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

We demonstrate a stable homodyne phase demodulation method with a double pulse based on an ultra-weak fiber Bragg grating (UWFBG) array. The technique divides one of the probe pulses into three sections and introduces successive 2π/3 phase differences into each section. By using a simple direct detection scheme, it can achieve distributed and quantitative vibration measurement along the UWFBG array. Compared to the traditional homodyne demodulation technique, the proposed technique is more stable and easier to accomplish. Moreover, the reflected light from the UWFBGs can provide a signal that is modulated uniformly by the dynamic strain and multiple results for averaging, resulting in a higher signal-to-noise ratio (SNR). We experimentally demonstrate the technique's effectiveness by monitoring different vibrations. The SNR for measuring a generic 100 Hz, 0.08 rad vibration in a 3 km UWFBG array with a reflectivity of -40 to -45 dB is estimated to be ∼44.92 dB.

Distributed dynamic strain measurement with a direct detection scheme by using a three-step-phase-shifted double pulse in a UWFBG array.

Near-surface structure investigation plays an important role in studying shallow active faults and has various engineering applications. Therefore, we developed a near-surface structure investigation method using ambient noise in a water environment. This newly developed seismic acquisition technology, fiber-optic distributed acoustic sensing (DAS), was used to acquire ambient noise from the Yangtze River. The recorded data were processed to reconstruct surface waves based on the theory of seismic interferometry. The fundamental-mode dispersion curves were extracted and inverted to obtain a shear-wave velocity model below the DAS line. We compared the inverted velocity model with the subsurface geological information from near the study area. The results from the inverted model were consistent with the prior geological information. Therefore, ambient noise in the water environment can be combined with DAS technology to effectively investigate near-surface structures.

/pdf/near-surface-structure-investigation-using-ambient-noise-in-1xkk1pb5.pdf

Near-Surface Structure Investigation Using Ambient Noise in the Water Environment Recorded by Fiber-Optic Distributed Acoustic Sensing

Airborne radar high-squint looking imaging is an important research for remote sensing. The traditional Doppler beam sharpening based on fast Fourier transform (FFT) has good real-time performance but low cross-range resolution. Many super-resolution methods have been proposed to enhance the cross-range resolution for airborne radar. However, these methods generally adopt the batch processing mode with high computational complexity and high memory usage, which lead to poor real-time performance. This paper proposes an online super-resolution imaging approach for airborne scanning radar based on sliding window recursive least square (SWRLS) algorithm. The current scattering estimation can be derived recursively through downdating and updating. The proposed method effectively improves the cross-range resolution as well as the real-time performance and memory occupancy, which is beneficial to high-quint continuous realtime imaging for airborne radar. Simulation results are given to demonstrate the effectiveness of the proposed method.

Online Super-Resolution Imaging for Airborne Scanning Radar Based on Sliding Window RLS Algorithm

Fund correlation analysis can guide investors' investment and wealth management, avoiding the selection of highly relevant funds in the investment process, which can make the risk sharing among funds. There is a strong dependence between the features of the fund data and a long-term dependence between the output of different time steps, which makes it difficult to obtain good performance in the fund data in the data analysis model used in the traditional intelligent investment system. This has brought difficulties to fund correlation analysis. In order to solve the above problems, this paper uses an encoder-decoder model combined with the attention mechanism--Improved RNN model. The Encoder-decoder model has made great strides in the application of financial time series analysis. And the attention mechanism can select specific feature inputs and previous time step outputs, both of which are highly correlated with the current output, making system predictions more efficient. This paper applies this model to the historical data set containing multiple public funds. The results show that the fund intelligent investment system proposed in this paper performs best.

Improved Recurrent Neural Networks (RNN) Based Intelligent Fund Transaction Model

To facilitate queries over XML and semi-structured data, various structural indexes acting as a structural summary have been proposed. Structural index is derived directly from the data and serve as indices for evaluating path expressions on XML and semi-structured data. We propose D2-Index: a path-based dynamic index method for querying XML and semi-structured data. D2-Index is prefix-based and based on the concept of ORDPATH and BSC. It uses binary fraction to encode and can be completely capable of static encoding. More importantly, it can insert nodes into any position efficiently without re-encoding the existing indexes. According to the experimental results, it shows that the query plans using D2-Index is feasibility, accuracy and efficiency.

Yin Zhang

Papers

Distributed dynamic strain measurement with a direct detection scheme by using a three-step-phase-shifted double pulse in a UWFBG array.

Near-Surface Structure Investigation Using Ambient Noise in the Water Environment Recorded by Fiber-Optic Distributed Acoustic Sensing

Online Super-Resolution Imaging for Airborne Scanning Radar Based on Sliding Window RLS Algorithm

Improved Recurrent Neural Networks (RNN) Based Intelligent Fund Transaction Model

D2-Index: A Dynamic Index Method for Querying XML and Semi-Structured Data