Fusion Analysis of Economic Data of the Medical and Health Industry Based on Blockchain Technology and Two-Way Spectral Cluster Analysis

Due to the huge potential in gene expression analysis, which is helpful for disease diagnosis, new drug development, and life science research, the two-way clustering algorithm was proposed and it was widely used in gene expression data research. In order to understand the economic data of medical and health industry, this paper analyzes the economic data of the medical and health industry in different regions of China based on blockchain technology and two-way spectral cluster analysis and makes statistics on the economic data of the medical and health industry in eastern, central, and western regions of China. This paper studies the development status of China’s medical and health industry and the factors affecting the agglomeration of medical and health service industry and analyzes them under the blockchain technology and two-way spectral cluster analysis method. The results show that the overall development trend of China’s medicine and health is from government-led to government, society, and individual sharing. After the transformation of blockchain technology and two-way spectral cluster analysis, the output value of the pharmaceutical industry increased by about 10%.

An EMI radiation grouping approach based on spectral cluster in near filed scanning for assessment

With the ever-increasing operating frequency in integrated circuit, it is very essentialto assess the radiation used to help the IC designer. Based on the similarity of electromagnetic patterns obtained from the radiation of ICs and their nonlinear edge, we develop a post-processing technique to group the electromagnetic patterns. A near field scanning is performed to obtain and extract the electromagnetic pattern that is used to validate the technique. Experiment results show that it can accurately group the electromagnetic patterns by which radiation assessment can be performed.

Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.

WHAT IDENTIFIES A WHALE BY ITS FLUKE? ON THE BENEFIT OF INTERPRETABLE MACHINE LEARNING FOR WHALE IDENTIFICATION

Interpretable and explainable machine learning have proven to be promising approaches to verify the quality of a data-driven model in general as well as to obtain more information about the quality of certain observations in practise. In this paper, we use these approaches for an application in the marine sciences to support the monitoring of whales. Whale population monitoring is an important element of whale conservation, where the identification of whales plays an important role in this process, for example to trace the migration of whales over time and space. Classical approaches use photographs and a manual mapping with special focus on the shape of the whale flukes and their unique pigmentation. However, this is not feasible for comprehensive monitoring. Machine learning methods, especially deep neural networks, have shown that they can efficiently solve the automatic observation of a large number of whales. Despite their success for many different tasks such as identification, further potentials such as interpretability and their benefits have not yet been exploited. Our main contribution is an analysis of interpretation tools, especially occlusion sensitivity maps, and the question of how the gained insights can help a whale researcher. For our analysis, we use images of humpback whale flukes provided by the Kaggle Challenge ”Humpback Whale Identification”. By means of spectral cluster analysis of heatmaps, which indicate which parts of the image are important for a decision, we can show that the they can be grouped in a meaningful way. Moreover, it appears that characteristics automatically determined by a neural network correspond to those that are considered important by a whale expert.

A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed “Spectral Cluster Discovery'' (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we utilize the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods.