Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments.

Global university rankings differ in methodology, yet their published positions are frequently compared across systems and monitored over time. This study examines two dimensions of ranking stability: cross-sectional consistency across ranking systems and temporal persistence within QS, while assessing whether machine-learning models improve predictive accuracy beyond a persistence baseline. The analysis included 57 universities from 20 countries and seven regions. Cross-ranking consistency among QS-2026, THE-2026, and ARWU 2025 was evaluated using pairwise rank correlations, bootstrap confidence intervals, and normalized percentile positions. Temporal predictability was assessed on a balanced panel of 40 universities with QS positions for 2021–2024 and 2026 using an expanding-window design, a persistence baseline, linear regression, Random Forest, XGBoost, and LightGBM. Pairwise correlations were positive within observed overlaps, but the ranking systems were not interchangeable. The persistence baseline achieved the lowest development MAE. On the final two-year holdout, Random Forest (MAE 5.073) did not significantly outperform persistence (MAE 5.150; p = 0.902), and the latest QS position was the only predictor with consistently positive importance. Historical persistence errors bracketed the 2024–2026 result, indicating that predictability was driven mainly by ranking persistence, whereas additional machine-learning complexity provided limited benefits in this sample.

We propose a secure biometric data protection (SBDP) system, which uses artificial intelligence (AI) and encryption methods to prevent forgery and keep the biometric data private and intact. The proposed SBDP approach integrates deep learning-based feature extraction with robust encryption and authentication mechanisms in a single pipeline. We use the optimized convolutional neural network (OCNN) to obtain unique features from multimodal biometric inputs like fingerprints, facial photos, and retinal scans. This works well because it learns how to represent data efficiently. To reduce the risks of raw biometric exposure, we adopt a generative adversarial network (GAN) to generate synthetic biometric representations that maintain essential characteristics while reducing sensitivity to data leakage. The biometric features and images are encrypted using the ElGamal cryptosystem to provide security assurance, while the digital signature scheme based on the SHA-256 hash function is used to provide data integrity and authenticity. Experimental results show good performance of all components of the framework. The optimized CNN obtains a classification accuracy of more than 99.8%, while the GAN shows stable training behavior with the discriminator and generator losses converging to around 0.3 and 4.0, respectively. The cryptographic module guarantees encryption dependability and signature verification efficacy across all evaluated scenarios. The integrated system provides effective protection of biometric data from unauthorized access, tampering and identity forgery. The SBDP framework is a promising solution for defense, healthcare and digital identity management, ensuring secure transmission and storage of biometric data.

The object of the study is the onboard diagnostic process in CubeSat nanosatellite systems, with particular emphasis on fault detection based on real-time analysis of multivariate telemetry data under real-time embedded operating conditions. The problem statement is the gap between the relatively low computational expenses and predictability of classical FDIR approaches and their limited ability to detect complex anomalies, and, conversely, the greater anomaly sensitivity and lower embedded performance of machine-learning models. A hybrid onboard diagnostic architecture that integrates telemetry acquisition, telemetry preprocessing, statistical feature extraction, a deterministic FDIR branch, a lightweight machine-learning branch, and decision fusion into one onboard diagnostics session has been elaborated. The proposed hybrid onboard diagnostic architecture has been evaluated based on hardware-in-the-loop testing using representative telemetry and fault cases for CubeSat systems. The evaluation metrics were the fault-detection accuracy, the detection latency, the CPU consumption, the memory consumption, and the resistance to telemetry noise. The hybrid onboard diagnostic architecture was compared to the conventional threshold-based FDIR system and standalone machine learning algorithms under the same HIL conditions. In the reported experiment setup, the proposed hybrid onboard diagnostic architecture demonstrated the fault-detection accuracy of 94%, while the classical FDIR method provided 71%, and the standalone machine-learning approach provided 88%. The average detection latency was decreased to 83 ms, in contrast to 120 ms and 95 ms, respectively. The embedded solution requires only 17.6% CPU and 58 KB of memory. Under the highest telemetry-noise level, the fault-detection accuracy of the proposed hybrid onboard diagnostic architecture decreases to 80%, whereas the standalone machine-learning and the classical FDIR baselines provide only 65% and 43% fault-detection accuracy, respectively.

Mobile-health systems generate repeated, time-stamped observations, but future-risk forecasts must be reliable rather than merely discriminative. We evaluate Temporal Calibrated Explainable Risk Forecasting (T-CERF) for irregular ecological momentary assessment histories. Study A used 5726 forecasting examples from 542 Corona Health participants, a 56-day history window, and a 28-day primary horizon. On the fixed participant-independent test set, T-CERF achieved a PR-AUC of 0.867, ROC-AUC of 0.946, and Brier score of 0.083. Random Forest matched its ROC-AUC (0.947 versus 0.946), while the Persistence baseline yielded a higher F1-score (0.786 versus 0.761). The PR-AUC difference from the matched current-only model was +0.023, but its paired participant-level bootstrap interval included zero. Across five additional participant-level splits, ΔPR-AUC averaged +0.0106 (SD 0.0262; range −0.0298 to +0.0399), with one split favoring the current-only model. Thus, a consistent discriminative benefit from temporal history was not established. Temperature Scaling improved calibration, while ensemble uncertainty supported selective prediction. A separate HBSC Kazakhstan analysis remains exploratory. T-CERF is therefore presented as a calibrated, uncertainty-aware mobile-health forecasting framework rather than as a hardware or network-level IoT implementation.