digital twins are also discussed. The study also discusses the lack of AI governance in 6G infrastructure, which includes data privacy, transparency of the algorithms, energy expenses, and global standardisation. This research focus reveals the highlights of the primary gaps in design and governance rationale that emerge through the lack of AI-integrated structural frameworks, resigns through the absence of a designed fabric needed to supplant the transcending potential of 6G enabled autonomous communication systems AI will irrevocably purge and define the naivety behind detonating the boundless potential AI entrenched paradigms will deliver.
Co-Authors Meher, Kunal Mishra, Smita Ranjan Sahu, Bharat Jyoti Singh, Amanveer Singh, Anuradha Sunil, M.P. Tejesh, Kukatla