Neurocomputing最新文献

{"title":"Improving rule-based classifiers by Bayes point aggregation","authors":"Luca Bergamin ,&nbsp;Mirko Polato ,&nbsp;Fabio Aiolli","doi":"10.1016/j.neucom.2024.128699","DOIUrl":"10.1016/j.neucom.2024.128699","url":null,"abstract":"<div><div>The widespread adoption of artificial intelligence systems with continuously higher capabilities is causing ethical concerns. The lack of transparency, particularly for state-of-the-art models such as deep neural networks, hinders the applicability of such black-box methods in many domains, like the medical or the financial ones, where model transparency is a mandatory requirement, and hence white-box models are largely preferred over potentially more accurate but opaque techniques.</div><div>For this reason, in this paper, we focus on ruleset learning, arguably the most interpretable class of learning techniques. Specifically, we propose Bayes Point Rule Classifier, an ensemble methodology inspired by the Bayes Point Machine, to improve the performance and robustness of rule-based classifiers. In addition, to improve interpretability, we propose a technique to retain the most relevant rules based on their importance, thus increasing the transparency of the ensemble, making it easier to understand its decision-making process.</div><div>We also propose FIND-RS, a greedy ruleset learning algorithm that, under mild conditions, guarantees to learn hypothesis with perfect accuracy on the training set while preserving a good generalization capability to unseen data points.</div><div>We performed extensive experimentation showing that FIND-RS achieves state-of-the-art classification performance at the cost of a slight increase in the ruleset complexity w.r.t. the competitors. However, when paired with the Bayes Point Rule Classifier, FIND-RS outperforms all the considered baselines.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid novel SWARA-ELECTRE-I method using probabilistic uncertain linguistic information for feature selection in image recognition","authors":"Sumera Naz ,&nbsp;Shariq Aziz Butt ,&nbsp;Muhammad Muneeb ul Hassan ,&nbsp;José Escorcia-Gutierrez ,&nbsp;Areej Fatima ,&nbsp;Farhat ul Ain","doi":"10.1016/j.neucom.2024.128615","DOIUrl":"10.1016/j.neucom.2024.128615","url":null,"abstract":"<div><div>In the digital age, the exponential growth of data poses significant challenges for analysts and machine learning algorithms in pattern detection due to its high dimensionality. This study addresses the dimensionality problem by leveraging Probabilistic Uncertain Linguistic Term Set (PULTS), which combine Uncertain Linguistic Term Set (ULTS) with associated probabilities to handle uncertainty in decision-making. We introduce the PUL-weighted average operator to integrate the opinions of multiple decision-makers and propose a novel ELimination and Choice Translating REality (ELECTRE-I) method for optimizing alternatives in multiple attribute group decision-making (MAGDM) scenarios. This method is enhanced by the Stepwise Weight Assessment Ratio Analysis (SWARA) method to determine the relative weight of each attribute. By integrating SWARA with the ELECTRE-I method, we develop a comprehensive approach to tackle MAGDM problems using PULTS. A numerical example involving feature selection in image recognition demonstrates the method’s effectiveness and accuracy. Comparative studies highlight the advantages of our approach in producing a small feature set with high classification accuracy. The proposed method offers a robust solution for feature selection in image recognition and other MAGDM problems, significantly improving decision-making accuracy and efficiency. The methodology’s simplicity and computational ease make it applicable across various domains requiring effective dimensionality reduction.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel ensemble over-sampling approach based Chebyshev inequality for imbalanced multi-label data","authors":"Weishuo Ren ,&nbsp;Yifeng Zheng ,&nbsp;Wenjie Zhang ,&nbsp;Depeng Qing ,&nbsp;Xianlong Zeng ,&nbsp;Guohe Li","doi":"10.1016/j.neucom.2024.128717","DOIUrl":"10.1016/j.neucom.2024.128717","url":null,"abstract":"<div><div>With the development of intelligent technology, data exhibits characteristics of multi-label and imbalanced distribution, which lead to the degradation of classification model performance. Therefore, addressing multi-label class imbalance has become a hot research topic. Nowadays, over-sampling approaches aim to generate a superset of the original dataset to deal with imbalanced data. However, traditional over-sampling methods only employ the central data point and its nearest neighbor samples to synthesize samples without considering the impact of data distribution. To address these issues, in this paper, we propose an ensemble multi-label over-sampling algorithm (MLCIO) based on Chebyshev inequality and a group optimization strategy. Firstly, to generate more representative and diverse samples, with the seed sample serving as the sphere’s center, Chebyshev inequality is utilized to ensure that synthetic samples fall within its <span><math><mi>m</mi></math></span> times the standard deviation. Secondly, a group optimization ranking weighting approach is employed to obtain more reliable and stable label information. Finally, comparative experiments are conducted on 11 imbalanced datasets from various domains using different evaluation metrics. The results demonstrate that our proposal achieves better performance than other approaches.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing MRI segmentation with CLIP-driven semi-supervised learning and semantic alignment","authors":"Bo Sun ,&nbsp;Kexuan Li ,&nbsp;Jingjuan Liu ,&nbsp;Zhen Sun ,&nbsp;Xuehao Wang ,&nbsp;Yuanbo He ,&nbsp;Xin Zhao ,&nbsp;Huadan Xue ,&nbsp;Aimin Hao ,&nbsp;Shuai Li ,&nbsp;Yi Xiao","doi":"10.1016/j.neucom.2024.128690","DOIUrl":"10.1016/j.neucom.2024.128690","url":null,"abstract":"<div><div>Precise segmentation and reconstruction of multi-structures within MRI are crucial for clinical applications such as surgical navigation. However, medical image segmentation faces several challenges. Although semi-supervised methods can reduce the annotation workload, they often suffer from limited robustness. To address this issue, this study proposes a novel CLIP-driven semi-supervised model, that includes two branches and a module. In the image branch, copy-paste is used as data augmentation method to enhance consistency learning. In the text branch, patient-level information is encoded via CLIP to drive the image branch. Notably, a novel cross-modal fusion module is designed to enhance the alignment and representation of text and image. Additionally, a semantic spatial alignment module is introduced to register segmentation results from different axial MRIs into a unified space. Three multi-modal datasets (one private and two public) were constructed to demonstrate the model’s performance. Compared to previous state-of-the-art methods, this model shows a significant advantage with both 5% and 10% labeled data. This study constructs a robust semi-supervised medical segmentation model, particularly effective in addressing label inconsistency and abnormal organ deformations. It also tackles the axial non-orthogonality challenges inherent in MRI, providing a consistent view of multi-structures.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MARA: A deep learning based framework for multilayer graph simplification","authors":"Cheick Tidiane Ba ,&nbsp;Roberto Interdonato ,&nbsp;Dino Ienco ,&nbsp;Sabrina Gaito","doi":"10.1016/j.neucom.2024.128712","DOIUrl":"10.1016/j.neucom.2024.128712","url":null,"abstract":"<div><div>In many scientific fields, complex systems are characterized by a multitude of heterogeneous interactions/relationships that are challenging to model. Multilayer graphs constitute valuable tools that can represent such complex systems, thus making possible their analysis for downstream decision-making processes. Nevertheless, modeling such complex information still remains challenging in real-world scenarios. On the one hand, holistically including all relationships may lead to noisy or computationally intensive graphs. On the other hand, limiting the amount of information to model through the selection of a portion of the available relationships can introduce boundary specification biases. However, the current research studies are demonstrating that it is more beneficial to retain as much information as possible and at a later stage perform graph simplification i.e., removing uninformative or redundant parts of the graph to facilitate the final analysis. While simplification strategies, based on deep learning methods, have been already extensively explored in the context of single-layer graphs, only a limited amount of efforts have been devoted to simplification strategies for multilayer graphs. In this work, we propose the MultilAyer gRaph simplificAtion (<span>MARA</span>) framework, a GNN-based approach designed to simplify multilayer graphs based on the downstream task. <span>MARA</span> generates node embeddings for a specific task by training jointly two main components: (i) an edge simplification module and (ii) a (multilayer) graph neural network. We tested <span>MARA</span> on different real-world multilayer graphs for node classification tasks. Experimental results show the effectiveness of the proposed approach: <span>MARA</span> reduces the dimension of the input graph while keeping and even improving the performance of node classification tasks in different domains and across graphs characterized by different structures. Moreover, deep learning-based simplification allows <span>MARA</span> to preserve and enhance important graph properties for the downstream task. To our knowledge, <span>MARA</span> represents the first simplification framework especially tailored for multilayer graphs analysis.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing aspect-based sentiment analysis with linking words-guided emotional augmentation and hybrid learning","authors":"Deling Huang,&nbsp;Ling Ren,&nbsp;Zanxiong Li","doi":"10.1016/j.neucom.2024.128705","DOIUrl":"10.1016/j.neucom.2024.128705","url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) is a sophisticated task in the field of natural language processing that aims to identify emotional tendencies related to specific aspects of text. However, ABSA often faces significant data shortages, which limit the availability of annotated data for training and affects the robustness of models. Moreover, when a text contains multiple emotional dimensions, these dimensions can interact, complicating the judgments of emotional polarity. In response to these challenges, this study proposes an innovative training framework: Linking words-guided multidimensional emotional data augmentation and adversarial contrastive training (LWEDA-ACT). Specifically, this method alleviates the issue of data scarcity by synthesizing additional training samples using four different text generators. To obtain the most representative samples, we selected them by calculating sentence entropy. Meanwhile, to reduce potential noise, we introduced linking words to ensure text coherence. Additionally, by applying adversarial training, the model is able to learn generalized feature representations to handle minor input perturbations, thereby enhancing its robustness and accuracy in complex emotional dimension interactions. Through contrastive learning, we constructed positive and negative sample pairs, enabling the model to more accurately identify and distinguish the sentiment polarity of different aspect terms. We conducted comprehensive experiments on three popular ABSA datasets, namely Restaurant, Laptop, and Twitter, and compared our method against the current state-of-the-art techniques. The experimental results demonstrate that our approach achieved an accuracy improvement of +0.98% and a macro F1 score increase of +0.52% on the Restaurant dataset. Additionally, on the challenging Twitter dataset, our method improved accuracy by +0.77% and the macro F1 score by +1.14%.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Diverse Knowledge Perception and Fusion network for detecting targets and key parts in UAV images","authors":"Hanyu Wang,&nbsp;Qiang Shen,&nbsp;Zilong Deng","doi":"10.1016/j.neucom.2024.128748","DOIUrl":"10.1016/j.neucom.2024.128748","url":null,"abstract":"<div><div>Detecting targets and their key parts in UAV images is crucial for both military and civilian applications, including optimizing damage assessment, evaluating infrastructure, and facilitating disaster response efforts. Traditional top-down approaches impose excessive constraints that struggle to address challenges such as variable definitions and quantities of key parts, potential target occlusion, and model redundancy. Conversely, end-to-end approaches often overlook the relationships between targets and key parts, resulting in low detection accuracy. Inspired by the remarkable human reasoning process, we propose the Diverse Knowledge Perception and Fusion (DKPF) network, which skillfully balances the trade-offs between stringent constraints and unconstrained methods while ensuring both detection precision and real-time performance. Specifically, our model integrates reasoning guided by three distinct forms of knowledge: contextual knowledge at the image level in an unsupervised manner; explicit semantic knowledge regarding the interactions between targets and key parts at the instance level; and implicit comprehensive knowledge about the relationships among different types of targets or key parts, such as shape similarity. These specific knowledge forms are extracted through a novel adaptive fusion strategy for multi-scale features, a binary region-to-region semantic knowledge graph, and a data-driven self-attention architecture, respectively. Experiments conducted on both simulated and real-world datasets reveal that our method significantly outperforms state-of-the-art techniques, regardless of the number of key parts in the target. Furthermore, extensive ablation studies and visualization analyses validate both the efficacy of our approach and the interpretability of the generated features.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep belief network with fuzzy parameters and its membership function sensitivity analysis","authors":"Amit K. Shukla ,&nbsp;Pranab K. Muhuri","doi":"10.1016/j.neucom.2024.128716","DOIUrl":"10.1016/j.neucom.2024.128716","url":null,"abstract":"<div><div>Over the last few years, deep belief networks (DBNs) have been extensively utilized for efficient and reliable performance in several complex systems. One critical factor contributing to the enhanced learning of the DBN layers is the handling of network parameters, such as weights and biases. The efficient training of these parameters significantly influences the overall enhanced performance of the DBN. However, the initialization of these parameters is often random, and the data samples are normally corrupted by unwanted noise. This causes the uncertainty to arise among weights and biases of the DBNs, which ultimately hinders the performance of the network. To address this challenge, we propose a novel DBN model with weights and biases represented using fuzzy sets. The approach systematically handles inherent uncertainties in parameters resulting in a more robust and reliable training process. We show the working of the proposed algorithm considering four widely used benchmark datasets such as: MNSIT, n-MNIST (MNIST with additive white Gaussian noise (AWGN) and MNIST with motion blur) and CIFAR-10. The experimental results show superiority of the proposed approach as compared to classical DBN in terms of robustness and enhanced performance. Moreover, it has the capability to produce equivalent results with a smaller number of nodes in the hidden layer; thus, reducing the computational complexity of the network architecture. Additionally, we also study the sensitivity analysis for stability and consistency by considering different membership functions to model the uncertain weights and biases. Further, we establish the statistical significance of the obtained results by conducting both one-way and Kruskal-Wallis analyses of variance tests.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-level adaptive incongruity-enhanced model for multimodal sarcasm detection","authors":"Qiaofeng Wu ,&nbsp;Wenlong Fang ,&nbsp;Weiyu Zhong ,&nbsp;Fenghuan Li ,&nbsp;Yun Xue ,&nbsp;Bo Chen","doi":"10.1016/j.neucom.2024.128689","DOIUrl":"10.1016/j.neucom.2024.128689","url":null,"abstract":"<div><div>Multimodal sarcasm detection leverages multimodal information, such as image, text, etc. to identify special instances whose superficial emotional expression is contrary to the actual emotion. Existing methods primarily focused on the incongruity between text and image information for sarcasm detection. Existing sarcasm methods in which the tendency of image encoders to encode similar images into similar vectors, and the introduction of noise in graph-level feature extraction due to negative correlations caused by the accumulation of GAT layers and the lack of representations for non-neighboring nodes. To address these limitations, we propose a Dual-Level Adaptive Incongruity-Enhanced Model (DAIE) to extract the incongruity between the text and image at both token and graph levels. At the token level, we bolster token-level contrastive learning with patch-based reconstructed image to capture common and specific features of images, thereby amplifying incongruities between text and images. At the graph level, we introduce adaptive graph contrast learning, coupled with negative pair similarity weights, to refine the feature representation of the model’s textual and visual graph nodes, while also enhancing the information exchange among neighboring nodes. We conduct experiments using a publicly available sarcasm detection dataset. The results demonstrate the effectiveness of our method, outperforming several state-of-the-art approaches by 3.33% and 4.34% on accuracy and F1 score, respectively.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based image encryption techniques: Fundamentals, current trends, challenges and future directions","authors":"Om Prakash Singh ,&nbsp;Kedar Nath Singh ,&nbsp;Amit Kumar Singh ,&nbsp;Amrit Kumar Agrawal","doi":"10.1016/j.neucom.2024.128714","DOIUrl":"10.1016/j.neucom.2024.128714","url":null,"abstract":"<div><div>In recent years, the number of digital images has grown exponentially because of the widespread use of fast internet and smart devices. The integrity authentication of these images is a major concern for the research community. So, the encryption schemes that are commonly used to protect these images are an important subject for many potential applications. This paper presents a comprehensive survey of recent image encryption techniques using deep learning models. First, we explain the reasons that image encryption using deep learning models is beneficial to researchers and the public. Second, we discuss various state-of-art encryption techniques using deep learning models and offer technical summaries of popular techniques. Third, we provide a comparative analysis of our survey and existing state-of-the-art surveys. Finally, by investigating existing deep learning-based encryption, we identify several important research challenges and possible solutions including standard security metrics. To the best of our knowledge, we are the first researchers to do a detailed survey of deep learning-based image encryption for digital images.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}