Weights Of Different Features Research Articles

Medical assisted-segmentation system based on global feature and stepwise feature integration for feature loss problem

This paper presents an advanced methodology that combines Convolutional Neural Networks (CNNs) and Transformers to tackle the issue of feature loss and elevate segmentation accuracy. While Transformers excel at capturing global features, there is a potential risk of losing crucial global information when CNNs concentrate on local feature extraction. To counteract this, we introduce the FM module, which re-extracts local information from the Encoder’s output, reinforcing local feature expression and enhancing segmentation accuracy. Additionally, our approach places a significant emphasis on the judicious fusion of features in the Decoder. By incorporating a technology that aggregates both global and local features, we aim to prevent the loss of feature information. To augment the effective representation of data features and heighten accuracy across diverse segmentation tasks, we introduce the GS Feature Combination method, which adjusts the weights of different features during the aggregation process. Experimental results demonstrate noteworthy performance improvements, achieving an Intersection over Union (IOU) of 71.3%, surpassing existing methods. This innovative approach bears substantial importance in the diagnosis, treatment, and prognosis prediction of osteosarcoma, affording doctors the opportunity to reduce workload and time while upholding diagnostic precision.

Exploiting local label correlation from sample perspective for multi-label classification via three-way decision theory

In multi-label classification, the expansion of output dimension seriously interferes learning performance, and even fails to build a joint prediction model. In order to restrain the proliferation of multi-label classifier’s hypothesis space, the current works focus on the application of global positive label correlation. However, the “black or white” mechanism ignore other possible forms of label correlation, such as negative or neutral correlation. By introducing the doctrine of the mean, three-way decision (3WD) theory provides a solution for in-depth research on local label correlation, and aims to handle the uncertainty of multi-label learning tasks. In this paper, a novel learning algorithm for multi-label joint classification, namely ML-3WD, is proposed by considering the 3WD label correlation from the perspective of samples. According to the weights of different features on any label, the comprehensive loss of each sample to three action strategies can be measured. Obviously, the 3WD rules for any label variable in multi-label output space is obtained. By aggregating the cutting thresholds between different labels, the division principles of 3WD label correlation are further established. Given any multi-label sample, the local fuzzy membership to co-occurrence or mutual state for label pair is examined based on kernelized fuzzy rough sets. The 3WD local label relevance of each sample is confirmed, that is, positive, negative or neutral. The global application strategy for multi-label classification is utilized to avoid over-fitting induced by local mining strategy. Based on the integral mean of the distribution of 3WD local label relevance in multi-label sample space, two different versions of empirical label relevance are constructed. By constraining the relative position between sub-separation hyperplanes, the 3WD label correlation distribution-based model for multi-label joint classification is designed. The experiment results on fifteen real world multi-label datasets reflect that our algorithm achieves good classification ability and versatility. The impact of core parameters on learning performance is also dissected.

AMM: An Adaptive Online Map Matching Algorithm

Online map matching is essential for some location-based services, such as car navigation. However, due to GPS measurement errors and/or the lack of sufficient information, the performance of most existing online algorithms will degrade in the increasingly complex traffic environment. In this paper, we propose an adaptive online map matching algorithm called AMM. The basic idea is that AMM should be able to calibrate GPS observation data for various measurement errors and under complex urban conditions. First, we establish a collaborative evaluation model between GPS points and candidate points to effectively filter low-quality GPS measurement points, dynamically set the weights of different features and comprehensively select the best candidate points. Second, we propose a retrospective correction mechanism to correct the previous matching results when more information is available, which will help improve the accuracy of future GPS points. Furthermore, we define parameter self-tuning rules for AMM to enhance its portability by avoiding time-consuming parameter tuning steps. We conduct extensive experiments to evaluate the performance of AMM on real vehicle trajectory datasets. The experiment results show that AMM outperforms its counterparts by up to 32% in terms of accuracy and its performance in different traffic conditions is more stable.

Adaptive Long-Neck Network With Atrous-Residual Structure for Instance Segmentation

Instance segmentation is an important yet challenging task in the computer vision field. Existing mainstream single-stage solution with parameterized mask representation has designed the neck models to fuse features of different layers; however, the performance of instance segmentation is still restricted to the layer-by-layer transmission scheme. In this article, an instance segmentation framework with an adaptive long-neck (ALN) network and atrous-residual structure is proposed. The long-neck network is composed of two bidirectional fusion units, which are cascaded to facilitate the information communication among features of different layers in top-down and bottom-up pathways. In particular, a new cross-layer transmission scheme is introduced in a top-down pathway to achieve a hybrid dense fusion of multiscale features and weights of different features are learned adaptively according to their respective contributions to promote the network convergence. Meanwhile, a bottom-up pathway further complements the features with more location clues. In this way, high-level semantic information and low-level location information are tightly integrated. Furthermore, an atrous-residual structure is added to the mask prototype branch of instance prediction to capture more contextual information. This contributes to the generation of high-quality masks. The experimental results indicate that the proposed method achieves effective segmentation and the outputted masks match the contours of objects.

An Improved Method for Ship Target Detection Based on YOLOv4

The resolution of remote sensing images has increased with the maturation of satellite technology. Ship detection technology based on remote sensing images makes it possible to monitor a large range and far sea area, which can greatly enrich the monitoring means of maritime departments. In this paper, we conducted research on small target detection and resistance to complex background interference. First, a ship dataset with four types of targets (aircraft carriers, warships, merchant ships and submarines) is constructed, and experiments are conducted on the dataset using the object detection algorithm YOLOv4. The Kmeans++ clustering algorithm is used for a priori frame selection, and the migration learning method is used to enhance the detection effect of the YOLOv4. Second, the model is improved to address the problems of missed detection of small ships and difficulty in resisting background interference: the RFB_s (Receptive Field Block) with dilated convolution is introduced instead of the SPP (Spatial Pyramid Pooling) to enlarge the receptive field and improve the detection of small targets; the attention mechanism CBAM (Convolutional Block Attention Module) is added to adjust the weights of different features to highlight salient features useful for ship detection task, which improve the detection performance of small ships and improve the model’s ability to resist complex background. Compared to YOLOv4, our proposed model achieved a large improvement in mAP (mean Average Precision) from 77.66% to 91.40%.

Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

Depression has become a major health and economic burden worldwide. Electroencephalography (EEG) data has been used by a growing number of researchers to study depression. EEG-based functional connectivity (FC) features have emerged since they can account for the relationships between different brain regions. In this paper, the time–frequency analysis technique is introduced into the construction of the FC matrix. Specifically, instead of directly building the FC matrix from the EEG signals, the intrinsic time-scale decomposition (ITD) method is employed to mine the time–frequency information, and then the Pearson correlation is used to measure the FC between channels. The results show the significant differences in the FC networks between different groups. Furthermore, the graph-based adaptive least absolute shrinkage and selection operator model (GA-LASSO) is proposed in this paper to learn the discriminative features from the FC matrix, which is mainly achieved by adding both the adaptive L1 and graph regularized terms to the original least absolute shrinkage and selection operator (LASSO) model. The advantages of GA-LASSO come from the processing of discriminative weights of different features, and the connections between features by graph topology. In addition, the effectiveness of the proposed strategy of depression detection is validated on the open dataset MODMA, as well as the self-collected dataset called EDRA. The experimental results show that the current study sheds new light on the pathological mechanism of subclinical depression and suggests that EEG resting-state FC analysis may identify potentially effective biomarkers for its clinical diagnosis.

Why a user prefers an artwork: A deep attention model for artwork recommendation

The combination of art market and emerging e-commerce has brought new trade opportunities and has achieved continuous growth in recent years. More and more people, especially young people, are keen to browse art information and buy artwork on the Internet. Therefore, designing an effective method of recommending artworks may not only effectively enhance the user experience in the art market but also benefit the greater transaction growth. The artwork recommendation task in e-commerce has not received much attention. Previous research works often regard the artworks as ordinary pictures and do not take into account the particularity of the artwork. To solve this problem, we modelled the aesthetic features into artwork recommendation and used the attention mechanism to learn user preferences for various features. We proposed a DAAR (Deep Attention Artwork Recommendation) model and used the attention mechanism to model the user’s preference weights for different features (including content features, aesthetic features and authors). To verify the validity of the proposed model, we collected data and conducted experiments on a real artwork community website. The experimental results show that the proposed DAAR model was better than the current state-of-the-art recommendation methods.

Chatter detection in milling of thin-walled parts using multi-channel feature fusion and temporal attention-based network

Chatter often occurs in the milling of thin-walled parts due to their high flexibility, which can seriously impair machining accuracy, surface quality, and production efficiency. In this paper, a chatter detection method in the milling of thin-walled parts based on deep learning is proposed. Multi-channel signal features in the time, frequency, and time–frequency domains are employed and their chatter sensitivities are evaluated. A novel temporal attention-based network is constructed. The self-attention mechanism, channel attention mechanism, and attentive statistics pooling are applied to capture the intra-feature correlation, assign the weights of different features, and calculate the variations and weights of features at multiple moments, respectively. The mutual information of distinct features is integrated into the channel attention mechanism to enhance the function of suppressing useless features and enhancing useful ones. The additive angular margin loss function is applied to strengthen intra-class aggregation and inter-class separation. Milling experiments on straight-wall parts are performed, and the classification performance of this model is evaluated in three scenarios. Afterward, milling experiments on curved thin-walled parts are conducted to verify the generalization performance. The results show that the proposed method can accurately detect chatter under varying machining conditions.

MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.

Exploring the interrelationships between microbes and disease can help microbiologists make decisions and plan treatments. Predicting new microbe-disease associations currently relies on biological experiments and domain knowledge, which is time-consuming and inefficient. Automated algorithms are used to uncover the intrinsic link between microbes and disease. However, due to data noise and inadequate understanding of relevant biology, the efficient prediction of microbe-disease associations is still crucial. This study develops a multi-view graph augmentation convolutional network (MVGCNMDA) to predict potential disease-associated microbes. First, we use two data augmentation methods, edge perturbation and node dropping, to remove the data noise in the preprocessing stage. Second, we calculate Gaussian interaction profile kernel similarity and cosine similarity. Therefore, the Graph Convolutional Network(GCN) can fully use multi-view features. Then, the multi-view features are fed into the multi-attention block to learn the weights of different features adaptively. Finally, the embedding results are obtained using a Convolutional Neural Network (CNN) combiner, and the matrix completion is used to predict the relationship between potential microbes and diseases. We test our model on the Human microbe-disease Association Database (HMDAD), Disbiome, and the Combined Dataset (Peryton and MicroPhenoDB). The area under PR curve (AUPR), area under ROC curve (AUC), F1 score, and RECALL value are calculated to evaluate the performance of the developed MVGCNMDA. The AUPR is 0.9440, AUC is 0.9428, F1 score is 0.9383, and RECALL value is 0.8858. The experiments show that our model can accurately predict potential microbe-disease associations compared with the state-of-the-art works on the global Leave-One-Out-Cross-Validation (LOOCV) and the fivefold Cross-Validation (fivefold CV). To further verify the effectiveness of the proposed graph data augmentation, we designed five different settings in the ablation study. Furthermore, we present two case studies that validate the prediction of the potential association between microbes and diseases by MVGCNMDA.

Full Vehicle Trajectory Planning Model for Urban Traffic Control Based on Imitation Learning

Connected and automated vehicles (CAVs) extend urban traffic control from temporal to spatiotemporal by enabling the control of CAV trajectories. Most of the existing studies on CAV trajectory planning only consider longitudinal behaviors (i.e., in-lane driving), or assume that the lane changing can be done instantaneously. The resultant CAV trajectories are not realistic and cannot be executed at the vehicle level. The aim of this paper is to propose a full trajectory planning model that considers both in-lane driving and lane changing maneuvers. The trajectory generation problem is modeled as an optimization problem and the cost function considers multiple driving features including safety, efficiency, and comfort. Ten features are selected in the cost function to capture both in-lane driving and lane changing behaviors. One major challenge in generating a trajectory that reflects certain driving policies is to balance the weights of different features in the cost function. To address this challenge, it is proposed to optimize the weights of the cost function by imitation learning. Maximum entropy inverse reinforcement learning is applied to obtain the optimal weight for each feature and then CAV trajectories are generated with the learned weights. Experiments using the Next Generation Simulation (NGSIM) dataset show that the generated trajectory is very close to the original trajectory with regard to the Euclidean distance displacement, with a mean average error of less than 1 m. Meanwhile, the generated trajectories can maintain safety gaps with surrounding vehicles and have comparable fuel consumption.

Multi-criteria linear optimization classifier with semantically weighted kernels for Chinese word formation pattern prediction

For many classification methods, different features in these classification models are usually organized into a flat and equal-length vector while semantically restrictive relations or nonparallel structures among them are often ignored. However, sometimes the semantic relations or structures among different attributes derived from the domain knowledge are very important, for instance, natural language processing and biological data mining, which can help to increase the performance of classification models considerably. For Chinese semantic word formation analysis with the large-scale Chinese corpus, the feature set is horizontally composed of different morpheme types, semantic types and part of speech (POS) while it vertically consists of three levels of big, medium and small classes. If a classifier model does not consider these relations or structures, then it can lead to information loss and distortion, and the degenerated classifier model can be ultimately obtained. In this paper, in view of the aforementioned limitations, by defining a new conceptual hierarchy graph, the semantic weights of different features are obtained, then semantically weighted kernels are formulated and introduced to multi-criteria linear optimization classifier (MCLOC). Thus, a new semantically weighted MCLOC (SW-MCLOC) using the linear and radius basic function (RBF) kernels is proposed and applied to Chinese word formation pattern prediction. The experimental results and comparison among MCLOC, SVM and SW-MCLOC show that our proposed model can effectively increase the separation of different word formation patterns, the predictive accuracy of target patterns and the generalization power of pattern classification on new compound words.

Hyperspectral Image Classification Based on Two-Branch Spectral–Spatial-Feature Attention Network

Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods.

Optimal-Setting for Ore and Water Feeding in Grinding Process Based on Improved Case-Based Reasoning

The grinding product particle size is the most crucial operational index of mineral grinding processes. The size and consistency of the product directly affects the subsequent dressing and sintering. In this paper, a novel expert system is proposed for guiding the operating variables to keep the product stable with the wildly varying ore properties. First, case-based reasoning (CBR) is introduced to describe the whole grinding process with the historical data and expert experience. Second, the generative adversarial network (GAN) is employed to extend the raw data to enhance the flexibility of CBR. Moreover, the weights of different features in CBR is optimized by improved non-dominated sorting genetic algorithm II (NSGA-II). Finally, the proposed method is validated by a set of actual data collected from a Chinese dressing plant. The experimental result demonstrates the effectiveness of the proposed method.

Simultaneously learning feature-wise weights and local structures for multi-view subspace clustering

Multi-view clustering integrates multiple feature sets, which usually have a complementary relationship and can reveal distinct insights of data from different angles, to improve clustering performance. It remains challenging to productively utilize complementary information across multiple views since there is always noise in real data, and their features are highly redundant. Moreover, most existing multi-view clustering approaches only aimed at exploring the consistency of all views, but overlooked the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because individual views generally present different geometric structures while admitting the same cluster structure. To ease the above issues, in this paper, a novel multi-view subspace clustering method is established by concurrently assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. In particular, a common clustering assignment regularization is adopted to explore the consistency among multiple views. An alternating iteration algorithm based on the augmented Lagrangian multiplier is also developed for optimizing the associated objective. Experiments conducted on diverse multi-view datasets manifest that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.

A Real Time Face Tracking System based on Multiple Information Fusion

Face tracking is one of key steps in real surveillance system. In this paper, a real-time face tracking system using multiple objects tracking algorithm is proposed. Multiple objects tracking algorithms typically consist of two parts: object detection and data association. In our system, we particularly use Multi-task convolutional neural network (MTCNN) to detect faces. Simultaneously, aiming at dealing with the tracking failure caused by object occlusion or rapid object movement, we use multiple features including appearance feature, motion feature, and shape feature for tracking. Furthermore, a judgement method is applied to measure whether tracking is successful or not. After that, depending on the tracking state, we adjust the weights of different features for feature fusion. From the experimental results, it can be concluded that, compared with the traditional tracking algorithms the proposed multi-object tracking algorithm has a better tracking effect in the real scenes.

Self-Weighted Clustering With Adaptive Neighbors.

Many modern clustering models can be divided into two separated steps, i.e., constructing a similarity graph (SG) upon samples and partitioning each sample into the corresponding cluster based on SG. Therefore, learning a reasonable SG has become a hot issue in the clustering field. Many previous works that focus on constructing better SG have been proposed. However, most of them follow an ideal assumption that the importance of different features is equal, which is not adapted in practical applications. To alleviate this problem, this article proposes a self-weighted clustering with adaptive neighbors (SWCAN) model that can assign weights for different features, learn an SG, and partition samples into clusters simultaneously. In experiments, we observe that the SWCAN can assign weights for different features reasonably and outperform than comparison clustering models on synthetic and practical data sets.

A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

Extracting road from high resolution remote sensing (HRRS) images is an economic and effective way to acquire road information, which has become an important research topic and has a wide range of applications. In this paper, we present a novel method for road extraction from HRRS images. Multi-kernel learning is first utilized to integrate the spectral, texture, and linear features of images to classify the images into road and non-road groups. A precise extraction method for road elements is then designed by building road shaped indexes to automatically filter out the interference of non-road noises. A series of morphological operations are also carried out to smooth and repair the structure and shape of the road element. Finally, based on the prior knowledge and topological features of the road, a set of penalty factors and a penalty function are constructed to connect road elements to form a complete road network. Experiments are carried out with different sensors, different resolutions, and different scenes to verify the theoretical analysis. Quantitative results prove that the proposed method can optimize the weights of different features, eliminate non-road noises, effectively group road elements, and greatly improve the accuracy of road recognition.

Incremental learning-based visual tracking with weighted discriminative dictionaries

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.

Ranking products with IF-based sentiment word framework and TODIM method

Purpose The purpose of this paper is to rank products by combining sentiment analysis (SA) and multiple attribute decision-making (MADM). Design/methodology/approach This research constructs intuitionistic fuzzy (IF)-based sentiment word framework and corresponding computation rules, which aim to calculate the sentiment score of each sentiment phase. Based on intuitionistic fuzzy weighted averaging operator, the authors aggregate the overall performance of each feature for different products. Then, the MADM method can be used, TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) in this paper, to rank product through online reviews. Findings The results of the research show the superiority and applicability of proposed method in ranking products with online reviews. Originality/value This paper proposes IF-based sentiment word framework and corresponding computation rules, which is a novel idea to express both the sentiment orientations (positive, negative and neutral) and emotional intensities. In addition, this research makes full use of knowledge from both experts and online reviewers. Further, attention degree of each feature is considered in the process of calculating weight of different features, which is rarely seen in current studies. This paper makes full use of SA, fuzzy control theory and MADM theory to handle vague information (online comments) and rank alternative products, which can promote future perspectives and developments.

Ranking products with online reviews: A novel method based on hesitant fuzzy set and sentiment word framework

Recently, sentiment analysis (SA) and multi-attribute decision making (MADA) have been extensively studied respectively, which aims to help decision makers make informed decisions. However, rather less attention has been paid to the field of combining SA and MADA. Therefore, in this paper, we propose a novel method to rank products through online reviews. To begin with, it is a novel idea to view different sentiment scores of one feature as the different membership degrees. Further, we propose the fuzzy sentiment word framework and corresponding computation rules to calculate the sentiment score of each feature in each review, which later can be used to obtain the overall performance of each feature concerning different products based on hesitant fuzzy set (HFS). Next, the attention degree of each feature is considered in the process of calculating weight of different features. In addition, based on 2-addiitive fuzzy measure and Choquet integral, we extend TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method, which concerns decision make’s psychological behavior, to deal with criteria interactions (positive, mutual independent and negative) in the process of MADM. Furthermore, we use a case study to demonstrate the efficiency and applicability of the proposed method.