Taylor's University

Modern broadband access networks increasingly carry heterogeneous, delay-sensitive traffic generated by cloud-assisted applications, real-time communication services, and short-form multimedia exchanges. These workloads have bursty ON/OFF packet dynamics, creating dense arrival clusters that conventional GPON/XGS-PON upstream schedulers struggle to handle. This work proposes an AI-driven Dynamic Bandwidth Allocation (AI-DBA) framework based on a Deep Q-Network (DQN) that observes global queue states for video, voice, and data traffic and performs adaptive admission/prioritization/scheduling decisions. The DQN agent learns an adaptive scheduling policy through experience replay and reward-driven interactions to anticipate burst formations, drain high-pressure queues, and smooth resource allocation. Simulation results show that DQN-DBA maintains near-zero buffer queues and eliminates observed packet loss across all traffic classes, and stablize the delay and jitter profiles, whereas TCP and QCT-ARED experience long queue buildup, increased loss rates, and high delay/jitter variability under the same bursty traffic conditions. Across both experiments, the proposed approach consistently outperforms all baselines. Against the Non-AI scheme, the AI-based model reduces packet loss by 72.84% and average waiting time (latency) by 41.73%. In the second experiment, across video, voice, and data traffic, DQN-DBA achieves a 100% reduction in observed packet loss compared to both TCP and QCT-ARED, reduces steady-state delay by approximately 74.7% relative to TCP and 66.3% relative to QCT-ARED, and improves throughput by about 558–634% over TCP and 343–384% over QCT-ARED.The results confirm that AI-driven upstream scheduling provides a robust, scalable, and highly adaptive solution for managing bursty multi-traffic loads in next-generation PON architectures.

In large scale image retrieval and big data analytics it is a big challenge to search similar images from high dimensional data. Mostly used algorithms are Locality Sensitive Hashing and Random Projection Based Hashing. They are widely used for approximate nearest neighbor searching. These two algorithms treat all input features uniformly while they ignore feature importance and class separability. In this research we aim to propose a lightweight hashing framework named Adaptive Feature Aware Hashing which integrates feature weighting prior to projection-based hashing. The algorithm computes data-driven feature weights using variance, between-class separability, and Fisher-style discriminative criteria to enhance discriminative power during hash code generation. We also incorporated multi table and multi probe hashing which enhances discriminative power during hash code generation. For this research we used MNISH dataset for experimental evaluation. We compared the results against a Baseline Locality-Sensitive Hashing (LSH) method using random projections. Our results indicate that The AFAH methods (v1 and v2 Fisher) significantly improved both precision and recall compared to the Baseline LSH, with AFAH v2 Fisher showing the highest precision (0.7557) and AFAH v1 having the highest recall (0.2285).

Purpose This study aims to examine the factors influencing WeChat users’ intention to share food photography at nostalgia-themed restaurants in China. Design/methodology/approach Data for this study were collected from 700 WeChat users in China and analyzed using SmartPLS 4.0 and artificial neural networks (ANNs). Findings Results from PLS and ANN showed that staff service and nostalgia emotion significantly influence users’ intention to share food photography. However, concerns about personal can inhibit sharing intentions, particularly when these intentions are motivated by status seeking or nostalgia emotions. Originality/value This study extends the application of uses and gratifications theory to the context of nostalgia-themed restaurants. It also enhances the understanding of Chinese WeChat users’ intention to share their gastronomy experiences by highlighting restaurant attributes. Additionally, this research enables stakeholders, such as restaurant operators and social media platform developers, to devise more effective strategies to encourage customers of nostalgic restaurants to share food photography.