As a classical convex optimization problem in geometry, computing the maximum inscribed ball (MaxIB) in ultra-high-dimensional polytopes is critical for enabling real-time IoT applications, such as optimal deployment of sensor networks, where polytopes model physical constraints arising from obstacles or coverage boundaries. However, existing methods suffer from the curse of dimensionality, leading to prohibitive computational costs. This paper develops a more efficient approach for computing the (1-\(\epsilon\))-approximate MaxIB in high-dimensional polytopes. To address these challenges, the problem is reformulated with adaptive penalty parameters to enforce strong convexity, enabling line... More >

Designing a reliable fiber optic communication network is crucial. High-speed optical networks are now a vital part of the communication system and the foundation of wireless and mobile networks, driven by their constant expansion and increasing demand. The high transmission rate improves spectral utilization, increases system capacity, and reduces overall system expenditure. To improve the communication channel and achieve high transmission performance and data rates, a spread-spectrum compensation scheme is required. The goal of fiber optic communication systems is to transmit as many bits per second as possible over the longest possible distance at an acceptable data rate. Two methods are... More >

The Internet of Things (IoT) continues to expand rapidly, resulting in increasingly heterogeneous and complex wireless sensor networks (WSNs). Traditional anomaly detection approaches cannot cope with dynamic traffic patterns, high data volumes, and strict resource constraints. This study presents a hybrid XGBoost–CNN model that integrates XGBoost-based feature selection with a lightweight Convolutional Neural Network optimized for IoT environments. The proposed model was evaluated using real-world IoT traffic data and benchmarked against XGBoost, KNN, and SVM. Experimental results show that the hybrid approach improves detection accuracy by over 1%, increases throughput by 22–40%, and r... More >

Accurate state estimation for dynamic targets is essential in fields such as target tracking, navigation, and autonomous driving. However, traditional estimation models struggle to handle the nonlinear motion patterns and sensor noise prevalent in real-world environments. To address these challenges, this paper proposes a novel end-to-end estimation model named Variational Neural Network with Planar Flow (VNNPF). The model integrates a Bayesian Gated Recurrent Unit (BGRU) as the process model, a planar flow-based variational autoencoder (PFVAE) as the measurement model, and a Bayesian hyperparameter optimization module inspired by Kalman filtering. The BGRU captures nonlinear temporal depend... More >

This literature review examines the state of Text-to-SQL technology, which translates natural language queries into SQL. It analyzes rule-based, neural, and hybrid approaches, assessing their strengths and weaknesses, and surveys commonly used datasets, benchmarks, and evaluation metrics. The study identifies research gaps concerning generalization, scalability, and interpretability, and suggests integrating user feedback and domain knowledge. To better understand the implementation and potential improvements of machine learning in this domain, we conducted a systematic literature review (SLR) of publications from 2015 to 2023. From 439 gathered papers, 23 were identified as highly relevant.... More >

This paper examines the vulnerability of Small and Medium-sized Enterprises (SMEs) exposed to evolving climate changes in Pakistan, specifically the impacts of extreme weather events, including floods and drought. The earlier literature illustrates that SMEs are affected by climate-related risks, but the current study takes the discussion further by implementing machine learning algorithms to measure the vulnerabilities of SMEs more objectively. A mixed-methods design was used to combine surveys with machine-learning techniques. PyCaret was employed to tune instruments such as Logistic Regression (LR), Random Forest (RF), ordered logistic regression, LightGBM, ADA Boost, SVM, KNN, GBC, and N... More >

The rapid accumulation of large-scale, long-term meteorological data presents unprecedented opportunities for data-driven weather modeling and high-resolution numerical weather prediction. While various deep learning techniques—such as Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs)—have been explored for weather forecasting, the complex spatial dependencies within historical meteorological data, particularly dynamic spatial correlations, remain insufficiently addressed. To tackle this challenge, we propose a Dynamic Spatio-Temporal Fusion Graph Network (DSTFGN), a novel module that integrates multivariate time-series analysis with graph-... More >

The growing role of citation relations in identifying research impact has spurred much investigation on assessing the most cited papers and their roles within datasets. Due to the richness of the CORA dataset, this study selects highly cited papers and measures the results of node classification, as well as the H-index of research articles. Besides, it explores the correlations and robustness with regard to the nodes by computing their chances and studying their connections. To these ends, linear transformation was utilized for mapping low-level node features to high-level, and the Graph Attention Networks (GAT) for node classification. The study was able to find highly cited papers and com... More >