https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/Head https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/assertion https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/provenance https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/pubinfo https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/assertion https://ieeexplore.ieee.org/document/10365540 http://purl.org/dc/terms/creator https://orcid.org/0000-0001-9487-5622 https://ieeexplore.ieee.org/document/10365540 http://purl.org/dc/terms/creator https://orcid.org/0000-0003-2911-8558 https://ieeexplore.ieee.org/document/10365540 http://purl.org/dc/terms/publisher https://ror.org/01n002310 https://ieeexplore.ieee.org/document/10365540 http://purl.org/dc/terms/subject http://edamontology.org/topic_3316 https://ieeexplore.ieee.org/document/10365540 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://ieeexplore.ieee.org/document/10365540 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://ieeexplore.ieee.org/document/10365540 http://www.w3.org/2000/01/rdf-schema#comment Accurate uncertainty quantification is necessary to enhance the reliability of deep learning (DL) models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of DL models. Such PIs are useful or “high-quality (HQ)” as long as they are sufficiently narrow and capture most of the probability density. In this article, we present a method to learn PIs for regression-based neural networks (NNs) automatically in addition to the conventional target predictions. In particular, we train two companion NNs: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean PI width and ensuring the PI integrity using constraints that maximize the PI probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher quality PIs. Major findings:The DualAQD framework produces "prediction intervals" that inform users of the confidence level associated with an AI model's specific estimate. This method generates narrower and more accurate confidence ranges than existing methods while maintaining high overall target accuracy. The system successfully identifies regions of high uncertainty in crop yield predictions, increasing the reliability of deep learning models for high-stakes decision-making. https://ieeexplore.ieee.org/document/10365540 http://www.w3.org/2000/01/rdf-schema#label Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation https://ieeexplore.ieee.org/document/10365540 https://schema.org/funder https://ror.org/021nxhr62 https://ieeexplore.ieee.org/document/10365540 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://ieeexplore.ieee.org/document/10365540 https://www.w3.org/ns/dcat#contactPoint john.sheppard@montana.edu https://ieeexplore.ieee.org/document/10365540 https://www.w3.org/ns/dcat#endDate 2023 https://ieeexplore.ieee.org/document/10365540 https://www.w3.org/ns/dcat#startDate 2022 https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/provenance https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://purl.org/dc/terms/created 2026-01-21T17:49:29.713Z https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://purl.org/nanopub/x/introduces https://ieeexplore.ieee.org/document/10365540 https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://purl.org/nanopub/x/supersedes https://w3id.org/np/RAAnumXKqMyA6FRjwlI0AWDgFT8rnYBqvgCwOHWrrhxT4 https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAoTD7udB2KtUuOuAe74tJi1t3VzK0DyWS7rYVAq1GRvw https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/sig http://purl.org/nanopub/x/hasSignature qylJh82YaRS7TbP6rl545D5lrOF/CMPedWEZKxBYuyyaRy65nJgbA1hPPA1H4hOGiKAp/0W7BspTXac0+7nwg6pM5nXM3fsRnchRYYeSwqqNXeSgCw6NVVTj/dtatFflwtEDCvzys43h+LeqsBpY8t/UuQ4VBB9CCA5k9bCYDnE9D6r1+7tJDlYaIh6Y8/d/UEqDpqmt8I2YgcFO2Z+Dc0G6YOkvZmLtxECK4D2tHvgudCry7xa8fSouk56zVi4bb/OVaihA46bi8tgkan7Lry8DdEsE5452V0ZfMCNFBKxixVFPg+t/25KtAI+ZkWvBMW6ZVlbqYcsvSv/N2w2NlQ== https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg https://w3id.org/np/RAFtXCAVvqQ1-Q2hPDAxolC4GC-N662igvuYlH4_TxqJg/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742