The growing number of heterogeneous Web and IoT APIs poses a fundamental challenge to the reliability and reusability of modern applications, as existing API composition frameworks are hindered by semantic fragmentation and a neglect of historical usage patterns. To address this problem, this research paper introduces a history-aware semantic framework that enhances the long-term robustness of service compositions by embedding APIs into a unified knowledge graph via a dedicated ontology that harmonizes heterogeneous data and services through semantic abstraction and domain-level alignment. Reliability of composition is further enhanced by incorporating historical composition data capturing co-occurrence and popularity. A genetic algorithm dynamically optimizes the weights of these metrics to select the Top-K most reliable mashup compositions. Extensive evaluation on the large-scale ProgrammableWeb benchmark (11,492 APIs and 7,336 mashups) demonstrates a statistically significant improvement of 26% in mashup reliability (p < 0.01) compared to state-of-the-art baselines. While this work focuses on Intelligent Media mashups as a representative application domain, the proposed framework is applicable to general Web and IoT API composition. The integration of semantic abstraction, historical analysis, and optimization provides a robust solution to generate resilient, adaptive, and highly reliable service mashups, offering a useful approach to support the long-term adaptability and reuse of complex service ecosystems.
This paper investigates a downlink cooperative non-orthogonal multiple access (NOMA) system assisted by a multi-antenna amplify-and-forward (AF) relay. Unlike conventional single-antenna relay configurations, the considered framework jointly exploits relay diversity and direct transmission links between the base station (BS) and users. Under independent Nakagami-k fading channels, closed-form expressions for the outage probability (OP) and ergodic capacity (EC) of both users are derived for scenarios with and without direct BS-user links. The analytical formulation explicitly captures the effects of the number of relay antennas, fading severity and power allocation coefficients on system performance. For the ergodic capacity analysis, an exact integral representation combined with a Gaussian-Chebyshev quadrature approach is developed to efficiently evaluate the performance under the max-SINR selection criterion. The analytical results are verified through Monte-Carlo simulations and compared with orthogonal multiple access (OMA) benchmarks. Numerical results demonstrate that increasing the number of relay antennas significantly improves reliability due to enhanced spatial diversity. Moreover, the NOMA scheme achieves superior outage performance for the far user compared with OMA, while the ergodic capacity of the near user exhibits a pronounced gain in the moderate-to-high SNR regime. These findings confirm the effectiveness of multi-antenna cooperative relaying in improving both reliability and spectral efficiency.
Wireless Sensor Networks (WSNs) have remained an active research field in both military and civilian domains, driven by the expanding diversity of their applications. In recent years, there has been a progressive shift toward integrating Artificial Intelligence to address the persistent challenge of energy optimization in WSNs. We introduce a novel adaptation of a Fixed Set Search (FSS) mechanism to WSNs. FSS adds a learning phase to the well-known GRASP metaheuristic. FSS-WSN approach guides the Base Station (BS) in a centralized multi-hop environment to select the optimal cluster-heads, thereby maximizing the global utility of the network. We evaluated our approach under documented fairness conditions, against a wide range of established baselines including classical clustering protocols (LEACH, HEED, SEP), widely used swarm optimizers (PSO, GWO, ABC), the recent multi-hop hybrid EEM-LEACH-ABC, and recent SO-GJO-family variants (SO, GJO, EMO-GJO, and ESO-GJO). The results demonstrate a statistically significant improvement (paired Wilcoxon test with Holm correction) over the best baseline regarding two key metrics-the number of delivered reports and the CPU time required for decision-making. These results suggest that our approach is a strong, practical option for many WSN use cases.
Arabic Text Classification (ATC) remains challenging due to the Arabic language's morphological richness and semantic complexity. This paper proposes ABPC-Net, a hybrid framework integrating a frozen Arabic Transformer encoder, a Bidirectional LSTM, parallel multi-scale CNN branches and a lightweight capsule-inspired vector projection head for hierarchical feature integration. Evaluated on the SANAD dataset and its subsets (AlArabiya, AlKhaleej and Akhbarona) over five independent runs, ABPC-Net achieves mean accuracies of 97.00 +/- 0.04%, 99.14 +/- 0.10%, 98.40 +/- 0.10% and 95.59 +/- 0.12%, respectively. Under identical experimental conditions, the proposed framework consistently outperforms re-implemented frozen and fully fine-tuned AraBERT and MARBERT baselines. Cross-dataset evaluation on BBC Arabic and CNN Arabic further provides evidence of intra-domain transferability and rapid few-shot adaptability across Arabic news sources. The reported results are scoped to Modern Standard Arabic news classification.
This paper examines the theoretical performance of rate-splitting multiple access (RSMA) in keyhole fading channels. This rank-deficient environment is notorious for degrading the performance of traditional single-input single-output (SISO) systems. For a two-user downlink channel, exact and asymptotic outage probability results of RSMA with perfect and imperfect successive interference cancellation (pSIC and ipSIC) are derived. Closed-form solutions are derived by comparing the product of two independent Nakagami-m fading channels that model the keyhole effect. To further demonstrate RSMA's robustness in such an environment, we also examine the diversity order and unveil the effect of keyhole-induced rank deficiency on system reliability. Our results demonstrate that RSMA retains a performance edge over non-orthogonal multiple access (NOMA), especially with imperfect SIC or low SNR. Numerical results and Monte-Carlo simulations confirm the theoretical formulae and show that RSMA can combat the harmful effects of the keyhole channel better than other conventional schemes. The results confirm the promise of RSMA for future wireless systems operating in severe-fading environments.
