RETRACTED ARTICLE: Classification of sentence level sentiment analysis using cloud machine learning techniques

Ramu, Arulmurugan; Sabarmathi, K R; Haldorai, Anandakumar

doi:10.1007/s10586-017-1200-1

Cited by 67 publications

(17 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As seen, AI plays a fundamental role in the Insight Generation layer. Thus, Cloud providers invested a considerable amount of money and time to build research teams to create stateof-art AI services and more specifically Cloud ML services [25]. Some of these important providers are [26]: Google Cloud Machine Learning Platform, Microsoft Cognitive Services, Amazon Machine Learning; and IBM Watson Analytics.…”

Section: Insight Generationmentioning

confidence: 99%

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

Moreno¹,

Carrasco²,

Herrera‐Viedma³

2019

IJIMAI

View full text Add to dashboard Cite

Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches).

show abstract

Section: Insight Generationmentioning

confidence: 99%

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

Moreno¹,

Carrasco²,

Herrera‐Viedma³

2019

IJIMAI

View full text Add to dashboard Cite

show abstract

“…Businesses and organisations should create programs that leverage knowledge to produce outcomes for their stakeholders. Some examples of "smart" companies include Uber and Lyft for personal mobility and Nextdoor for knowledge sharing [22].…”

Section: Ecosystem Framework -A Modelmentioning

confidence: 99%

An Analysis of Trends, Developments and Transparent Issues in the making of Indian Smart Cities

Jamshed¹

2018

EAI Endorsed Transactions on Smart Cities

View full text Add to dashboard Cite

During the past few years, interest has grown in the creation and application of methods, structures for smart city evaluation. This reflects the increasing awareness of the value of systems for better planning and architecture of efficient and stable communities. A city is a vast and permanent human community that offers many resources and opportunities for its people. The rapid urbanization and growth have put a lot of pressure on local services and service delivery. The research discusses many potential resources that can make a city smart across growing city dimensions and also discusses the initiatives of the Indian government towards a Smart city. This paper is a survey of a variety of papers, covering the actual case study from a more comprehensive framework, such as e-mobility, which covers the smart city subject from.

show abstract

“…Vast amounts of sentiment data from social networks are learned by decision trees, k-nearest neighbor, SVM, and Bayes, to manipulates people's sentiments, opinions towards products, events, topics, etc. [5,6]. Large-scale driving video datasets are trained by the convolutional neural network (CNN) to predict driver action [7].…”

Section: Introductionmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

SN Appl. Sci.

View full text Add to dashboard Cite

In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

show abstract

RETRACTED ARTICLE: Classification of sentence level sentiment analysis using cloud machine learning techniques

Cited by 67 publications

References 25 publications

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

An Analysis of Trends, Developments and Transparent Issues in the making of Indian Smart Cities

A novel parallel learning algorithm for pattern classification

Contact Info

Product

Resources

About