Fake review detection in low-resource and code-mixed languages remains challenging due to informal writing styles, transliterated regional expressions, linguistic variability, and the limited availability of annotated datasets. This paper presents a hybrid LaBSE semantic and handcrafted feature fusion approach with machine learning for fake review detection in Roman Marathi code-mixed text. A real-time dataset comprising 2,287 Roman Marathi reviews collected from multiple online platforms is utilized to evaluate the proposed approach. The framework integrates opinion-mining features with multilingual semantic representations generated using Language-agnostic BERT Sentence Embedding (LaBSE) and handcrafted linguistic, behavioural, contextual, temporal, and metadata features to construct a comprehensive hybrid feature representation. The dataset is balanced using random oversampling and subsequently partitioned into training and testing subsets using an 80:20 ratio. Four machine learning classifiers, namely Random Forest, XGBoost, Support Vector Machine, and K-Nearest Neighbour, are evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that XGBoost achieves the best performance with 94.60% accuracy, 95.63% precision, 93.47% recall, and 94.54% F1-score, outperforming the other evaluated classifiers. The findings demonstrate the effectiveness of combining multilingual semantic information with explicit linguistic and contextual characteristics for identifying deceptive reviews in Roman Marathi code-mixed environments and establish an initial benchmark for fake review detection in this low-resource setting.
Swapnil S. Nehar, R. Keole, Pravin P. Karde· International journal of com...· 0 citations
Quantum machine learning (QML) is increasingly investigated for network intrusion detection, but the practical value of current quantum models remains uncertain because many studies rely on small simulated data sets, idealized execution, weak classical baselines, or isolated accuracy comparisons. This study develops and evaluates a resource-aware hybrid quantum-classical intrusion detection framework that combines fixed fidelity quantum kernels, repaired Quantum Kernel Alignment (QKA), a Variational Quantum Classifier (VQC), LightGBM, prototype-based kernel reduction, and probabilistic stacking. Experiments use the UNSW-NB15 benchmark under a leakage-aware preprocessing protocol and repeated evaluation across five random seeds. The experimental program covers 4- and 6-qubit representations, multiple training sizes, prototype budgets, and finite-shot configurations, yielding 260 experimental configurations/runs. The strongest quantum-assisted model, a 6-qubit QKA-VQC-LightGBM stack at 100 samples per class, obtains mean F1=0.8480, MCC=0.7092, ROC-AUC=0.9398, and PR-AUC=0.9302. Repaired QKA improves F1 from 0.8043 for the corresponding fixed kernel to 0.8089, while 50-prototype QKA attains F1=0.8257 with a more favorable kernel-workload trade-off than larger prototype budgets. Finite-shot experiments between 1,024 and 4,096 shots remain stable in simulation. However, full-data LightGBM remains stronger overall (F1=0.8957, MCC=0.8068, ROC-AUC=0.9755, PR-AUC=0.9704), and corrected statistical tests do not establish general quantum superiority. These findings motivate a Selective Quantum Intrusion Detection System (S-QIDS), in which classical inference handles high-confidence traffic and quantum processing is reserved for uncertain, novel, or high-risk observations. The study therefore reframes near-term quantum intrusion detection from unconditional quantum advantage toward measurable quantum utility, quantum-classical complementarity, and resource-aware quantum intervention.
Rahul R. Bhoge, R. Keole, Pravin P. Karde· International journal of com...· 0 citations
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