Data Mining Practical Machine Learning Tools and Techniques

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

Giant and isotropic magnetoresistance as huge as −53% was observed in magnetic manganese oxide La0.72Ca0.25MnOz films with an intrinsic antiferromagnetic spin structure. We ascribe this magnetoresistance to spin‐dependent electron scattering due to spin canting of the manganese oxide.

31p-S-4 Giant Magnetoresistance in Magnetic Manganese Oxide with Intrinsic Antiferromagnetic Spin Structure

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.

Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control

Wireless sensor networks deployment and operation are most likely to take place under hazardous conditions. One extreme scenario is the deployment of a wireless sensor network in a mountainous and forested region in which a fire has ignited, for the purpose of localizing and /or tracking, in real time, its spread. Some methods developed in this paper are expected to provide superior performances under these conditions. Existing fires in the deployment region will also affect the nodes' coverage because the nodes that will fall in the middle of the fire will likely cease to operate. In our future work, we show that these routing can be used in many applications.

Coverage Hole Bypassing in Wireless Sensor Networks

A name disambiguation module for intelligent robotic consultant in industrial internet of things

The 〈001〉-textured Pb(Nb0.03Zr0.50Ti0.47)O3/La0.75Sr0.11Ca0.14MnO3 heterostructure deposited on SrTiO3(001) substrates by pulsed laser deposition

The deconvolution methods are the effective approaches to realize high cross-range resolution of real-beam scanning radar. However, the computational time of these algorithms cannot meet the real-time requirements of radar imaging, which is a key limitation to apply this technology into engineering application. In order to overcome this problem, we propose a parallel processing plan based on Graphics Processing Unit (GPU) frame to achieve the real-time imaging by the Bayesian deconvolution algorithm. Because of the processing of each range bin is independent, the characteristics of GPU parallel computing can be used to settle the problem of large computation burden. In this paper, the method to realize parallel computing on GPU frame will be explained in detail. At last, the Poisson-based maximum a posteriori (MAP) algorithm [11] is taken as an example to compare the efficiency of the GPU frame with Central Processing Unit (CPU).

Realization of airborne forward-looking radar super-resolution algorithm based on GPU frame

Limited SAR training data could incur overfitting in multilayer convolutional neural network (CNN), which restricts the recognition performance severely. In this paper, we proposed a new SAR target recognition method based on heterogeneous CNN ensemble. The proposed network is constructed with the noncomplete connection scheme and multiple filters stack, which is able to reduce the number of free parameters and improve the training efficiency. Based on ensemble learning theory, heterogeneous network structures are effectively combined, the generalization ability and recognition performance of the network are improved, and the amount of training samples is also further reduced. Experimental results have shown the superiority of the proposed network based on the MSTAR dataset.

Yin Zhang

Papers

Coverage Hole Bypassing in Wireless Sensor Networks

A name disambiguation module for intelligent robotic consultant in industrial internet of things

The 〈001〉-textured Pb(Nb0.03Zr0.50Ti0.47)O3/La0.75Sr0.11Ca0.14MnO3 heterostructure deposited on SrTiO3(001) substrates by pulsed laser deposition

Realization of airborne forward-looking radar super-resolution algorithm based on GPU frame

Target recognition for SAR images based on heterogeneous CNN ensemble