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Volume 3 Issue 3
Published:05 September 2020

Shuhui Yang, Zimu Yuan, Wei Li

2020, 3(3): 155-170.   doi:10.26599/BDMA.2020.9020001
Abstract ( 81 HTML ( 3   PDF(705KB) ( 45 )

The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal Strength (RSS) is a convenient and low-cost measurement that has been adopted in many localization approaches. However, using RSS data for localization needs to solve a fundamental problem, that is...

Dongxiao Yu, Lifang Zhang, Qi Luo, Xiuzhen Cheng, Jiguo Yu, Zhipeng Cai

2020, 3(3): 171-180.   doi:10.26599/BDMA.2020.9020002
Abstract ( 145 HTML ( 0   PDF(1206KB) ( 52 )

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has $d$ ($d$

Wei Zhong, Ning Yu, Chunyu Ai

2020, 3(3): 181-195.   doi:10.26599/BDMA.2020.9020003
Abstract ( 197 HTML ( 2   PDF(5676KB) ( 103 )

With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures, a machine learning based Intrusion Detection Systems (IDS) has become a vital component to protect our economic and national security. Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection. The single learning model approach may experience problems to understand increasingly complicated data distrib...

Sunitha Basodi, Chunyan Ji, Haiping Zhang, Yi Pan

2020, 3(3): 196-207.   doi:10.26599/BDMA.2020.9020004
Abstract ( 118 HTML ( 0   PDF(6851KB) ( 17 )

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges, one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradient...

Zigeng Wang, Xia Xiao, Sanguthevar Rajasekaran

2020, 3(3): 208-224.   doi:10.26599/BDMA.2020.9020005
Abstract ( 128 HTML ( 0   PDF(4015KB) ( 53 )

Feature selection is a crucial problem in efficient machine learning, and it also greatly contributes to the explainability of machine-driven decisions. Methods, like decision trees and Least Absolute Shrinkage and Selection Operator (LASSO), can select features during training. However, these embedded approaches can only be applied to a small subset of machine learning models. Wrapper based methods can select features independently from machine learning models but they often suffer from a hi...

Zaobo He, Junxiu Zhou

2020, 3(3): 225-233.   doi:10.26599/BDMA.2020.9020008
Abstract ( 121 HTML ( 0   PDF(6116KB) ( 41 )

The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information, leading to one of the most important concerns — genomic data privacy. Since personal biomedical data are highly correlated with relatives, with the increasing availability of genomes and personal traits online (i.e., leakage unwittingly, or after their releasing intentionally to genetic service platforms), kin-genomic data privacy is thre...