Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to devel...
详细信息
In Mobile Ad hoc Networks (MANET), Ad hoc On Demand Distance Vector (AODV) protocol suffers from the black hole attacks that disrupts the functionality of the network by displaying false information during routing pro...
详细信息
Accurately forecasting financial risk, particularly in bankruptcy forecasting, remains a significant challenge due to the non-linear and complex nature of economic indicators. Conventional statistical techniques often...
详细信息
Accurately forecasting financial risk, particularly in bankruptcy forecasting, remains a significant challenge due to the non-linear and complex nature of economic indicators. Conventional statistical techniques often fail to capture the intricate relationships within financial datasets. This research addresses these shortcomings using a Kernel-based Extreme Learning Machine (KELM). KELM stands out for its capability to discern complex patterns, yet the model’s full potential is often underutilized due to the intricate hyperparameter tuning process. We identify a critical research gap: the need for sophisticated algorithms that can effectively fine-tune KELM tailored for financial risk forecasting. To bridge this gap, our study develops the Objective-based Survival Individual Enhancement Chimp Optimization Algorithm (OBSIECOA). This innovative algorithm elevates hyperparameter optimization by bolstering weaker predictions and preserving diversity among the strongest. It strategically reallocates the elite candidates during iterative cycles, enabling an extensive and dynamic exploration of the hyperparameter space. Our methodology emphasizes the importance of precision in financial forecasting models, and the OBSIECOA’s contribution is quantitatively assessed through rigorous evaluation against several KELM variants: standard KELM, KELM-DBOA, KELM-MOA, KELM-NFVOA, and KELM-WOBCOA. The assessment uses two real-world financial datasets, the Japanese Dataset (JPNBDS) and the Wieslaw dataset, with performance metrics including RMSE, NSEF, and bias. The KELM-OBSIECOA framework significantly improves forecasting accuracy over traditional models, confirming its efficacy as a superior early warning system for financial distress. Our findings underscore the necessity for and effectiveness of KELM approaches in overcoming the inherent challenges of financial risk prediction. The research advocates for integrating such advanced models in financial analysis, highlighting their s
The integration of fifth-generation/sixth-generation (5G/6G) ultra-reliable low-latency communication (URLLC) with industrial Internet of Things (IIoT) applications is revolutionizing Industry 4.0, and enhancing IIoT ...
The integration of fifth-generation/sixth-generation (5G/6G) ultra-reliable low-latency communication (URLLC) with industrial Internet of Things (IIoT) applications is revolutionizing Industry 4.0, and enhancing IIoT performance through artificial intelligence (AI) simulations. The critical need for low latency and high reliability in IIoT devices can be effectively addressed by leveraging AI techniques, optimizing data processing, and making decisions in real time. Traditional methods achieve some level of efficiency and safety, but AI offers significant improvements in decision-making, safety, quality prediction, and employee adoption. Integrating AI into IIoT applications enhances industrial workflows, while presenting opportunities and challenges. Machine learning (ML) and deep learning (DL) algorithms enable industrial applications to operate efficiently and intelligently. This paper outlines the requirements for reliable and low-latency communication links between IIoT devices and primary research areas where AI algorithms can be employed, such as fault diagnosis, intelligent anomaly detection, edge computing, network performance, and intrusion detection systems in IIoT applications. Special attention is paid to the role of AI techniques in enhancing IIoT system performance and efficiency, highlighting its advantages, applications, and challenges. The current state-of-the-art challenges and future directions of AI in IIoTs are discussed, providing insights for further research. Potential areas for further research include developing new techniques, integrating 5G/6G technologies, autonomous decision-making, self-optimization, addressing mission-critical applications, and shifting AI processing to the edge. This comprehensive review will benefit academics, researchers, professionals in AI and IIoT, and industries seeking to leverage AI technologies to enhance IIoT performance and efficiency.
In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adso...
In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system. While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of are a complicated task. Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage. To overcome these issues, this paper proposed gene expression programming (GEP) with -nearest neighbour (GEP-KNN). The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different values produced the highest accuracy at and of 95.12% and 95.67%; the lowest accuracy is of 65.34%.
In this paper, we propose a new algorithm for computing Hahn polynomial coefficients (HPCs) for higher polynomial order, which greatly reduces the spread of numerical defects associated with Hahn polynomials (HPs) usi...
详细信息
ISBN:
(数字)9781728180410
ISBN:
(纸本)9781728180427
In this paper, we propose a new algorithm for computing Hahn polynomial coefficients (HPCs) for higher polynomial order, which greatly reduces the spread of numerical defects associated with Hahn polynomials (HPs) using conventional methods. The proposed method is used to reconstruct large 2D images. The reliability and effectiveness of the new approach were often linked to standard repetition algorithms. The findings show that the method proposed is efficient and effective in terms of calculation accuracy and stability of high order Hahn moments.
After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully crafted samples. Although empirical resul...
详细信息
暂无评论