Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful understanding pathogenesis, diagnosis, and prevention, as well identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease achieved impressive performance. However, there are two main drawbacks to these methods. The first underutilize biometric...

Despite the advances reached along last 20 years, anomaly detection in networks is still an immature technology, Nevertheless, benefits which could be obtained from a better understanding of problem itself as well improvement these methods. Therefore, this paper we present survey on networks. In order to distinguish between different approaches used for structured way, have classified those methods into four categories: statistical detection, classifier based using machine learning and...

In this paper, with respect to multiple criteria group decision making (MCGDM) problems in which values are expressed by Pythagorean fuzzy uncertain linguistic variables (PFULVs), we propose an extended TODIM method. Firstly, define the set, and pr opose operational laws, Hamming distance, score function accuracy of PFULVs. Then method is presented solve MCGDM under environment, a numerical example information given show effectiveness proposed Further, analyze influence different parameter...

Failure mode and effect analysis (FMEA) method has been widely utilized to solve the problem of risk assessment in all walks life. An FMEA decision support model considering expert clustering attitude is constructed. First, information processed cloud environment. The behavior experts simulated based on trust relationship, opinion similarity similarity. Second, consensus opinions are formed through evolution, group weight determination constructed size level. Finally, a linear programming...

In the intricate decision-making problems, preference information of decision makers may be difficultly stated by numerical values due to ambiguity human thinking about complex objective things in real world, and sometimes, it easily expressed LTs (linguistic terms). Th us, how solve fuzzy MAGDM (multiple attribute group decision-making) problem which attributes are described neutrosophic linguistic weights for fully unknown, has become an important research direction. To achieve this goal,...

Schweizer–Sklar (SS) operation can make information aggregation more flexible, and the Muirhead mean (MM) operator take into account correlation between inputs by a variable parameter. Because traditional MM is only available for real numbers single-valued neutrosophic set (SVNS) better express incomplete uncertain in decision systems, this paper, we applied operators to sets (SVNSs) presented two new with SS operation, i.e., (SVNSSMM) weighted (WSVNSSMM) operator. We listed some properties...

There are many practical decision-making problems in people’s lives, but the information given by decision makers (DMs) is often unclear and how to describe this of critical importance. Therefore, we introduce interval neutrosophic linguistic numbers (INLNs) represent less clear uncertain give their operational rules comparison methods. In addition, since Maclaurin symmetric mean (MSM) operator has special characteristic capturing interrelationships among multi-input arguments, further...

Geological exploration plays a fundamental and crucial role in geological engineering. The most frequently used method is to obtain borehole videos using an axial view camera system (AVBCS) pre-drilled borehole. This approach surveying the internal structure of based on video playback screenshot analysis. One drawbacks AVBCS that it provides only qualitative description information with forward-looking video, but quantitative analysis data, such as width dip angle fracture, are unavailable....

The Maclaurin symmetric mean (MSM) operator has a good aggregation effect. It can capture the relationships between multiple input parameters, and neutrosophic uncertain linguistic numbers well represent some indeterminate incomplete information. In this paper, we combine MSM with singled-valued set propose operators based on single-valued environment, such as mean(SVNULMSM) generalized mean(SVNULGMSM) operator. First of all, according to numbers, give operating rules. Furthermore,...

When solving multiple attribute decision making (MADM) problems, the 2-tuple linguistic variable is an effective tool that can not only express complex cognitive information but also prevent loss of in calculation. The picture fuzzy set (PFS) has three degrees and more freedom to information. In addition, Archimedean t-conorm t-norm (ATT) generalize most existing t-conorms t-norms Maclaurin symmetric mean (MSM) operators catch relationships among multi-input parameters. Therefore, we...

Protein content (PC) is a crucial factor that determines the end-use and nutritional quality of wheat (Triticum aestivum). Glutamine synthetase (GS), which major participant in nitrogen metabolism, can convert inorganic into organic nitrogen. Although many studies have been conducted on PC GS, dynamic analysis all filling stages has not conducted. Therefore, 115 F9-10 recombinant inbred lines 'R131/R142' were used to analyze GS activity during different developmental stages, using...

In order to solve multiple-attribute group decision-making (MAGDM) problems under a trapezoid intuitionistic fuzzy linguistic (TIFL) environment and the relationships between multiple input parameters needed, in this paper, we extend Maclaurin symmetric mean (MSM) operators TIFL numbers (TIFLNs). Some new aggregation are proposed, including (TIFLMSM) operator, generalized (TIFLGMSM) weighted (TIFLWMSM) operator (TIFLWGMSM) operator. Next, based on TIFLWMSM TIFLWGMSM operators, two methods...

Aiming at multiple attribute group decision making (MAGDM) problems, especially the values of 2-tuple linguistic numbers and interrelationships between each needing to be considered, this paper proposes a new method analysis. Firstly, we developed few aggregation operators, like dependent weighted Maclaurin symmetric mean (2TLDWMSM) operator, generalized (2TLDWGMSM) geometric (2TLDWGeoMSM) operator. In above (MSM) operators can take relationships into account mitigate unfair parameters’...

In recent years, a single medical image is prone to lose hidden features with low resolution and salient high noise but rich information, ignoring the connection of multiple images. Therefore, remedy for shortcomings, this paper proposes multi-modal classification method combined graph convolutional neural networks(MB-pGCN). First, first modality image, we use ResNet-152 model extract features, obtain local disease-related region inter-group comparison method. It utilizes weak attention...