This study provides an applicable methodological procedure applying Artificial Intelligence (AI)-based supervised Machine Learning (ML) algorithms in detecting fake reviews of online review platforms and identifies the best ML algorithm as well as the most critical fake review determinants for a given restaurant review dataset. Our empirical findings from analyzing 16 determinants (review-related, reviewer-related, and linguistic attributes) measured from over 43,000 online restaurant reviews reveal that among the seven ML algorithms, the random forest algorithm outperforms the other algorithms and, among the 16 review attributes, time distance is found to be the most important, followed by two linguistic (affective and cognitive cues) and two review-related attributes (review depth and structure). The present study contributes to the literature on fake online review detection, especially in the hospitality field and the body of knowledge on supervised ML algorithms.

The continental margin of the northern South China Sea is considered to be a magma-poor rifted margin. This work uses new seismic, bathymetric, gravity, and magnetic data to reveal how extensively magmatic processes have reshaped the latter continental margin. Widespread hydrothermal vent complexes and magmatic edifices such as volcanoes, igneous sills, lava flows, and associated domes are confirmed in the broader area of the northern South China Sea. Newly identified hydrothermal vents have crater- and mound-shaped surface expressions, and occur chiefly above igneous sills and volcanic edifices. Detailed stratigraphic analyses of volcanoes and hydrothermal vents suggest that magmatic activity took place in discrete phases between the early Miocene and the Quaternary. Importantly, the occurrence of hydrothermal vents close to the present seafloor, when accompanied by shallow igneous sills, suggest that fluid seepage is still active, well after main phases of volcanism previously documented in the literature. After combining geophysical and geochemical data, this study postulates that the extensive post-rift magmatism in the northern South China Sea is linked to the effect of a mantle plume over a long time interval. We propose that prolonged magmatism resulted in contact metamorphism in carbon-rich sediments, producing large amounts of hydrothermal fluid along the northern South China Sea. Similar processes are expected in parts of magma-poor margins in association with CO₂/CH₄ and heat flow release into sea water and underlying strata.

This study provides an applicable methodological approach applying artificial intelligence (AI)-based supervised machine learning (ML) algorithms in risk a

Machine learning-based intrusion detection systems (IDS) deployed in real-world environments frequently degrade due to concept drift, where evolving traffic patterns invalidate assumptions learned during training. This challenge is especially pronounced in Internet of Things (IoT) environments, where device behavior changes over time due to user interaction, firmware updates, and emerging attack strategies. Prior work introduced FIRCE, a framework that integrates conformal evaluation into streaming IDS pipelines to enable uncertainty-aware drift detection and adaptive retraining. In this journal extension, we present FADES, a framework for adaptive drift estimation that generalizes drift monitoring beyond prediction-space uncertainty by supporting both conformal evaluation and representation-space detectors within a unified streaming architecture. FADES incorporates multiple conformal evaluation variants, including Approximate Cross-Conformal Evaluation, which preserves the statistical structure of cross-conformal evaluation while eliminating repeated model training, as well as an Adaptive Chunking Controller that dynamically balances detection responsiveness and computational cost. We extend prior work through three major contributions: (i) a variance-aware evaluation protocol comprising 375 simulations across multiple seeds and runs, (ii) integration of a contrastive autoencoder-based detector to enable direct comparison between prediction-space and representation-space drift detection, and (iii) expanded evaluation across in-domain and cross-dataset transfer settings using UNSW-NB15, CICIDS2018, and a real-world IoT testbed. Approx-CCE achieves performance comparable to standard cross-conformal evaluation across hundreds of simulations, providing empirical evidence that the statistical benefits of CCE derive primarily from its disjoint calibration partition structure rather than fold-specific model diversity, a finding with implications for conformal evaluation in repeated recalibration settings more broadly. In contrast, representation-space drift detection via CADE incurs substantial computational cost under repeated retraining, limiting its practicality in streaming settings. These findings demonstrate that conformal evaluation provides a statistically grounded and computationally efficient foundation for real-time drift-aware intrusion detection, and that FADES enables flexible, unified evaluation of drift detection strategies under realistic deployment conditions.