Applied Intelligence最新文献

{"title":"Complex layout generation for large-scale floor plans via deep edge-aware GNNs","authors":"Zhengyang Lu,&nbsp;Yifan Li,&nbsp;Feng Wang","doi":"10.1007/s10489-025-06311-w","DOIUrl":"10.1007/s10489-025-06311-w","url":null,"abstract":"<div><p>In architectural layout generation, deep learning techniques have advanced the residential generation in multiple scenarios. However, current approaches fail to extract complex graph features from large-scale layouts, neglecting large-scale global context. Additionally, the lack of robust, quantitative evaluation metrics for layouts hampers the objective comparison of different generative approaches. To address these issues, we propose a multi-scale applicable layout generation method based on deep edge-aware GNNs, stressing edge-specific and non-local spatial information. Next, we introduce quantitative metrics to assess layout quality, including room accessibility index and space property proportion, whose purpose is to establish layout standards in the computer-aided design field. Lastly, we create the Public Space Floor Plan Dataset (P-PLAN), a collection of 4,535 annotated layout samples designed to serve as a robust evaluation platform for large-scale layout models. We conducted extensive qualitative and quantitative experiments on the Residential Floor Plan Dataset (R-PLAN) and P-PLAN dataset to demonstrate the effectiveness of the proposed method. Notably, with the proposed evaluation metrics, our method significantly outperforms existing models in accessibility and diversity.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107786","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":"MOEA/D with adaptive weight vector adjustment and parameter selection based on Q-learning","authors":"Fei Xue,&nbsp;Yuezheng Chen,&nbsp;Tingting Dong,&nbsp;Peiwen Wang,&nbsp;Wenyu Fan","doi":"10.1007/s10489-024-05906-z","DOIUrl":"10.1007/s10489-024-05906-z","url":null,"abstract":"<div><p>Multi-objective evolutionary algorithms (MOEAs) are widely utilized for addressing multi-objective optimization problems (MOPs), demonstrating effectiveness in handling low-dimensional and regular Pareto fronts (PFs) MOPs. However, when the number of objectives increases (&gt;3) and the PFs become increasingly intricate, maintaining both the convergence and diversity of solutions presents a significant challenge. To address this, an adaptive weight vector adjustment and parameter selection based on Q-learning (QLMOEA/D-AWA) is proposed. In the algorithm, Q-learning is employed to select both the Tchebycheff value and the number of weight vectors, aiming to balance convergence and diversity. To enhance the convergence, an improved Tchebycheff approach is proposed. To better solve problems in high-dimensional objective spaces, the niche technique is adopted to retain elite individuals. In addition, to address MOPs with irregular PFs, a two-stage weight vector deletion strategy is proposed to remove invalid weight vectors, and a certain number of weight vectors are added based on sparsity rules. An experiment study of objective numbers ranging from 2 to 10 is conducted on DTLZ, WFG, MaF and multi-objective traveling salesman problem (MOTSP). Among 115 benchmark problems, QLMOEA/D-AWA achieves 54 and 49 best results in IGD and HV, respectively.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107796","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":"Semi-supervised symmetric non-negative matrix factorization with graph quality improvement and constraints","authors":"Xiaowan Ren,&nbsp;Youlong Yang","doi":"10.1007/s10489-025-06282-y","DOIUrl":"10.1007/s10489-025-06282-y","url":null,"abstract":"<div><p>Symmetric non-negative matrix factorization (SNMF) decomposes a similarity matrix into the product of an indicator matrix and its transpose, allowing clustering results to be directly extracted from the indicator matrix without additional clustering methods. Furthermore, SNMF has been shown to be effective in clustering nonlinearly separable data. SNMF-based clustering methods significantly depend on the quality of the pairwise similarity matrix, yet their effectiveness is often hindered by the reliance on predefined matrices in most semi-supervised SNMF approaches. Thus, we propose a novel algorithm, named semi-supervised symmetric non-negative matrix factorization with graph quality improvement and constraints (<span>(text {S}^{3}text {NMFGC})</span>), addressing this limitation by employing an integrated clustering strategy that dynamically generates and adaptively updates the similarity matrices. This is accomplished by integrating a weighted graph construction based on multiple clustering results, a label propagation algorithm, and pairwise constraint terms into a unified optimization framework that enhances the semi-supervised SNMF model. Subsequently, we adopt an alternating iterative update method to solve the optimization problem and prove its convergence. Rigorous experiments highlight the superiority of our model, which outperforms seven state-of-the-art NMF methods across six datasets.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107781","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":"Multiple instance learning with hierarchical discrimination and smoothing attention for histopathological diagnosis","authors":"Jing Zhao,&nbsp;Zhikang Zhao,&nbsp;Xueru Song,&nbsp;Shiliang Sun","doi":"10.1007/s10489-025-06300-z","DOIUrl":"10.1007/s10489-025-06300-z","url":null,"abstract":"<div><p>The microscopic structure of human tissue can be observed by pathological slides, which provides a strong basis for cancer diagnosis. However, the serious lack of experienced pathologists and the complexity of the diagnostic process have facilitated the development of computer-aided pathological image analysis. Pathological slides generally have high resolution, and multiple instance learning (MIL) has been widely used in histopathological whole slide image (WSI) analysis, where each WSI has a large number of unlabelled patches and only a WSI-level label is given. The bag-based MIL methods often learn the decision boundary at the bag level, and thus hard to learn the discriminative features at the instance level. Furthermore, the difficulty of identification varies between positive instances in a bag, and the existing attention-based aggregation methods always assign higher attention scores for the easy-to-identify positive instances, but assign lower attention scores for the difficult-to-identify positive instances and thus cannot learn these difficult instances sufficiently. In this paper, we propose a novel MIL method with hierarchical discrimination learning and a smoothing attention strategy for cancer subtype diagnosis. Particularly, to learn hierarchical discriminative features, the proposed MIL method simultaneously trains a bag classifier and multiple instance classifiers, where the multi-way attention scores of each instance for different categories are used to guide the selection of training samples for the instance classifimer. The smoothing strategy is designed to trade off the attention weights between the easily and hardly identifiable positive instances. We conducted experiments on histopathological diagnosis datasets and achieved state-of-the-art performance. Codes are available at https://github.com/bravePinocchio/HDSA-MIL.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110011","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":"Computational analysis of virus-host protein-protein interactions using gene ontology and natural language processing","authors":"Pınar Cihan,&nbsp;Zeynep Banu Ozger,&nbsp;Zeynep Cakabay","doi":"10.1007/s10489-024-06223-1","DOIUrl":"10.1007/s10489-024-06223-1","url":null,"abstract":"<div><p>The role of in-silico computational methods in identifying protein-protein interactions (PPIs) between target and host proteins is crucial for developing effective infection treatments. These methods are essential for quickly determining high-quality and accurate PPIs, predicting protein pairs with the highest likelihood of physical interaction from a large pool, and reducing the need for experimental confirmation or prioritizing pairs for experiments. This study proposes using gene ontology and natural language processing (NLP) approaches to extract and quantify features from protein sequences. In the first step, proteins were represented using gene ontology terms, and a set of features was generated. In the second step, NLP techniques treated gene ontology terms as a word dictionary, creating numerical vectors using the bag of words (BoW), count vector, term frequency-inverse document frequency (TF-IDF), and information content methods. In the third step, different machine learning methods, including Decision Tree, Random Forest, Bagging-RepTree, Bagging-RF, BayesNet, Deep Neural Network (DNN), Logistic Regression, Support Vector Machine (SVM), and VotedPerceptron, were employed to predict protein interactions in the datasets. In the fourth step, the Max-Min Parents and Children (MMPC) feature selection algorithm was applied to improve predictions using fewer features. The performance of the developed method was tested on the SARS-CoV-2 protein interaction dataset. The MMPC algorithm reduced the feature count by over 99%, enhancing protein interaction prediction. After feature selection, the DNN method achieved the highest predictive performance, with an AUC of 0.878 and an F-Measure of 0.793. Sequence-based protein encoding methods AAC, APAAC, CKSAAPP, CTriad, DC, and PAAC were applied to proteins in the SARS-CoV-2 interaction dataset and their performance was compared with GO-NLP. The performance of the relevant methods was measured separately and combined. The highest performance was obtained from the combined dataset with an AUC value of 0.888. This study demonstrates that the proposed gene ontology and NLP approach can successfully predict protein-protein interactions for antiviral drug design with significantly fewer features using the MMPC-DNN model.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-06223-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110010","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":"Hypergraph denoising neural network for session-based recommendation","authors":"Jiawei Ding,&nbsp;Zhiyi Tan,&nbsp;Guanming Lu,&nbsp;Jinsheng Wei","doi":"10.1007/s10489-025-06283-x","DOIUrl":"10.1007/s10489-025-06283-x","url":null,"abstract":"<div><p>Session-based recommendation (SBR) predicts the next interaction of users based on their clicked items in a session. Previous studies have shown that hypergraphs are superior in capturing complex item transitions which contribute to SBR performance. However, existing hypergraph-based methods fail to model item co-occurrence and sequential patterns simultaneously, limiting the improvement of recommendation performance. Moreover, they are more sensitive to noisy items than conventional graph models due to the item association mechanism. In this paper, we propose a novel hypergraph-based method named Hypergraph Denoising Neural Network (HDNN) for SBR to tackle the abovementioned problems. The proposed method involves two newly-designed modules: a sequential pattern learning module (SPLM) and an adaptive attention selection module (AASM). In particular, SPLM models item sequential patterns to complement the hypergraph-based models which only focus on co-occurrence patterns. Meanwhile, AASM employs learnable attention score thresholds to exclude items with low attention scores, mitigating the impact of noisy items in hypergraphs. Furthermore, the sequential denoising unit (SDU) designed in SPLM is employed to eliminate noise in item sequential patterns, thus realizing the dual denoising purpose. Extensive experiments are conducted on three real-world datasets. The results of the experiments show that our HDNN framework shows better performance than the state-of-the-art models. In particular, all evaluation metrics in Tmall and RetailRocket showed improvements of over 15% and 5%, respectively.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110012","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":"Applications of pre-trained CNN models and data fusion techniques in Unity3D for connected vehicles","authors":"Mojtaba Norouzi,&nbsp;Seyed Hossein Hosseini,&nbsp;Mohammad Khoshnevisan,&nbsp;Behzad Moshiri","doi":"10.1007/s10489-024-06213-3","DOIUrl":"10.1007/s10489-024-06213-3","url":null,"abstract":"<div><p>Intelligent Transportation Systems (ITS) aim to enhance road safety and Internet of Things (IoT)-related solutions are crucial in achieving this objective. By leveraging Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies, drivers can access valuable information about their surroundings. This research utilized the Unity 3D game engine to simulate various traffic scenarios, exploring a stochastic environment with two data sources: camera and road sign labels. We developed a full-duplex communication system to enable the communication between Python and Unity. This allows the vehicle to capture images in Unity and classify them using Convolutional Neural Network (CNN) models coded in Python. To improve road sign detection accuracy, we applied multi-sensor Data Fusion (DF) techniques to fuse the information received from the sources. We applied DF methods such as the Kalman filter, Dempster-Shafer theory, and Fuzzy Integral Operators to combine the two sources of information. Furthermore, our proposed CNN model incorporates an Ordered Weighted Averaging (OWA) layer to fuse information from three pre-trained CNN models. Our results show that the proposed model integrating the OWA layer achieved an accuracy of 98.81%, outperforming six state-of-the-art models. We compared the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). In our work, EKF exhibited a lower execution time (0.02 seconds), yielding less accurate results. UKF, however, provided a more accurate estimate while being more computationally complex. Furthermore, the Dempster-Shafer model showed approximately 30% better accuracy compared to the Fuzzy Integral Operator. Using this methodology on autonomous vehicles in our virtual environment led to making more accurate decisions, even in a variety of weather conditions and accident scenarios. The findings of this research contribute to the development of more efficient and safer vehicles.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110113","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":"Feature selections based on uncertainty measurements from dual-quantitative improvement and double-hierarchical fusion","authors":"Qian Wang,&nbsp;Xianyong Zhang,&nbsp;Zhiying Lv,&nbsp;Zhiwen Mo","doi":"10.1007/s10489-024-06075-9","DOIUrl":"10.1007/s10489-024-06075-9","url":null,"abstract":"<div><p>Feature selections promote classification learning, and rough set theory offers effective mathematical methods; in practice, the performance enhancement of feature selection algorithms formulates a research target and challenge, and the corresponding problem solving usually resorts to improvement constructions of uncertainty measures. By fitting fuzzy rough sets (FFRSs), the relative dependency complement mutual information (FDCIE) motivates a recent algorithm of feature selection, called FNRDCI; however, FDCIE has improvement space of quantification view and fusion hierarchy, so the corresponding feature selection and heuristic algorithm can be advanced. In this paper, the dependency is improved by information localization, while the mutual information is enriched by information fuzzification and decision-class combination, so improved fusion measures and robuster feature selections are established by double-hierarchical fusion on decision classification and class. At first, the correctional dependency is proposed by fuzzy decision localization, and it induces a classification fusion measure (i.e. FCDCIE); based on two types of fuzzy decisions, two types of mutual information (i.e. FRCEmI and FRCFmI) are established by information fuzzification and class combination. Then, two types of dependency and two types of mutual information combinedly generate <span>(2times 2=4)</span> classification fusion measures (i.e. IFRDCEmI, IFRDCFmI, IFRCDCEmI, IFRCDCFmI) by pursuing class-level priority fusion; these new measures acquire semantics uncertainty, system equations, and granulation monotonicity/nonmonotonicity. Furthermore, <span>(1+2times 2=5)</span> fusion measures yield 5 novel feature selections with heuristic algorithms. Finally, experimental comparisons demonstrate the effectiveness and efficiency of the proposed novel methods of uncertainty measures and selection algorithms.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110024","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":"Adaptive sparsity detection-based evolutionary algorithm for large-scale sparse multi-objective optimization problems","authors":"Feiyue Qiu,&nbsp;Donghui Long,&nbsp;Qi Chen,&nbsp;Huizhen Hu,&nbsp;Qicang Qiu","doi":"10.1007/s10489-025-06291-x","DOIUrl":"10.1007/s10489-025-06291-x","url":null,"abstract":"<div><p>Large-scale sparse multi-objective optimization problems (LSSMOPs) widely exist in practical applications, which have large-scale decision variables and sparse Pareto optimal solutions. Existing algorithms have some shortcomings in dealing with LSSMOPs: (1) failing to make full use of the knowledge of the sparsity of the Pareto optimal solutions, leading to insufficient sparsity detection; (2) ignoring the connection between binary and real variables, leading to insufficient variables optimization. This paper proposes an adaptive sparsity detection-based evolutionary algorithm (ASD-MOEA) to address these issues, which is a two-stage algorithm. The first stage performs an adaptive sparsity detection strategy, which dynamically adjusts the probability of binary variables flipping and the fitness of decision variables according to the iteration ratio. Then, non-zero variables are mined based on fitness. The second stage performs a variable grouping-based optimization strategy, grouping decision variables according to their sparsity in the set of non-dominated solutions to reduce the search space, then performs genetic operations in the subspace. Finally, we compare ASD-MOEA with six mainstream algorithms. The results show that the proposed algorithm significantly outperforms the existing algorithms in dealing with LSSMOPs, and achieves a balance between sparsity maintenance and variable optimization.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109531","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":"Multi-view human point cloud registration method with overlapping regions semantic constraints and feature weighting","authors":"Ming Li,&nbsp;Guiqin Li,&nbsp;Xihang Li,&nbsp;Tiancai Li","doi":"10.1007/s10489-025-06296-6","DOIUrl":"10.1007/s10489-025-06296-6","url":null,"abstract":"<div><p>Multi-view human point cloud registration is a crucial step in 3D human reconstruction tasks. The symmetric structures and similar geometric features in human point clouds often lead to feature mismatches in point cloud registration. Therefore, we propose a pipeline for game tree registration based on semantic constraints and feature weighting (GTR-SCFW) that enhances the stability and accuracy of feature matching, thereby improving the registration precision of multi-view point clouds. First, we calculate and compare the feature similarity between multi-view point clouds and use a generalized best-first search (BFS) method to construct a multi-layered registration game tree. At each game node, overlapping regions are divided into multiple sub-regions based on semantic information, and global fast registration is used to determine the matching relationships of features within each sub-region. Then, the best matching points in each sub-region are selected based on the confidence of feature pairs, and the weights of all the best point pairs are calculated. Finally, the initial rigid transformation matrix is computed using weighted least squares (WLS), and ICP is employed to achieve fast fine registration. GTR-SCFW effectively avoids incorrect matching relationships caused by geometric feature similarity during the initial transformation estimation, providing a good initial pose for iterative closest point (ICP) fine registration. For point clouds with different initial poses, the registration’s rotational error approaches 0<span>(^circ )</span>, while the translational error is as low as 1.203e-4 mm. Comparative experimental results show that this method outperforms existing feature-based registration methods regarding robustness, reliability, and computational efficiency.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109532","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}