Pub Date : 2025-07-04DOI: 10.1109/TAI.2025.3586238
Rachmad Vidya Wicaksana Putra;Muhammad Shafique
Embedded AI systems are expected to incur low power/energy consumption for solving machine learning tasks, as these systems are usually power constrained (e.g., object recognition task in autonomous mobile agents with portable batteries). These requirements can be fulfilled by spiking neural networks (SNNs), since their bio-inspired spike-based operations offer high accuracy and ultra low-power/energy computation. Currently, most of SNN architectures are derived from artificial neural networks whose neurons’ architectures and operations are different from SNNs, and/or developed without considering memory budgets from the underlying processing hardware of embedded platforms. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose SpikeNAS, a novel fast memory-aware neural architecture search (NAS) framework for SNNs that quickly finds an appropriate SNN architecture with high accuracy under the given memory budgets from targeted embedded systems. To do this, our SpikeNAS employs several key steps: analyzing the impacts of network operations on the accuracy, enhancing the network architecture to improve the learning quality, developing a fast memory-aware search algorithm, and performing quantization. The experimental results show that our SpikeNAS improves the searching time and maintains high accuracy compared to state-of-the-art while meeting the given memory budgets (e.g., 29$boldsymbol{times}$, 117$boldsymbol{times}$, and 3.7$boldsymbol{times}$ faster search for CIFAR10, CIFAR100, and TinyImageNet200, respectively, using an Nvidia RTX A6000 GPU machine), thereby quickly providing the appropriate SNN architecture for the memory-constrained embedded AI systems.
{"title":"SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-Based Embedded AI Systems","authors":"Rachmad Vidya Wicaksana Putra;Muhammad Shafique","doi":"10.1109/TAI.2025.3586238","DOIUrl":"https://doi.org/10.1109/TAI.2025.3586238","url":null,"abstract":"Embedded AI systems are expected to incur low power/energy consumption for solving machine learning tasks, as these systems are usually power constrained (e.g., object recognition task in autonomous mobile agents with portable batteries). These requirements can be fulfilled by spiking neural networks (SNNs), since their bio-inspired spike-based operations offer high accuracy and ultra low-power/energy computation. Currently, most of SNN architectures are derived from artificial neural networks whose neurons’ architectures and operations are different from SNNs, and/or developed without considering memory budgets from the underlying processing hardware of embedded platforms. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose <italic>SpikeNAS</i>, a novel fast memory-aware neural architecture search (NAS) framework for SNNs that quickly finds an appropriate SNN architecture with high accuracy under the given memory budgets from targeted embedded systems. To do this, our SpikeNAS employs several key steps: analyzing the impacts of network operations on the accuracy, enhancing the network architecture to improve the learning quality, developing a fast memory-aware search algorithm, and performing quantization. The experimental results show that our SpikeNAS improves the searching time and maintains high accuracy compared to state-of-the-art while meeting the given memory budgets (e.g., 29<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>, 117<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>, and 3.7<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> faster search for CIFAR10, CIFAR100, and TinyImageNet200, respectively, using an Nvidia RTX A6000 GPU machine), thereby quickly providing the appropriate SNN architecture for the memory-constrained embedded AI systems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 2","pages":"947-959"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175972","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 : 2025-07-04DOI: 10.1109/TAI.2025.3585868
Himani Daulat;Krishna Chauhan;Tarun Varma
Filter bank design remains a critical challenge in signal processing, particularly in achieving high-performance metrics while maintaining computational efficiency. Current methods, including various optimization algorithms, have made strides in addressing these challenges but often need to improve in balancing perfect reconstruction (PR) and magnitude response accuracy. This research addresses these gaps by introducing the Gaussian distribution crayfish optimization algorithm (GD-COA), an enhanced version of the crayfish optimization algorithm (COA), for designing a four-channel finite impulse response (FIR) filter bank. The GD-COA formulates the design problem as a meta-heuristic optimization task, integrating PR and magnitude criteria to guide the filter design. It applies to uniform (critically sampled and oversampled) and nonuniform filter banks, accommodating various sampling rates. Our results show that GD-COA achieves significant improvements in filter bank performance. Specifically, for a critically sampled uniform filter bank, it attained a PR Error of $7.2219boldsymboltimes 10^{-16}$ and a Magnitude Response Approximation Error (MRAE) of $3.8018boldsymboltimes 10^{-16}$. In an oversampled uniform filter bank, the PR Error was $1.7321boldsymboltimes 10^{-5}$ with an MRAE of $7.2444boldsymboltimes 10^{-6}$. The algorithm yielded a PR Error of $3.2831boldsymboltimes 10^{-4}$ and an MRAE of $8.5113boldsymboltimes 10^{-5}$ for a nonuniform filter bank with a consistent sampling set. When applied to a variable filter bank with an inconsistent sampling set, the PR Error was $1.1403boldsymboltimes 10^{-4}$, and the MRAE was $2.34423boldsymboltimes 10^{-5}$. These results demonstrate the GD-COA’s effectiveness in optimizing filter coefficients, ensuring minimal reconstruction errors, and satisfactory magnitude response across various design scenarios.
{"title":"Application of Gaussian Distribution Crayfish Optimization in Adaptive FIR Filter Bank: Four-Channel Uniform and Nonuniform Designs","authors":"Himani Daulat;Krishna Chauhan;Tarun Varma","doi":"10.1109/TAI.2025.3585868","DOIUrl":"https://doi.org/10.1109/TAI.2025.3585868","url":null,"abstract":"Filter bank design remains a critical challenge in signal processing, particularly in achieving high-performance metrics while maintaining computational efficiency. Current methods, including various optimization algorithms, have made strides in addressing these challenges but often need to improve in balancing perfect reconstruction (PR) and magnitude response accuracy. This research addresses these gaps by introducing the Gaussian distribution crayfish optimization algorithm (GD-COA), an enhanced version of the crayfish optimization algorithm (COA), for designing a four-channel finite impulse response (FIR) filter bank. The GD-COA formulates the design problem as a meta-heuristic optimization task, integrating PR and magnitude criteria to guide the filter design. It applies to uniform (critically sampled and oversampled) and nonuniform filter banks, accommodating various sampling rates. Our results show that GD-COA achieves significant improvements in filter bank performance. Specifically, for a critically sampled uniform filter bank, it attained a PR Error of <inline-formula><tex-math>$7.2219boldsymboltimes 10^{-16}$</tex-math></inline-formula> and a Magnitude Response Approximation Error (MRAE) of <inline-formula><tex-math>$3.8018boldsymboltimes 10^{-16}$</tex-math></inline-formula>. In an oversampled uniform filter bank, the PR Error was <inline-formula><tex-math>$1.7321boldsymboltimes 10^{-5}$</tex-math></inline-formula> with an MRAE of <inline-formula><tex-math>$7.2444boldsymboltimes 10^{-6}$</tex-math></inline-formula>. The algorithm yielded a PR Error of <inline-formula><tex-math>$3.2831boldsymboltimes 10^{-4}$</tex-math></inline-formula> and an MRAE of <inline-formula><tex-math>$8.5113boldsymboltimes 10^{-5}$</tex-math></inline-formula> for a nonuniform filter bank with a consistent sampling set. When applied to a variable filter bank with an inconsistent sampling set, the PR Error was <inline-formula><tex-math>$1.1403boldsymboltimes 10^{-4}$</tex-math></inline-formula>, and the MRAE was <inline-formula><tex-math>$2.34423boldsymboltimes 10^{-5}$</tex-math></inline-formula>. These results demonstrate the GD-COA’s effectiveness in optimizing filter coefficients, ensuring minimal reconstruction errors, and satisfactory magnitude response across various design scenarios.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 2","pages":"931-946"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175994","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 : 2025-07-01DOI: 10.1109/TAI.2025.3585090
Erich Malan;Valentino Peluso;Andrea Calimera;Enrico Macii
This article investigates the impact of client and server learning rates on training deep neural networks in federated learning (FL). While previous research has primarily focused on optimizing the initial values of these learning rates, we demonstrate that this approach alone is insufficient for maximizing model performance and training efficiency. To address this weakness, we propose a revised two-sided learning rate optimization strategy that integrates learning rate decay schedules as tunable variables and adjusts the learning rate configurations based on the target training budget, allowing for more effective optimization. We conduct an extensive experimental evaluation to quantify the improvements offered by our approach. The results reveal that: 1) integrating decay schedules into the tuning process leads to significant performance enhancements; and 2) the optimal configuration of client-server decay schedules is strongly influenced by the training round budget. Based on these findings, we claim that performance evaluations of new FL algorithms should extend beyond the fine-tuning of the initial learning rate values, as done in the state-of-the-art approach, and include the optimization of decay schedules according to the available training budget.
{"title":"Refined Two-Sided Learning Rate Tuning for Robust Evaluation in Federated Learning","authors":"Erich Malan;Valentino Peluso;Andrea Calimera;Enrico Macii","doi":"10.1109/TAI.2025.3585090","DOIUrl":"https://doi.org/10.1109/TAI.2025.3585090","url":null,"abstract":"This article investigates the impact of client and server learning rates on training deep neural networks in federated learning (FL). While previous research has primarily focused on optimizing the initial values of these learning rates, we demonstrate that this approach alone is insufficient for maximizing model performance and training efficiency. To address this weakness, we propose a revised two-sided learning rate optimization strategy that integrates learning rate decay schedules as tunable variables and adjusts the learning rate configurations based on the target training budget, allowing for more effective optimization. We conduct an extensive experimental evaluation to quantify the improvements offered by our approach. The results reveal that: 1) integrating decay schedules into the tuning process leads to significant performance enhancements; and 2) the optimal configuration of client-server decay schedules is strongly influenced by the training round budget. Based on these findings, we claim that performance evaluations of new FL algorithms should extend beyond the fine-tuning of the initial learning rate values, as done in the state-of-the-art approach, and include the optimization of decay schedules according to the available training budget.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 2","pages":"906-917"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176029","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 : 2025-07-01DOI: 10.1109/TAI.2025.3584905
Junfeng Zhang;Hao Ji;Tarek Raïssi;Haoyue Yang
This article investigates the practical group consensus of type-1 and type-2 T-S fuzzy positive multiagent systems (MASs). First, a positive disturbance observer and a distributed positive compensator are proposed. A group consensus protocol is designed by integrating event-triggered mechanism, which utilizes the state information of the compensator. Some feasible conditions are addressed for practical group positive consensus in the form of linear programming (LP). The key novelties are threefold: 1) a novel positive disturbance observer and compensator framework is constructed; 2) a fuzzy positive group consensus protocol is established; and 3) LP is employed for describing the corresponding conditions. Finally, two examples are provided to verify the effectiveness of the theory findings.
{"title":"Practical Group Consensus of T-S Fuzzy Positive Multiagent Systems Using Compensative Control","authors":"Junfeng Zhang;Hao Ji;Tarek Raïssi;Haoyue Yang","doi":"10.1109/TAI.2025.3584905","DOIUrl":"https://doi.org/10.1109/TAI.2025.3584905","url":null,"abstract":"This article investigates the practical group consensus of type-1 and type-2 T-S fuzzy positive multiagent systems (MASs). First, a positive disturbance observer and a distributed positive compensator are proposed. A group consensus protocol is designed by integrating event-triggered mechanism, which utilizes the state information of the compensator. Some feasible conditions are addressed for practical group positive consensus in the form of linear programming (LP). The key novelties are threefold: 1) a novel positive disturbance observer and compensator framework is constructed; 2) a fuzzy positive group consensus protocol is established; and 3) LP is employed for describing the corresponding conditions. Finally, two examples are provided to verify the effectiveness of the theory findings.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 2","pages":"892-905"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176017","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 : 2025-07-01DOI: 10.1109/TAI.2025.3585095
Cong Hu;Jiangtao Song;Xiao-Jun Wu
Semisupervised learning (SSL) is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods have gained interest due to their efficient learning procedures. However, they are confined to pseudolabel or feature representation-level perturbations, negating the benefit of having both forms in a single framework. This leads to the model remaining robust to either the pseudolabel or the feature representation. To this end, we propose pseudograph regularization (PGR) for semisupervised classification, which leverages graph-based contrastive learning to unify pseudolabels and feature embeddings in a single semisupervised framework. The model imposes graph regularization on both pseudolabels and feature embeddings of unlabeled data to retain the intrinsic geometric structure. Feature embeddings into the model impose constraints on the class probability, forcing the class probability distributions of unlabeled data subject to different perturbations to be consistent. The pseudolabels regularly optimize the embedding space’s structure through graph-based contrastive learning, which allows data with similar pseudolabels to have similar feature embeddings in latent space. PGR unifies pseudolabel and feature representation of unlabeled data to improve the ability of model to resist noise interference and generalization ability. Extensive experiments on four benchmark datasets demonstrate that PGR can generate higher quality pseudolabels for unlabeled data, and is superior to the state-of-the-art (SOTA) methods.
{"title":"PGR: Pseudograph Regularization for Semisupervised Classification","authors":"Cong Hu;Jiangtao Song;Xiao-Jun Wu","doi":"10.1109/TAI.2025.3585095","DOIUrl":"https://doi.org/10.1109/TAI.2025.3585095","url":null,"abstract":"Semisupervised learning (SSL) is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods have gained interest due to their efficient learning procedures. However, they are confined to pseudolabel or feature representation-level perturbations, negating the benefit of having both forms in a single framework. This leads to the model remaining robust to either the pseudolabel or the feature representation. To this end, we propose pseudograph regularization (PGR) for semisupervised classification, which leverages graph-based contrastive learning to unify pseudolabels and feature embeddings in a single semisupervised framework. The model imposes graph regularization on both pseudolabels and feature embeddings of unlabeled data to retain the intrinsic geometric structure. Feature embeddings into the model impose constraints on the class probability, forcing the class probability distributions of unlabeled data subject to different perturbations to be consistent. The pseudolabels regularly optimize the embedding space’s structure through graph-based contrastive learning, which allows data with similar pseudolabels to have similar feature embeddings in latent space. PGR unifies pseudolabel and feature representation of unlabeled data to improve the ability of model to resist noise interference and generalization ability. Extensive experiments on four benchmark datasets demonstrate that PGR can generate higher quality pseudolabels for unlabeled data, and is superior to the state-of-the-art (SOTA) methods.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 2","pages":"918-930"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176000","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 : 2025-06-30DOI: 10.1109/TAI.2025.3584288
Ziyan Zhang;Fei Xu;Bo Jiang;Jin Tang
To alleviate the local receptive issue of graph convolutional network (GCN), transformers have been exploited to capture the long-range dependence of nodes for graph data representation and learning. However, existing graph transformers generally employ a regular self-attention module for all node-to-node message passing, which needs to learn the affinities/relationships between all node’s pairs, leading to high computational cost issue. Also, they are usually sensitive to graph noises. To overcome this issue, we propose a novel graph transformer architecture, termed anchor graph transformer (AGFormer), by leveraging an anchor graph model. To be specific, AGFormer first obtains some representative anchors and then converts node-to-node message passing into anchor-to-anchor and anchor-to-node message passing processes. Thus, AGFormer performs much more efficiently and also robustly than regular node-to-node transformers. Extensive experiments on several benchmark datasets demonstrate the benefits of the proposed AGFormer. Specifically, when the number of graph nodes reaches 15 000, AGFormer achieves a training speed that is three times faster than that of GraphTrans. Furthermore, AGFormers perform more robustly on the noised NCI109 dataset compared to GraphTrans.
{"title":"Efficient Graph Representation With Anchor-Graph Transformer","authors":"Ziyan Zhang;Fei Xu;Bo Jiang;Jin Tang","doi":"10.1109/TAI.2025.3584288","DOIUrl":"https://doi.org/10.1109/TAI.2025.3584288","url":null,"abstract":"To alleviate the local receptive issue of graph convolutional network (GCN), transformers have been exploited to capture the long-range dependence of nodes for graph data representation and learning. However, existing graph transformers generally employ a regular self-attention module for all node-to-node message passing, which needs to learn the affinities/relationships between all node’s pairs, leading to high computational cost issue. Also, they are usually sensitive to graph noises. To overcome this issue, we propose a novel graph transformer architecture, termed anchor graph transformer (AGFormer), by leveraging an anchor graph model. To be specific, AGFormer first obtains some representative anchors and then converts node-to-node message passing into anchor-to-anchor and anchor-to-node message passing processes. Thus, AGFormer performs much more efficiently and also robustly than regular node-to-node transformers. Extensive experiments on several benchmark datasets demonstrate the benefits of the proposed AGFormer. Specifically, when the number of graph nodes reaches 15 000, AGFormer achieves a training speed that is three times faster than that of GraphTrans. Furthermore, AGFormers perform more robustly on the noised NCI109 dataset compared to GraphTrans.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"7 2","pages":"1201-1209"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176001","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 : 2025-06-12DOI: 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.
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