RETRACTED ARTICLE: Multimedia based intelligent network big data optimization model

Multimedia Tools and Applications - The data of a city space is very large. In the city, there are tens of the thousands of sensors of video, audio and image at the same time. Real-time...     

RETRACTED ARTICLE: The big data visualization technology based ecosystem cycle on high speed network

Multimedia Tools and Applications - Big Data is becoming a key strategy in the business sector, with the increasing number of corporate customer data tracking and collection practices, and the...     

RETRACTED ARTICLE: Designing statistical assessment healthcare information system for diabetics analysis using big data

Health services research provides a multi-disciplinary area of scientific exploration in relation to financial systems, social factors, organizational processes, and health technologies. With the help of big data, the huge amount of data can well be stored and handled effectively for diagnosis and also proper treatment of diseases can be monitored with these emerging technologies. In recent years, Diabetes Mellitus is non-transmittable illnesses that are a matter of concern in most of the developing countries. This paper proposes a model of a statistical assessment, healthcare information system for Diabetes Analysis employing big data. The performance metric such as accuracy and F-measure for the proposed statistical assessment model is evaluated by Hadoop framework, the results are comparatively higher than existing methods.

     

RETRACTED ARTICLE: An effective stadium monitoring control algorithm based on big data in emerging mobile networks

Multimedia Tools and Applications - In the process of monitoring the gymnasium by the traditional radio frequency technology, the parallel computing problem of the large data environment in the...     

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

     

RETRACTED ARTICLE: Smart communication using tri-spectral sign recognition for hearing-impaired people

The Journal of Supercomputing - In recent years, new technology developments have been proposed and implemented to support people with hearing impairment and speech loss. It is a severe...     

RETRACTED ARTICLE: Optimization analysis of sport pattern driven by machine learning and multi-agent

Neural Computing and Applications - The intelligent simulation of Sports can match the actual game and is of great significance to the development of Sports. Sports is a system in which multiple...     

RETRACTED ARTICLE: Design of traffic object recognition system based on machine learning

Neural Computing and Applications - In recent years, researchers have proposed many methods to solve the problem of obstacle detection. However, computer vision-based vehicle detection and...     

Social big data: Recent achievements and new challenges

Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.     

Brain decoding: Opportunities and challenges for pattern recognition

The neuroimaging community heavily relies on statistical inference to explain measured brain activity given the experimental paradigm. Undeniably, this method has led to many results, but it is limited by the richness of the generative models that are deployed, typically in a mass-univariate way. Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data.Over the recent years, techniques from pattern recognition have brought new insights into where and how information is stored in the brain by prediction of the stimulus or state from the data. Pattern recognition is intrinsically multivariate and the underlying models are data-driven. Moreover, the predictive setting is more powerful for many applications, including clinical diagnosis and brain–computer interfacing. This special issue features a number of papers that identify and tackle remaining challenges in this field. The specific problems at hand constitute opportunities for future research in pattern recognition and neurosciences.     

Feature selection techniques in the context of big data: taxonomy and analysis

Applied Intelligence - Recent advancements in Information Technology (IT) have engendered the rapid production of big data, as enormous volumes of data with high dimensional features grow...     

RETRACTED ARTICLE: Using multimedia based big data for child abuse prevention system (Based on the establishment of e-child welfare support system)

Multimedia Tools and Applications - With the case of a child’s death by child abuse in 2013, public attention has grown on child abuse cases and a variety of policies to protect child abuse...     

大数据产业现状和挑战

系统地论述了大数据产业现状和挑战,介绍了大数据产业基本概念和范畴,分析了大数据产业生态中各环节,总结了大数据产业发展的政策环境以及投融资环境,最后,总结了大数据产业发展在数据共享、交易、隐私保护等方面面临的挑战。     

大数据分析关键技术与挑战

全面论述了大数据分析技术的概念、现状、技术及应用,包括大数据基本概念及特点、大数据分析产生的时代背景、关键技术以及大数据带来的变革和挑战,在大数据分析的关键技术中重点对大数据的清洗与融合、大数据处理框架、大数据的建模与分析进行了介绍。     

RETRACTED ARTICLE: Chinese regional economic cooperative development model based on network analysis and multimedia data visualization

Since the reform and opening up, China’s economic development has been accelerating. In the current world economic system, China occupies a very important position. However, there is a phenomenon of uneven economic growth among different regions, namely, there are large differences in economic growth rates and economic development levels between different provinces and cities. In recent years, a large number of studies have shown that China’s regional economic growth has obvious spatial correlation. In this paper, we adopt the method of network analysis to study and explain the spatial correlation of regional economic growth. Multimedia mining is a combination of data mining technology and multimedia technology. It is a cross-disciplinary field of knowledge discovery, data mining, artificial intelligence, machine learning, database technology, and multimedia technology. Therefore, data visualization technology can be used to study the coordinated development model of regional economy. Multimedia data visualization is an evolving concept whose boundaries are constantly expanding, mainly referring to technologically advanced technical methods that allow the use of graphics, image processing, computer vision, and user interfaces. Visualize data by expressing, modeling, and displaying stereo, surface, attributes, and animations. Compared with special technical methods such as stereo modeling, the technical methods covered by data visualization are much broader. The simulation results prove that the propose model can obtain the better overall perforamcne.