Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms into dental healthcare systems is considered a promising approach for enhancing the reliability and efficiency of diagnostic processes. In this context, an integrated artificial intelligence model is proposed to enhance model performance and interpretability. The basic idea of the proposed model is to augment the deep learning approach with Ensemble methods to improve the accuracy and robustness of dental healthcare. In the proposed model, a Non-Maximum Suppression (NMS) ensembled technique is employed to improve the accuracy of predictions along with combining outputs from multiple single models (YOLO8 and RT-DETR) to make a final decision. Experimental results on real-world datasets show that the proposed model gives high accuracy in miscellaneous dental diseases. The results show that the proposed model achieves 18% time reductions as well as 30% improvements in accuracy compared with other competitive deep learning algorithms. In addition, the effectiveness of the proposed integrated model, achieved 74% mAP50 and 58% mAP50-90, outperforming existing models. Furthermore, the proposed model grants a high degree of system reliability.

Nowadays, Big Data becomes a research focus in industrial, banking, social network, and other fields. In addition, the explosive increase of data and information require efficient processing solutions. Therefore, Spark is considered as a promising candidate of Large-Scale Distributed Computing Systems for big data processing. One primary challenge is the straggler problem that occurred due to the presence of heterogeneity where a machine takes an extra-long time to finish execution of a task, which decreases the system throughput. To mitigate straggler tasks, Spark adopts speculative execution mechanism, in which the scheduler launches additional backup to avoid slow task processing and achieve acceleration. In this paper, a new Optimized Straggler Mitigation Framework is proposed. The proposed framework uses a dynamic criterion to determine the closest straggler tasks. This criterion is based on multiple coefficients to achieve a reliable straggler decision. Also, it integrates the historical data analysis and online adaptation for intelligent straggler judgment. This guarantees the effectiveness of speculative tasks by improving cluster performance. Experimental results on various benchmarks and applications show that the proposed framework achieves 23.5% to 30.7% execution time reductions, and 25.4 to 46.3% increase of the cluster throughputs compared with spark engine.

Intrusion Detection Systems are critical for safeguarding networked environments against evolving cyber threats. Machine Learning offers a variety of ID models; however, assessing their performance to select the one that best fit Decision-Makers needs is particularly challenging since it involves balancing conflicting performance criteria. This paper proposes an integrated framework that combines multiple Multi-Criteria Decision-Making approaches to identify the most effective ID model across diverse operational contexts acknowledging that a single method may not yield a robust and trusted assessment. In this framework, thirty-one ID models based on representative ML algorithms (e.g., DT, NN, etc) are benchmarked on three widely used ID datasets, NSL-KDD, UNSW-NB15, and CIC-IDS2018, using nine performance criteria (e.g., accuracy, recall, etc). To capture differing priorities across criteria, objective weights for the criteria are derived via four distinct methods (CILOS, Shannon Entropy, CRITIC, and MEREC). Afterwards, ten distinct ranking algorithms (e.g., TOPSIS, GRA, etc) are applied with each weighting scheme; this yields 120 individual rank-orders which are then synthesized through Majority voting, Borda count and Copeland integration methods into a final, consensus ranking, thus boosting result stability and enhancing stakeholders’ confidence. In addition, a sensitivity analysis is carried out at different levels, including weighting methods, ranking methods, dataset level and criteria level to evaluate the robustness of the framework. The results consistently highlight the 1D-CNN architecture as the top performer, while also revealing complementary specialist and all-rounder models under different aggregation schemes. By integrating extensive algorithmic diversity with principled weight computation, a robust, low-bias mechanism for ID model selection is delivered by the framework, which can be adapted by practitioners to shifting threat landscapes and evaluation priorities.