-
Graph neural networks with selective attention and path reasoning for document-level relation extraction Appl. Intell. (IF 5.3) Pub Date : 2024-04-20 Tingting Hang, Jun Feng, Yunfeng Wang, Le Yan
-
AFSRNet: learning local descriptors with adaptive multi-scale feature fusion and symmetric regularization Appl. Intell. (IF 5.3) Pub Date : 2024-04-20 Dong Li, Haowen Liang, Kin-Man Lam
-
Cross-domain Fisher Discrimination Criterion: A Domain Adaptive Method Based on the Nature of Classifier Appl. Intell. (IF 5.3) Pub Date : 2024-04-20 Yuchuan Liu, Lianzhi Li, Jia Tan, Yu Rao, Xiaoheng Tan, Yongsong Li
-
Non-local self-attention network for image super-resolution Appl. Intell. (IF 5.3) Pub Date : 2024-04-20 Kun Zeng, Hanjiang Lin, Zhiqiang Yan, Jinsheng Fang, Taotao Lai
-
Supporting ANFIS interpolation for image super resolution with fuzzy rough feature selection Appl. Intell. (IF 5.3) Pub Date : 2024-04-20 Muhammad Ismail, Changjing Shang, Jing Yang, Qiang Shen
-
Revisiting clustering for efficient unsupervised dialogue structure induction Appl. Intell. (IF 5.3) Pub Date : 2024-04-18 Maarten De Raedt, Fréderic Godin, Chris Develder, Thomas Demeester
-
Outlier detection for incomplete real-valued data via information entropy and class-consistent technology Appl. Intell. (IF 5.3) Pub Date : 2024-04-18 Xiaopeng Cai, Zhaowen Li
-
A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples Appl. Intell. (IF 5.3) Pub Date : 2024-04-18 Peixuan Ding, Yi Xu, Pan Qin, Xi-Ming Sun
-
Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography Appl. Intell. (IF 5.3) Pub Date : 2024-04-17 Oleksandr Kuznetsov, Emanuele Frontoni, Kyrylo Chernov
-
Lifelong learning gets better with MixUp and unsupervised continual representation Appl. Intell. (IF 5.3) Pub Date : 2024-04-17 Prashant kumar, Durga Toshniwal
-
CA-PDBPR: category-aware privacy preserving POI recommendation using decentralized Bayesian personalized ranking Appl. Intell. (IF 5.3) Pub Date : 2024-04-17 Qinyun Gao, Shenbao Yu, Bilian Chen, Langcai Cao
-
F2D-SIFPNet: a frequency 2D Slow-I-Fast-P network for faster compressed video action recognition Appl. Intell. (IF 5.3) Pub Date : 2024-04-16 Yue Ming, Jiangwan Zhou, Xia Jia, Qingfang Zheng, Lu Xiong, Fan Feng, Nannan Hu
-
An efficient center-based method for real-time pig posture recognition and tracking Appl. Intell. (IF 5.3) Pub Date : 2024-04-16 Morann Mattina, Abdesslam Benzinou, Kamal Nasreddine, Francis Richard
-
IMCN: Improved modular co-attention networks for visual question answering Appl. Intell. (IF 5.3) Pub Date : 2024-04-16 Cheng Liu, Chao Wang, Yan Peng
-
A highly efficient ADMM-based algorithm for outlier-robust regression with Huber loss Appl. Intell. (IF 5.3) Pub Date : 2024-04-15 Tianlei Wang, Xiaoping Lai, Jiuwen Cao
-
Graph attention autoencoder model with dual decoder for clustering single-cell RNA sequencing data Appl. Intell. (IF 5.3) Pub Date : 2024-04-15 Shudong Wang, Yu Zhang, Yuanyuan Zhang, Yulin Zhang, Shanchen Pang, Jionglong Su, Yingye Liu
-
A robot grasping detection network based on flexible selection of multi-modal feature fusion structure Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 Yuhan Wang, Zhibo Guo, Yu Chen, Chaiqi Guo, Meizhen Xia, Tingyue Qi
-
PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 Yu Fu, Xindi Liu, Bin Yu
-
Relational reasoning and adaptive fusion for visual question answering Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 Xiang Shen, Dezhi Han, Liang Zong, Zihan Guo, Jie Hua
-
SS-MVMETRO: Semi-supervised multi-view human mesh recovery transformer Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 Silong Sheng, Tianyou Zheng, Zhijie Ren, Yang Zhang, Weiwei Fu
-
Polyphonic sound event localization and detection using channel-wise FusionNet Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 Spoorthy V., Shashidhar G. Kooolagudi
-
BiLSTM-TANet: an adaptive diverse scenes model with context embeddings for few-shot learning Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 He Zhang, Han Liu, Lili Liang, Wenlu Ma, Ding Liu
-
SPROSAC: Streamlined progressive sample consensus for coarse–fine point cloud registration Appl. Intell. (IF 5.3) Pub Date : 2024-04-13 Zeyuan Liu, Xiaofeng Yue, Juan Zhu
-
Multiple reference points-based multi-objective feature selection for multi-label learning Appl. Intell. (IF 5.3) Pub Date : 2024-04-11 Yangtao Chen, Wenbin Qian
-
Modeling essay grading with pre-trained BERT features Appl. Intell. (IF 5.3) Pub Date : 2024-04-11 Annapurna Sharma, Dinesh Babu Jayagopi
-
Causal inference in the medical domain: a survey Appl. Intell. (IF 5.3) Pub Date : 2024-04-11 Xing Wu, Shaoqi Peng, Jingwen Li, Jian Zhang, Qun Sun, Weimin Li, Quan Qian, Yue Liu, Yike Guo
-
Multi-object behaviour recognition based on object detection cascaded image classification in classroom scenes Appl. Intell. (IF 5.3) Pub Date : 2024-04-11 Min Dang, Gang Liu, Hao Li, Qijie Xu, Xu Wang, Rong Pan
-
Self-knowledge distillation enhanced binary neural networks derived from underutilized information Appl. Intell. (IF 5.3) Pub Date : 2024-04-11 Kai Zeng, Zixin Wan, HongWei Gu, Tao Shen
-
Incremental feature selection approach to multi-dimensional variation based on matrix dominance conditional entropy for ordered data set Appl. Intell. (IF 5.3) Pub Date : 2024-04-10 Weihua Xu, Yifei Yang, Yi Ding, Xiyang Chen, Xiaofang Lv
-
Domain generalization based on domain-specific adversarial learning Appl. Intell. (IF 5.3) Pub Date : 2024-04-09 Ziping Wang, Xiaohang Zhang, Zhengren Li, Fei Chen
-
Resource allocation in heterogeneous network with node and edge enhanced graph attention network Appl. Intell. (IF 5.3) Pub Date : 2024-04-08 Qiushi Sun, Yang He, Ovanes Petrosian
-
FeSTGCN: A frequency-enhanced spatio-temporal graph convolutional network for traffic flow prediction under adaptive signal timing Appl. Intell. (IF 5.3) Pub Date : 2024-04-08 Hai-chao Huang, Zhi-heng Chen, Bo-wen Li, Qing-hai Ma, Hong-di He
-
A hybrid information-based two-phase expansion algorithm for community detection with imbalanced scales Appl. Intell. (IF 5.3) Pub Date : 2024-04-06
Abstract The scale of communities in real-world networks is often imbalanced, which has a significant impact on community detection performance. Existing approaches exhibit a trade-off between accuracy and computational cost, with global methods offering higher accuracy but requiring intensive computations, and local methods accelerating the detection at the expense of accuracy. Despite these challenges
-
Network traffic grant classification based on 1DCNN-TCN-GRU hybrid model Appl. Intell. (IF 5.3) Pub Date : 2024-04-06
Abstract Accurate grant classification of network traffic not only assists service providers in making acceptable allocations based on actual business demands, but also ensures service quality. To further improve the accuracy of traffic classification, we propose a hybrid method of 1DCNN-TCN-GRU for traffic data authorized classification. The proposed hybrid model extracts more complex features by
-
Visual contextual relationship augmented transformer for image captioning Appl. Intell. (IF 5.3) Pub Date : 2024-04-06
Abstract The image captioning task is among the most important tasks in computer vision. Most existing methods mine more useful contextual information from image features. Similarly, to mine more contextual information, this paper proposes a visual contextual relationship augmented transformer (VRAT) method for improving the correctness of image description statements. In VRAT, visual contextual features
-
Resolution-sensitive self-supervised monocular absolute depth estimation Appl. Intell. (IF 5.3) Pub Date : 2024-04-05 Yuquan Zhou, Chentao Zhang, Lianjun Deng, Jianji Fu, Hongyi Li, Zhouyi Xu, Jianhuan Zhang
Depth estimation is an essential component of computer vision applications for environment perception, 3D reconstruction and scene understanding. Among the available methods, self-supervised monocular depth estimation is noteworthy for its cost-effectiveness, ease of installation and data accessibility. However, there are two challenges with current methods. Firstly, the scale factor of self-supervised
-
A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system Appl. Intell. (IF 5.3) Pub Date : 2024-04-04 Theodora Sanida, Minas Dasygenis
The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) for rapid and accurate identification of lung diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing the five-fold cross-validation
-
R-CCF: region-aware continual contrastive fusion for weakly supervised object detection Appl. Intell. (IF 5.3) Pub Date : 2024-04-03 Yongqiang Zhang, Rui Tian, Yin Zhang, Zian Zhang, Yancheng Bai, Mingli Ding, Wangmeng Zuo
Weakly-supervised learning has emerged as a compelling method for object detection by reducing the fully annotated labels requirement in the training procedure. Recently, some works have treated the detection task as a classification task, resulting in highlighting only discriminative object parts. Moreover, fully-supervised object detectors use specific modules (e.g. feature pyramid networks (FPN)
-
Conditional probability table limit-based quantization for Bayesian networks: model quality, data fidelity and structure score Appl. Intell. (IF 5.3) Pub Date : 2024-04-03 Rafael Rodrigues Mendes Ribeiro, Jordão Natal, Cassio Polpo de Campos, Carlos Dias Maciel
Bayesian Networks (BN) are robust probabilistic graphical models mainly used with discrete random variables requiring discretization and quantization of continuous data. Quantization is known to affect model accuracy, speed and interpretability, and there are various quantization methods and performance comparisons proposed in literature. Therefore, this paper introduces a novel approach called CPT
-
Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing Appl. Intell. (IF 5.3) Pub Date : 2024-04-03 Benjamin Uhrich, Nils Pfeifer, Martin Schäfer, Oliver Theile, Erhard Rahm
In 3D printing processes, there are many thermal stress related defects that can have a significant negative impact on the shape and size of the structure. Such anomalies in the heat transfer of the printing process need to be detected at an early stage. Understanding heat transfer is crucial, and simulation models can offer insights while reducing the need for costly experiments. Traditional numerical
-
Multi-state delayed echo state network with empirical wavelet transform for time series prediction Appl. Intell. (IF 5.3) Pub Date : 2024-04-03 Xianshuang Yao, Huiyu Wang, Yanning Shao, Zhanjun Huang, Shengxian Cao, Qingchuan Ma
In this paper, considering the effect of multiple delayed states on the reservoir itself, based on the advantage of the empirical wavelet transform, an improved ESN with multiple delayed states is proposed, called multi-state delayed echo state network with empirical wavelet transform (EWT-MSD-ESN). Firstly, the empirical wavelet transform is used to decompose the input signal, and then the main features
-
Task attention-based multimodal fusion and curriculum residual learning for context generalization in robotic assembly Appl. Intell. (IF 5.3) Pub Date : 2024-04-03 Chuang Wang, Ze Lin, Biao Liu, Chupeng Su, Gang Chen, Longhan Xie
In the domain of flexible manufacturing, Deep Reinforcement Learning (DRL) has emerged as a pivotal technology for robotic assembly tasks. Despite advancements in sample efficiency and interaction safety through residual reinforcement learning with initial policies, challenges persist in achieving context generalization amidst stochastic systems characterized by large random errors and variable backgrounds
-
A two-stage approach solo_GAN for overlapping cervical cell segmentation based on single-cell identification and boundary generation Appl. Intell. (IF 5.3) Pub Date : 2024-04-02
Abstract Accurate cell segmentation is a pivotal step throughout the cervical cancer treatment continuum, encompassing early screening, guiding treatment decisions, and assessing long-term prognosis. Currently, in clinical practice, pathologists rely on microscopic examination of cell characteristics followed by manual annotation, leveraging their expert knowledge. Nonetheless, this approach is labor-intensive
-
Complex visual question answering based on uniform form and content Appl. Intell. (IF 5.3) Pub Date : 2024-04-02 Deguang Chen, Jianrui Chen, Chaowei Fang, Zhichao Zhang
Abstract Complex visual question answering holds the potential to enhance artificial intelligence proficiency in understanding natural language, stimulate advances in computer vision technologies, and expand the range of practical applications. However, achieving desirable answers is often hindered by factors such as inconsistent form and content of pre-training and fine-tuning tasks, and the involvement
-
Video-based beat-by-beat blood pressure monitoring via transfer deep-learning Appl. Intell. (IF 5.3) Pub Date : 2024-04-01 Osama A. Omer, Mostafa Salah, Loay Hassan, Ahmed Abdelreheem, Ammar M. Hassan
Abstract Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal
-
Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimization Appl. Intell. (IF 5.3) Pub Date : 2024-04-01
Abstract To effectively address large-scale optimization problems, this paper proposes an evolutionary dynamic grouping (EDG) based cooperative co-evolution (CC) algorithm. In the proposed algorithm, a novel decomposition method is designed to generate the sub-components of decision variables dynamically. Additionally, an evolutionary search method based on the fireworks search strategy is proposed
-
Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults Appl. Intell. (IF 5.3) Pub Date : 2024-03-28 Jose Alberto Maestro-Prieto, José Miguel Ramírez-Sanz, Andrés Bustillo, Juan José Rodriguez-Díez
-
SelfPAB: large-scale pre-training on accelerometer data for human activity recognition Appl. Intell. (IF 5.3) Pub Date : 2024-03-28
Abstract Annotating accelerometer-based physical activity data remains a challenging task, limiting the creation of robust supervised machine learning models due to the scarcity of large, labeled, free-living human activity recognition (HAR) datasets. Researchers are exploring self-supervised learning (SSL) as an alternative to relying solely on labeled data approaches. However, there has been limited
-
Audio-visual speech synthesis using vision transformer–enhanced autoencoders with ensemble of loss functions Appl. Intell. (IF 5.3) Pub Date : 2024-03-27 Subhayu Ghosh, Snehashis Sarkar, Sovan Ghosh, Frank Zalkow, Nanda Dulal Jana
-
Multi-start team orienteering problem for UAS mission re-planning with data-efficient deep reinforcement learning Appl. Intell. (IF 5.3) Pub Date : 2024-03-27
Abstract In this paper, we study the Multi-Start Team Orienteering Problem (MSTOP), a mission re-planning problem where vehicles are initially located away from the depot and have different amounts of fuel. We consider/assume the goal of multiple vehicles is to travel to maximize the sum of collected profits under resource (e.g., time, fuel) consumption constraints. Such re-planning problems occur
-
DS-MSFF-Net: Dual-path self-attention multi-scale feature fusion network for CT image segmentation Appl. Intell. (IF 5.3) Pub Date : 2024-03-27 Xiaoqian Zhang, Lei Pu, Liming Wan, Xiao Wang, Ying Zhou
-
LA-RCNN: Luong attention-recurrent- convolutional neural network for EV charging load prediction Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Djamel Eddine Mekkaoui, Mohamed Amine Midoun, Yanming Shen
-
Heterogeneous graph neural network with graph-data augmentation and adaptive denoising Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Xiaojun Lou, Guanjun Liu, Jian Li
-
Mediating effects of NLP-based parameters on the readability of crowdsourced wikipedia articles Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Simran Setia, Anamika Chhabra, Amit Arjun Verma, Akrati Saxena
-
SRENet: Structure recovery ensemble network for single image deraining Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Dan Zhang, Yingbing Xu, Liyan Ma, Xiaowei Li, Xiangyu Zhang, Yan Peng, Yaoran Chen
-
Generative adversarial network for newborn 3D skeleton part segmentation Appl. Intell. (IF 5.3) Pub Date : 2024-03-26
Abstract Childbirth simulations have been studied in order to predict and prevent difficult delivery issues. The reconstruction of the maternal pelvic model, which consists of a comprehensive fetal model with articulated joints, is important for therapeutic purposes. However, it is difficult and time-consuming to segment the various bones using classical image processing approaches. The aim of this
-
Metric learning for monotonic classification: turning the space up to the limits of monotonicity Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Juan Luis Suárez, Germán González-Almagro, Salvador García, Francisco Herrera
-
Hybrid density-based adaptive weighted collaborative representation for imbalanced learning Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Yanting Li, Shuai Wang, Junwei Jin, Hongwei Tao, Chuang Han, C. L. Philip Chen
-
Multi-level relation learning for cross-domain few-shot hyperspectral image classification Appl. Intell. (IF 5.3) Pub Date : 2024-03-26 Chun Liu, Longwei Yang, Zheng Li, Wei Yang, Zhigang Han, Jianzhong Guo, Junyong Yu
-
Cross-modal contrastive learning for multimodal sentiment recognition Appl. Intell. (IF 5.3) Pub Date : 2024-03-25 Shanliang Yang, Lichao Cui, Lei Wang, Tao Wang