With the increase in Internet usage over the last half century, technology has also benefited from the growth and development of the Internet. It is no wonder that smart grids are emerging as one of the most popular technologies of today. As smart grids are connected to the internet, they are constantly vulnerable to cyberattacks. It is always possible for attackers to gain access to metering system infrastructure which is a major and critical part of smart grids. When this system is not working properly because of some security threats, irreparable damage is in the place for a country and its industries. This article suggests utilizing dynamic Applications that Participate in Their Own Defense defenses to secure such systems and infrastructures.
Accurate diabetes prediction is critical for early clinical intervention, yet existing machine-learning pipelines often fail due to class imbalance, noisy physiological measurements, and redundant or weakly informative features. To address these unresolved limitations, this study establishes a unified fuzzy-evolutionary-ensemble framework designed to uncover a more discriminative feature space for type 2 diabetes prediction. The workflow integrates selective fuzzification of nonlinear clinical variables, SMOTE-ENN-based balancing and noise filtering, and Bat-Algorithm-driven evolutionary feature selection prior to training a Random Forest classifier. Applied to the Pima Indians Diabetes Dataset, the framework compresses the expanded fuzzy-numerical space into an optimized 10-feature subset and yields substantial performance gains: 96.11% accuracy, 95.14% precision, 98.00% recall, and a 98.23% F1-score. Relative to a baseline Random Forest using all 16 features (74.45% accuracy; 59.86% F1), the system achieves more than a 1.6-fold improvement in F1-score while preserving over 99% stability in majority-class detection and surpassing earlier published 16-feature models impacted by imbalance and weak feature relevance. Collectively, these results demonstrate that the integrated pipeline produces an enhanced fuzzy-numerical representation space that improves diabetes risk stratification and provides a scalable, uncertainty-aware foundation for diagnostic modeling across broader clinical datasets.
As smart organizations increasingly rely on technologies like AI, big data, and IoT, securing these systems is crucial. This research introduces a dynamic multi-factor authentication (MFA) system within the Zero Trust Architecture (ZTA) framework, which enforces continuous verification. The system randomly selects biometric authentication factors, such as fingerprint, iris, and face recognition using a Markov chain model to ensure adaptability and reduce predictability in security measures. Using CASIA biometric datasets and AES-128 encryption, the system securely manages user data and authentication. Performance analysis shows that the dynamic selection process balances security and efficiency, making it suitable for smart organizations, particularly in e-government contexts. This MFA system strengthens cybersecurity by providing a flexible, real-time defense against evolving threats.