Frontiers in Artificial Intelligence最新文献

{"title":"Beyond diagnostic accuracy: navigating the artificial intelligence data tsunami toward value-based implementation in gastroenterology.","authors":"Konstantinos Ekmektzoglou, Theodore Rokkas","doi":"10.3389/frai.2026.1943025","DOIUrl":"10.3389/frai.2026.1943025","url":null,"abstract":"<p><p>Artificial intelligence (AI) in gastrointestinal (GI) endoscopy has evolved from its early application in simple edge detection to highly sophisticated technologies designed to improve detection of neoplastic and preneoplastic lesions and support clinical decision-making. However, this rapid technological evolution has generated a \"<i>data tsunami</i>\" or \"<i>datanami</i>\": an overwhelming and continuously expanding volume of information that, frequently, exceeds the ability of clinicians and patients to process, interpret, and apply it effectively. Substantial investments in AI have been driven by expectations of improved efficiency and reduced healthcare costs. Nevertheless, the metrics used to justify the adoption of AI-assisted diagnostic and therapeutic strategies remain inadequately standardized, leaving the true cost-effectiveness of these technologies uncertain. Current AI research in gastroenterology has primarily focused on improving diagnostic performance rather than evaluating long-term clinical outcomes or economic value. To address this limitation, investigators employ stochastic microsimulation models, which simulate individual patient trajectories and enable more robust assessments of cost-effectiveness. As the field progresses from AI to extended intelligence and, ultimately, intelligent assistance, the role of humans becomes increasingly central. Rather than replacing clinical judgment, AI should augment human expertise, with healthcare professionals serving as the critical link between technological innovation and meaningful clinical benefit. This review examines whether AI applications in GI diseases can be implemented cost-effectively in routine clinical practice for diagnosis and treatment. It further explores the indispensable human dimension of AI adoption, emphasizing accountability, reliability, ethical oversight, and responsible implementation as essential safeguards for current and future clinical applications.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1943025"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216726","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":"Cost-sensitive random forest with adaptive threshold optimization for imbalanced financial fraud detection.","authors":"Jafar Ali, Rahman Shafique, Daniel Gavilanes, Manuel Masias Vergara, Isabel de la Torre Diez, Dae Wan Kim","doi":"10.3389/frai.2026.1938836","DOIUrl":"10.3389/frai.2026.1938836","url":null,"abstract":"<p><strong>Background: </strong>Financial transaction fraud detection is a challenging binary classification problem characterized by severe class imbalance, rare fraudulent cases, and asymmetric misclassification costs. In real-world banking systems, failing to detect fraudulent transactions can lead to substantial financial losses, whereas false alarms mainly increase operational review costs. Therefore, fraud detection models should not be optimized only for overall accuracy, as accuracy-oriented classifiers may favor the majority non-fraud class and fail to identify minority fraud cases effectively. To address this issue, this study proposes a cost-sensitive ML framework for financial transaction fraud detection under class imbalance and asymmetric financial risk.</p><p><strong>Methods: </strong>In this study, a cost-sensitive RF framework with adaptive probability threshold optimization is proposed for binary fraud classification. Misclassification costs are incorporated during model training through class-weighted learning, allowing the classifier to assign greater importance to minority fraud cases. In addition, instead of using the default probability threshold of 0.5, the decision threshold is optimized using validation data to minimize the expected misclassification cost and improve minority-class detection. The proposed framework was evaluated on two publicly available financial fraud datasets with different transaction characteristics: the Credit Card Fraud Detection dataset and the Financial Fraud Prediction dataset. Its performance was compared with conventional ML baselines using fraud-oriented evaluation metrics, including precision, recall, F1-score, false-negative rate, false-positive rate, and expected misclassification cost.</p><p><strong>Results: </strong>The proposed framework achieved strong and consistent performance across both datasets. On the Credit Card Fraud Detection dataset, it achieved a precision of 94.4%, recall of 85.7%, and F1-score of 89.8%. On the Financial Fraud Prediction dataset, it achieved a precision of 93.1%, a recall of 83.8%, and an F1-score of 88.2%. Compared with conventional fixed-threshold ML models, the adaptive threshold strategy reduced false-negative predictions while maintaining high precision. These results indicate that the proposed cost-sensitive framework provides a more balanced decision strategy for fraud detection under highly imbalanced transaction distributions.</p><p><strong>Conclusion: </strong>The findings demonstrate that combining class-weighted learning with adaptive threshold optimization can improve fraud detection performance under asymmetric misclassification costs. The proposed framework offers a practical and interpretable decision-support approach for banking fraud detection systems, particularly in settings where reducing undetected fraudulent transactions is more important than maximizing overall accuracy alone.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1938836"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216834","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":"Beyond the black box in healthcare operations: a structured literature review and governance framework for explainable AI in clinical process intelligence systems.","authors":"Albert Adusei Brobbey, Narayan Bhosale, James Ackom","doi":"10.3389/frai.2026.1864565","DOIUrl":"10.3389/frai.2026.1864565","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Healthcare artificial intelligence has produced substantial diagnostic capabilities, yet clinical operations remain underserved by AI transparency research. Existing reviews of Explainable AI (XAI) in healthcare concentrate on diagnostic imaging, oncology, radiology, and clinical decision support. The governance needs of clinical process intelligence (CPI) systems, which support patient flow, resource allocation, throughput management, and care coordination, remain comparatively underdeveloped.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This original research article reports a structured literature review with systematic search and screening, followed by framework development. It characterizes healthcare XAI research across application domains and introduces the CPI-XAI Governance Framework, a theory-informed model for operationalizing algorithmic transparency as an organizational information-systems capability in clinical process intelligence environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and CINAHL for peer-reviewed studies published from January 2015 to April 2026. Screening and extraction used transparent procedures adapted from PRISMA flow-reporting practices. Two authors independently screened records and extracted domain, XAI method, setting, evaluation, and governance variables; disagreements were resolved by author adjudication. Framework development used a documented hybrid deductive-inductive coding pipeline, source-contribution rules, negative-case review, and an auditable evidence-to-framework mapping anchored in IT Capability Theory, the DeLone-McLean IS Success Model, implementation science, sociotechnical systems theory, and health AI governance guidance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 187 included studies, 175 (93.6%) addressed diagnostic, disease-specific, imaging, or general clinical decision-support contexts, while operational and workflow-embedded AI accounted for 9 studies (4.8%). The primary result is descriptive: XAI research in healthcare remains concentrated in diagnostic domains, while explicit explainability-governance work for clinical operations is sparse within the search boundary. Five evidence gaps informed the framework: limited operational XAI evaluation standards, role-blind explanation design, weak causal and counterfactual support, poor workflow embedding, and absence of longitudinal XAI governance. A preliminary 0-15 scoring rubric and two hypothetical organizational assessments demonstrate how the framework can be applied analytically; they do not constitute external validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Healthcare XAI remains concentrated in diagnostic and clinical decision-support contexts, while explicit explainability-governance research for operational AI systems remains thinly represented. The CPI-XAI Governance Framework addresses this gap by treating explainability as an organizat","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1864565"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216717","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":"Explainable AI for phishing URL detection: a Bayesian-optimized stacking ensemble framework with SHAP-guided feature learning.","authors":"Hafiz Aziz Khan, Sonia Akter, Abdur Rahman Lindon, Taslima Akter, Iftekhar Rasul, Mamunur Rahman, Nasrin Akter Tohfa, Iftekhar Hossain","doi":"10.3389/frai.2026.1854934","DOIUrl":"10.3389/frai.2026.1854934","url":null,"abstract":"<p><strong>Introduction: </strong>Phishing remains one of the most persistent and financially damaging threats facing modern organizations, with over 4.7 million incidents recorded in 2023 alone. Existing AI-based phishing detection frameworks are constrained by limited benchmarking scope, absent model interpretability, and insufficient statistical validation - three limitations that collectively restrict operational utility in real-world security environments.</p><p><strong>Methods: </strong>We present an explainable, end-to-end machine learning pipeline evaluated on a large public benchmark of 247,950 URLs described by 41 structural and lexical features. The pipeline integrates SHAP-driven feature selection (reducing 41 to 24 features via a 95% cumulative-signal rule), a systematic benchmark of 12 classifiers spanning seven algorithmic families, Bayesian hyperparameter optimization via Optuna TPE sampling (40 trials each for XGBoost and CatBoost), and a heterogeneous stacking ensemble combining Optuna-tuned XGBoost, CatBoost, Extra Trees, and Random Forest under a logistic-regression meta-learner. A four-layer statistical validation protocol - comprising a Friedman omnibus test, Wilcoxon signed-rank tests, paired t-tests, and Cohen's d effect sizes - was applied to five-fold cross-validation accuracy distributions to assess directional consistency, with the limited inferential resolution of five folds explicitly acknowledged.</p><p><strong>Results: </strong>SHAP-driven selection reduced the feature space by 41.5% while retaining 95% of predictive signal. The stacking ensemble achieved 96.75% accuracy, 96.74% F1-score, and AUC of 0.9947, attaining the lowest Brier score among all 13 models (0.0246), indicating superior probability calibration. The Friedman omnibus test confirmed significant performance differences across models (χ²F = 59.84, <i>p</i> < 0.0001), and all 12 Wilcoxon pairwise comparisons yielded the minimum attainable p-value (<i>p</i> = 0.0313), confirming the ensemble never lost a cross-validation fold against any baseline. Post-hoc SHAP analysis identified subdomain structure, URL length, and URL entropy as the dominant phishing indicators at both ensemble and base-learner levels.</p><p><strong>Discussion: </strong>The co-leaders - the stacking ensemble and Extra Trees - demonstrate that rigorous, interpretable AI pipelines can advance phishing detection accuracy and transparency simultaneously. The framework's calibrated risk scores, threshold flexibility, and multi-level SHAP explainability support analyst-facing decision-making in security operations, while its leakage-free.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1854934"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216954","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":"Intelligent defense at the edge: a comprehensive survey of federated learning, TinyML and explainable AI for intrusion detection in IoT and IIoT ecosystems.","authors":"S Sarath Kumar, M Asha Jerlin","doi":"10.3389/frai.2026.1900815","DOIUrl":"10.3389/frai.2026.1900815","url":null,"abstract":"<p><p>The proliferation of Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies has fundamentally transformed contemporary computing infrastructures by interconnecting large heterogeneous devices, sensors, embedded systems, and cyber-physical platforms. These ecosystems support diverse applications including smart cities, intelligent transportation, industrial automation, medical surveillance, precision agriculture and home automation. Despite their operational advantages, the vast connectivity and inherent resource constraints of IoT devices significantly expand the cyberattack surface. Limited processing power, limited memory and restricted energy budgets render conventional cybersecurity solutions infeasible for IoT environments. Consequently, IoT systems have become prime targets for advanced threats, including malware, botnets, Distributed Denial of Service (DDoS) attacks, ransomware, insider attacks and data manipulation. Traditional Intrusion Detection Systems (IDS), designed for enterprise networks, rely on centralized data collection and computationally intensive machine learning models that are unsuitable for IoT deployments due to scalability limitations, communication overhead, latency constraints and privacy concerns. Recent research has therefore shifted toward lightweight, intelligent and distributed cybersecurity frameworks capable of providing robust protection under severe resource limitations. This survey comprehensively reviews recent advances in AI-based intrusion detection and malware analysis for IoT ecosystems, covering lightweight machine learning models (ML), deep learning (DL) architectures for edge deployment, federated learning (FL) based privacy-preserving detection, TinyML on-device intelligence, blockchain-based trust management, Explainable AI (XAI) driven transparent monitoring and adaptive cyber defense. A unified conceptual framework is proposed integrating lightweight detection, distributed collaborative learning, trust-based orchestration and adaptive defense.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1900815"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216891","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":"Context-aware KazRoBERTa with transformer-based candidate re-ranking for Kazakh spelling correction.","authors":"Zhanar Azhibekova, Daniyar Sultan, Laura Baitenova","doi":"10.3389/frai.2026.1925050","DOIUrl":"10.3389/frai.2026.1925050","url":null,"abstract":"<p><p>Automatic spelling correction for Kazakh is difficult because productive morphology, vowel harmony, and Cyrillic keyboard substitutions generate many orthographically plausible alternatives. This study proposes Context-Aware KazRoBERTa, a three-stage framework that combines token-level error detection, linguistically constrained candidate generation, and masked-language-model candidate re-ranking with a shared Transformer encoder. Candidate generation integrates inverse ka2ru mapping, vowel-harmony restoration, Optimal String Alignment distance, and lexicon filtering; contextual probability, orthographic similarity, and lexical frequency are then combined for selection. Evaluation was performed on a synthetic corpus of 2,615,771 parallel sentence pairs derived from 922,798 clean sentences. On the held-out synthetic test set, the framework achieved 97.7% detection F1-score and 98.8% detection accuracy, while sentence correction accuracy reached 85.8%, word correction accuracy reached 95.6%, and character error rate decreased to 1.86%. The 95% Wilson interval for sentence correction accuracy was 85.36-86.23%. Relative to the reported two-stage baseline, the 5.6-point sentence-accuracy margin implies a conservative continuity-corrected McNemar lower bound of χ² > = 230.3 (<i>p</i> < 0.001) across all paired contingency tables compatible with the reported rounded margins. Ablation results attribute the largest gain to contextual re-ranking, particularly for deletion and substitution errors. The evidence nevertheless remains limited to one fixed-seed synthetic evaluation: no natural-error test corpus, direct ByT5/mT5/NLLB or sequence-tagging baseline, paired prediction archive, repeated-run variance, or measured latency benchmark was supplied. The web interface therefore demonstrates system integration rather than external accuracy. The scientific contribution is the Kazakh-specific, parameter-sharing hybrid design; claims of real-world generalization or state-of-the-art superiority require external benchmarking and fully archived configurations.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1925050"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216746","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":"Artificial intelligence, explainable deep learning, and generative models for osteoporosis and bone disease prediction: a systematic review and future research roadmap.","authors":"M Raja, Avulapalli Jayaram Reddy","doi":"10.3389/frai.2026.1930437","DOIUrl":"10.3389/frai.2026.1930437","url":null,"abstract":"<p><p>Osteoporosis is a major global health burden, with considerable morbidity, mortality, and healthcare costs. Artificial intelligence (AI) has opened new avenues for osteoporosis screening, bone mineral density quantification, fracture-risk prediction, and clinical decision support, but progress remains fragmented across machine learning, deep learning, explainable AI (XAI), generative models, and multimodal learning. This systematic review brings these strands together and identifies priority research needs for AI-based osteoporosis prediction. Following the PRISMA 2020 statement, studies were identified from Scopus and Google Scholar, published between January 2020 and the final search date of 30 June 2026, with two reviewers screening independently and resolving disagreements by consensus. Design-related risk-of-bias indicators, such as retrospective vs. prospective design, validation strategy, and reference standard, were recorded for every study; a full PROBAST, QUADAS-2, and Newcastle-Ottawa Scale appraisal is planned but not yet complete. Fifty-eight studies met the inclusion criteria and were synthesized narratively and descriptively, without formal meta-analysis, given substantial heterogeneity. Findings spanned five themes: classical machine learning, deep learning for imaging, explainable AI, generative models, and multimodal learning. Deep learning achieved strong predictive accuracy (AUC 0.82-0.96), XAI improved clinical interpretability, generative models helped offset data scarcity, and multimodal approaches boosted performance by combining imaging and clinical data. Key gaps remain, including limited external validation, heavy reliance on retrospective data, poor generalisability, and limited regulatory readiness. Building on these findings, this review proposes MXGNet, a multimodal, explainable, generative framework incorporating privacy-preserving federated learning, intended to guide development of robust, interpretable, clinically deployable AI tools for osteoporosis prediction.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"9 ","pages":"1930437"},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216753","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":"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}