Genetic programming (GP) has been widely applied to evolve scheduling heuristics for dynamic flexible job shop scheduling (DFJSS). However, the evaluation of GP individuals is computationally expensive, especially in large scale DFJSS scenarios. A k-nearest neighbor (KNN) based surrogate has been successfully used to reduce individual evaluation time for GP by predicting the fitness of an individual with the most similar sample in KNN. Particularly, the phenotypes of GP individuals have been utilized to generate samples for KNN-based surrogates with a precondition that the fitness of individuals with the same phenotype is the same or similar. However, their real fitness may differ greatly due to different input decision situations for fitness calculations in DFJSS. Thus, only considering phenotypes of GP individuals to extract samples could decrease the accuracy of KNN surrogates. This article proposes a KNN-based surrogate assisted GP algorithm by considering both the phenotype and genotype of GP individuals to generate samples. Specifically, a genotypic characterization based on terminal frequency is designed to measure the similarity of individual genotypes. The results show that with the same training time, the proposed algorithm can converge fast and achieve better scheduling heuristics than the state-of-the-art algorithms in most examined scenarios. With the same number of generations, the proposed algorithm can obtain comparable performance but only needs about one third of the training time of baseline GP. The effectiveness of the proposed algorithm is also verified from different aspects, e.g., relation between genotype correlation and fitness difference of individuals, and population diversity.
{"title":"Phenotype and Genotype Based Sample Aware Surrogate-Assisted Genetic Programming in Dynamic Flexible Job Shop Scheduling","authors":"Luyao Zhu;Fangfang Zhang;Xiaodong Zhu;Ke Chen;Mengjie Zhang","doi":"10.1109/TAI.2025.3562161","DOIUrl":"https://doi.org/10.1109/TAI.2025.3562161","url":null,"abstract":"Genetic programming (GP) has been widely applied to evolve scheduling heuristics for dynamic flexible job shop scheduling (DFJSS). However, the evaluation of GP individuals is computationally expensive, especially in large scale DFJSS scenarios. A k-nearest neighbor (KNN) based surrogate has been successfully used to reduce individual evaluation time for GP by predicting the fitness of an individual with the most similar sample in KNN. Particularly, the phenotypes of GP individuals have been utilized to generate samples for KNN-based surrogates with a precondition that the fitness of individuals with the same phenotype is the same or similar. However, their real fitness may differ greatly due to different input decision situations for fitness calculations in DFJSS. Thus, only considering phenotypes of GP individuals to extract samples could decrease the accuracy of KNN surrogates. This article proposes a KNN-based surrogate assisted GP algorithm by considering both the phenotype and genotype of GP individuals to generate samples. Specifically, a genotypic characterization based on terminal frequency is designed to measure the similarity of individual genotypes. The results show that with the same training time, the proposed algorithm can converge fast and achieve better scheduling heuristics than the state-of-the-art algorithms in most examined scenarios. With the same number of generations, the proposed algorithm can obtain comparable performance but only needs about one third of the training time of baseline GP. The effectiveness of the proposed algorithm is also verified from different aspects, e.g., relation between genotype correlation and fitness difference of individuals, and population diversity.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 12","pages":"3232-3247"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612210","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 : 2025-04-17DOI: 10.1109/TAI.2025.3562160
Yuxing Xing;Caixia Chen;Jie Wu;Jie Chen
The potential game has been widely used to describe multiagent task allocation. However, the application of traditional game-theoretic algorithms has shown unsatisfactory performance in scenarios with a high agent count. For this, we employ reinforcement learning algorithm to enable each agent to independently make decision in response to other agents’ decisions and variations in the number of agents, ultimately working towards achieving a desired goal. First, we construct a potential game for multiagent task allocation and design a corresponding utility function for each agent. Then, we propose a deep q-network algorithm based on graph neural network, and enhance the agent selection mechanism in this learning algorithm. During each iteration, a task is randomly selected for an agent from the participant set, and each agent updates its strategy accordingly. Finally, by comparing several representative game theoretical algorithms, the numerical simulations highlight the advantages and performance of our proposed GDQ-Net algorithm across various tasks and numbers of agents under the constructed model.
{"title":"Reinforcement Learning for Efficient Multiagent Task Allocation in Potential Game Model","authors":"Yuxing Xing;Caixia Chen;Jie Wu;Jie Chen","doi":"10.1109/TAI.2025.3562160","DOIUrl":"https://doi.org/10.1109/TAI.2025.3562160","url":null,"abstract":"The potential game has been widely used to describe multiagent task allocation. However, the application of traditional game-theoretic algorithms has shown unsatisfactory performance in scenarios with a high agent count. For this, we employ reinforcement learning algorithm to enable each agent to independently make decision in response to other agents’ decisions and variations in the number of agents, ultimately working towards achieving a desired goal. First, we construct a potential game for multiagent task allocation and design a corresponding utility function for each agent. Then, we propose a deep q-network algorithm based on graph neural network, and enhance the agent selection mechanism in this learning algorithm. During each iteration, a task is randomly selected for an agent from the participant set, and each agent updates its strategy accordingly. Finally, by comparing several representative game theoretical algorithms, the numerical simulations highlight the advantages and performance of our proposed GDQ-Net algorithm across various tasks and numbers of agents under the constructed model.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 12","pages":"3217-3231"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612204","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 : 2025-04-16DOI: 10.1109/TAI.2025.3560921
Hailong Hu;Jun Pang
Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners. Therefore, it is critical to technically protect the intellectual property of GANs. Prior works need to tamper with the training set or training process to verify the ownership of a GAN. In this article, we show that these methods are not robust to emerging model extraction attacks. Then, we propose a new method GAN-Guards which utilizes the common characteristics of a target model and its stolen models for ownership infringement detection. Our method can be directly applicable to all well-trained GANs as it does not require retraining target models. Extensive experimental results show that our new method achieves superior detection performance, compared with the watermark-based and fingerprint-based methods. Finally, we demonstrate the effectiveness of our method with respect to the number of generations of model extraction attacks, the number of generated samples, and adaptive attacks.
{"title":"Ownership Infringement Detection for Generative Adversarial Networks Against Model Stealing","authors":"Hailong Hu;Jun Pang","doi":"10.1109/TAI.2025.3560921","DOIUrl":"https://doi.org/10.1109/TAI.2025.3560921","url":null,"abstract":"Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners. Therefore, it is critical to technically protect the intellectual property of GANs. Prior works need to tamper with the training set or training process to verify the ownership of a GAN. In this article, we show that these methods are not robust to emerging model extraction attacks. Then, we propose a new method GAN-Guards which utilizes the common characteristics of a target model and its stolen models for ownership infringement detection. Our method can be directly applicable to all well-trained GANs as it does not require retraining target models. Extensive experimental results show that our new method achieves superior detection performance, compared with the watermark-based and fingerprint-based methods. Finally, we demonstrate the effectiveness of our method with respect to the number of generations of model extraction attacks, the number of generated samples, and adaptive attacks.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 11","pages":"3018-3029"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455916","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}
Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training powerful models with enormous self-collected data and providing application programming interfaces (APIs) for users, such as Chat-GPT. To avoid model leakage, users are required to upload their data to the server. This approach is particularly advantageous for devices with limited computational capabilities, as it offloads computation to the server, easing the workload on the devices themselves. However, this way raises public concerns about the privacy disclosure risk. Hence, to alleviate related concerns, we propose to directly denoise LDCT in the encrypted domain to achieve privacy-preserving cloud services without exposing private data to the server. Concretely, we employ homomorphic encryption to encrypt private LDCT, which is then transferred to the server model trained with plaintext LDCT for further denoising. Since fundamental DL operations, such as convolution and linear transformation, cannot be directly used in the encrypted domain, we transform the fundamental mathematic operations in the plaintext domain into the operations in the encrypted domain. Moreover, we present two interactive frameworks for linear and nonlinear models, both of which can achieve lossless operating. In this way, the proposed methods can achieve two merits, the data privacy is well protected, and the server model is free from the risk of model leakage. Moreover, we provide theoretical proof to validate the lossless property of our framework. Finally, experiments were conducted to demonstrate that the transferred contents are well protected and cannot be reconstructed.1
The codes are released at https://github.com/Zi-YuanYang/Encrypt_LDCT_Recon
{"title":"A Novel Privacy-Enhancing Framework for Low-Dose CT Denoising","authors":"Ziyuan Yang;Huijie Huangfu;Maosong Ran;Zhiwen Wang;Hui Yu;Mengyu Sun;Yi Zhang","doi":"10.1109/TAI.2025.3561092","DOIUrl":"https://doi.org/10.1109/TAI.2025.3561092","url":null,"abstract":"Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training powerful models with enormous self-collected data and providing application programming interfaces (APIs) for users, such as Chat-GPT. To avoid model leakage, users are required to upload their data to the server. This approach is particularly advantageous for devices with limited computational capabilities, as it offloads computation to the server, easing the workload on the devices themselves. However, this way raises public concerns about the privacy disclosure risk. Hence, to alleviate related concerns, we propose to directly denoise LDCT in the encrypted domain to achieve privacy-preserving cloud services without exposing private data to the server. Concretely, we employ homomorphic encryption to encrypt private LDCT, which is then transferred to the server model trained with plaintext LDCT for further denoising. Since fundamental DL operations, such as convolution and linear transformation, cannot be directly used in the encrypted domain, we transform the fundamental mathematic operations in the plaintext domain into the operations in the encrypted domain. Moreover, we present two interactive frameworks for linear and nonlinear models, both of which can achieve lossless operating. In this way, the proposed methods can achieve two merits, the data privacy is well protected, and the server model is free from the risk of model leakage. Moreover, we provide theoretical proof to validate the lossless property of our framework. Finally, experiments were conducted to demonstrate that the transferred contents are well protected and cannot be reconstructed.<xref><sup>1</sup></xref><fn><label><sup>1</sup></label><p>The codes are released at <uri>https://github.com/Zi-YuanYang/Encrypt_LDCT_Recon</uri></p></fn>","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 11","pages":"3043-3055"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455995","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}
Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal discovery usually relies on expert validation or prior analysis to improve the reliability of recovered causality, which is yet limited by the scarcity of expert resources. Recently, large language models (LLM) have been used for causal analysis across various domain-specific scenarios, suggesting its potential as autonomous expert roles in guiding data-based structure learning. However, integrating LLMs into causal discovery faces challenges due to inaccuracies in LLM-based reasoning on revealing the actual causal structure. To address this challenge, we propose an error-tolerant LLM-driven causal discovery framework. The error-tolerant mechanism is designed three-fold with sufficient consideration on potential inaccuracies. In the LLM-based reasoning process, an accuracy-oriented prompting strategy restricts causal analysis to a reliable range. Next, a knowledge-to-structure transition aligns LLM-derived causal statements with structural causal interactions. In the structure learning process, the goodness-of-fit to data and adherence to LLM-derived priors are balanced to further address prior inaccuracies. Evaluation of eight real-world causal structures demonstrates the efficacy of our LLM-driven approach in improving data-based causal discovery, along with its robustness to inaccurate LLM-derived priors.
{"title":"Integrating Large Language Model for Improved Causal Discovery","authors":"Taiyu Ban;Lyuzhou Chen;Derui Lyu;Xiangyu Wang;Qinrui Zhu;Qiang Tu;Huanhuan Chen","doi":"10.1109/TAI.2025.3560927","DOIUrl":"https://doi.org/10.1109/TAI.2025.3560927","url":null,"abstract":"Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal discovery usually relies on expert validation or prior analysis to improve the reliability of recovered causality, which is yet limited by the scarcity of expert resources. Recently, large language models (LLM) have been used for causal analysis across various domain-specific scenarios, suggesting its potential as autonomous expert roles in guiding data-based structure learning. However, integrating LLMs into causal discovery faces challenges due to inaccuracies in LLM-based reasoning on revealing the actual causal structure. To address this challenge, we propose an error-tolerant LLM-driven causal discovery framework. The error-tolerant mechanism is designed three-fold with sufficient consideration on potential inaccuracies. In the LLM-based reasoning process, an accuracy-oriented prompting strategy restricts causal analysis to a reliable range. Next, a knowledge-to-structure transition aligns LLM-derived causal statements with structural causal interactions. In the structure learning process, the goodness-of-fit to data and adherence to LLM-derived priors are balanced to further address prior inaccuracies. Evaluation of eight real-world causal structures demonstrates the efficacy of our LLM-driven approach in improving data-based causal discovery, along with its robustness to inaccurate LLM-derived priors.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 11","pages":"3030-3042"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456013","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}
Open set domain adaptation (OSDA) copes with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target domain. Most existing OSDA approaches, depending on the final image feature space of deep models, require manually-tuned thresholds, and may easily misclassify unknown samples as known classes. Mixture-of-experts (MoE) could be a remedy. Within an MoE, different experts handle distinct input features, producing unique expert routing patterns for various classes in a routing feature space. As a result, unknown class samples may display different expert routing patterns to known classes. This article proposes dual-space detection, which exploits the inconsistencies between the image feature space and the routing feature space to detect unknown class samples without any threshold. A graph router is further introduced to better make use of the spatial information among the image patches. Experiments on three datasets validated the effectiveness and superiority of our approach.
{"title":"Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach","authors":"Zhenbang Du;Jiayu An;Yunlu Tu;Jiahao Hong;Dongrui Wu","doi":"10.1109/TAI.2025.3560590","DOIUrl":"https://doi.org/10.1109/TAI.2025.3560590","url":null,"abstract":"Open set domain adaptation (OSDA) copes with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target domain. Most existing OSDA approaches, depending on the final image feature space of deep models, require manually-tuned thresholds, and may easily misclassify unknown samples as known classes. Mixture-of-experts (MoE) could be a remedy. Within an MoE, different experts handle distinct input features, producing unique expert routing patterns for various classes in a routing feature space. As a result, unknown class samples may display different expert routing patterns to known classes. This article proposes dual-space detection, which exploits the inconsistencies between the image feature space and the routing feature space to detect unknown class samples without any threshold. A graph router is further introduced to better make use of the spatial information among the image patches. Experiments on three datasets validated the effectiveness and superiority of our approach.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 12","pages":"3207-3216"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612207","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 : 2025-04-14DOI: 10.1109/TAI.2025.3560592
Guojie Li;Zhiwen Yu;Kaixiang Yang;Ziwei Fan;C. L. Philip Chen
Broad learning system (BLS) has been widely researched and applied in the field of semisupervised learning. However, current semisupervised BLS methods rely on predefined graph structures. High-dimensional small-sample data, characterized by abundant redundant and noisy features with complex distribution patterns, often leads to the construction of poor-quality predefined graphs, thereby constraining the model’s performance. Additionally, the random generation of feature and enhancement nodes in BLS, combined with limited data labels, results in suboptimal model performance. To address these issues, this article first proposes a broad learning system with adaptive locality preservation (BLS-ALP). This method employs adaptive locality preservation constraints in the output space to ensure that similar samples share the same label, iteratively updating the graph structure. To further enhance the performance of BLS-ALP, an incremental ensemble framework (IBLS-ALP) is proposed. This framework effectively mitigates the impact of redundant and noisy features by using multiple random subspaces instead of the original high-dimensional space. Additionally, IBLS-ALP enhances the utilization of a small number of labels by incorporating residual labels, thereby significantly improving the model’s overall performance. Extensive experiments conducted on various high-dimensional small-sample datasets demonstrate that IBLS-ALP exhibits superior performance.
{"title":"Incremental Semisupervised Learning With Adaptive Locality Preservation for High-Dimensional Data","authors":"Guojie Li;Zhiwen Yu;Kaixiang Yang;Ziwei Fan;C. L. Philip Chen","doi":"10.1109/TAI.2025.3560592","DOIUrl":"https://doi.org/10.1109/TAI.2025.3560592","url":null,"abstract":"Broad learning system (BLS) has been widely researched and applied in the field of semisupervised learning. However, current semisupervised BLS methods rely on predefined graph structures. High-dimensional small-sample data, characterized by abundant redundant and noisy features with complex distribution patterns, often leads to the construction of poor-quality predefined graphs, thereby constraining the model’s performance. Additionally, the random generation of feature and enhancement nodes in BLS, combined with limited data labels, results in suboptimal model performance. To address these issues, this article first proposes a broad learning system with adaptive locality preservation (BLS-ALP). This method employs adaptive locality preservation constraints in the output space to ensure that similar samples share the same label, iteratively updating the graph structure. To further enhance the performance of BLS-ALP, an incremental ensemble framework (IBLS-ALP) is proposed. This framework effectively mitigates the impact of redundant and noisy features by using multiple random subspaces instead of the original high-dimensional space. Additionally, IBLS-ALP enhances the utilization of a small number of labels by incorporating residual labels, thereby significantly improving the model’s overall performance. Extensive experiments conducted on various high-dimensional small-sample datasets demonstrate that IBLS-ALP exhibits superior performance.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 11","pages":"2990-3004"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455763","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}
One of the main challenges in electroencephalography (EEG) emotion recognition is the lack of understanding of the biological properties of the brain and how they relate to emotions. To address this issue, this article proposes an implicit emotion regulatory mechanism inspired contrastive learning framework (CLIER) for EEG emotion recognition. The framework simulates the complex relationship between emotions and the underlying neurobiological processes; to achieve this, the mechanism is mainly simulated through three parts. First, to leverage the interindividual variability of emotional expression, the emotion features of the individual are captured by a dynamic connection graph in the subject-dependent setting. Subsequently, reverse regulation is simulated by contrast learning based on label information and data augmentation to capture more biologically specific emotional features. Finally, caused by the asymmetry between the left and right hemispheres of the human brain in response to emotions, brain lateralization mutual learning facilitates the fusion of the hemispheres in determining emotions. Experiments on SEED, SEED-IV, SEED-V, and EREMUS datasets show impressive results: 93.4% accuracy on SEED, 90.2% on SEED-IV, 82.46% on SEED-V, and 41.63% on EREMUS. Employing an identical experimental protocol, our model demonstrated superior performance relative to the majority of existing methods, thus showcasing its effectiveness in the realm of EEG emotion recognition.
{"title":"EEG Emotion Recognition Based on an Implicit Emotion Regulatory Mechanism","authors":"Dongdong Li;Zhishuo Jin;Yujun Shen;Zhe Wang;Suo Jiang","doi":"10.1109/TAI.2025.3560593","DOIUrl":"https://doi.org/10.1109/TAI.2025.3560593","url":null,"abstract":"One of the main challenges in electroencephalography (EEG) emotion recognition is the lack of understanding of the biological properties of the brain and how they relate to emotions. To address this issue, this article proposes an implicit emotion regulatory mechanism inspired contrastive learning framework (CLIER) for EEG emotion recognition. The framework simulates the complex relationship between emotions and the underlying neurobiological processes; to achieve this, the mechanism is mainly simulated through three parts. First, to leverage the interindividual variability of emotional expression, the emotion features of the individual are captured by a dynamic connection graph in the subject-dependent setting. Subsequently, reverse regulation is simulated by contrast learning based on label information and data augmentation to capture more biologically specific emotional features. Finally, caused by the asymmetry between the left and right hemispheres of the human brain in response to emotions, brain lateralization mutual learning facilitates the fusion of the hemispheres in determining emotions. Experiments on SEED, SEED-IV, SEED-V, and EREMUS datasets show impressive results: 93.4% accuracy on SEED, 90.2% on SEED-IV, 82.46% on SEED-V, and 41.63% on EREMUS. Employing an identical experimental protocol, our model demonstrated superior performance relative to the majority of existing methods, thus showcasing its effectiveness in the realm of EEG emotion recognition.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 11","pages":"3005-3017"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455996","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 : 2025-04-11DOI: 10.1109/TAI.2025.3560248
Zhiqiang Ge;Duxin Chen;Wenwu Yu
Recently, probabilistic latent variable models have played an important role in data analytics in various industrial application scenarios, such as process monitoring, fault diagnosis, and soft sensing. Inspired by the idea of lightweight deep learning, this article proposes a new deep residual learning method for the probabilistic’ partial least squares (PLSs) model. First, layerwise probabilistic modeling is carried out to extract supervised latent variables in different hidden layers of the deep model using a well-designed expectation-maximization algorithm for parameter optimization. Through this layerwise residual learning process, more target-related latent variables can be extracted, which are supervised by the outputs of the predictive model. Next, an additional probabilistic model is constructed for information fusion and further extraction of supervised latent variables which are highly related to the modeling target. In fact, this step can be considered as an ensemble learning strategy, which has great potentials in decreasing modeling error and reducing prediction uncertainty. A soft-sensing strategy is then developed for online prediction of key variables. The performance is evaluated using two industrial examples. Compared to the shallow probabilistic model, the performance of the deep model has been improved by 10%–20%.
{"title":"Deep Residual Learning of a Probabilistic’ Partial Least Squares Model for Predictive Data Analytics","authors":"Zhiqiang Ge;Duxin Chen;Wenwu Yu","doi":"10.1109/TAI.2025.3560248","DOIUrl":"https://doi.org/10.1109/TAI.2025.3560248","url":null,"abstract":"Recently, probabilistic latent variable models have played an important role in data analytics in various industrial application scenarios, such as process monitoring, fault diagnosis, and soft sensing. Inspired by the idea of lightweight deep learning, this article proposes a new deep residual learning method for the probabilistic’ partial least squares (PLSs) model. First, layerwise probabilistic modeling is carried out to extract supervised latent variables in different hidden layers of the deep model using a well-designed expectation-maximization algorithm for parameter optimization. Through this layerwise residual learning process, more target-related latent variables can be extracted, which are supervised by the outputs of the predictive model. Next, an additional probabilistic model is constructed for information fusion and further extraction of supervised latent variables which are highly related to the modeling target. In fact, this step can be considered as an ensemble learning strategy, which has great potentials in decreasing modeling error and reducing prediction uncertainty. A soft-sensing strategy is then developed for online prediction of key variables. The performance is evaluated using two industrial examples. Compared to the shallow probabilistic model, the performance of the deep model has been improved by 10%–20%.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 11","pages":"2977-2989"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455952","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 : 2025-04-09DOI: 10.1109/TAI.2025.3558183
Nilufar Zaman;Angshuman Jana
In today’s world, online services have revolutionized human activities and thus the consumers expect their service providers to make their online experiences more fruitful by recommending the relevant services to them. In this case, it becomes really challenging for the service providers to provide recommendation to a user whose information’s and preferences are unavailable. This issue is handled by cross-domain approach, which explores similar users across various domains in the same platform. However, the main concern with this cross-domain approach is that the information needs to be available in any domain of one platform. Thus, a multidomain recommendation is designed to optimize the recommendation system performance by analyzing the information obtained from multiple platforms. However, existing multidomain recommendation model has mainly two challenges. First, there are no overlapping users to understand the similarities between them. Second, the transfer learning approach in multidomain allows the transfer of information from only the source to the target domain. Therefore, our proposed approach consider the parallel inductive shift learning (PISL) model to address these two above-mentioned challenges. For the first challenge, we have focused to identify the similarities between user–user and user–item by considering various features of user and item. For the next challenge, our proposed model analyzes the source and the target domain simultaneously and thus does a parallel transfer of information from the source to the target domain and vice versa. We have tested our model for three real-life movie and book datasets i.e. for the movie dataset we have used Movielens, Amazon, and Netflix datasets. In contrast, for the book dataset, we have used the Amazon, Good Reads, and Book Crossing dataset, which proves to outperform the other state-of-the-art approaches.
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