scholarly journals Incremental autoencoders for text streams clustering in social networks

2021 ◽  
Vol 27 (11) ◽  
pp. 1203-1221
Author(s):  
Amal Rekik ◽  
Salma Jamoussi
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Streams ◽  
High Speed ◽  
Clustering Method ◽  
Text Streams ◽  
Data Field ◽  
Learning Technique ◽  
Clustering Data

Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.

Research on O2O Platform and Promotion Algorithm of Sports Venues Based on Deep Learning Technique

2018 ◽  
Vol 13 (3) ◽  
pp. 73-84
Author(s):  
Kaiyan Han ◽  
Qin Wang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Joint Distribution ◽  
Big Data Analysis ◽  
Practical Significance ◽  
Hidden Variable ◽  
Optimal Value ◽  
Learning Technique ◽  
Sports Venues

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.

scholarly journals FINET: Fast Inferring NETwork

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Anyou Wang ◽  
Rong Hai
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Regulatory Network ◽  
High Speed ◽  
Biological Network ◽  
High Accuracy ◽  
Massive Data ◽  
Command Line ◽  
Gene Regulatory ◽  
User Friendly

Abstract Objectives Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity from big data. Results The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with over 94% precision. The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET’s implementations with Julia, users with no background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data.

scholarly journals A survey of Big Data dimensions vs Social Networks analysis

2020 ◽  
Author(s):  
Michele Ianni ◽  
Elio Masciari ◽  
Giancarlo Sperlí
Keyword(s):  
Social Networks ◽  
Big Data ◽  
High Speed ◽  
User Behavior ◽  
Real Life ◽  
Heterogeneous Data ◽  
User Interactions ◽  
Link Type ◽  
Audio Data ◽  
Traditional Approaches

Abstract The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).

scholarly journals Application of Machine Learning in Rhinology: A State of the Art Review

2020 ◽  
Vol 63 (8) ◽  
pp. 341-349
Author(s):  
Myeong Sang Yu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Records ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Machine Learning Applications

The revolutionary development of artificial intelligence (AI) such as machine learning and deep learning have been one of the most important technology in many parts of industry, and also enhance huge changes in health care. The big data obtained from electrical medical records and digitalized images accelerated the application of AI technologies in medical fields. Machine learning techniques can deal with the complexity of big data which is difficult to apply traditional statistics. Recently, the deep learning techniques including convolutional neural network have been considered as a promising machine learning technique in medical imaging applications. In the era of precision medicine, otolaryngologists need to understand the potentialities, pitfalls and limitations of AI technology, and try to find opportunities to collaborate with data scientists. This article briefly introduce the basic concepts of machine learning and its techniques, and reviewed the current works on machine learning applications in the field of otolaryngology and rhinology.

scholarly journals Big Data Analytics With Reference To Wide Variety of Domains

2019 ◽  
Vol 8 (10) ◽  
pp. 1100-1104
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Government Sector ◽  
Big Data Technologies ◽  
Survey Results

In the current day scenario, a huge amount of data is been generated from various heterogeneous sources like social networks, business apps, government sector, marketing, health care system, sensors, machine log data which is created at such a high speed and other sources. Big Data is chosen as one among the upcoming area of research by several industries. In this paper, the author presents wide collection of literature that has been reviewed and analyzed. This paper emphasizes on Big Data Technologies, Application & Challenges, a comparative study on architectures, methodologies, tools, and survey results proposed by various researchers are presented

Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark

2017 ◽  
Vol 47 (10) ◽  
pp. 2727-2739 ◽  
Author(s):  
Sergio Ramirez-Gallego ◽  
Bartosz Krawczyk ◽  
Salvador Garcia ◽  
Michal Wozniak ◽  
Jose Manuel Benitez ◽  
...  
Keyword(s):  
Big Data ◽  
Data Streams ◽  
High Speed ◽  
Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Big Data Streams ◽  
Neighbor Classification

Integrating deep learning, social networks, and big data for healthcare system

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Riad Belkeiri ◽  
Abd Sattar Khaouazm
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Deep Learning ◽  
Social Network ◽  
Healthcare System ◽  
Health Sector ◽  
Daily Basis ◽  
X Rays ◽  
X Ray ◽  
Data Layer

AbstractThis paper aims to propose a deep learning model based on big data for the healthcare system to predict social network data. Social network users post large amounts of healthcare information on a daily basis and at the same time hospitals and medical laboratories store very large amounts of healthcare data, such as X-rays. The authors provide an architecture that can integrate deep learning, social networks, and big data. Deep learning is one of the most challenging areas of research and is becoming increasingly popular in the health sector. It uses deep analysis to extract knowledge with optimum precision. The proposed architecture consists of three layers: the deep learning layer, the big data layer, and the social networks layer. The big data layer includes data for health care, such as X-ray images. For the deep learning layer, three Convolution Neuronal Network models are proposed for X-ray image classification. As a result, social network layer users can access the proposed system to predict their X-ray image posts.

scholarly journals An Energy-Efficient Silicon Photonic-Assisted Deep Learning Accelerator for Big Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mengkun Li ◽  
Yongjian Wang
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Deep Learning ◽  
Wavelength Division Multiplexing ◽  
High Speed ◽  
Human Performance ◽  
Feature Maps ◽  
Wavelength Division ◽  
Large Bandwidth ◽  
Silicon Photonic

Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing architecture due to their advantages, such as low energy consumption, large bandwidth, and high speed. Therefore, we propose a silicon photonic-assisted deep learning accelerator for big data. The accelerator uses microring resonators (MRs) to form a photonic multiplication array. It combines photonic-specific wavelength division multiplexing (WDM) technology to achieve multiple parallel calculations of input feature maps and convolution kernels at the speed of light, providing the promise of energy efficiency and calculation speed improvement. The proposed accelerator achieves at least a 75x improvement in computational efficiency compared to the traditional electrical design.

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