{"status":"ok","message-type":"work-list","message-version":"1.0.0","message":{"facets":{},"total-results":278537,"items":[{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T08:43:22Z","timestamp":1723020202459},"reference-count":0,"publisher":"ISROSET: International Scientific Research Organization for Science, Engineering and Technology","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ijcse"],"DOI":"10.26438\/ijcse\/v6i3.400402","type":"journal-article","created":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T11:11:53Z","timestamp":1528974713000},"page":"400-402","source":"Crossref","is-referenced-by-count":5,"title":["A Review Speech Emotion Recognition"],"prefix":"10.26438","volume":"6","author":[{"given":"G.N.","family":"Peerzade","sequence":"first","affiliation":[]},{"name":"Department of CS and IT, Dr. B. A. M. U, Aurangabad (MS), India","sequence":"first","affiliation":[]},{"given":"R.R.","family":"Deshmukh","sequence":"additional","affiliation":[]},{"name":"Department of CS and IT, Dr. B. A. M. U, Aurangabad (MS), India","sequence":"additional","affiliation":[]},{"given":"S.D.","family":"Waghmare","sequence":"additional","affiliation":[]},{"name":"Department of CS and IT, Dr. B. A. M. U, Aurangabad (MS), India","sequence":"additional","affiliation":[]}],"member":"10981","published-online":{"date-parts":[[2018,3,30]]},"container-title":["International Journal of Computer Sciences and Engineering"],"deposited":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T11:12:30Z","timestamp":1528974750000},"score":19.449139,"resource":{"primary":{"URL":"http:\/\/www.ijcseonline.org\/full_paper_view.php?paper_id=1816"}},"issued":{"date-parts":[[2018,3,30]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3,30]]}},"URL":"https:\/\/doi.org\/10.26438\/ijcse\/v6i3.400402","ISSN":["2347-2693"],"issn-type":[{"type":"electronic","value":"2347-2693"}],"published":{"date-parts":[[2018,3,30]]}},{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:05:48Z","timestamp":1771956348536,"version":"3.50.1"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ajis"],"abstract":"<jats:p>This study addresses the emerging problem that quantum attacks can undermine classical public key cryptography that secures global cloud platforms, leaving long lived, cross border data at risk, and evaluates how far organizations have progressed toward post quantum cryptography (PQC) frameworks. The purpose is to quantify PQC framework maturity and its security, compliance, and performance implications in cloud and enterprise cases. Using a quantitative, cross sectional, case-based design, survey data were collected from 220 organizations that provide or consume multi region cloud services, with key informants in security, cloud architecture, and compliance rating Likert five-point items. Core variables included PQC awareness, adoption intention, regulatory and contractual pressure, security governance capability, perceived performance impact, PQC framework maturity, perceived quantum resilient security posture, perceived regulatory compliance, and perceived operational performance. Descriptive statistics, reliability and validity tests, Pearson correlations, and multiple regression models with sector, size, region, and deployment model as controls were applied. Results show moderate PQC maturity (mean 3.21) but higher awareness (3.68) and adoption intention (3.55). PQC maturity correlated strongly with quantum resilient security posture (r = 0.68) and regulatory compliance (r = 0.64), and significantly predicted both outcomes (\u03b2 = 0.59 and \u03b2 = 0.55, R\u00b2 = 0.47 and 0.42). Regulatory pressure (\u03b2 = 0.34) and governance capability (\u03b2 = 0.18) were also significant drivers of maturity. These findings imply that building systematic, governance anchored PQC frameworks can measurably strengthen cloud security and compliance while maintaining acceptable performance, guiding prioritized, phased PQC migration for global cloud providers and enterprise users.<\/jats:p>","DOI":"10.63125\/dhbvry98","type":"journal-article","created":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T09:34:51Z","timestamp":1765100091000},"page":"101-134","source":"Crossref","is-referenced-by-count":1,"title":["POST-QUANTUM CRYPTOGRAPHY FRAMEWORKS FOR SECURING GLOBAL CLOUD SYSTEMS"],"prefix":"10.63125","volume":"02","author":[{"name":"MS in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2021,12,1]]},"container-title":["American Journal of Interdisciplinary Studies"],"deposited":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T09:34:51Z","timestamp":1765100091000},"score":19.217585,"resource":{"primary":{"URL":"https:\/\/ajisresearch.com\/index.php\/ajis\/article\/view\/76"}},"issued":{"date-parts":[[2021,12,1]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2021,12,1]]}},"URL":"https:\/\/doi.org\/10.63125\/dhbvry98","published":{"date-parts":[[2021,12,1]]}},{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T20:18:38Z","timestamp":1649189918033},"reference-count":0,"publisher":"Harborside Press, LLC","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JADPRO"],"published-print":{"date-parts":[[2010,12,1]]},"DOI":"10.6004\/jadpro.2010.1.4.3","type":"journal-article","created":{"date-parts":[[2015,4,29]],"date-time":"2015-04-29T18:50:19Z","timestamp":1430333419000},"source":"Crossref","is-referenced-by-count":0,"title":["Biomarkers in Colorectal Cancer: Implications for Nursing Practice"],"prefix":"10.6004","volume":"1","author":[{"given":"Carolyn","family":"Grande, CRNP, AOCNP\u00ae","sequence":"first","affiliation":[]},{"given":"Pamela","family":"Hallquist Viale, RN, MS, CS, ANP, AOCNP\u00ae","sequence":"additional","affiliation":[]},{"given":"Deanna","family":"Yamamoto, RN, MS, CS, AOCNP\u00ae","sequence":"additional","affiliation":[]}],"member":"4047","published-online":{"date-parts":[[2010,12,1]]},"container-title":["Journal of the Advanced Practitioner in Oncology"],"deposited":{"date-parts":[[2015,4,29]],"date-time":"2015-04-29T18:50:23Z","timestamp":1430333423000},"score":19.150894,"resource":{"primary":{"URL":"http:\/\/www.advancedpractitioner.com\/issues\/volume-1,-number-4-(novdec-2010)\/biomarkers-in-colorectal-cancer-implications-for-nursing-practice.aspx"}},"issued":{"date-parts":[[2010,12,1]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2010,12,1]]},"published-print":{"date-parts":[[2010,12,1]]}},"URL":"https:\/\/doi.org\/10.6004\/jadpro.2010.1.4.3","ISSN":["2150-0878"],"issn-type":[{"value":"2150-0878","type":"print"}],"published":{"date-parts":[[2010,12,1]]}},{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T08:08:11Z","timestamp":1772266091692,"version":"3.50.1"},"reference-count":0,"publisher":"eLife Sciences Publications, Ltd","content-domain":{"domain":[],"crossmark-restriction":false},"DOI":"10.7554\/elife.23040.017","type":"component","created":{"date-parts":[[2017,7,25]],"date-time":"2017-07-25T11:00:13Z","timestamp":1500980413000},"source":"Crossref","is-referenced-by-count":0,"title":["Figure 6\u2014figure supplement 1. Properties of intracellular complex spikes in novel and familiar mazes."],"prefix":"10.7554","member":"4374","deposited":{"date-parts":[[2018,8,24]],"date-time":"2018-08-24T06:56:33Z","timestamp":1535093793000},"score":19.030647,"resource":{"primary":{"URL":"https:\/\/elifesciences.org\/articles\/23040\/figures#fig6s1"}},"subtitle":["(A)-(C) CS width (at half-maximum, ms) (A), CS area (mV*ms) (B), and number of spikes per CS (C) (mean \u00b1 SE) for all CSs in NOV and FAM mazes. (D) Left: Fraction of cells recorded in NOV and FAM mazes that had at least one CS in a NOV or FAM maze. Right: Fraction of all NOV and FAM maze epochs in which at least one CS occurred in the maze. (E) CS rate as a function of relative (to lap baseline, left) and absolute (right) subthreshold Vm. Here, subthreshold Vm is computed as the mean value in 150 ms windows (sliding every 10 ms)."],"issued":{"date-parts":[[null]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7554\/elife.23040.017","relation":{"is-component-of":[{"id-type":"doi","id":"10.7554\/eLife.23040","asserted-by":"object"}]}},{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:25:55Z","timestamp":1761006355409,"version":"build-2065373602"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["RAST"],"abstract":"<jats:p>This study examined the critical roles of explain ability and fairness in advancing trustworthy artificial intelligence (AI) within large-scale decision systems. As AI technologies increasingly shape consequential decisions in domains such as healthcare, finance, employment, and judicial processes, ensuring transparency, equity, and legitimacy has become paramount. Drawing on a comprehensive review of 152 peer-reviewed studies, this research synthesized conceptual foundations, methodological advancements, and empirical findings to build a robust framework for understanding how explain ability and fairness jointly contribute to trustworthiness. A quantitative research design was employed, incorporating large-scale datasets and multi-phase statistical analyses to evaluate how explanation fidelity, stability, and sparsity influence comprehension, trust, and perceived fairness, and how fairness interventions impact model performance and equity outcomes. Results demonstrated that explanation fidelity significantly enhanced user comprehension, while stability strongly predicted trust, highlighting the importance of consistent and faithful explanations in shaping user confidence. Fairness metrics such as demographic parity and equal opportunity gaps were powerful predictors of perceived fairness, and reductions in these disparities substantially increased user acceptance of AI decisions. Interaction analyses revealed that combining counterfactual explanations with fairness constraints produced synergistic effects, improving both equity and trust without excessively compromising predictive performance. The study also quantified trade-offs, showing that fairness interventions slightly reduced accuracy but delivered substantial gains in legitimacy and social acceptability. Human-cantered outcomes such as trust and reliance were closely linked to technical measures, illustrating that the social impact of AI is deeply intertwined with its design. By integrating findings across technical, ethical, and behavioural dimensions, this study contributed new empirical evidence and theoretical insights into how explain ability and fairness shape trustworthy AI. The results provide a comprehensive foundation for designing, evaluating, and governing AI systems that are transparent, equitable, and socially aligned in large-scale decision-making contexts.<\/jats:p>","DOI":"10.63125\/3w9v5e52","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:41:52Z","timestamp":1760949712000},"page":"54-93","source":"Crossref","is-referenced-by-count":0,"title":["TRUSTWORTHY AI: EXPLAINABILITY &amp; FAIRNESS IN LARGE-SCALE DECISION SYSTEMS"],"prefix":"10.63125","volume":"02","author":[{"name":"Ms in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri, USA","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2023,12,1]]},"container-title":["Review of Applied Science and Technology"],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:41:52Z","timestamp":1760949712000},"score":18.930717,"resource":{"primary":{"URL":"https:\/\/rast-journal.org\/index.php\/RAST\/article\/view\/47"}},"issued":{"date-parts":[[2023,12,1]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2023,12,1]]}},"URL":"https:\/\/doi.org\/10.63125\/3w9v5e52","published":{"date-parts":[[2023,12,1]]}},{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T08:51:54Z","timestamp":1772268714563,"version":"3.50.1"},"reference-count":0,"publisher":"PeerJ","content-domain":{"domain":[],"crossmark-restriction":false},"DOI":"10.7717\/peerj.17801\/table-1","type":"component","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T03:26:19Z","timestamp":1721618779000},"source":"Crossref","is-referenced-by-count":0,"title":["Table 1: Comparative analysis of trait responses under three levels of drought stress (CM, CS, MS)."],"prefix":"10.7717","member":"4443","deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T03:26:22Z","timestamp":1721618782000},"score":18.6526,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/17801\/table-1"}},"issued":{"date-parts":[[null]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7717\/peerj.17801\/table-1","relation":{"is-component-of":[{"id-type":"doi","id":"10.7717\/peerj.17801","asserted-by":"object"}]}},{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:36:29Z","timestamp":1759365389243,"version":"build-2065373602"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ijbei"],"abstract":"<jats:p>Nation-state cyberattacks represent one of the most complex and evolving threats to global security, often leveraging sophisticated strategies that exploit structural vulnerabilities across interconnected digital ecosystems. Traditional machine learning models, while effective for anomaly detection and malware classification, struggle to capture the relational and temporal dependencies inherent in coordinated cyber campaigns. This study explores the integration of Graph Neural Networks (GNNs) into AI-augmented cybersecurity frameworks to enhance predictive capabilities against nation-state cyberattacks. By modeling cyber infrastructures, threat intelligence, and attack pathways as graph-structured data, GNNs can identify latent patterns and interdependencies between threat actors, targets, and tactics. The proposed framework incorporates multi-source data, including network telemetry, open-source intelligence, and historical incident reports, to construct dynamic attack graphs that evolve in near real time. Experimental evaluations demonstrate that GNN-based models outperform conventional deep learning architectures in forecasting multi-stage intrusions, achieving higher precision in distinguishing state-sponsored campaigns from generic cyber threats. Furthermore, explainability modules embedded within the GNN pipeline improve interpretability by revealing critical nodes, links, and features driving predictions, thereby supporting actionable decision-making for security analysts and policymakers. This work underscores the strategic potential of AI-augmented approaches in advancing national resilience, providing early-warning capabilities, and enabling proactive defense strategies against adversarial state actors.<\/jats:p>","DOI":"10.63125\/z3dy9737","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T07:12:33Z","timestamp":1759302753000},"page":"35-59","source":"Crossref","is-referenced-by-count":0,"title":["AI-AUGMENTED CYBERSECURITY: GRAPH NEURAL NETWORKS FOR PREDICTING NATION-STATE CYBERATTACKS"],"prefix":"10.63125","volume":"04","author":[{"name":"MS in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2024,12,1]]},"container-title":["International Journal of Business and Economics Insights"],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T07:12:34Z","timestamp":1759302754000},"score":18.598427,"resource":{"primary":{"URL":"https:\/\/ijbei-journal.org\/index.php\/ijbei\/article\/view\/26"}},"issued":{"date-parts":[[2024,12,1]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.63125\/z3dy9737","published":{"date-parts":[[2024,12,1]]}},{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T07:48:33Z","timestamp":1772264913435,"version":"3.50.1"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AJSRI"],"abstract":"<jats:p>This study provides a comprehensive systematic review of federated learning as a framework for privacy-preserving healthcare data sharing and its potential to enable global artificial intelligence collaboration. In total, 124 peer-reviewed articles were examined following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency, rigor, and reproducibility. The review highlights how federated learning has evolved from conceptual discussions to practical applications across multiple healthcare domains, including medical imaging, electronic health records, biosignals, and genomic analysis. Key findings indicate that federated architectures, particularly server\u2013client models, have become the dominant deployment strategy, while peer-to-peer approaches are gaining attention for their resilience and decentralization. Privacy-preserving mechanisms\u2014such as differential privacy, secure aggregation, and cryptographic computation\u2014emerged as central to ensuring compliance with regulatory and ethical standards, with adaptive strategies allowing for an effective balance between confidentiality and model utility. Evidence from multi-institutional collaborations shows that federated learning not only improves predictive performance but also enhances inclusivity, enabling smaller or resource-limited institutions to contribute meaningfully without relinquishing data ownership. At the same time, empirical studies identified adversarial risks such as gradient inversion, membership inference, and poisoning attacks, underscoring the necessity for layered safeguards and strong governance structures. Collectively, the findings demonstrate that federated learning is more than a technical innovation; it represents a socio-technical paradigm that integrates privacy, equity, and collaboration into the development of global healthcare AI. This review positions federated learning as a cornerstone for building secure, ethical, and scalable artificial intelligence systems that address the dual imperatives of advancing medical innovation while safeguarding patient confidentiality.<\/jats:p>","DOI":"10.63125\/jga18304","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:47:01Z","timestamp":1756486021000},"page":"320-351","source":"Crossref","is-referenced-by-count":1,"title":["FEDERATED LEARNING FOR PRIVACY-PRESERVING HEALTHCARE DATA SHARING: ENABLING GLOBAL AI COLLABORATION"],"prefix":"10.63125","volume":"04","author":[{"name":"Ms in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2025,1,1]]},"container-title":["American Journal of Scholarly Research and Innovation"],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:47:02Z","timestamp":1756486022000},"score":18.578882,"resource":{"primary":{"URL":"https:\/\/researchinnovationjournal.com\/index.php\/AJSRI\/article\/view\/63"}},"issued":{"date-parts":[[2025,1,1]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2025,1,1]]}},"URL":"https:\/\/doi.org\/10.63125\/jga18304","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1186\/s12263-025-00790-9","asserted-by":"object"}]},"published":{"date-parts":[[2025,1,1]]}},{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:13:27Z","timestamp":1757618007062,"version":"3.44.0"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["asrc"],"abstract":"<jats:p>This study conducts a meta-analysis of scholarly and policy literature on blockchain as an enabler of decentralized digital identity with a specific focus on cross-border authentication and immigration contexts. The analysis integrates evidence from more than 200 reviewed publications spanning information systems, cryptography, law, governance, and humanitarian studies published between 2000 and 2022. Findings reveal a sharp increase in academic and policy attention since 2015, reflecting the growing recognition of identity as a critical application of blockchain beyond finance. Decentralized identity frameworks demonstrate substantial advantages over centralized and federated systems, including reductions in cyber vulnerabilities, improved privacy through zero-knowledge proofs and selective disclosure, and operational efficiency gains such as a 45 percent average reduction in authentication time. Evidence from pilot projects highlights measurable benefits in humanitarian contexts, where blockchain-based systems reduced aid distribution costs by up to 98 percent and provided refugees with portable credentials that preserved continuity of healthcare, education, and financial services across borders. Despite these advances, significant gaps persist in interoperability, scalability, governance, and inclusivity. Divergent national regulations and fragmented technical standards continue to limit cross-border adoption, while usability challenges and risks of digital exclusion hinder accessibility for vulnerable populations. The study concludes that blockchain-driven identity systems can deliver transformative improvements in security, privacy, and portability but require harmonized global standards, stronger governance frameworks, and inclusive design strategies to ensure equitable adoption. By synthesizing evidence across disciplines, this research contributes a comprehensive assessment of the current state and limitations of decentralized digital identity in cross-border contexts.<\/jats:p>","DOI":"10.63125\/sr1rz960","type":"journal-article","created":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T16:10:11Z","timestamp":1757175011000},"page":"63-88","source":"Crossref","is-referenced-by-count":0,"title":["BLOCKCHAIN-BASED DECENTRALIZED IDENTITY FOR CROSS-BORDER AUTHENTICATION: ENHANCING CYBERSECURITY AND IMMIGRATION APPLICATIONS"],"prefix":"10.63125","volume":"02","author":[{"name":"Ms in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2022,1,1]]},"container-title":["ASRC Procedia: Global Perspectives in Science and Scholarship"],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T16:10:12Z","timestamp":1757175012000},"score":18.509594,"resource":{"primary":{"URL":"https:\/\/global.asrcconference.com\/index.php\/asrc\/article\/view\/26"}},"issued":{"date-parts":[[2022,1,1]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2022,1,1]]}},"URL":"https:\/\/doi.org\/10.63125\/sr1rz960","published":{"date-parts":[[2022,1,1]]}},{"institution":[{"name":"OSD or Non-Service DoD Agency","acronym":["OSD or Non-Service DoD Agency"],"department":["Funding"]},{"name":"CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE","department":["Performing"]}],"indexed":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T06:40:15Z","timestamp":1724913615479},"publisher-location":"Fort Belvoir, VA","reference-count":0,"publisher":"Defense Technical Information Center","content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[1993,9,1]]},"DOI":"10.21236\/ada276523","type":"report","created":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T18:00:01Z","timestamp":1504202401000},"source":"Crossref","is-referenced-by-count":0,"title":["An MS-DOS File System for UNIX"],"prefix":"10.21236","author":[{"given":"Alessandro","family":"Forin","sequence":"first","affiliation":[]},{"given":"Gerald R.","family":"Malan","sequence":"first","affiliation":[]}],"member":"9879","original-title":[""],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T05:28:50Z","timestamp":1724909330000},"score":18.374916,"resource":{"primary":{"URL":"http:\/\/www.dtic.mil\/docs\/citations\/ADA276523"}},"subtitle":[""],"issued":{"date-parts":[[1993,9,1]]},"references-count":0,"alternative-id":["CMU-CS-93-196"],"URL":"https:\/\/doi.org\/10.21236\/ada276523","published":{"date-parts":[[1993,9,1]]}},{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T06:12:13Z","timestamp":1767334333474,"version":"3.48.0"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ajates"],"abstract":"<jats:p>Robust and verifiable large language models (LLMs) are increasingly considered for high-stakes decision-support in healthcare, defense, and finance, yet empirical evidence on their reliability, security, and audit readiness remains limited. This quantitative study evaluated four LLM system configurations\u2014baseline, retrieval-grounded, schema\/rule-constrained, and tool-augmented verification\u2014across 360 domain-specific cases and 5,760 evaluated case-instances under clean, perturbation, out-of-distribution, and adversarial conditions. Descriptive and multivariable analyses showed that tool-augmented verification achieved the highest overall task correctness at 80% on clean inputs, compared to 64% for baseline, while maintaining higher decision stability under perturbations at 81% versus 61%. Evidence support rates increased from 58% in baseline outputs to 82% in tool-augmented configurations, and schema validity exceeded 94% under constrained outputs across domains. Under adversarial testing, retrieval-grounded systems exhibited the highest policy violation rate at 18.9%, whereas schema\/rule-constrained and tool-augmented systems reduced violations to 7.2% and 6.9%, respectively. However, stricter controls increased false refusals, rising from 2.3% in baseline to 7.0% in schema-constrained configurations. Mixed-effects regression results indicated that tool augmentation more than doubled the odds of task correctness relative to baseline, while schema constraints reduced policy violations by nearly 50%. Out-of-distribution conditions reduced correctness across all configurations, with the smallest degradation observed in tool-augmented systems. Overall, the findings demonstrated that robustness and verifiability in high-stakes LLM decision-support depended on layered grounding, constraint enforcement, and deterministic verification mechanisms, and that measurable tradeoffs emerged between security controls and operational utility across domains.<\/jats:p>","DOI":"10.63125\/xv9bab19","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T06:07:15Z","timestamp":1767334035000},"page":"01-50","source":"Crossref","is-referenced-by-count":0,"title":["ROBUST AND VERIFIABLE LLMS FOR HIGH-STAKES DECISION-MAKING (HEALTHCARE, DEFENSE, FINANCE)"],"prefix":"10.63125","volume":"06","author":[{"name":"MS in CS Candidate, University of Central Missouri, USA","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, Campbellsville University, USA","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2026,1,1]]},"container-title":["American Journal of Advanced Technology and Engineering Solutions"],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T06:07:15Z","timestamp":1767334035000},"score":18.329613,"resource":{"primary":{"URL":"https:\/\/ajates-scholarly.com\/index.php\/ajates\/article\/view\/63"}},"issued":{"date-parts":[[2026,1,1]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2026,1,1]]}},"URL":"https:\/\/doi.org\/10.63125\/xv9bab19","published":{"date-parts":[[2026,1,1]]}},{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:51:27Z","timestamp":1775141487279,"version":"3.50.1"},"reference-count":0,"publisher":"American Scholarly Publishing Group Inc","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jsdp"],"abstract":"<jats:p>The study on Explainable Reinforcement Learning (XRL) for High-Stakes Decision Systems: Developing Interpretable RL Models for Autonomous Vehicles, Healthcare, or Finance had been conducted to investigate how interpretability in reinforcement learning enhances performance, trust, and accountability in critical decision-making environments. This research had reviewed and synthesized findings from 126 peer-reviewed papers spanning the past decade, focusing on the integration of explain ability mechanisms into reinforcement learning models applied to safety-critical and ethically sensitive domains. The study aimed to identify quantitative relationships between key explain ability constructs\u2014fidelity, stability, and comprehensibility\u2014and measurable human or system outcomes such as decision accuracy, response time, trust calibration, and accountability perception. Using a mixed quantitative framework, the research combined simulation-based performance data, human-centered evaluation metrics, and statistical modeling to assess how explainable RL architectures perform compared to non-explainable counterparts. The findings revealed that explainable reinforcement learning models consistently outperformed traditional opaque systems across all three domains. In autonomous vehicles, explanations improved driver response times and reduced intervention rates; in healthcare, they enhanced clinician confidence and treatment decision accuracy; and in finance, they improved risk-adjusted returns and investor trust. Regression and correlation analyses demonstrated that explanation fidelity strongly predicted decision accuracy, while explanation stability and comprehensibility were significant predictors of trust and accountability. Furthermore, repeated-measures ANOVA confirmed statistically significant improvements in user trust and performance under explainable conditions, supported by large effect sizes. The study also identified several persistent challenges, including the trade-off between interpretability and performance, variability in user comprehension, and limitations in real-time explanation delivery. Overall, the review and empirical analysis provided a comprehensive understanding of how explainable reinforcement learning contributes to safer, more transparent, and ethically accountable AI-driven decision systems. The insights derived from the 126 reviewed studies establish a robust foundation for developing future XRL frameworks capable of balancing performance optimization with human interpretability in complex, high-stakes environments such as autonomous vehicles, clinical systems, and financial analytics.<\/jats:p>","DOI":"10.63125\/53crx355","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T12:27:32Z","timestamp":1762518452000},"page":"31-70","source":"Crossref","is-referenced-by-count":1,"title":["EXPLAINABLE REINFORCEMENT LEARNING FOR HIGH-STAKES DECISION SYSTEMS DEVELOPING INTERPRETABLE RL MODELS FOR AUTONOMOUS VEHICLES, HEALTHCARE, OR FINANCE"],"prefix":"10.63125","volume":"01","author":[{"name":"Ms in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri, USA","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2022,12,1]]},"container-title":["Journal of Sustainable Development and Policy"],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T12:27:32Z","timestamp":1762518452000},"score":17.83031,"resource":{"primary":{"URL":"https:\/\/jsdp-journal.org\/index.php\/jsdp\/article\/view\/24"}},"issued":{"date-parts":[[2022,12,1]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2022,12,1]]}},"URL":"https:\/\/doi.org\/10.63125\/53crx355","published":{"date-parts":[[2022,12,1]]}},{"indexed":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T04:11:04Z","timestamp":1751602264024,"version":"3.41.0"},"reference-count":0,"publisher":"Academic Science Publications and Distributions","issue":"9s","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. 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EI helps to manage our emotions in positive ways. The objective of the present research paper is to study all the Intra PA, Inter PA, Intra PM, Inter PM dimensions of gathered dataset regarding EI and its impact on stress detection. The goal of this paper is to make the gender smart comparative evaluation on nowadays society for this the dataset is created for the same by way of the usage of psychometric test via the statistical analysis on the identical self-created database it has been discovered that female should improve her EI Dimensions to overcome stress &amp; by using t-test it proves that there may be a statistically huge difference between male and female close to normal strain however it can be different if there is another parameter is becoming a member of.<\/jats:p>","DOI":"10.35940\/ijitee.l7960.1091220","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T11:10:30Z","timestamp":1602069030000},"page":"251-253","update-policy":"http:\/\/dx.doi.org\/10.35940\/beiesp.crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Role of Emotional Intelligence Dimensions in Stress Detection"],"prefix":"10.35940","volume":"9","author":[{"name":"M.Phil. 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