Pub Date : 2025-12-08DOI: 10.1109/TAI.2025.3641262
{"title":"2025 Index IEEE Transactions on Artificial Intelligence","authors":"","doi":"10.1109/TAI.2025.3641262","DOIUrl":"https://doi.org/10.1109/TAI.2025.3641262","url":null,"abstract":"","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 12","pages":"1-61"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11283132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729281","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}
Pub Date : 2025-07-14DOI: 10.1109/TAI.2025.3582067
Nikhil Laxminarayana;Nimish Mishra;Prayag Tiwari;Sahil Garg;Bikash K. Behera;Ahmed Farouk
N. Laxminarayana, N. Mishra, P. Tiwari, S. Garg, B. K. Behera, and A. Farouk, “Quantum-assisted activation for supervised learning in healthcare-based intrusion detection systems,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, pp. 977–984, Mar. 2024.
N. Laxminarayana， N. Mishra， P. Tiwari， S. Garg， B. K. Behera， A. Farouk，“基于医疗保健的入侵检测系统中监督学习的量子辅助激活”，《IEEE人工智能学报》，第5卷，第5期。3，第977-984页，2024年3月。
{"title":"Retraction Notice: Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems","authors":"Nikhil Laxminarayana;Nimish Mishra;Prayag Tiwari;Sahil Garg;Bikash K. Behera;Ahmed Farouk","doi":"10.1109/TAI.2025.3582067","DOIUrl":"https://doi.org/10.1109/TAI.2025.3582067","url":null,"abstract":"N. Laxminarayana, N. Mishra, P. Tiwari, S. Garg, B. K. Behera, and A. Farouk, “Quantum-assisted activation for supervised learning in healthcare-based intrusion detection systems,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, pp. 977–984, Mar. 2024.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"606-606"},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898252","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}
Pub Date : 2025-06-12DOI: 10.1109/TAI.2025.3577605
Tanish Singh Rajpal;Akshit Naithani
In response to the critical vulnerabilities exposed by quantum computing and AI-driven cryptanalysis in traditional encryption systems, this article introduces NeuroCrypt—a neuro-symbolic AI framework that synergizes adaptive cryptography, decentralized governance, and postquantum security. NeuroCrypt employs three AI groups: CryptAI (multialgorithm encryption), GenAI (neuro-symbolic algorithm synthesis), and TestAI (adversarial validation), to dynamically generate and deploy quantum-resistant cryptographic techniques. The framework uniquely combines five-layer encryption (randomly ordered classical and AI-generated algorithms, e.g., lattice–chaotic hybrids) with metadata-driven security, where encrypted logic is distributed via Shamir’s secret sharing (SSS) over VPNs, eliminating key-exchange dependencies. A permissioned blockchain enforces tamper-proof updates validated by TestAI consensus ($n/2 + 1$ threshold), while dynamic threshold adaptation adjusts SSS shard requirements based on real-time threat levels. Evaluations demonstrate NeuroCrypt’s superiority: 2.3$times$ higher entropy than AES-256, 94.3% shard survival under 30% compromise, and 220 ms encryption latency for 1 MB data on edge devices. The system’s lattice-based encryption (1024-dimensional) and frequent AI-driven updates resist Shor/Grover attacks, validated through simulated quantum oracles achieving $mathcal{O}(10^{38})$ operations for 256-bit keys. Compliance with GDPR, NIST PQC, and FIPS 140-2 ensures readiness for healthcare, fintech, and government applications. NeuroCrypt’s architecture—backward-compatible with legacy systems and optimized for IoT/cloud ecosystems—sets a precedent for self-evolving security, offering a 15% storage overhead trade-off for metadata-driven keyless decryption. Future work will optimize edge-device performance and integrate 6G network protocols, establishing NeuroCrypt as a foundational framework for postquantum cybersecurity.
{"title":"NeuroCrypt: A Neuro Symbolic AI Ecosystem for Advanced Cryptographic Data Security and Transmission","authors":"Tanish Singh Rajpal;Akshit Naithani","doi":"10.1109/TAI.2025.3577605","DOIUrl":"https://doi.org/10.1109/TAI.2025.3577605","url":null,"abstract":"In response to the critical vulnerabilities exposed by quantum computing and AI-driven cryptanalysis in traditional encryption systems, this article introduces <italic>NeuroCrypt</i>—a neuro-symbolic AI framework that synergizes adaptive cryptography, decentralized governance, and postquantum security. NeuroCrypt employs three AI groups: <italic>CryptAI</i> (multialgorithm encryption), <italic>GenAI</i> (neuro-symbolic algorithm synthesis), and <italic>TestAI</i> (adversarial validation), to dynamically generate and deploy quantum-resistant cryptographic techniques. The framework uniquely combines five-layer encryption (randomly ordered classical and AI-generated algorithms, e.g., lattice–chaotic hybrids) with metadata-driven security, where encrypted logic is distributed via Shamir’s secret sharing (SSS) over VPNs, eliminating key-exchange dependencies. A permissioned blockchain enforces tamper-proof updates validated by <italic>TestAI</i> consensus (<inline-formula><tex-math>$n/2 + 1$</tex-math></inline-formula> threshold), while dynamic threshold adaptation adjusts SSS shard requirements based on real-time threat levels. Evaluations demonstrate NeuroCrypt’s superiority: 2.3<inline-formula><tex-math>$times$</tex-math></inline-formula> higher entropy than AES-256, 94.3% shard survival under 30% compromise, and 220 ms encryption latency for 1 MB data on edge devices. The system’s lattice-based encryption (1024-dimensional) and frequent AI-driven updates resist Shor/Grover attacks, validated through simulated quantum oracles achieving <inline-formula><tex-math>$mathcal{O}(10^{38})$</tex-math></inline-formula> operations for 256-bit keys. Compliance with GDPR, NIST PQC, and FIPS 140-2 ensures readiness for healthcare, fintech, and government applications. NeuroCrypt’s architecture—backward-compatible with legacy systems and optimized for IoT/cloud ecosystems—sets a precedent for self-evolving security, offering a 15% storage overhead trade-off for metadata-driven keyless decryption. Future work will optimize edge-device performance and integrate 6G network protocols, establishing NeuroCrypt as a foundational framework for postquantum cybersecurity.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"512-521"},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we apply topic modeling using nonnegative matrix factorization (NMF) on the COVID-19 open research dataset (CORD-19) to uncover the underlying thematic structure and its evolution within the extensive body of COVID-19 research literature. NMF factorizes the document-term matrix into two nonnegative matrices, effectively representing the topics and their distribution across the documents. This helps us to see how strongly documents relate to topics and how topics relate to words. We describe the complete methodology, which involves a series of rigorous preprocessing steps to standardize the available text data while preserving the context of phrases and subsequently feature extraction using the term frequency-inverse document frequency (tf-idf), which assigns weights to words based on their frequency and rarity in the dataset. To ensure the robustness of our topic model, we conduct a stability analysis. This process assesses the stability scores of the NMF topic model for different numbers of topics, enabling us to select the optimal number of topics for our analysis. Through our analysis, we track the evolution of topics over time within the CORD-19 dataset. Our findings contribute to the understanding of the knowledge structure of the COVID-19 research landscape, providing a valuable resource for future research in this field.
{"title":"Exploring Topic Trends in COVID-19 Research Literature Using Nonnegative Matrix Factorization","authors":"Divya Patel;Vansh Parikh;Om Patel;Agam Shah;Bhaskar Chaudhury","doi":"10.1109/TAI.2025.3579459","DOIUrl":"https://doi.org/10.1109/TAI.2025.3579459","url":null,"abstract":"In this work, we apply topic modeling using nonnegative matrix factorization (NMF) on the COVID-19 open research dataset (CORD-19) to uncover the underlying thematic structure and its evolution within the extensive body of COVID-19 research literature. NMF factorizes the document-term matrix into two nonnegative matrices, effectively representing the topics and their distribution across the documents. This helps us to see how strongly documents relate to topics and how topics relate to words. We describe the complete methodology, which involves a series of rigorous preprocessing steps to standardize the available text data while preserving the context of phrases and subsequently feature extraction using the term frequency-inverse document frequency (tf-idf), which assigns weights to words based on their frequency and rarity in the dataset. To ensure the robustness of our topic model, we conduct a stability analysis. This process assesses the stability scores of the NMF topic model for different numbers of topics, enabling us to select the optimal number of topics for our analysis. Through our analysis, we track the evolution of topics over time within the CORD-19 dataset. Our findings contribute to the understanding of the knowledge structure of the COVID-19 research landscape, providing a valuable resource for future research in this field.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"586-595"},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-10DOI: 10.1109/TAI.2025.3578007
Zhenqin Chen;Yiwei Lin;Qiong Luo;Jinshan Xu
Fetal electrocardiography (FECG) is a crucial tool for assessing fetal cardiac health and pregnancy status. Direct invasive FECG provides reliable fetal heart rate signals, but poses risks and is limited to use during labor. Conversely, non-invasive monitoring of the fetal heart is possible via abdominal electrocardiography (AECG), which detects fetal heart waveforms using electrodes positioned on the mother’s abdomen. However, this method is often subject to interference from maternal cardiac activity and other external sources. To address this issue, we propose a novel diffusion method, DIFF-FECG, aimed at improving the extraction of FECG signals from AECG recordings. This method leverages a condition-driven diffusion process to learn specific conditional probability distributions, enabling the effective separation of high-quality FECG signals from noisy AECG data. By adaptively managing the inherent non-Gaussian noise characteristics of MECG within the AECG, DIFF-FECG achieves more effective FECG reconstruction. Furthermore, the quality of the generated FECG signals is also enhanced by adding reconstruction loss and multiple reconstructions. Experimental results on two public databases demonstrate that the proposed DIFF-FECG method yields satisfactory results, with an average Pearson correlation coefficient of 0.922 for the estimated FECG. These findings underscore the potential of diffusion probabilistic models in advancing FECG signal extraction techniques, thereby contributing to improved fetal health monitoring.
{"title":"DIFF-FECG: A Conditional Diffusion-Based Method for Fetal ECG Extraction From Abdominal ECG","authors":"Zhenqin Chen;Yiwei Lin;Qiong Luo;Jinshan Xu","doi":"10.1109/TAI.2025.3578007","DOIUrl":"https://doi.org/10.1109/TAI.2025.3578007","url":null,"abstract":"Fetal electrocardiography (FECG) is a crucial tool for assessing fetal cardiac health and pregnancy status. Direct invasive FECG provides reliable fetal heart rate signals, but poses risks and is limited to use during labor. Conversely, non-invasive monitoring of the fetal heart is possible via abdominal electrocardiography (AECG), which detects fetal heart waveforms using electrodes positioned on the mother’s abdomen. However, this method is often subject to interference from maternal cardiac activity and other external sources. To address this issue, we propose a novel diffusion method, DIFF-FECG, aimed at improving the extraction of FECG signals from AECG recordings. This method leverages a condition-driven diffusion process to learn specific conditional probability distributions, enabling the effective separation of high-quality FECG signals from noisy AECG data. By adaptively managing the inherent non-Gaussian noise characteristics of MECG within the AECG, DIFF-FECG achieves more effective FECG reconstruction. Furthermore, the quality of the generated FECG signals is also enhanced by adding reconstruction loss and multiple reconstructions. Experimental results on two public databases demonstrate that the proposed DIFF-FECG method yields satisfactory results, with an average Pearson correlation coefficient of 0.922 for the estimated FECG. These findings underscore the potential of diffusion probabilistic models in advancing FECG signal extraction techniques, thereby contributing to improved fetal health monitoring.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 1","pages":"534-546"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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