Frontiers in Artificial Intelligence最新文献

{"title":"Self-evaluative reliability: a missing safety property in adaptive clinical AI governance.","authors":"Rasit Dinc, Nurittin Ardic","doi":"10.3389/frai.2026.1973339","DOIUrl":"10.3389/frai.2026.1973339","url":null,"abstract":"","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1973339"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing vehicle routing problem through a qubit-efficient Symmetric Arc Reduction using Quantum Approximate Optimization Algorithm.","authors":"Jayaraja A, Senthil Kumaran U","doi":"10.3389/frai.2026.1900887","DOIUrl":"10.3389/frai.2026.1900887","url":null,"abstract":"<p><strong>Introduction: </strong>Finding the most efficient routes for a fleet of delivery vehicles remains a critical and costly challenge for nearly any logistics business. This core idea is known as the Vehicle Routing Problem (VRP). The primary goal is to minimize operational costs, such as the total travel distance or the number of trucks deployed, while ensuring all the customers are served. Because VRP is NP-hard, finding a perfect solution is often impossible, forcing industries to rely on classical approximate algorithms. However, quantum variational algorithms offer a new paradigm by iteratively optimizing parameters to navigate the solution space, presenting a new and promising avenue for finding better and faster solutions.</p><p><strong>Methods: </strong>In this paper, we introduce Symmetric Arc Reduction in Quantum Approximate Optimization Algorithm (SAR-QAOA), which systematically eliminates redundant symmetric routing variables in the binary integer programming formulation prior to the Quadratic Unconstrained Binary Optimization (QUBO) formulation. The proposed SAR-QAOA encodes edge-based VRP while reducing the qubit requirement from <i>n</i>(<i>n</i>-1) to <i>n</i>(<i>n</i>-1)/2, thereby improving the resource efficiency of the QUBO formulation by prioritizing regions of the solution space that are more likely to contain high-quality solutions; SAR enhances both search accuracy and computational efficiency.</p><p><strong>Results: </strong>Experimental results on noiseless quantum simulators validate the feasibility of the proposed formulation and its integration with the QAOA framework, providing a promising foundation for future investigations on larger routing problems and evolving quantum hardware.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1900887"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India.","authors":"S Boomika, R Sitharthan, R Tanukasree, R Senthil Kumar","doi":"10.3389/frai.2026.1958933","DOIUrl":"10.3389/frai.2026.1958933","url":null,"abstract":"<p><strong>Introduction: </strong>Groundwater resources in Chennai, Tamil Nadu, India are under increased stress due to rapid urbanization, erratic monsoon patterns and largely unregulated groundwater extraction. Addressing this challenge requires not only continuous monitoring but also accurate forecasting of groundwater fluctuations.</p><p><strong>Methods: </strong>This study presents a data driven framework that combines Internet of Things (IoT) enabled sensing with Machine Learning model. Real-time measurements such as groundwater depth, rainfall, temperature, and soil moisture are combined with historical hydro-meteorological data to build a reliable prediction system. A hybrid modelling approach is adopted in this study, where Random Forest is used to identify key influencing factors and capture non-linear relationships, while Long Short-Term Memory (LSTM) networks are employed to model temporal variations.</p><p><strong>Results: </strong>The combined RF-LSTM model surpasses traditional techniques, delivering an RMSE of 0.38 m, an MAE of 0.29 m, and R² value of 0.96. Relative to a standalone LSTM model, the hybrid approach cuts the RMSE by approximately 22%, and it also outperforms Random Forest, Support Vector Regression, and Linear Regression individually.</p><p><strong>Discussion: </strong>The model successfully tracks seasonal shifts in groundwater capturing groundwater recharge during the Northeast monsoon and depletion during the summer season. Through timely and dependable forecasts, this system aids more effective groundwater management, strengthens rainwater harvesting efforts and supports sustainable urban water practices going forward.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1958933"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endophytic actinomycetes as a reservoir of novel chemotherapeutic leads against multidrug-resistant cancer.","authors":"Dhanvee Ganatra, Mahendrapal Singh Rajput, Gaurav Sanghvi","doi":"10.3389/frai.2026.1974583","DOIUrl":"10.3389/frai.2026.1974583","url":null,"abstract":"<p><p>Multidrug resistance (MDR) has emerged as a major barrier to effective cancer control, undermining the efficacy of chemotherapy, targeted agents and, increasingly, immunotherapies. This review synthesizes current knowledge on the molecular and cellular bases of MDR in cancer, including ABC transporter-mediated drug efflux, enhanced DNA damage responses, survival-signaling rewiring, tumor-microenvironmental protection and cancer stem cell-driven plasticity. Particular emphasis is placed on endophytic actinomycetes and their metabolites as an underexploited reservoir of MDR modulating chemotypes with potent cytotoxic, resistance-reversal and microenvironment-modulating activities. By integrating recent mechanistic and pharmacological data, the review first delineates the crisis of drug-resistant cancer, then catalogs actinomycete-derived metabolites with documented efficacy against resistant cell lines, including those that inhibit efflux pumps, restore pro-apoptotic signaling or interfere with angiogenesis, epithelial-mesenchymal transition and stemness traits. Finally, translational challenges, such as suboptimal pharmacokinetics, off-target toxicity, herb-drug interactions and the paucity of biomarker-driven clinical trials, are critically examined alongside emerging strategies involving semi-synthetic optimization, nanocarrier delivery and rational combination regimens. A forward-looking discovery pipeline is proposed that couple's genome mining of endophytic actinomycetes with systematic MDR-mechanism profiling and medicinal chemistry, aiming to prioritize the most promising metabolites for preclinical and clinical development as next-generation MDR-targeted therapeutics.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1974583"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UniPET: a unified approach for whole-body CT to PET translation.","authors":"Francesco Di Feola, Valerio Guarrasi, Mikael Johansson, Paolo Soda","doi":"10.3389/frai.2026.1919564","DOIUrl":"10.3389/frai.2026.1919564","url":null,"abstract":"<p><p>Positron emission tomography (PET) provides critical metabolic information for oncological imaging, yet its use is constrained by radiation exposure, cost, and limited availability. Synthesizing PET-like images from computed tomography (CT) has been proposed as a way to approximate metabolic information; however, existing approaches either fail to capture region-specific variability or rely on multiple organ-specific models that do not scale to whole-body imaging. In this work, we introduce UniPET, a unified approach for whole-body CT-to-PET translation based on curriculum learning. By progressively incorporating anatomical regions of increasing complexity, UniPET enables a single network to learn coherent morpho-metabolic relationships across different regions. We evaluate UniPET on a public dataset of 900 patients and an external cohort of 579 patients. The model achieves image quality and metabolic consistency comparable to region-specific approaches, while improving over conventional whole-body models and maintaining stable performance under distribution shifts. By capturing region-specific variability within a single model, UniPET provides a promising approach for augmenting CT-based workflows with synthetic metabolic information, with potential applications in screening triage, in low-resource environments where PET is unavailable, and as an additional input for multimodal analysis pipelines. The code is available at: https://github.com/arco-group/UniPET.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1919564"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale conversational data auditability via cryptographic commitments and hash-chained audit-ledger verification.","authors":"Iván Ortiz-Gárces, Pablo Palacios, Washington Loza-Herrera, William Villegas-Ch","doi":"10.3389/frai.2026.1929127","DOIUrl":"10.3389/frai.2026.1929127","url":null,"abstract":"<p><p>The auditability of large language model-based systems requires verifiable evidence without exposing the sensitive content of interactions. This article proposes a hash-chained audit-ledger framework to transform public LLM conversations into canonical off-chain units, generate cryptographic commitments using SHA-256 and HMAC-SHA-256, build a lightweight ledger with Merkle roots, and verify integrity against induced tampering. The study integrates three heterogeneous sources, LMSYS-Chat-1M, WildChat, and Chatbot Arena, and produces 3,971,887 auditable units. In the full-corpus HMAC-SHA-256 experiment, the 11,899.55 MiB normalized repository was represented by 1,473.44 MiB of hash-only audit records, 2.03 MiB of ledger block headers, and 380.52 MiB for the operational ledger plus membership structures required for record-level verification. Five induced tampering categories, Merkle and decision-hash integrity, hash-chain linkage integrity, block ordering integrity, membership completeness, and dataset-membership consistency, were detected without false acceptance; a separate clean-ledger baseline validated the untampered ledger with zero error signals. Load experiments showed near-linear growth of mean total pipeline time with respect to the number of audit records. Operational ledger-stage throughput increased across the intermediate workloads but declined at the largest evaluated load, while full record-set verification throughput remained comparatively stable up to 250,000 interactions before decreasing in the 500,000-interaction scenario. This contribution demonstrates that the traceability of decisions generated by LLM can be formulated as a measurable, private, and cryptographically verifiable experimental chain. The framework provides a documented numerical-audit basis for independent consistency assessment, regulatory compliance, and forensic analysis when ledger states are evaluated against preserved artifacts, trusted checkpoints, or externally anchored commitments.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1929127"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stock health analysis and forecasting using risk-adjusted metrics and LSTM-guided Monte Carlo simulation.","authors":"Balakrishnan P, A Anny Leema, Ananaey Garg, Shakti Patidar","doi":"10.3389/frai.2026.1811451","DOIUrl":"10.3389/frai.2026.1811451","url":null,"abstract":"<p><p>The complex and nonlinear behaviors of financial markets make it exceedingly challenging for average investors to evaluate the performance of a specific asset or make meaningful predictions about its future behavior. Investing in stocks involves various evaluation techniques. However, many of these techniques are restricted to a small set of variables or measurements that are static and historical, limiting their ability to assess the evolving risk-return tradeoff. The objective of this research is to develop a more sophisticated technique that integrates stock evaluation and forecasting by combining traditional financial performance indicators and machine learning time-series forecasting techniques. The result is a system that provides a more organized, simplified, and transparent representation of asset performance and prospective trends. The system provides risk-adjusted performance health scores based on traditional financial metrics such as annualized return, volatility, Sharpe ratio, and drawdown, and supplements this assessment with a Long Short-Term Memory (LSTM) network, which is trained on historical return patterns to generate the predictive signal. Monte Carlo simulation technique is implemented in this work to expect future price paths which, on one hand, are mathematically constrained by the financial limits and, on the other hand, are statistically based on the past distributions. Besides, the framework applies explainable AI methods to interpret the forecasted variations in terms of publicly available market and macroeconomic data, ultimately aiming at opening the \"black box\" to the users and building their trust. The suggested method offers consistent performance evaluation, stable long-term forecasts, and comprehensible insights, which makes it appropriate for decision-support applications in retail investment analysis, according to experimental evaluation using Indian equity and mutual fund data.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1811451"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Nuchal Translucency analysis for early prenatal risk stratification.","authors":"Smita S Pawar, Mangesh D Nikose, Prakash G Burade","doi":"10.3389/frai.2026.1914775","DOIUrl":"10.3389/frai.2026.1914775","url":null,"abstract":"<p><p>Nuchal Translucency (NT) is a measurement taken from ultrasound images described as the gathering of fluid behind the fetus's neck, during 11-13 weeks. Increased Nuchal Translucency can lead to high risk of chromosomal abnormalities like Down Syndrome. The existing AI model may struggle to detect the subtle difference due to low resolution fetal movement, which can lead to errors in measurements and diagnosis. In addition, traditional NT measurement methods rely heavily on manual caliper placement, making the process operator-dependent and reducing consistency and automation. To address these challenges, this paper proposes an Enhanced Multi-Scale Spatial Temporal Graph Attention Network (EMSSTGAN) to predict the risk of chromosomal abnormalities in NT. Initially, the input images of NT are pre-processed using feature refinement attention network (FRANet) for denoising and Multi-Scale Attention Retinex Network (MARN) for contrast enhancement. NT detection is then performed by localizing the nuchal translucency region using bounding box annotations, followed by U-Net-based segmentation for precise extraction of the NT area. The Active Contour Model (ACM) is subsequently applied to measure the NT thickness based on boundary geometry. The thickness when compared with threshold values is used to identify the risk of chromosomal abnormalities as low, medium and high. The proposed model achieves an accuracy of 98.15%, an error of 1.85%, and a FDR of 2.44%, respectively.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1914775"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid neuro-symbolic graph explanation framework for fraud detection in financial transaction networks.","authors":"Neeraj S Kumar, Idhikash J, Jannath Nisha O S, Shreenidhi S R","doi":"10.3389/frai.2026.1904459","DOIUrl":"10.3389/frai.2026.1904459","url":null,"abstract":"<p><strong>Introduction: </strong>This research focuses on the development of a hybrid neuro-symbolic system for fraud detection in rapidly expanding digital financial systems.</p><p><strong>Methods: </strong>A bipartite customer-merchant graph is constructed from the Nigerian Financial Transactions dataset, and a 3-layer Graph Attention Network (FraudGAT) is used to model structural dependencies while a symbolic reasoning layer and constrained LLM prompting generate auditor-readable explanations.</p><p><strong>Results: </strong>On the curated evaluation split, FraudGAT achieves AUC 0.874, AUPRC 0.771, and F1 0.609. We additionally benchmark against GCN, GraphSAGE, GIN, a graphtransformer baseline, and tabular baselines (Random Forest, XGBoost, LightGBM): tabular ensembles perform best on this sampled split, while FraudGAT remains stronger than several graph baselines and supports an interpretable detect-explain workflow.</p><p><strong>Discussions: </strong>Therefore, we position the contribution as an auditable neuro-symbolic graph framework and an empirical foundation for scaling to larger, richer transaction graphs.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1904459"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exhaustive feature-subset search for data-driven post-correction of satellite atmospheric processors: a case study on chlorophyll-a retrieval in inland reservoirs.","authors":"Victoria Amores-Chaparro, Fernando Broncano, Jesús A Torrecilla-Pinero, Aurora Cuartero","doi":"10.3389/frai.2026.1947948","DOIUrl":"https://doi.org/10.3389/frai.2026.1947948","url":null,"abstract":"<p><p>Satellite retrieval of chlorophyll-a (Chl-a) is central to monitoring eutrophication in inland reservoirs, yet the atmospheric-correction processors it depends on, trained on oceanic and large-lake conditions, are systematically biased over small water bodies. A data-driven post-correction layer is increasingly used to reduce this bias; for such a layer over the joint output of the C2-Net processors (C2RCC, C2X, and C2XC), we ask which engineered features robustly carry the corrective signal rather than chasing an accuracy headline. Enumerating all 2¹⁷-1 = 131, 071 subsets of a symmetric 17-feature pool exposes a broad performance plateau on which three mainstream heuristics pick inequivalent \"best\" subsets at near-identical error, so any single optimum is an artifact of selection on the cross-validated statistic. Stability selection instead isolates a small robust core (led by <math> <msqrt><mrow><mo>|</mo> <mtext>C</mtext> <mn>2</mn> <mtext>X</mtext> <msub><mrow><mtext>C</mtext></mrow> <mrow><mi>r</mi></mrow> </msub> <mo>|</mo></mrow> </msqrt> </math> , chosen in 98% of subsamples at <i>k</i> = 6), the transformations that dominate the lowest-error subset being selected near chance. On 252 satellite-<i>in-situ</i> pairs from five Guadiana-Basin reservoirs (Spain), the correction improves pooled RMSE over raw C2RCC modestly but, for the Huber model, significantly (~10-16%, <i>p</i> = 0.047 under a cross-validation-corrected test), with the three estimators statistically indistinguishable from one another; under Leave-One-Reservoir-Out validation the degradation runs from ~15% (pooled) to ~34% (per-reservoir mean), both within the wide bootstrap intervals and thus approximate rather than precise. We distill a compact reporting template: an RMSE-vs.-<i>k</i> curve with variance bands, stability selection in place of a single \"best\" subset, a relative-error metric (MdAPE) alongside RMSE, and LORO as a minimum transferability check. All code, data, and models are released openly.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1947948"},"PeriodicalIF":6.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}