The purpose of this study is to develop an effective hybrid model for automatic document classification by combining statistical and semantic text vectorization techniques with machine learning algorithms. The methodology integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Word2Vec embeddings with classifiers such as Support Vector Machine (SVM) and Random Forest. The proposed approach includes data preprocessing (tokenization, normalization, stop word removal, and lemmatization), feature extraction, model training, and evaluation using classification metrics such as accuracy, F1-score, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa. Experimental results demonstrate that the Word2Vec + SVM model outperforms other configurations, achieving 90.2% accuracy and an F1-score of 82.52%, thus highlighting the advantage of incorporating semantic context into vector representation. The study concludes that hybrid methods combining TF-IDF and Word2Vec with robust classifiers improve both the precision and generalizability of document analysis models. Practical implications include potential applications in sentiment analysis, topic modeling, text classification for legal and healthcare domains, and multilingual contexts. This research provides a foundation for developing high-performance text analysis systems applicable to various real-world natural language processing tasks.

Decentralized cyber–physical systems (CPSs) improve scalability and fault tolerance but expose local controllers and neighbor-to-neighbor coordination to integrity, availability, and multi-agent attacks. This study presents a PRISMA-guided systematic mapping of cyber-resilience methods for decentralized CPSs and integrates the evidence into a taxonomy and assessment framework. A Scopus search exported on 14 September 2026 yielded 366 records; 337 primary records were screened, 163 underwent eligibility assessment, 79 were included, and 72 met a six-criterion quality threshold for Core synthesis. Power systems and microgrids represented 55.6% of Core studies, false-data/deception attacks were the largest primary attack family (31.9%), and only 18.1% evaluated explicit multi-vector or hybrid attacks. Simulation-only validation dominated (70.8%), while 29.2% provided strong experimental, real-time, hardware-in-the-loop, laboratory, or co-simulation evidence. The synthesis shows comparatively mature work on resilient control, detection, observer-based compensation, and consensus but limited evidence on recovery, self-healing, safety preservation, cross-domain transfer, and reproducible multi-vector evaluation. The proposed framework connects attack models, topology assumptions, resilience mechanisms, validation strength, and measurable outcomes, providing a traceable basis for comparing methods and designing stronger future evaluations.