Wireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. P...
详细信息
Wireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. Precise collecting information from sensor nodes for aggregating data in Cluster Head (CH) is an essential demand for a successful WSN application. This paper proposes a new scheme of identifying collected information correctness for aggregating data in CHs in hierarchical WSN based on improving classification of Support vector machine (SVM). The optimal parameter SVM is implemented by an improved flower pollination algorithm (IFPA) to achieve classification accuracy. The collecting environmental information like temperature, humidity, etc., from sensor nodes to CHs that classify data fault, aggregate, and transfer them to the BS. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.
Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labor-intensive labeling. In contrast, weakly...
详细信息
Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labor-intensive labeling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide-level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, CAMEL is proposed, which achieves comparable results to those of fully supervised baselines in pixel-level segmentation. However, CAMEL requires 1280 x 1280 image-level binary annotations for positive WSIs. Here, CAMEL2 is presented, by introducing a threshold of the cancerous ratio for positive bags, it allows one to better utilize the information, consequently enabling us to scale up the image-level setting from 1280 x 1280 to 5120 x 5120 while maintaining accuracy. The results with various datasets demonstrate that CAMEL2, with the help of 5120 x 5120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level classifications. Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labor-intensive labeling. Herein, CAMEL2 is presented, with the help of 5120 x 5120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level *** (c) 2024 WILEY-VCH GmbH
Recently, nonnegative matrix factorization (NMF) with part-based representation has been widely used for appearance modeling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as &qu...
详细信息
Recently, nonnegative matrix factorization (NMF) with part-based representation has been widely used for appearance modeling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as "parts" unless some rigorous conditions are satisfied. To avoid this problem, this paper introduces NMF's variants into the visual tracking framework in the view of data clustering for appearance modeling. First, an initial target appearance model based on NMF is proposed to describe the target's appearance with the incorporated local coordinate factorization constraint, orthogonality of the bases, and L-1,L-1 norm regularized sparse residual error constraint. Second, an inverse NMF model is proposed in which each learned base vector is regarded as a clustering center in a low-dimensional subspace. Potential target samples (from the foreground) will be clustered around base vectors, while the candidate samples (from the background) are very likely to spread irregularly over the entire clustering space. Such differences can be fully exploited by the inverse NMF model to produce more discriminative encoding vectors than the conventional NMF method. Furthermore, incremental updating model is introduced into the tracking framework for online updating the initial appearance model. Experiments on object tracking benchmark suggest that our tracker is able to achieve promising performance when compared with some state-of-the-art methods in deformation, occlusion, and other challenging situations.
Manifold learning is one of the representative nonlinear dimensionality reduction techniques and has had many successful applications in the fields of information processing, especially pattern classification, and com...
详细信息
Manifold learning is one of the representative nonlinear dimensionality reduction techniques and has had many successful applications in the fields of information processing, especially pattern classification, and computer vision. However, when it is used for supervised classification, in particular for hierarchical classification, the result is still unsatisfactory. To address this issue, a novel supervised approach, namely hierarchical manifold learning (HML) is proposed. HML takes into account both the between-class label information and the within-class local structural information of the training sets simultaneously to guide the dimension reduction process for classification purpose. In this process, we extract sharing features to represent the parent manifold's information, and better solve the out-of-sample problem of manifold learning by using the generalized regression neural network at considerably lower computational cost, thereby making the proposed HML more suitable for supervised classification. Experimental results demonstrate the feasibility and effectiveness of our proposed algorithm.
A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequen...
详细信息
A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.
暂无评论