Wang–Mendel (WM) fuzzy system is an effective and interpretable model for solving tabular data classification problem. However, original WM weak in handling dataset with high dimensionality or large volume. Meanwhile, its capability of characterizing narrow, which results from lacking hierarchical transformation features like deep learning-based models. In this article, we propose a rule-based (DFRBCS) based on improved method, technique learning strategy are combined to make desirable...

Traditional diagnostic tools for scoliosis screening necessitate a substantial number of specialized personnel and equipment, leading to inconvenience that can result in missed opportunities early diagnosis optimal treatment. We have developed deep learning-based image segmentation model enhance the efficiency screening. A total 350 patients with 108 healthy subjects were included this study. The dataset was created using their bare back images standing full-length anteroposterior spinal...

Survival prediction serves as a pivotal component in precision oncology, enabling the optimization of treatment strategies through mortality risk assessment. While integration histopathological images and genomic profiles offers enhanced potential for patient stratification, existing methodologies are constrained by two fundamental limitations: (i) insufficient attention to fine-grained local features favor global representations, (ii) suboptimal cross-modal fusion that either neglect...

Non-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying interaction between ncRNA and protein is fundamental to understand ncRNA. Under pressures from expensive cost experimental techniques, developing an accuracy computational predictive model has become indispensable way identify ncRNA-protein interaction. A powerful predicting needs a good feature set...

Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes occurrence development tumors. Accumulating studies focus on prediction miRNA-lncRNA interaction, mostly are concerned biological experiments machine learning methods. These methods found long cycles, high costs, requiring over much human intervention. In this paper, a data-driven hierarchical deep framework was proposed, which composed capsule...

Traffic metrics at application level are critical for protocol research, abnormity detection, accounting and network operation. There great challenges to identify packets since dynamic ports packet encryption deployed popularly. several different methods of traffic identification being proposed in recently research corresponding applications. It is impossible with any one method alone. A methodology online named multi-phases (MPI) based on flow this paper. two stages the methodology. The...

Image classification is an important task in content-based image retrieval, which can be regarded as intermediate component to handle large-scale datasets for improving the accuracy of retrieval. Traditional methods generally utilize Support Vector Machines (SVM) classifier. However, there are several drawbacks using SVM, such high computational cost and large number parameters optimized. In this paper we propose Extreme Learning Machine (ELM) based Multi-modality Classifier Combination...

<title>Abstract</title> <bold>Background: </bold>Traditional scoliosis screening necessitates a substantial number of specialized personnel and equipment, leading to inconvenience that can result in missed opportunities for early diagnosis optimal treatment. We have developed deep learning-based image segmentation model enhance the efficiency screening. <bold>Methods: </bold>A total 350 patients with 108 healthy subjects were included this study. The dataset comprised bare back images...

Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular information, greatly enhancing our understanding interactions within tissues. Accurate identification domains is crucial for comprehending tissue organization. However, effective integration location and still faces significant challenges. To address this challenge, we propose a novel self-supervised graph representation learning framework named stHGC identifying...

In this paper, we consider a continuous map f: X→X, where X is compact metric space, and discuss the existence of chaotic set f specially (as X=[0,1]). We prove that has positively topological entropy if only it an uncountably in which each point recurrent not weakly periodic.

Abstract-Community network discovery algorithm requires many different community data to evaluate the performance. The sets currently we use are from reality. During experiment, these require a large number of pre-work, but range their parameters is relatively limited. This paper presents two random generation algorithms. They can respectively generate uniform and non-uniform network. At same time, this builds visible platform. Our experiment results show that algorithms for generating...

Resource allocation in grid environment is a complex undertaking due to the heterogeneity and dynamic nature aroused by wide area sharing. To address heterogeneous computationally intractable problem of resource optimization grid, this paper presents an algorithm for parallel tasks based on particle swarm optimization. The user tackled introducing universal utility function. And that computational intractability solved using iterative searching swarm. Experimental results show proposed...

. Cross-modal retrieval is an important research in the field of multi-modal learning, which aims to map samples from different modalities into a shared feature space for comparing and retrieving samples. However, existing methods are unable capture potential semantic correlations coexistence information modal data, resulting lack effective features. To address this issue, we propose novel approach called Learning Semantic Structure Correlation with Deep Alignment Network Cross- Modal...