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

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. However, some studies in recent years have shown that the LSTM (Long Short-Term Memory) model has good time series processing capability, and the Encoder-decoder model has made great progress in the application of financial time series analysis. Based on the above research, this paper constructs the DLIFT system using an Improved RNN model combined with attention mechanism.. 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, and compares it with several other intelligent investment systems. The results show that the fund intelligent investment system proposed in this paper performs best. The research in this paper is of great significance to the use of deep learning methods to solve fund correlation analysis problems, and provides new ideas for the research in the field of smart finance.

CIFT: Connected Intelligent Fund Transaction System Based on Deep Learning

The emerging hierarchical-infeed LCC-HVDC (HIDC) system has been recently commissioned for the long distance bulk power transmission in China. Compared to the conventional multi-infeed LCC-HVDC (MIDC) system with inverters coupling only in the ac-side, different inter-inverter interactions exist in the HIDC system where inverters couple in both the ac- and dc- sides. It is beneficial to reveal the underlying difference of the inter-inverter interactions in the HIDC and MIDC systems for determining which system should be adopted in the planning study. However, this difference has not been clearly revealed yet in earlier works. Thus in this paper, the inter-inverter voltage interaction factor (IVIF) index is firstly introduced to quantitatively assess the inter-inverter interactions. The analytical calculation methods of the IVIF for the HIDC and MIDC systems are also presented for the satisfactory accuracy and efficiency. Moreover, these methods are further used for comparative analysis of parametric dependence of the IVIF in the HIDC and MIDC systems considering various system parameters, operation variables and dc control modes. The analysis results clearly reveal the underlying difference of the inter-inverter interactions in the HIDC and MIDC systems. Finally, the effectiveness of the theoretical assertions is verified by experiments based on the hardware-in-the-loop platform.

Comparative Analysis of Inter-Inverter Interactions in Emerging Hierarchical-Infeed and Conventional Multi-infeed LCC-HVDC Systems

Radar high resolution imaging becomes a research hotspot in recent years. Synthetic Aperture Radar (SAR) uses motion of single platform to obtain large aperture. However, it can only obtain a limited resolution at a specific perspective. Distributed radar, which utilize platforms to expand in space and form a larger aperture. It records echo from multiple signal channels which can achieve high resolution in all directions. The existing distributed radar system mainly realizes the imaging of static target with lots of platforms. In this paper, we studied moving target imaging of distributed radar in sparse array condition. In this system, all the array elements can work flexibly as transmitter or receiver. Some methods of spectrum estimation are used to solve distributed radar imaging model. But they are all applied to static target and it is difficult to estimate the number of scattered points accurately, especially for moving target. We established the signal model of moving target in a period of observation time and solved it by using truncated singular value decomposition (TSVD) method. Simulation results verified the feasibility of the imaging model and excellent performance of proposed method.

Sparse Array Distributed Radar Imaging Based on TSVD for Moving Target

Background and Objectives: Standard suction technique (SST), slow-pull technique (SPT), and wet suction technique (WEST) of EUS-FNA are designed to improve the diagnostic yields of solid and solid-cystic lesions. We conducted a multicenter, prospective, randomized crossover trial to compare SST, SPT, and WEST on specimen quality and diagnostic accuracy using a 22G needle. Methods: Patients with solid or solid-cystic lesions referred for EUS-FNA at four tertiary hospitals from December 2017 to August 2019 were considered eligible. All lesions were sampled using a 22G needle by the three techniques performed consecutively in a randomized order. The primary outcome was quality of the specimen acquired by each technique regarding blood contamination, tissue integrity and cellularity for diagnosis, graded on a predefined scale. The secondary outcomes were the diagnostic yield of EUS-FNA and the incidence of adverse events. ClinicalTrial. gov registration number: NCT03567863. Results: A total of 300 patients (mean age, 60.6 years, 188 men) were enrolled. WEST was superior (mean score 4.02 ± 1.51) over SST (3.67 ± 1.57, P = 0.018), but comparable to SPT (3.83 ± 1.55, P = 0.370) in overall specimen quality evaluation. WEST produced better tissue integrity (1.42 ± 0.74) and higher cellularity (1.32 ± 0.80) than SST and SPT. SPT (1.43 ± 0.69) was superior to SST (1.27 ± 0.72, P = 0.004) and WEST (1.28 ± 0.71, P = 0.006) in avoiding blood contamination. WEST achieved a diagnostic accuracy of 74.7%, higher than SST (64.4%, P = 0.007) and SPT (65.0%, P = 0.012). One bleeding event occurred with a pancreatic lesion. Conclusions: WEST was comparable to SPT and superior to SST in the overall quality of the specimen and achieved highest diagnostic yield.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688131

Comparison of specimen quality among the standard suction, slow-pull, and wet suction techniques for EUS-FNA: A multicenter, prospective, randomized controlled trial

https://doi.org/10.1016/j.onehlt.2023.100513

Yin Zhang

Papers

CIFT: Connected Intelligent Fund Transaction System Based on Deep Learning

Comparative Analysis of Inter-Inverter Interactions in Emerging Hierarchical-Infeed and Conventional Multi-infeed LCC-HVDC Systems

Sparse Array Distributed Radar Imaging Based on TSVD for Moving Target

Comparison of specimen quality among the standard suction, slow-pull, and wet suction techniques for EUS-FNA: A multicenter, prospective, randomized controlled trial

Prevalence of Streptococcus suis in pigs in China during 2000–2021: A systematic review and meta-analysis