Big Data analytics for Advanced Viticulture

Big data analytics involve systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involve grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It apply feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. System also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. Overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 72% and it can be increased as a future work by including deep learning with time series grape leaf images.

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SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE

Journal of Information Technology and Digital World - September 2019 ◽

10.36548/ijtdw.2019.2.003 ◽

2019 ◽

Vol 01 (02) ◽

pp. 77-86

Author(s):

Smys S

Keyword(s):

Health Care ◽

Big Data ◽

Data Analytics ◽

New Technologies ◽

Big Data Analytics ◽

Health Care Industry ◽

Research Area ◽

Care Industry ◽

The Core ◽

The Cost

The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhancing the practical healthcare using the predictive modeling acquiring a deeper individual measures. This affords the researches to go through the huge set of data and identify the patterns along with the trends and delivering solutions improvising the medical care, minimizing the cost and he regulating the health admittance, ensuring the safety of human lives. The paper provides the survey on the predictive big data analysis and accuracy it provides in the health care system.

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Role of Big Data Analytics in Healthcare

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1421.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 4174-4177 ◽

Cited By ~ 1

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Modern Life ◽

Affected Person ◽

Commercial Enterprise ◽

The Right ◽

Use Of Knowledge ◽

Massive Information

Big data analytics has turn out to be the principle essence of just about every area in modern life such as healthcare, commercial enterprise and plenty of different industries. Recent improvements in technology have absolutely changed the way our every day existence operates as hastily evolving advancements are leading towards bigger use of knowledge in the direction of high-quality lifestyles. Recently, investigators in healthcare technology are generating complex and excessivedimensional records using diverse datasets and for this reason, these disciplines become more record-intensive. Hence, this may be considered as the right time to efficiently use the statistics analytics in healthcare and medical research to enhance remedy and affected person care. One side, big data analytics is coupled with some drawbacks and demanding situations than the existing conventional techniques. However, massive statistics is the imperative part of diverse researches like in human genome, which holds the promising future for subsequent generations. This makes it feasible to achieve a consolidated data associated with patient’s health which allows analysing the expected effects precisely. Therefore, these types of innovations have made it possible to use massive information in healthcare for improving the clinical operations, financial strategies in clinical sectors with digitized record upkeep and early ailment detection. Big data revolution has widened the horizons of healthcare and biomedical technology as it gives open record pool of affected person’s previous health facts for better analysis & assessment in future and thereby improving the medical practices with powerful scientific services.

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Big Data Analytics Correlation Taxonomy

Information ◽

10.3390/info11010017 ◽

2019 ◽

Vol 11 (1) ◽

pp. 17 ◽

Cited By ~ 2

Author(s):

Laden Husamaldin ◽

Nagham Saeed

Keyword(s):

Big Data ◽

Data Analytics ◽

Design Research ◽

Big Data Analytics ◽

Research Area ◽

Analytic Methods ◽

Lack Of Information ◽

The Difference ◽

Type Data ◽

Research Account

Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.

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Big data analytics with swarm intelligence

Industrial Management & Data Systems ◽

10.1108/imds-06-2015-0222 ◽

2016 ◽

Vol 116 (4) ◽

pp. 646-666 ◽

Cited By ~ 29

Author(s):

Shi Cheng ◽

Qingyu Zhang ◽

Quande Qin

Keyword(s):

Big Data ◽

Data Processing ◽

Swarm Intelligence ◽

Data Analytics ◽

Large Scale ◽

Big Data Analytics ◽

Underlying Mechanism ◽

Research Area ◽

Future Research ◽

Content Type

Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

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A Better Understanding of Big Data and Marketing Analytics

Advances in Marketing, Customer Relationship Management, and E-Services - Advanced Digital Marketing Strategies in a Data-Driven Era ◽

10.4018/978-1-7998-8003-5.ch001 ◽

2021 ◽

pp. 1-15

Author(s):

Francisco J. S. Lacárcel ◽

Leticia Polanco-Diges ◽

Felipe Debasa

Keyword(s):

Data Mining ◽

Big Data ◽

Literature Review ◽

Data Analytics ◽

Big Data Analytics ◽

Research Area ◽

Marketing Analytics ◽

Key Concepts ◽

Business Sectors ◽

Practical Implications

Data mining and analysis is consolidating as a crucial practice in economic, educational, social, and business sectors. In this context, this study aims to identify and categorize the main strategies, metrics, and concepts that are derived from big data analytics (BDA) and marketing analytics (MA). This study follows a systematic literature review (SLR) of important scientific contributions made so far in this research area. The authors have identified through this study 13 key concepts related to big data analytics and 13 related to marketing analytics, which are classified and categorized according to their application in technologies or actions in digital marketing. The chapter concludes with a discussion between theoretical and practical implications on the results for future researchers.

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Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions

Mathematical Problems in Engineering ◽

10.1155/2021/9022558 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Haruna Chiroma ◽

Shafi’i M. Abdulhamid ◽

Ibrahim A. T. Hashem ◽

Kayode S. Adewole ◽

Absalom E. Ezugwu ◽

...

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

High Performance ◽

Theoretical Perspective ◽

Big Data Analytics ◽

Research Area ◽

Future Research ◽

Internet Of Vehicles ◽

Active Attention

The Internet of Vehicles (IoV) is a developing technology attracting attention from the industry and the academia. Hundreds of millions of vehicles are projected to be connected within the IoV environments by 2035. Each vehicle in the environment is expected to generate massive amounts of data. Currently, surveys on leveraging deep learning (DL) in the IoV within the context of big data analytics (BDA) are scarce. In this paper, we present a survey and explore the theoretical perspective of the role of DL in the IoV within the context of BDA. The study has unveiled substantial research opportunities that cut across DL, IoV, and BDA. Exploring DL in the IoV within BDA is an infant research area requiring active attention from researchers to fully understand the emerging concept. The survey proposes a model of IoV environment integrated into the cloud equipped with a high-performance computing server, DL architecture, and Apache Spark for data analytics. The current developments, challenges, and opportunities for future research are presented. This study can guide expert and novice researchers on further development of the application of DL in the IoV within the context of BDA.

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SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE

Journal of Information Technology and Digital World - September 2019 ◽

10.36548/jitdw.2019.2.003 ◽

2019 ◽

Vol 01 (02) ◽

pp. 77-86

Author(s):

Smys S

Keyword(s):

Health Care ◽

Big Data ◽

Data Analytics ◽

New Technologies ◽

Big Data Analytics ◽

Health Care Industry ◽

Research Area ◽

Care Industry ◽

The Core ◽

The Cost

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Current Approaches to Advanced Research in Data Science at International Top League Universities

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8944 ◽

2020 ◽

Vol 17 (6) ◽

pp. 2806-2811

Author(s):

Wahidah Hashim ◽

A/L Jayaretnam Prathees ◽

Marini Othman ◽

Andino Maseleno

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

World Data ◽

The People ◽

Software Engineers ◽

Data Scientist ◽

The Right

Data Science also known as Analytics, has a high demand in the industries right now, where professionals who are well trained in this field are being recruited by many large companies. Before the existence of data science, companies and industries search for software engineers and data analysis to sort IT related problems. However, as the internet start to being used by most of the people in the world, data keep on pouring in a large volume and velocity, software engineers and data analysis could not handle it anymore. Analyzing the tremendous size of data is called Big Data Analytics. Corporate companies have already started to realize that data scientists are the right person to tackle Big Data related problems. Low supply of data scientist has hiked in the salary of the data scientist, as the pay for a data scientist many more time higher compare to other IT related professionals. Knowledge in data science can solve any data related problems in this world. Data scientist are not only recruited by tech-giants like Google and Amazon, medium organizations also started to understand the importance of data science and they too recruit data scientist for their company. In this paper, we will explore on the requirement and knowledges of data science that can be covered in UNITEN’s computer science syllabus.

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