IEEE transactions on artificial intelligence最新文献

{"title":"Bridging the Climate Gap: Multimodel Framework With Explainable Decision-Making for IOD and ENSO Forecasting","authors":"Harshit Tiwari;Prashant Kumar;Ramakant Prasad;Kamlesh Kumar Saha;Anurag Singh;Hocine Cherifi;Rajni","doi":"10.1109/TAI.2024.3489535","DOIUrl":"https://doi.org/10.1109/TAI.2024.3489535","url":null,"abstract":"Accurate forecasting of the Indian Ocean Dipole (IOD) and El-Niño-Southern Oscillation (NINO3.4) is crucial for understanding regional weather patterns in the Indian subcontinent. Addressing the challenges associated with IOD and NINO3.4 prediction, a robust multitask autoregressive deep learning (DL) model is introduced for precise forecasting of these indices and key grid projections sea surface temperature (SST), surface-level pressure gradient (SLG), and horizontal wind velocity (U-Comp) over a short to mid-term window (20 months). Utilizing spatiotemporal (SST, SLG, U-Comp) and temporal (IOD and NINO3.4) modalities, the proposed model predicts future IOD and NINO3.4, as well as SST, SLG, and U-Comp, in an autoregressive scheme. The multitask learning component regularizes the model, effectively capturing the evolving dynamics of global patterns conditioned on IOD and NINO3.4. The comprehensive evaluation explores various task settings, including a duo-setting that predicts IOD or NINO3.4 with spatiotemporal information, showcasing notable proficiency. In a multitask environment, where both temporal IOD, NINO3.4, and spatiotemporal SST, SLG, U-Comp are predicted, the model successfully forecasts IOD and NINO3.4 indices alongside grid projections with modest accuracy in root mean square error (RMSE). To enhance the model's interpretability regarding spatiotemporal dynamics, a tailored version of Grad-CAM is employed, providing critical insights for climate prediction. This research advances climate prediction models, offering a comprehensive framework with significant implications for informed decision-making in the Indian subcontinent's climatic context.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"661-675"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594377","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":"Model-Heterogeneous Federated Graph Learning With Prototype Propagation","authors":"Zhi Liu;Hanlin Zhou;Xiaohua He;Haopeng Yuan;Jiaxin Du;Mengmeng Wang;Guojiang Shen;Xiangjie Kong;Feng Xia","doi":"10.1109/TAI.2024.3490557","DOIUrl":"https://doi.org/10.1109/TAI.2024.3490557","url":null,"abstract":"Federated graph learning (FGL) enables clients to collaboratively train a robust graph neural network (GNN) while ensuring their private graph data never leaves the local. However, existing FGL frameworks require all clients to train the identical GNN model, which limits their real-world applicability. Although many model-heterogenous frameworks have been proposed for traditional nongraph federated learning settings, directly transferring them to the FGL setting typically results in suboptimal performance. To fill the gap, this article presents federated prototype propagation network (FedPPN), a lightweight FGL framework that supports clients to train fully customized models. FedPPN only transmits prototypes between clients and the server for knowledge sharing. The core idea is propagating global prototypes on each client's local graph, generating prototype-based node representations and predictions. The prototype-based prediction can then be ensembled with the prediction of local GNN, allowing clients to achieve accurate prediction. We evaluate our FedPPN on six benchmark datasets with different heterogeneous model setups. Experimental results show that our FedPPN outperforms advanced baselines in model accuracy without adding any trainable parameters on clients or the server. Besides, FedPPN's communication cost is significantly lower than methods that rely on model parameter exchange.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"676-689"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583148","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":"Neural Network-Based Ensemble Learning Model to Identify Antigenic Fragments of SARS-CoV-2","authors":"Syed Nisar Hussain Bukhari;Kingsley A. Ogudo","doi":"10.1109/TAI.2024.3487149","DOIUrl":"https://doi.org/10.1109/TAI.2024.3487149","url":null,"abstract":"The development of epitope-based vaccines (EBVs) necessitates the identification of antigenic fragments (AFs) of the target pathogen known as T-cell epitopes (TCEs). TCEs are recognized by immune system, specifically by T cells, B cells, and antibodies. Traditional wet lab methods for identifying TCEs are often costly, challenging, and time-consuming compared to computational approaches. In this study, we propose a neural network-based ensemble machine learning (ML) model trained on physicochemical properties of SARS-CoV-2 peptides sequences to predict TCE sequences. The performance of the model assessed using test dataset demonstrated an accuracy of >95%, surpassing the results of other ML classifiers that were employed for comparative analysis. Through fivefold cross-validation technique, a mean accuracy of approximately 95% was reported. Additionally, when compared to other existing TCE prediction methods using a blind dataset, the proposed model was found to be more accurate and effective. The predicted epitopes may have a strong probability to act as potential vaccine candidates. Nonetheless, it is imperative to subject these epitopes to further scientific examination both in vivo and in vitro, to confirm their suitability as vaccine candidates.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"651-660"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583135","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":"NICASU: Neurotransmitter Inspired Cognitive AI Architecture for Surveillance Underwater","authors":"Mehvish Nissar;Badri Narayan Subudhi;Amit Kumar Mishra;Vinit Jakhetiya","doi":"10.1109/TAI.2024.3486675","DOIUrl":"https://doi.org/10.1109/TAI.2024.3486675","url":null,"abstract":"The human brain is exceedingly good at learning rich narratives from highly limited experiences. One of the ways this is achieved in our brain is through neuromodulators or neurotransmitters, such as dopamine and nor-epinephrine, in cortical circuits. In terms of symbolic processing, these neuromodulators add “salience” to various emotions and experiences. A salience-based neural network (SANN) architecture was proposed in <xref>[1]</xref>. We have taken this architecture and have developed a discriminator to enable efficient change detection for underwater applications. In the context of underwater, surveillance can be elucidated as one of the processes of detecting and tracking the moving objects present in underwater videos. Several researchers working on the same tried to develop different techniques for identifying moving objects from outdoor scenes. However, while applying the same for underwater environments, it is found to be unable to preserve the minute details that are important for defining an object's boundary. This is mainly due to the complex scene dynamics of the aquatic environment. Moreover, the intricate natural properties of water and some of its characteristics, such as excessive turbidity, scattering, and low visibility, also make the task of detecting the object present in underwater videos extremely challenging. In this regard, we put forth an adversarial learning-based end-to-end deep learning architecture inspired by the way neurotransmitters work in the human brain to detect underwater moving objects. The proposed architecture uses two modules for underwater object detection. The initial module is a generator composed of a probabilistic learner which is based on multiple down- and up-sampling modules. Further, the discriminator network is composed of a multilevel feature-concatenation component, which can perpetuate specifics at distinct levels. The effectiveness of the proposed method (PM) is confirmed using the underwater change detection and Fish4Knowledge benchmark datasets by contrasting its outcomes with those of different state-of-the-art methods.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"626-638"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583262","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":"Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds","authors":"Kai Liu;Kang You;Pan Gao;Manoranjan Paul","doi":"10.1109/TAI.2024.3486676","DOIUrl":"https://doi.org/10.1109/TAI.2024.3486676","url":null,"abstract":"With the great progress of three-dimensional (3-D) sensing and acquisition technology, the volume of point cloud data has grown dramatically, which urges the development of efficient point cloud compression methods. In this article, we focus on the task of learned lossy point cloud attribute compression (PCAC). We propose an efficient attention-based method for lossy compression of point cloud attributes leveraging on an autoencoder architecture. Specifically, at the encoding side, we conduct multiple downsampling to best exploit the local attribute patterns, in which effective external cross attention (ECA) is devised to hierarchically aggregate features by intergrating attributes and geometry contexts. At the decoding side, the attributes of the point cloud are progressively reconstructed based on the multiscale representation and the zero-padding upsampling tactic. To the best of our knowledge, this is the first approach to introduce attention mechanism to point-based lossy PCAC task. We verify the compression efficiency of our model on various sequences, including human body frames, sparse objects, and large-scale point cloud scenes. Experiments show that our method achieves an average improvement of 1.15 and 2.13 dB in Bjontegaard delta (BD)-peak signal-to-noise ratio (BD-PSNR) of Y channel and YUV channel, respectively, when comparing with the state-of-the-art point-based method deep-PCAC. Codes of this article are available at <uri>https://github.com/I2-Multimedia-Lab/Att2CPC</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"639-650"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583177","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":"Image Tampering Detection With Frequency-Aware Attention and Multiview Fusion","authors":"Xu Xu;Junxin Chen;Wenrui Lv;Wei Wang;Yushu Zhang","doi":"10.1109/TAI.2024.3486671","DOIUrl":"https://doi.org/10.1109/TAI.2024.3486671","url":null,"abstract":"Manipulated images are flooding our daily lives, which poses a threat to social security. Recently, many studies have focused on image tampering detection. However, they have poor performance on independent validation due to differences in image scenes and tampering methods. The key question is how to design a network that is able to adaptively enhance the tampering information and suppress the generalization features during training. To this end, we propose a dual-branch network with a frequency adaptation paradigm and a feature fusion module for robust tampering image detection. First, this paradigm is designed to adaptively highlight tampering features through frequency conversion and learnable weight. Second, a feature fusion module is developed to filter redundant features and dynamically fuse two-branch features. Experiments on eight typical datasets demonstrate that our model has advantages over state-of-the-art algorithms, and our paradigm can well empower semantic segmentation networks for tampering detection.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"614-625"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583261","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":"Learning Empirical Inherited Intelligent MPC for Switched Systems With Network Security Communication","authors":"Yiwen Qi;Yiwen Tang;Wenke Yu","doi":"10.1109/TAI.2024.3486276","DOIUrl":"https://doi.org/10.1109/TAI.2024.3486276","url":null,"abstract":"This article studies learning empirical inherited intelligent model predictive control (LEII-MPC) for switched systems. For complex environments and systems, an intelligent control method design with learning ability is necessary and meaningful. First, a switching law that coordinates the iterative learning control action is devised according to the average dwell time approach. Second, an intelligent MPC mechanism with the iteration learning experience is designed to optimize the control action. With the designed LEII-MPC, sufficient conditions for the switched systems stability equipped with the event-triggering schemes (ETSs) in both the time domain and the iterative domain are presented. The ETS in the iterative domain is to solve unnecessary iterative updates. The ETS in the time domain is to deal with potential denial of service (DoS) attacks, which includes two parts: 1) for detection, an attack-dependent event-triggering method is presented to determine attack sequence and reduce lost packets; and 2) for compensation, a buffer is used to ensure system performance during the attack period. Last, a numerical example shows the effectiveness of the proposed method.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6342-6355"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810188","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 Learning-Based Dual Watermarking for Image Copyright Protection and Authentication","authors":"Sudev Kumar Padhi;Archana Tiwari;Sk. Subidh Ali","doi":"10.1109/TAI.2024.3485519","DOIUrl":"https://doi.org/10.1109/TAI.2024.3485519","url":null,"abstract":"Advancements in digital technologies make it easy to modify the content of digital images. Hence, ensuring digital images’ integrity and authenticity is necessary to protect them against various attacks that manipulate them. We present a deep learning (DL) based dual invisible watermarking technique for performing source authentication, content authentication, and protecting digital content copyright of images sent over the internet. Beyond securing images, the proposed technique demonstrates robustness to content-preserving image manipulation attacks. It is also impossible to imitate or overwrite watermarks because the cryptographic hash of the image and the dominant features of the image in the form of perceptual hash are used as watermarks. We highlighted the need for source authentication to safeguard image integrity and authenticity, along with identifying similar content for copyright protection. After exhaustive testing, our technique obtained a high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), which implies there is a minute change in the original image after embedding our watermarks. Our trained model achieves high watermark extraction accuracy and satisfies two different objectives of verification and authentication on the same watermarked image.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6134-6145"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810193","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":"MTECC: A Multitask Learning Framework for Esophageal Cancer Analysis","authors":"Jianpeng An;Wenqi Li;Yunhao Bai;Huazhen Chen;Gang Zhao;Qing Cai;Zhongke Gao","doi":"10.1109/TAI.2024.3485524","DOIUrl":"https://doi.org/10.1109/TAI.2024.3485524","url":null,"abstract":"In the field of esophageal cancer diagnostics, the accurate identification and classification of tumors and adjacent tissues within whole slide images (WSIs) are critical. However, this task is complicated by the difficulty in annotating normal tissue on tumor-bearing slides, as the infiltration results in a blend of different tissue types, making annotation difficult for pathologists. To overcome this challenge, we introduce the multitask esophageal cancer classification (MTECC) framework, featuring an innovative dual-branch architecture that operates at both global and local levels. The framework initially employs a masked autoencoder (MAE) for self-supervised learning. A distinctive feature of MTECC is the integration of RandoMix, an innovative image augmentation technique that randomly exchanges patches between different images. This method significantly enhances the model's generalization ability, especially for recognizing tissues within cancerous slides. MTECC ingeniously integrates two tasks: tumor detection using global tokens, and fine-grained tissue classification at the patch level using local tokens. The empirical evaluation of the MTECC on our extensive esophageal cancer dataset substantiates its efficacy. The performance metrics indicate robust results, with an accuracy of 0.811, an F1 score of 0.735, and an AUC of 0.957. The MTECC method represents a significant advancement in applying deep learning to complex pathological image analysis, offering valuable tools for pathologists in diagnosing and treating esophageal cancer.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6739-6751"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825947","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":"MDA-GAN: Multiscale and Dual Attention Generative Adversarial Network for Bone Suppression in Chest X-Rays","authors":"Anushikha Singh;Rukhshanda Hussain;Rajarshi Bhattacharya;Brejesh Lall;B.K. Panigrahi;Anjali Agrawal;Anurag Agrawal;Balamugesh Thangakunam;D.J. Christopher","doi":"10.1109/TAI.2024.3483731","DOIUrl":"https://doi.org/10.1109/TAI.2024.3483731","url":null,"abstract":"The bone structure in a chest x-ray creates trouble for a radiologist to examine the organs, manifestation of disease, and hidden tiny abnormalities. Bone suppression in chest x-rays allows better examination of lung fields. This has the potential to improve diagnostic accuracy. Dual-energy subtraction imaging is a standard bone suppression technique that delivers a higher dose of radiation and requires specific hardware. This article proposes a novel multiscale and dual attention-guided generative adversarial network (MDA-GAN) to transform chest x-rays into bone-suppressed x-rays in an unsupervised manner. We incorporate a spatial attention module to generate attention maps that were further concatenated with the coarsely generated bone segmentation mask. This dual attention is introduced to the generator at multiple scales in between the skip connection of the encoder and decoder layer. The proposed dual attention multiscale mechanism helps the generator to learn that only bones need to be removed on the chest x-ray without tempering the remaining parts. The proposed MDA-GAN is trained with adversarial loss combined with deep supervised cycle consistency and structure similarity for unpaired training images. We employ supervision heads in all the decoder layers to convert the activation maps into an output comparable to the scaled-down images and minimize the cycle consistency loss in a deep supervised manner. Experiments are conducted on an unpaired dataset including the public and our in-house Indian dataset and results show that incorporating dual attention at multiple scales and deep cycle consistency in translation networks significantly improves the quality of bone-suppressed images. (<uri>https://github.com/rB080/ribsup.git</uri>.)","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"604-613"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583289","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}