IEEE transactions on artificial intelligence最新文献_第6页

Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis 动态恶意软件行为的生成对抗网络：全面回顾，分类和分析

IEEE transactions on artificial intelligence Pub Date : 2025-02-04 DOI: 10.1109/TAI.2025.3537966

Ghebrebrhan Gebrehans;Naveed Ilyas;Khouloud Eledlebi;Willian Tessaro Lunardi;Martin Andreoni;Chan Yeob Yeun;Ernesto Damiani

引用次数: 0

HDL: Hybrid and Dynamic Learning for Fake Face Recognition 假人脸识别的混合和动态学习

IEEE transactions on artificial intelligence Pub Date : 2025-02-04 DOI: 10.1109/TAI.2025.3537963

Baojin Huang;Jiaqi Ma;Guangcheng Wang;Hui Wang

{"title":"HDL: Hybrid and Dynamic Learning for Fake Face Recognition","authors":"Baojin Huang;Jiaqi Ma;Guangcheng Wang;Hui Wang","doi":"10.1109/TAI.2025.3537963","DOIUrl":"https://doi.org/10.1109/TAI.2025.3537963","url":null,"abstract":"Face swapping aims to replace a source face with a target face, generating a fake face that is indistinguishable from the real one to the human eye. Existing face recognition methods usually discriminate the fake face as the target face identity, which happens to be misguided. To address this embarrassment, we pioneer a new task called “fake face recognition,” which seeks to discover the identity of the source face based on the fake face. Besides, we design a hybrid and dynamic learning strategy for fake face recognition. Specifically, we hybridize the existing real face recognition dataset with the fake face dataset. Based on the popular margin-based face recognition approach, we achieve dynamic learning by adjusting the margin for the fake face samples. The deep network is guided to first focus on real samples and then explores the identity of implicit commonalities between real and fake samples. To verify the performance of the fake face recognition model, we further organize the existing fake face datasets into face pairs. Extensive experiments on the fake face datasets show that our proposed hybrid and dynamic learning strategy achieves superior average accuracy (98.46%) compared to benchmark studies.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2073-2082"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751127","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}

引用次数: 0

SAWL-Net: A Statistical Attentions and Wavelet Aided Lightweight Network for Classification of Cancers in Histopathological Images SAWL-Net：一种统计关注和小波辅助的轻量级网络用于组织病理图像中的癌症分类

IEEE transactions on artificial intelligence Pub Date : 2025-01-30 DOI: 10.1109/TAI.2025.3536424

Surya Majumder;Aishik Paul;Friedhelm Schwenker;Ram Sarkar

{"title":"SAWL-Net: A Statistical Attentions and Wavelet Aided Lightweight Network for Classification of Cancers in Histopathological Images","authors":"Surya Majumder;Aishik Paul;Friedhelm Schwenker;Ram Sarkar","doi":"10.1109/TAI.2025.3536424","DOIUrl":"https://doi.org/10.1109/TAI.2025.3536424","url":null,"abstract":"Addressing the formidable challenges posed by the diagnosis and management of various types of cancer, including breast, colon, lung, and colorectal cancer, demands innovative solutions to streamline histopathological analysis processes. In this study, we propose a novel lightweight convolutional neural network (CNN) called statistical attentions and wavelet aided lightweight network (SAWL-Net) architecture based on MobileNetV2 equipped with hybrid statistical similarity and wave format-aided attention mechanisms, specifically tailored to the demands of cancer histopathology. By leveraging the capabilities, our model incorporates a lightweight design while ensuring high-performance outcomes. We introduce a unique combination of Pearson correlation coefficient, Spearman rank correlation, and cosine similarity metrics, alongside a specialized wave conversion technique to enhance the detection of similarities across different channels of histopathological data, while providing a holistic approach to the model. In this study, we have considered breast, colorectal, and lung & colon cancer datasets for experimentation. Notably, our model surpasses prevailing state-of-the-art methodologies, showcasing its efficacy in optimizing diagnostic accuracy and expediting treatment strategies for varied cancer types. Our codes are publicly available at the GitHub repository.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2051-2060"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751125","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}

引用次数: 0

Communication-Efficient Distributed Learning for Nash Equilibrium of Aggregative Games Over Time-Varying Digraphs 时变有向图聚集博弈纳什均衡的通信高效分布式学习

IEEE transactions on artificial intelligence Pub Date : 2025-01-29 DOI: 10.1109/TAI.2025.3535458

Mingfei Chen;Dong Wang;Xiaopeng Xu;Wenli Yao;Bingyang Zhu

{"title":"Communication-Efficient Distributed Learning for Nash Equilibrium of Aggregative Games Over Time-Varying Digraphs","authors":"Mingfei Chen;Dong Wang;Xiaopeng Xu;Wenli Yao;Bingyang Zhu","doi":"10.1109/TAI.2025.3535458","DOIUrl":"https://doi.org/10.1109/TAI.2025.3535458","url":null,"abstract":"Communication efficiency is a major challenge in learning the Nash equilibrium (NE) of aggregative games in a distributed manner. To address this problem, this article focuses on designing a communication-efficient algorithm under unbalanced digraphs, where the cost function of each player is affected by its own actions and the average aggregation function. In particular, the considered games have no central node, and no player has direct access to the aggregation function. To estimate the aggregation function, an auxiliary variable is employed to estimate the right Perron eigenvector of the column-stochastic weight matrix, which extends the dynamic average consensus protocol to time-varying digraphs. Additionally, players exchange information periodically and perform multistep local updates with local information between two consecutive communications. By combining the above two strategies with the gradient descent method, a communication-efficient algorithm is proposed and achieves a linear convergence rate. Then, the communication period selection method is provided to determine the best tradeoff between local updates and information exchange under limited resources. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2041-2050"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750865","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}

引用次数: 0

SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation SAMScore：一种用于图像翻译评价的内容结构相似度度量

IEEE transactions on artificial intelligence Pub Date : 2025-01-29 DOI: 10.1109/TAI.2025.3535456

Yunxiang Li;Meixu Chen;Kai Wang;Jun Ma;Alan C. Bovik;You Zhang

引用次数: 0

COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks 基于循环重叠彩票的卷积神经网络快速剪枝

IEEE transactions on artificial intelligence Pub Date : 2025-01-28 DOI: 10.1109/TAI.2025.3534745

Md. Ismail Hossain;Mohammed Rakib;M. M. Lutfe Elahi;Nabeel Mohammed;Shafin Rahman

{"title":"COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks","authors":"Md. Ismail Hossain;Mohammed Rakib;M. M. Lutfe Elahi;Nabeel Mohammed;Shafin Rahman","doi":"10.1109/TAI.2025.3534745","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534745","url":null,"abstract":"Pruning refers to the elimination of trivial weights from neural networks. The sub-networks within an overparameterized model produced after pruning are often called lottery tickets. This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve accuracy similar to that of the original unpruned network. We introduce a novel winning ticket called cyclic overlapping lottery ticket (COLT) by data splitting and cyclic retraining of the pruned network from scratch. We apply a cyclic pruning algorithm that keeps only the overlapping weights of different pruned models trained on different data segments. Our results demonstrate that COLT can achieve similar accuracies (obtained by the unpruned model) while maintaining high sparsities. Based on object recognition and detection tasks, we show that the accuracy of COLT is on par with the winning tickets of the lottery ticket hypothesis and, at times, is better. Moreover, COLTs can be generated using fewer iterations than tickets generated by the popular iterative magnitude pruning method. In addition, we also notice that COLTs generated on large datasets can be transferred to small ones without compromising performance, demonstrating its generalizing capability. We conduct all our experiments on Cifar-10, Cifar-100, TinyImageNet, and ImageNet datasets and report superior performance than the state-of-the-art methods. The codes are available at: <uri>https://github.com/ismail31416/COLT</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1664-1678"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196577","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}

引用次数: 0

AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain 基于注意力的深度学习方法在DCT领域的时间序列分类

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534141

Amine Haboub;Hamza Baali;Abdesselam Bouzerdoum

{"title":"AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain","authors":"Amine Haboub;Hamza Baali;Abdesselam Bouzerdoum","doi":"10.1109/TAI.2025.3534141","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534141","url":null,"abstract":"This article proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2-D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multilayer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on 13 different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1626-1638"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855682","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196917","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}

引用次数: 0

HWEFIS: A Hybrid Weighted Evolving Fuzzy Inference System for Nonstationary Data Streams HWEFIS：一种非平稳数据流的混合加权演化模糊推理系统

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534755

Tao Zhao;Haoli Li

{"title":"HWEFIS: A Hybrid Weighted Evolving Fuzzy Inference System for Nonstationary Data Streams","authors":"Tao Zhao;Haoli Li","doi":"10.1109/TAI.2025.3534755","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534755","url":null,"abstract":"For the problem of concept drift of nonstationary data streams, most evolving fuzzy inference systems (EFISs) still encounter problems. First, a single EFIS has difficulty quickly adjusting its own structure and parameters to adapt itself in an environment with obvious dynamic changes (such as sudden drift). Second, most ensemble EFISs adjust their weights according to errors, which is prone to the risk of model undertraining and repeated training. In this article, a new ensemble EFIS, referred to as a hybrid weighted evolving fuzzy inference system (HWEFIS), is proposed. The HWEFIS uses a detection method based on the edge heterogeneous distance (EHD) to mine similarity information between data distributions after data chunks arrive and uses Dempster–Shafer (DS) evidence theory to combine similarity and error information to generate hybrid weights. In addition, a forgetting factor and penalty mechanism are introduced into each base learner, which increases the ability of the base learner to address nonstationary problems. Experiments are carried out on synthetic datasets and real-world datasets. The experimental results show that the HWEFIS can achieve better performance in nonstationary data streams with complex drift, effectively suppresses the influence of concept drift, and is insensitive to the size of the data chunks.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1679-1694"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196581","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}

引用次数: 0

Behavioral Decision-Making of Mobile Robots Simulating the Functions of Cerebellum, Basal Ganglia, and Hippocampus 模拟小脑、基底神经节和海马体功能的移动机器人行为决策

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534150

Dongshu Wang;Qi Liu;Yihai Duan

{"title":"Behavioral Decision-Making of Mobile Robots Simulating the Functions of Cerebellum, Basal Ganglia, and Hippocampus","authors":"Dongshu Wang;Qi Liu;Yihai Duan","doi":"10.1109/TAI.2025.3534150","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534150","url":null,"abstract":"In unknown environments, behavioral decision-making of mobile robots is a crucial research topic in the field of robotics applications. To address the low learning ability and the difficulty of learning from the unknown environments for mobile robots, this work proposes a new learning model that integrates the supervised learning of the cerebellum, reinforcement learning of the basal ganglia, and memory consolidation of the hippocampus. First, to reduce the impact of noise on inputs and enhance the network's efficiency, a multineuron winning strategy and the refinement of the top-<inline-formula><tex-math>$k$</tex-math></inline-formula> competition mechanism have been adopted. Second, to increase the network's learning speed, a negative learning mechanism has been designed, which allows the robot to avoid obstacles more quickly by weakening the synaptic connections between error neurons. Third, to enhance the decision ability of cerebellar supervised learning, simulating the hippocampal memory consolidation mechanism, memory replay during the agent's offline state enables autonomous learning in the absence of real-time interactions. Finally, to better adjust the roles of cerebellar supervised learning and basal ganglia reinforcement learning in robot behavioral decision-making, a new similarity indicator has been designed. Simulation experiments and real-world experiments validate the effectiveness of the proposed model in this work.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1639-1650"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196570","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}

引用次数: 0

FLyer: Federated Learning-Based Crop Yield Prediction for Agriculture 5.0 传单：基于联邦学习的农业作物产量预测5.0

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534149

Tanushree Dey;Somnath Bera;Anwesha Mukherjee;Debashis De;Rajkumar Buyya

{"title":"FLyer: Federated Learning-Based Crop Yield Prediction for Agriculture 5.0","authors":"Tanushree Dey;Somnath Bera;Anwesha Mukherjee;Debashis De;Rajkumar Buyya","doi":"10.1109/TAI.2025.3534149","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534149","url":null,"abstract":"Crop yield prediction is a significant area of precision agriculture. In this article, we propose a crop yield prediction framework named FLyer, based on federated learning and edge computing. In FLyer, the soil and environmental data are locally processed inside the edge servers, and the model parameters are transmitted between the edge servers and the cloud with encrypted gradients. LSTM is used as the local and global models for data analysis. As the LSTM model can capture the temporal dependencies and hold the sequential nature of the data, we use LSTM in FLyer. By encrypting the gradients, the gradient information leakage ratio is reduced, and data privacy is protected. For gradient encryption, we use AES-256, and for data encryption during local storage we use RSA and AES-256. The results demonstrate that FLyer diminishes the latency by <inline-formula><tex-math>$boldsymbol{sim}$</tex-math></inline-formula>39% and energy consumption by <inline-formula><tex-math>$boldsymbol{sim}$</tex-math></inline-formula>40% than the conventional edge-cloud framework respectively. The experimental results show that the global model in FLyer achieves above 99% accuracy, precision, recall, and F1-score in crop yield prediction. The results also present that the local models also achieve <inline-formula><tex-math>$boldsymbol{>}$</tex-math></inline-formula>94% accuracy in crop yield prediction.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1943-1952"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519354","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}

引用次数: 0