The Cancer Genome Atlas (TCGA) is a public funded project that aims to catalogue and discover major cancer-causing genomic alterations to create a comprehensive "atlas" of cancer genomic profiles. So ...far, TCGA researchers have analysed large cohorts of over 30 human tumours through large-scale genome sequencing and integrated multi-dimensional analyses. Studies of individual cancer types, as well as comprehensive pan-cancer analyses have extended current knowledge of tumorigenesis. A major goal of the project was to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer. This review discusses the current status of TCGA Research Network structure, purpose, and achievements.
The study aims to conduct big data analysis (BDA) on the massive data generated in the smart city Internet of things (IoT), make the smart city change to the direction of fine governance and ...efficient and safe data processing. Aiming at the multi-source data collected in the smart city, the study introduces the deep learning (DL) algorithm while using BDA, and puts forward the distributed parallelism strategy of convolutional neural network (CNN). Meantime, the digital twins (DTs) and multi-hop transmission technology are introduced to construct the smart city DTs multi-hop transmission IoT-BDA system based on DL, and further simulate and analyze the performance of the system. The results reveal that in the energy efficiency analysis of model data transmission, the energy efficiency first increases and then decrease as the minimum energy collected α0 increases. But a more suitable power diversion factor ρ is crucial to the signal transmission energy efficiency of the IoT-BDA system. The prediction accuracy of the model is analyzed and it suggests that the accuracy of the constructed system reaches 97.80%, which is at least 2.24% higher than the DL algorithm adopted by other scholars. Regarding the data transmission performance of the constructed system, it is found that when the successful transmission probability is 100% and the exponential distribution parameters λ is valued 0.01∼0.05, it is the closest to the actual result, and the data delay is the smallest, which is maintained at the ms level. To sum up, improving the smart city’s IoT-BDA system using the DL approach can reduce data transmission delay, improve data forecasting accuracy, and offer actual efficacy, providing experimental references for the digital development of smart cities in the future.
•The DCNNPS is proposed in parallel with BDA to process the multi-source data collected from smart cities.•The smart city’s IoT-BDA system can reduce data transmission delay and improve data forecasting accuracy.•Distributed CNN Parallelism Strategy is proposed in parallel with BDA.
In the recent year, Internet of Things (IoT) is industrializing in several real-world applications such as smart transportation, smart city to make human life reliable. With the increasing ...industrialization in IoT, an excessive amount of sensing data is producing from various sensors devices in the Industrial IoT. To analyzes of big data, Artificial Intelligence (AI) plays a significant role as a strong analytic tool and delivers a scalable and accurate analysis of data in real-time. However, the design and development of a useful big data analysis tool using AI have some challenges, such as centralized architecture, security, and privacy, resource constraints, lack of enough training data. Conversely, as an emerging technology, Blockchain supports a decentralized architecture. It provides a secure sharing of data and resources to the various nodes of the IoT network is encouraged to remove centralized control and can overcome the existing challenges in AI. The main goal of our research is to design and develop an IoT architecture with blockchain and AI to support an effective big data analysis. In this paper, we propose a Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence that provides an efficient way of converging blockchain and AI for IoT with current state-of-the-art techniques and applications. We evaluate the proposed architecture and categorized into two parts: qualitative analysis and quantitative analysis. In qualitative evaluation, we describe how to use AI and Blockchain in IoT applications with “AI-driven Blockchain” and “Blockchain-driven AI.” In quantitative analysis, we present a performance evaluation of the BlockIoTIntelligence architecture to compare existing researches on device, fog, edge and cloud intelligence according to some parameters such as accuracy, latency, security and privacy, computational complexity and energy cost in IoT applications. The evaluation results show that the proposed architecture performance over the existing IoT architectures and mitigate the current challenges.
•Study Blockchain and AI for IoT.•Propose a Blockchain-enabled Intelligent IoT architecture with AI.•Evaluate the proposed architecture with standard measures.•Comparison of our research work with existing researches.
Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using ...wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions.
•Smartphones, wearable sensors, and social networks provide a new approach to collect patient data.•Continuous patient monitoring generates a large amount of unstructured healthcare data.•Existing approaches cannot deal with huge amounts of healthcare data extracted from various sources.•Traditional ML techniques are unable to handle extracted healthcare data for abnormality prediction.•A big data analytics engine is proposed to precisely analyze different sources of healthcare data.
•Survey on the practice of big data analysis in agriculture.•Detailed review of 34 high-impact relevant research studies.•Discussion on the status and potential of big data analysis in ...agriculture.•Open problems and challenges, barriers for wider adoption and use.•Ways to overcome barriers and potential future applications in agriculture.
To tackle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. This can happen by means of modern digital technologies that monitor continuously the physical environment, producing large quantities of data in an unprecedented pace. The analysis of this (big) data would enable farmers and companies to extract value from it, improving their productivity. Although big data analysis is leading to advances in various industries, it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employ the recent practice of big data analysis, in order to solve various relevant problems. Thirty-four different studies are presented, examining the problem they address, the proposed solution, tools, algorithms and data used, nature and dimensions of big data employed, scale of use as well as overall impact. Concluding, our review highlights the large opportunities of big data analysis in agriculture towards smarter farming, showing that the availability of hardware and software, techniques and methods for big data analysis, as well as the increasing openness of big data sources, shall encourage more academic research, public sector initiatives and business ventures in the agricultural sector. This practice is still at an early development stage and many barriers need to be overcome.
•An intelligent SOH estimation framework for EVs big data platform is presented.•The capacity estimation model is established based on neural network algorithm.•The dataset is generated from ...real-word data of EVs under actual operation.•Big data analysis is used for extraction of health feature parameters and selection of training data.•Accurate SOH evaluation can be obtained by using the historical operating data.
State of health (SOH) of in-vehicle lithium-ion batteries not only directly determines the acceleration performance and driving range of electric vehicles (EVs), but also reflects the residual value of the batteries. Especially, with the development of data acquisition and analysis technologies, using big data to realize on-line evaluation of battery SOH shows vital significance. In this paper, we propose an intelligent SOH estimation framework based on the real-world data of EVs collected by the big data platform. Defined by the more accessible detection, the health features are extracted from historical operating data. Then, the deep learning process is implemented in feedforward neural network driven by the degradation index. The estimation method is validated by the one-year monitoring dataset from 700 vehicles with different driving mode. The result shows that the proposed framework can effectively estimate SOH with the maximum relative error of 4.5% and describe the aging trend of battery pack based on big data platform.
Using multi-source sensing data based on the Internet of Things (IoT) with artificial intelligence and big data processing technology to achieve predictive maintenance of mechanical equipment can ...remarkably improve the service life of the machine and reduce labor costs when diagnosing mechanical faults, and it has become a highly relevant research topic. In this paper, the multi-source sensing data fusion models and fusion algorithms are studied and discussed. First, the Joint Directors of Laboratories (JDL) fusion model and the Hierarchical fusion model are compared and analyzed. Then, various types of fusion algorithms based on Neural Networks and Deep Learning, including Dempster-Shafer (D-S) evidence theory and their applications in mechanical fault diagnosis and fault prediction, are studied and compared. The findings reveal that exploring and designing a more intelligent fusion model incorporating the beneficial characteristics of different fusion algorithms are challenging and have a certain value for promoting the development of mechanical fault diagnosis and prediction.
In this article, we propose a big data based analysis framework to analyze and extract network behaviors in cellular networks for Industry 4.0 applications from a big data perspective, using Hadoop, ...Hive, HBase, and so on. The data prehandling and traffic flow extraction approaches are presented to construct effective traffic matrices. Accordingly, we can capture network behaviors in cellular networks from a networkwide perspective. Although there have been a number of prior studies on cellular network usage, to the best of our knowledge, this article is a first study that characterizes network behaviors using the big data analytics to analyze a network big data of call detail records over a longer duration (five months), with more users (five million), more records (several hundred million lines) and nationwide coverage. The call pattern analysis and network behavior extraction approaches are designed to perform big data analysis and feature extractions. Then, the corresponding algorithms are proposed to characterize network behaviors, i.e., cellular call patterns and network resource usage. The detailed evaluation is proposed to validate our method. For example, we find that some unpopular calls can last longer time and thus consume more network resources.
Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. ...Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan's Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
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