An innovative approach to passport quality assessment based on the possibility q-rung ortho-pair fuzzy hypersoft set.

This research article aims to evaluate the quality of passports issued by different countries. Passport quality assessment is critical in ensuring secure and efficient international travel. By leveraging this novel structure, we address the limitations of existing methods and provide a comprehensive and accurate evaluation of passport quality. Our proposed PQROFHS (possibility q-rung orthopair fuzzy hypersoft set) based structure integrates various attributes related to passport quality, considering the inherent uncertainties and imprecisions associated with each attribute. Through extensive experimentation, we demonstrate the superior performance of our approach, achieving higher accuracy, reliability, and consistency than traditional methods. The flexibility of the PQROFHS framework allows for a nuanced representation of uncertainty, enabling informed decision-making in real-world scenarios. Implementing the presented approach can enhance global travel security, streamline immigration processes, and facilitate seamless international travel experiences. An explanatory example of a real-world problem is shown to demonstrate the suggested method.

Temperature Ramp Strategy for Regulating Oxygen Vacancies in NiCo2 O4 Nanoneedles Towards Enhanced Electrocatalytic Water Splitting.

Oxide-based systems often suffer from higher overpotentials compared to transition metal sulfides and phosphides for the electrochemical hydrogen evolution reaction (HER). Interestingly, the generation of oxygen vacancy/defect has been seen as the strategy for further activating transition metal oxides (NiCo2 O4 as a model system) for an electrochemical water-splitting process. Herein, we employ the temperature ramp strategy (ambient air calcination) for the generation of oxygen vacancies in NiCo2 O4 (NCO) towards the tuning of electrocatalytic enhancements. The NiCo2 O4 synthesized at temperature ramp rates of 2 °C/min (NCO-2), 5 °C/min (NCO-5), and 10 °C/ min (NCO-10) depicts contrasting structural features and varying Ni : Co : O surface composition. The decrease in the crystallite size and converse trend in the particle strain were observed from NCO-2 to NCO-10. Interestingly, the surface Ni : Co : O ratios of 1 : 0.78 : 3.6, 1 : 0.81 : 3.3, and 1 : 0.69 : 2.8 for NCO-2, NCO-5, and NCO-10, respectively, were observed. The reduced relative oxygen ratio in the latter implies the generation of an ample amount of oxygen vacancy defects. HER performance depicts a consistent trend with enhanced oxygen defect concentration with the overpotential requirement of 700, 647, and 597 mV for NCO-2, NCO-5, and NCO-10, respectively, for the generation of a cathodic current of 25 mA cm-2 . The same trend in an electrocatalytic enhancement is observed for other cathodic currents.

Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants.

Automatic modulation recognition (AMR) is used in various domains-from general-purpose communication to many military applications-thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.