Rajib Mall

Souvik Deb, Shankar K. Ghosh, Avirup Das et al.

Device-to-device (D2D) communication was proposed to enhance the coverage of cellular base stations. In a D2D enabled non-standalone fifth generation cellular network (NSA), service demand of a user equipment (UE) may be served in four \emph{modes}: through LTE only, through NR only, through LTE via D2D and through NR via D2D. Such mode selection should consider the service requirements of the UEs (e.g., high data rate, low latency, ultra-reliability, etc.) and the overhead incurred by handovers. In existing mode selection approaches for D2D enabled NSA, the service requirements of the UEs have been largely ignored. To address this, in this paper, we propose a mode selection algorithm for D2D enabled NSA based on a two-level Analytic Hierarchy Process (AHP). The proposed AHP-based mechanism considers the service requirements of the UEs in level 1; and mode selection options (i.e., LTE only, NR only, LTE via D2D and NR via D2D) in level 2. Thereafter, a novel mode selection algorithm is proposed by combining the static ranking computed by the proposed two-level AHP and the variation of Reference Signal Received Power (RSRP) in different modes, thus capturing the impact of UE mobility and reducing unnecessary handovers. Simulation results show that our proposed algorithm outperforms the best performing related work in terms of the major Key performance indicators (KPIs) for all three slices, i.e., enhanced mobile broadband (eMBB), ultra reliable low latency (uRLLc) and massive machine type communications (mMTc).

Vaarunay Kaushal, Rajib Mall

Early identification of at-risk students is critical for effective intervention in online learning environments. This study extends temporal prediction analysis to Week 20 (50% of course duration), comparing Decision Tree and Long Short- Term Memory (LSTM) models across six temporal snapshots. Our analysis reveals that different performance metrics matter at different intervention stages: high recall is critical for early intervention (Weeks 2-4), while balanced precision-recall is important for mid-course resource allocation (Weeks 8-16), and high precision becomes paramount in later stages (Week 20). We demonstrate that static demographic features dominate predictions (68% importance), enabling assessment-free early prediction. The LSTM model achieves 97% recall at Week 2, making it ideal for early intervention, while Decision Tree provides stable balanced performance (78% accuracy) during mid-course. By Week 20, both models converge to similar recall (68%), but LSTM achieves higher precision (90% vs 86%). Our findings also suggest that model selection should depend on intervention timing, and that early signals (Weeks 2-4) are sufficient for reliable initial prediction using primarily demographic and pre-enrollment information.