Applied Intelligence最新文献

{"title":"An optimized path planning approach for automatic parking using hybrid A* bidirectional search","authors":"Wenrui Jin,&nbsp;Jiaxue Li,&nbsp;Xiaoxiao Lv,&nbsp;Tao Zhang","doi":"10.1007/s10489-024-05915-y","DOIUrl":"10.1007/s10489-024-05915-y","url":null,"abstract":"<div><p>Path planning in automatic parking is a significant challenge due to constrained parking spaces and numerous obstacles. To enhance both the safety and efficiency of the planned path, this paper proposes a bidirectional hybrid A* algorithm for narrow spaces with a high density of obstacles. A vehicle obstacle avoidance method that incorporates rectangular expansion through numerical analysis is proposed to achieve collision-free navigation. Meanwhile, a safety cost is integrated into the hybrid A* search algorithm to maintain a sufficient safety distance between the planned path and obstacles. Additionally, to enhance the efficiency of path planning, a bidirectional search method is combined with the hybrid A* algorithm, with the addition of a bidirectional cohesive item cost. Finally, simulation experiments are conducted to generate parking paths for both vertical and parallel parking scenarios. The simulation results indicate that the proposed algorithm obtains a sufficient safety distance, reduced search time, and fewer expanded nodes. Meanwhile, the stability and adaptability of the proposed method are analyzed. The comparison with other algorithms suggests that the proposed algorithm provides a larger safety distance and shorter search time.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859601","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 self-adaptation feature correspondences identification algorithm in terms of IMU-aided information fusion for VINS","authors":"Zhelin Yu","doi":"10.1007/s10489-024-06120-7","DOIUrl":"10.1007/s10489-024-06120-7","url":null,"abstract":"<div><p>Feature correspondences identification between consecutive frames is a critical prerequisite in the monocular Visual-Inertial Navigation System (VINS). In this paper, we propose a novel self-adaptation feature point correspondences identification algorithm in terms of IMU-aided information fusion at the level of feature tracking for nonlinear optimization framework-based VINS. This method starts with an IMU pre-integration predictor to predict the pose of each new coming frame. In weak texture scenes and motion blur situations, in order to increase the number of feature correspondences and improve the track lengths of feature points, we introduce a novel predicting-matching based feature point tracking strategy to build new matches. On the other hand, the predicted pose is incorporated into the outliers rejection step to deal with mismatch caused by dynamic objects. Finally, the proposed self-adaptation feature correspondences identification algorithm is implemented based on VINS-Fusion and validated through public datasets. The experimental results show that it effectively improves the accuracy and tracking length of feature matching, and demonstrates better performance in terms of camera pose estimation as compared to state-of-the-art approaches.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870287","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":"An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images","authors":"Faiqa Maqsood,&nbsp;Zhenfei Wang,&nbsp;Muhammad Mumtaz Ali,&nbsp;Baozhi Qiu,&nbsp;Tahir Mahmood,&nbsp;Raheem Sarwar","doi":"10.1007/s10489-024-06047-z","DOIUrl":"10.1007/s10489-024-06047-z","url":null,"abstract":"<div><p>Renal cell carcinoma (RCC) represents the primary type of kidney cancer, responsible for approximately 85% of kidney cancer-related fatalities. Precise grading of this cancer is pivotal for tailoring effective treatments. Detecting RCC early, before metastasis, significantly improves survival rates. While Artificial intelligence-based classification methods have emerged for RCC, advancements in accuracy, processing efficiency, and memory utilization remain imperative. This study introduces the Efficient Enhanced Feature Framework (EFF-Net), a deep neural network architecture designed for RCC grading using histopathological image analysis. EFF-Net amalgamates potent feature extraction from convolutional layers with efficient Separable convolutional layers, aiming to accelerate model inference, reduce trainable parameters, mitigate overfitting, and elevate RCC grading precision. Evaluation across three distinct datasets showcases the EFF-Net's outstanding performance: achieving 91.90% accuracy, a precision of 91.4%, a recall of 91.8%, and a harmonic mean of precision and recall (F1 score) of 91.9% on the Kasturba Medical College (KMC) dataset. Additionally, on the Lung and Colon Dataset, EFF-Net achieved 99.8% accuracy, a precision of 99.7%, a recall of 99.9%, and a 98.7% F1 score. Similarly, the Acute Lymphoblastic Leukaemia dataset demonstrated remarkable performance: 99.8% accuracy, a precision of 99%, a recall of 99%, and a 99.7% F1 score. EFF-Net's superior accuracy surpasses existing state-of-the-art approaches while exhibiting reduced trainable parameters and computational requirements.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859456","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":"Low-light image enhancement via an attention-guided deep Retinex decomposition model","authors":"Yu Luo,&nbsp;Guoliang Lv,&nbsp;Jie Ling,&nbsp;Xiaomin Hu","doi":"10.1007/s10489-024-06044-2","DOIUrl":"10.1007/s10489-024-06044-2","url":null,"abstract":"<div><p>Images acquired from optical imaging devices in a low-light or back-lit environment usually lead to a poor visual experience. The poor visibility and the attendant contrast or color distortion may degrade the performance of subsequent vision processing. To enhance the visibility of low-light image and mitigate the degradation of vision systems, an attention-guided deep Retinex decomposition model, dubbed Ag-Retinex-Net, is proposed. Inspired by the Retinex theory, the Ag-Retinex-Net first decomposes the input low-light image into two layers under an elaborate multi-term regularization, and then recomposes the refined two layers to obtain the final enhanced images via attention-guided generative adversarial learning. The multi-term constraints in the decomposition module can help better regularize and extract the decomposed illumination and reflectance. And the attention-guided generative adversarial learning in the recomposition module is utilized to help remove the degradation. The experimental results show that the proposed Ag-Retinex-Net outperforms other Retinex-based methods in terms of both visual quality and several objective evaluation metrics.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859459","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":"An interactive multi-task ESG classification method for Chinese financial texts","authors":"Han Zhang,&nbsp;Yazhou Zhang,&nbsp;Xinyu Wang,&nbsp;Lei Zhang,&nbsp;Lixia Ji","doi":"10.1007/s10489-024-06068-8","DOIUrl":"10.1007/s10489-024-06068-8","url":null,"abstract":"<div><p>In view of the problems existing in the ESG classification task of Chinese financial texts, such as feature loss caused by excessively long texts, this paper proposes an interactive multi-task model AmultiESG for ESG classification of Chinese financial texts. The model divides Chinese financial text ESG classification and financial sentiment dictionary expansion into primary and secondary tasks. First, BiLSTM model is used to learn the original representation of the text. Then, in the secondary task, the attention mechanism and full connection layers are combined with the domain dictionary to realize the extraction of emotional words. In the main task, in order to prevent feature loss due to the excessively long texts, we process the text again and divide it into blocks according to the period. Meanwhile, we learned new feature representation of the text by combining text label representation, text block representation, BiLSTM output features and domain dictionary features. And we introduce an interactive information transfer mechanism to iteratively improve the predicted results of the two tasks and strengthen the association between them. It has been experimentally demonstrated that the proposed method shows superior performance compared to other baselines for the ESG classification task of Chinese financial text, especially for long-text classification tasks.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858655","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":"Iterative local search for preserving data privacy","authors":"Alejandro Arbelaez,&nbsp;Laura Climent","doi":"10.1007/s10489-024-05909-w","DOIUrl":"10.1007/s10489-024-05909-w","url":null,"abstract":"<div><p>k-Anonymization is a popular approach for sharing datasets while preserving the privacy of personal and sensitive information. It ensures that each individual is indistinguishable from at least k-1 others in the anonymized dataset through data suppression or generalization, which inevitably leads to some information loss. The goal is to achieve k-anonymization with minimal information loss. This paper presents an efficient local search framework designed to address this challenge using arbitrary information loss metrics. The framework leverages anytime capabilities, allowing it to balance computation time and solution quality, thereby progressively improving the quality of the anonymized data. Our empirical evaluation shows that the proposed local search framework significantly reduces information loss compared to current state-of-the-art solutions, providing performance improvements of up to 54% and 43% w.r.t. the k-members and l-greedy heuristic solutions, the leading algorithms for large datasets. Additionally, our solution approach outperforms the Hun-garian-based solution, the best solution approach for small-size instances, by up to 4.7% on these instances.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05909-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858656","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":"MSDM: multi-space diffusion with dynamic loss weight","authors":"Zhou Liu,&nbsp;Zheng Ye,&nbsp;Jing Liu,&nbsp;Jun Qin,&nbsp;Ben He,&nbsp;Cathal Gurrin","doi":"10.1007/s10489-024-06043-3","DOIUrl":"10.1007/s10489-024-06043-3","url":null,"abstract":"<div><p>Diffusion models have achieved remarkable results in image generation. However, due to the slow convergence speed, room for enhancement remains in existing loss weight strategies. In one aspect, the predefined loss weight strategy based on signal-to-noise ratio (SNR) transforms the diffusion process into a multi-objective optimization problem. However, it takes a long time to reach the Pareto optimal. In contrast, the unconstrained optimization weight strategy can achieve lower objective values, but the loss weights of each task change unstably, resulting in low training efficiency. In addition, the imbalance of lossy compression and semantic information in latent space diffusion also leads to missing image details. To solve these problems, a new loss weight strategy combining the advantages of predefined and learnable loss weights is proposed, effectively balancing the gradient conflict of multi-objective optimization. A high-dimensional multi-space diffusion method called Multi-Space Diffusion is also introduced, and a loss function that considers both structural information and robustness is designed to achieve a good balance between lossy compression and fidelity. The experimental results indicate that the proposed model and strategy significantly enhance the convergence speed, being <b>3.7 times</b> faster than the Const strategy, and achieve an advanced <b>FID = 3.35</b> score on the ImageNet512.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858652","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":"Graph-based multi-label feature selection with dynamic graph constraints and latent representation learning","authors":"Jianxia Bai,&nbsp;Yanhong Wu","doi":"10.1007/s10489-024-06116-3","DOIUrl":"10.1007/s10489-024-06116-3","url":null,"abstract":"<div><p>Currently, multi-label feature selection with joint manifold learning and linear mapping has received much attention. However, the low-quality graph matrix used by existing methods leads to model limitations. Traditional linear mapping cannot learn the coupling relationship between different outputs. In addition, existing approaches ignore latent supervisory information in label correlation. To this end, we obtain a dynamic graph matrix with Laplace rank constraints by the <span>(L_{1})</span> norm with a conventional graph matrix. We also mine more reliable supervised information from label correlations by introducing latent representation learning. Moreover, we integrate all the above terms into a linear mapping learning framework based on improved matrix decomposition, and design a simple and effective scheme based on alternating iterations to optimize this framework. Numerous experimental results validate the competitive advantage of the proposed method over existing state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858654","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":"LMTformer: facial depression recognition with lightweight multi-scale transformer from videos","authors":"Lang He,&nbsp;Junnan Zhao,&nbsp;Jie Zhang,&nbsp;Jiewei Jiang,&nbsp;Senqing Qi,&nbsp;Zhongmin Wang,&nbsp;Di Wu","doi":"10.1007/s10489-024-05908-x","DOIUrl":"10.1007/s10489-024-05908-x","url":null,"abstract":"<div><p>Depression will become the most common mental disorder worldwide by 2030. A number of models based on deep learning are proposed to help the clinicians to assess the severity of depression. However, two issues remain unresolved: (1) few studies have not considered to encode multi-scale facial behaviors. (2) the current studies have the high computational complexity to hinder the proposed architecture in clinical application. To mitigate the above issues, an end-to-end, lightweight, multi-scale transformer based architecture, termed LMTformer, for sequential video-based depression analysis (SVDA), is proposed. In LMTformer, which consists of the three models: coarse-grained feature extraction (CFE) block, light multi-scale transformer (LMST), final Beck Depression Inventory–II (BDI–II) predictor (FBP). In CFE, coarse-grained features are extracted for LMST. In LMST, a multi-scale transformer is proposed to model the potential local and global features at the different receptive field. In addition, multi-scale global feature aggregation (MSGFA) is also proposed to model the global features. For FBP, two fully connected layers are used. Our novel architecture LMTformer is evaluated on the AVEC2013/AVEC2014 depression databases, and the former dataset with a root mean square error (RMSE) of 7.75 and a mean absolute error (MAE) of 6.12 for AVEC2013, and a RMSE of 7.97 and a MAE of 6.05 for AVEC2014. On the LMVD dataset, we obtain the best performances with F1-score of 82.74%. Additionally, the model represents the excellent computational complexity while only need 0.95M parameters and 1.1G floating-point operations per second (FLOPs). Code will be available at: https://github.com/helang818/LMTformer/.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05908-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859458","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 robot path tracking method based on manual guidance and path reinforcement learning","authors":"Yong Pan,&nbsp;Chengjun Chen,&nbsp;Dongnian Li,&nbsp;Zhengxu Zhao","doi":"10.1007/s10489-024-06098-2","DOIUrl":"10.1007/s10489-024-06098-2","url":null,"abstract":"<div><p>Controlling the movement of an industrial robot along specific edges of a workpiece in a complex environment, where multiple paths intersect, is crucial for tasks such as welding and gluing. Traditional robot teaching methods restrict robots to fixed task environments using pre-programmed motion planning schemes. Although vision-guided robotic path-tracking systems can automatically extract paths, the presence of multiple intersections complicates autonomous path determination and tracking using conventional vision-based algorithms. To address this challenge, this study proposed a robot path-tracking approach that integrates manual guidance with path reinforcement learning. This strategy leverages both visual- and human-guided information to learn complex manipulation skills that require precise positional constraints and continuous motion, such as welding or gluing, in environments with intersecting paths. A user-friendly robot path teaching framework was designed, allowing operators to select key positions on the robot manipulator’s motion path (2D guide pixel points) from color images using a mouse to generate guide images. However, these interactively selected 2D guide pixel points may introduce biases relative to the ideal robot path (i.e., the edge of the workpiece that needs to be tracked). To mitigate this, a path reinforcement learning technique was proposed that uses the edge image of the workpiece along with manual guidance to determine the necessary actions (2D pixel tracking path points) for tracking specific edges in complex environments. This process is constrained by guide images and an invalid action mask matrix. An invalid action mask matrix, calculated from the guide points, prevents the exploration of suboptimal trajectories during path reinforcement learning. The robot’s 6- degrees of freedom (DOF) path was then derived from the 2D pixel-tracking path points and depth images. Finally, the accuracy of 2D pixel path tracking was tested in a virtual environment, yielding an average error of 0.363 pixels and a standard deviation of 0.594 pixels. The effectiveness of the proposed path-tracking approach in scenarios with multiple intersecting paths was verified in a physical environment.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859457","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}