IEEE transactions on artificial intelligence最新文献

{"title":"Ensuring Reliable Learning in Graph Convolutional Networks: Convergence Analysis and Training Methodology","authors":"Xinge Zhao;Chien Chern Cheah","doi":"10.1109/TAI.2025.3550458","DOIUrl":"https://doi.org/10.1109/TAI.2025.3550458","url":null,"abstract":"Recent advancements in learning from graph-structured data have highlighted the importance of graph convolutional networks (GCNs). Despite some research efforts on the theoretical aspects of GCNs, a gap remains in understanding their training process, especially concerning convergence analysis. This study introduces a two-stage training methodology for GCNs, incorporating both pretraining and fine-tuning phases. A two-layer GCN model is used for the convergence analysis and case studies. The convergence analysis that employs a Lyapunov-like approach is performed on the proposed learning algorithm, providing conditions to ensure the convergence of the model learning. Additionally, an automated learning rate scheduler is proposed based on the convergence conditions to prevent divergence and eliminate the need for manual tuning of the initial learning rate. The efficacy of the proposed method is demonstrated through case studies on the node classification problem. The results reveal that the proposed method outperforms gradient descent-based optimizers by achieving consistent training accuracies within a variation of 0.1% across various initial learning rates, without requiring manual tuning.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2510-2525"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926893","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}

{"title":"Detection of Unknown-Unknowns in Human-in-Loop Human-in-Plant Safety Critical Systems","authors":"Aranyak Maity;Ayan Banerjee;Sandeep K. S. Gupta","doi":"10.1109/TAI.2025.3550913","DOIUrl":"https://doi.org/10.1109/TAI.2025.3550913","url":null,"abstract":"Errors in artificial intelligence (AI)-enabled autonomous systems (AASs) where both the cause and effect are unknown to the human operator at the time they occur are referred to as “unknown-unknown” errors. This article introduces a methodology for preemptively identifying “unknown-unknown” errors in AAS that arise due to unpredictable human interactions and complex real-world usage scenarios, potentially leading to critical safety incidents through unsafe shifts in operational data distributions. We posit that AAS functioning in human-in-the-loop and human-in-the-plant modes must adhere to established physical laws, even when unknown-unknown errors occur. Our approach employs constructing physics-guided models from operational data, coupled with conformal inference for assessing structural breaks in the underlying model caused by violations of physical laws, thereby facilitating early detection of such errors before unsafe shifts in operational data distribution occur. Validation across diverse contexts—zero-day vulnerabilities in autonomous vehicles, hardware failures in artificial pancreas systems, and design deficiencies in aircraft in maneuvering characteristics augmentation systems (MCASs)—demonstrates our framework's efficacy in preempting unsafe data distribution shifts due to unknown-unknowns. This methodology not only advances unknown-unknown error detection in AAS but also sets a new benchmark for integrating physics-guided models and machine learning to ensure system safety.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2526-2541"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926900","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}

{"title":"A Comprehensive Survey on Diagnostic Microscopic Imaging Modalities, Challenges, Taxonomy, and Future Directions for Cervical Abnormality Detection and Grading","authors":"Anindita Mohanta;Sourav Dey Roy;Niharika Nath;Abhijit Datta;Mrinal Kanti Bhowmik","doi":"10.1109/TAI.2025.3551669","DOIUrl":"https://doi.org/10.1109/TAI.2025.3551669","url":null,"abstract":"Cancer is one of the most severe diseases, affecting the lives of many people in the modern world. Among the various types of cancer, cervical cancer is one of the most frequently occurring cancers in the female population. In most cases, doctors and practitioners can typically only identify cervical cancer in its latter stages. Planning cancer therapy and increasing patient survival rates become very difficult as the disease progresses. As a result, diagnosing cervical cancer in its initial stages has become imperative to arrange proper therapy and surgery. In this article, we present a survey of automatic computerized methods for diagnosing cervical abnormalities based on microscopic imaging modalities. The present survey was conducted by defining a novel taxonomy of the surveyed techniques based on the approaches they used. We also discuss the challenges and subchallenges associated with an automatic cervical cancer diagnosis based on microscopic imaging modalities. Additionally, surveys on various public and private datasets used by the research community for developing new methods are presented. In this article, the performances of published papers are compared. The article concludes by suggesting possible research directions in these fields.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2354-2383"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926904","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}

{"title":"Unimodal Distributions for Ordinal Regression","authors":"Jaime S. Cardoso;Ricardo P. M. Cruz;Tomé Albuquerque","doi":"10.1109/TAI.2025.3549740","DOIUrl":"https://doi.org/10.1109/TAI.2025.3549740","url":null,"abstract":"In many real-world prediction tasks, the class labels contain information about the relative order between the labels that are not captured by commonly used loss functions such as multicategory cross-entropy. In ordinal regression, many works have incorporated ordinality into models and loss functions by promoting unimodality of the probability output. However, current approaches are based on heuristics, particularly nonparametric ones, which are still insufficiently explored in the literature. We analyze the set of unimodal distributions in the probability simplex, establishing fundamental properties and giving new perspectives to understand the ordinal regression problem. Two contributions are then proposed to incorporate the preference for unimodal distributions into the predictive model: 1) UnimodalNet, a new architecture that by construction ensures the output is a unimodal distribution, and 2) Wasserstein regularization, a new loss term that relies on the notion of projection in a set to promote unimodality. Experiments show that the new architecture achieves top performance, while the proposed new loss term is very competitive while maintaining high unimodality.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2498-2509"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Artificial Intelligence Publication Information","authors":"","doi":"10.1109/TAI.2025.3546710","DOIUrl":"https://doi.org/10.1109/TAI.2025.3546710","url":null,"abstract":"","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-uniform Illumination Attack for Fooling Convolutional Neural Networks","authors":"Akshay Jain;Shiv Ram Dubey;Satish Kumar Singh;KC Santosh;Bidyut Baran Chaudhuri","doi":"10.1109/TAI.2025.3549396","DOIUrl":"https://doi.org/10.1109/TAI.2025.3549396","url":null,"abstract":"Convolutional neural networks (CNNs) have made remarkable strides; however, they remain susceptible to vulnerabilities, particularly to image perturbations that humans can easily recognize. This weakness, often termed as “attacks,” underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel nonuniform illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely accepted datasets including CIFAR10, TinyImageNet, CalTech256, and NWPU-RESISC45 focusing on image classification with 12 different NUI masks. The resilience of VGG, ResNet, MobilenetV3-small, InceptionV3, and EfficientNet_b0 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models’ classification accuracy when subjected to NUI attacks, due to changes in the image pixel value distribution, indicating their vulnerability under NUI. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models’ resilience against NUI attacks. A comparative study with other attack techniques shows the effectiveness of the NUI attack and defense technique.<xref>¹</xref><fn><p>¹The code is available at <uri>https://github.com/Akshayjain97/Non-Uniform_Illumination</uri></p></fn>","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2476-2485"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926890","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}

{"title":"MalaNet: A Small World Inspired Neural Network for Automated Malaria Diagnosis","authors":"Shubham Dwivedi;Kartikeya Pandey;Kumar Shubham;Om Jee Pandey;Achyut Mani Tripathi;Tushar Sandhan;Rajesh M. Hegde","doi":"10.1109/TAI.2025.3549406","DOIUrl":"https://doi.org/10.1109/TAI.2025.3549406","url":null,"abstract":"In this work, a novel neural network architecture called MalaNet is proposed for the detection and diagnosis of malaria, an infectious disease that poses a major global health challenge. The proposed neural network architecture is inspired by small-world network principles, which generally involve the introduction of new links. A small-world neural network is realized by establishing new connections, thereby reducing the average path length and increasing clustering coefficient. These characteristics are known to enhance interconnectivity and improve feature propagation within the network. In the context of malaria diagnosis, these characteristics of MalaNet can enhance detection accuracy and enable better generalization in scenarios with limited data availability. Broadly, two variants of MalaNet are proposed in this work. First, a small-world-inspired feed-forward neural network (FNN) is developed for symptom and categorical feature-based diagnosis, providing an accessible solution when blood smear images are unavailable. Subsequently, a small-world-inspired convolutional neural network (CNN) is developed for precise and automated diagnosis when blood smear images are available. Both variants of MalaNet are rigorously validated using the National Institute of Health Malaria dataset, a clinical dataset from Federal Polytechnic Ilaro Medical Centre, Nigeria, and the APTOS dataset. Comparative results against several state-of-the-art neural network models in the literature demonstrate MalaNet’s superior performance, generalization capability, and computational efficiency. The small-world neural network architecture proposed in this work enhances feature learning, diagnostic accuracy, and adaptability in limited-data and resource-constrained settings, motivating its application in disease diagnosis where timely and accurate results are critical.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2486-2497"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926892","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}

{"title":"An Adaptive Deep Learning Based Short-Term Wind Speed Forecasting Model for Variable Terrain Conditions","authors":"Sourav Malakar;Saptarsi Goswami;Bhaswati Ganguli;Amlan Chakrabarti","doi":"10.1109/TAI.2025.3547685","DOIUrl":"https://doi.org/10.1109/TAI.2025.3547685","url":null,"abstract":"Wind flow can be highly unpredictable suffer substantial fluctuations in speed and direction due to the shape and height of hills, mountains, and valleys, making accurate wind speed (WS) forecasting essential in complex terrain. Hourly WS data at 50 meters above ground, from MERRA-2, NASA (2015–2021), collected from five Indian wind stations for plain and complex terrain. This article presents a novel and adaptive model for short-term WS forecasting. The article's key contributions are as follows. (a) the partial auto correlation function (PACF) is utilized to minimize the dimension of the set of intrinsic mode functions (IMF), hence reducing training time; (b) The sample entropy (SampEn) was used to calculate the complexity of the reduced set of IMFs. Since a particular deep learning (DL) model-feature-combination was selected based on complexity, the proposed method is adaptive; (c) a novel bidirectional feature-LSTM framework for complicated IMFs has been suggested, resulting in improved forecasting accuracy; (d) the proposed model shows 55.94% superior forecasting performance compared to the persistence, hybrid, ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and variational mode decomposition (VMD)-based DL models. It has achieved the lowest prediction variance between simple and complex terrain at 0.70%, ensuring robust forecasting performance. Dimension reduction of IMF's and complexity-based model-feature selection helps reduce the training time by 68.77%, additionally forecasting quality is improved by 58.58% on average. These benefits highlight the model's adaptability, effectiveness, and resilience in addressing WS forecasting challenges on complex terrain.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2437-2447"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926897","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}

{"title":"Predicting Postgraduate Student Engagement Using Artificial Intelligence (AI)","authors":"Niusha Shafiabady;Tebbin Koo;Fareed Ud Din;Kabir Sattarshetty;Margaret Yen;Mamoun Alazab;Ethar Alsharaydeh","doi":"10.1109/TAI.2025.3548016","DOIUrl":"https://doi.org/10.1109/TAI.2025.3548016","url":null,"abstract":"The increasing number of international students (IS) enrolled in Australian higher education institutions, combined with the widespread adoption of online and hybrid learning, has significant implications for understanding the factors that influence engagement among this diverse student group. Early identification of students with low engagement facilitates academic success, prevents poor outcomes, optimizes resource allocation, improves teaching strategies, increases motivation, and supports long-term success. This study's main aim is to examine the use of AI to predict student engagement. Development of a theoretically informed survey that aimed to elicit postgraduate students' engagement was developed and validated by expert judgment. In total, 200 copies of the survey were distributed, 121 responses were received, and 96 were considered for this study representing a response rate of 48%. This study promotes a multidimensional approach, utilizing AI and ML methodologies, to determine the influence of social and cultural contexts on student engagement. This approach enables educators and institutions to create effective strategies for enhancing the learning experience of postgraduate students. Multiple AI and ML techniques have been utilized including synthetic data generation methods such GaussianCopula, triplet-based variational autoencoder, generative adversarial networks, CopulaGAN, and conditional tabular generative adversarial network. These techniques are specifically employed to predict various dimensions of engagement, including personal, academic, intellectual, social, and professional engagement. The performance of AI/ML algorithms, including support vector machine, K-nearest neighbors, decision trees, gradient boosting machine, random forest, Naive Bayes, logistic regression, and extra trees, was assessed using several metrics including F1 score, sensitivity, specificity, confusion matrix, and accuracy. The models used in this study achieved up to 85% accuracy, offering a solid foundation for guidelines and support to enhance decision making processes in higher education. These findings provide valuable insights for both academics and policy makers, laying the groundwork for evidence-based strategies to improve student engagement.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2464-2475"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926899","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}

{"title":"Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location","authors":"Mohsen Saffari;Mahdi Khodayar;Mohammad E. Khodayar;Seyed Saeed Fazlhashemi","doi":"10.1109/TAI.2025.3547878","DOIUrl":"https://doi.org/10.1109/TAI.2025.3547878","url":null,"abstract":"Accurate fault classification and location are critical to ensure the reliability and resilience of large-scale power distribution systems (PDSs). The existing data-driven works in this area struggle to capture essential space-time correlations of PDS measurements and often rely on deterministic and shallow neural architectures. Furthermore, they encounter challenges such as over-smoothing and the inability to capture deep correlations. To overcome these limitations, a novel deep space-time generative graph convolutional autoencoder (SGGCA) is proposed. First, the PDS is modeled as a space-time graph where the nodes and edges show the bus measurements and line impedance values, respectively. The proposed SGGCA's encoder captures deep correlations of the space-time graph using a new graph convolution with early connections and identity transformations to mitigate the over-smoothing. Our encoder encompasses a new recurrent method to adjust graph convolution parameters without relying on node embeddings on the temporal dimension. Additionally, it incorporates generative modeling by capturing the probability distribution function of the latent representation through a conditional normalizing flow model. The extracted generative space-time features are enhanced by a multi-head attention mechanism to better capture task-relevant characteristics of the PDS measurements. The extracted features are fed to sparse decoders to classify and locate the faults in the PDS. The feature sparsity of decoders ensures a high generalization capacity and avoids overfitting. The proposed method is evaluated on the IEEE 69-bus and 123-bus systems. It achieves substantial improvements in fault classification accuracy by 3.33% and 6.26% and enhances fault location accuracy by 6.33% and 5.73% for the respective PDSs compared with state-of-the-art models.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2448-2463"},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926898","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}