Application of Data Mining in WITMED: Identification of Prognostic Genes in Oral Cancer

In recent years, the booming development of big data, cloud computing, Internet of Things, and other technologies provides conditions for the popularization and application of smart city. The combination of big data and medical information produces the emerging field of WITMED (Wise Information Technology of Med). WITMED is essential for the prospering growth of smart cities, which assumed a high quality of medical service is the most challenging goal for the city government. In this paper, the main attention is paid to the method of targeted gene therapy, which provides a new method for the treatment of oral cancer in inhibiting the growth, differentiation, invasion, and metastasis of oral cancer cells; therefore, the physical and psychological adverse effects of surgery and chemotherapy on patients are reduced and the survival and prognosis of patients are improved. Targeted gene therapy methods need to select the appropriate gene; that is, data mining methods are used to analyze a large number of complex genetic data from smart cities to obtain appropriate genetic markers, which makes the effect of targeted gene therapy better, and also provide some reference for the research of oral cancer gene direction and provide some basis for clinical treatment.

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Sustainable governance in smart cities and use of supervised learning based opinion mining

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i1.pp498-497 ◽

2021 ◽

Vol 11 (1) ◽

pp. 498

Author(s):

Hena Iqbal ◽

Sujni Paul ◽

Khaliquzzaman Khan

Keyword(s):

Big Data ◽

Policy Evaluation ◽

Opinion Mining ◽

Smart Cities ◽

Life Quality ◽

Future Prospects ◽

Indian Government ◽

Data Set ◽

Governance Model

Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model.

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Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

Advances in Data Mining and Database Management - Handbook of Research on Big Data and the IoT ◽

10.4018/978-1-5225-7432-3.ch002 ◽

2019 ◽

pp. 22-41

Author(s):

Onur Dogan ◽

Omer Faruk Gurcan

Keyword(s):

Big Data ◽

Traffic Congestion ◽

Urban Areas ◽

Smart Cities ◽

Digital Data ◽

Rapid Urbanization ◽

Data Resource ◽

Parallel Data ◽

Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

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Big Data IoT Analytics for Smart Cities With Cloud Computing Technique

Advances in Web Technologies and Engineering - Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing ◽

10.4018/978-1-7998-3111-2.ch007 ◽

2021 ◽

pp. 104-126

Author(s):

Chellaswamy C. ◽

Sathiyamoorthi V.

Keyword(s):

Big Data ◽

Smart Cities ◽

Computing Technique ◽

Strong Connectivity ◽

The Public ◽

Environmental Footprints ◽

5G Network ◽

Information And Communication ◽

Data Analytic

Currently, cities are being reconstructed to smart cities that use an information and communication technology (ICT) framework alongside the internet of things (IoT) technology to increase efficiency and also share information with the public, helping to improve the quality of government services citizens' welfare. This large, diverse set of information called big data is obtained by ICT and IoT technologies from smart cities. This information does not have any meaning of its own but a high potential to make use of smart city services. Therefore, the information collected is mined and processed through use of big data analytic techniques. The environmental footprints in smart cities can be monitored and controlled with the help of ICT. Big data analytic techniques help enhance the functionalities of smart cities and the 4G and 5G network provides strong connectivity for professional devices.

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Big Data Quality for Data Mining in Business Intelligence Applications

Advances in Business Information Systems and Analytics - Integration Challenges for Analytics, Business Intelligence, and Data Mining ◽

10.4018/978-1-7998-5781-5.ch004 ◽

2021 ◽

pp. 64-91

Author(s):

Arun Thotapalli Sundararaman

Keyword(s):

Data Mining ◽

Big Data ◽

Data Quality ◽

Business Intelligence ◽

Future Research ◽

Quality Factors ◽

Data Volume ◽

Mining Algorithms ◽

Data Mining Application

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

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Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽

10.3390/s18092994 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2994 ◽

Cited By ~ 28

Author(s):

Bhagya Silva ◽

Murad Khan ◽

Changsu Jung ◽

Jihun Seo ◽

Diyan Muhammad ◽

...

Keyword(s):

Big Data ◽

Data Processing ◽

Real Time ◽

Smart City ◽

Data Analytics ◽

Smart Cities ◽

Big Data Analytics ◽

Time Data ◽

Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

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Leveraging Applications of Data Mining in Healthcare Using Big Data Analytics

Advances in Healthcare Information Systems and Administration - Handbook of Research on Data Science for Effective Healthcare Practice and Administration ◽

10.4018/978-1-5225-2515-8.ch015 ◽

2017 ◽

pp. 345-359

Author(s):

Mohammad Hossein Tekieh ◽

Bijan Raahemi ◽

Eric I. Benchimol

Keyword(s):

Data Mining ◽

Big Data ◽

Distributed Algorithms ◽

Data Analytics ◽

Big Data Analytics ◽

Health Data ◽

Tree Structure ◽

Massive Data ◽

Potential Applications

Big data analytics has been introduced as a set of scalable, distributed algorithms optimized for analysis of massive data in parallel. There are many prospective applications of data mining in healthcare. In this chapter, the authors investigate whether health data exhibits characteristics of big data, and accordingly, whether big data analytics can leverage the data mining applications in healthcare. To answer this interesting question, potential applications are divided into four categories, and each category into sub-categories in a tree structure. The available types of health data are specified, with a discussion of the applicable dimensions of big data for each sub-category. The authors conclude that big data analytics can provide more advantages for the quality of analysis in particular categories of applications of data mining in healthcare, while having less efficacy for other categories.

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Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining

Security and Communication Networks ◽

10.1155/2017/3847092 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Shuo Li ◽

Zhanjie Song ◽

Wenhuan Lu ◽

Daniel Sun ◽

Jianguo Wei

Keyword(s):

Data Mining ◽

Big Data ◽

Least Significant Bit ◽

Speech Watermarking ◽

Big Data Mining ◽

Data Mining Approach ◽

Watermark Embedding ◽

Data Infrastructures ◽

Original Speech

The privacy is a major concern in big data mining approach. In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining. In the framework, the watermark is the compressed version of the original speech. The watermark is embedded into the least significant bit (LSB) layers. At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech. To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter. This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations. Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method. Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen. Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly. This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures.

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