Predictive analytics for banking user data using AWS Machine Learning cloud service

Ramesh, Ranjith

doi:10.1109/iccct2.2017.7972282

Cited by 14 publications

(8 citation statements)

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“…Different types of inference as a service are accessible on-demand nowadays, such as language services (e.g., text analytics or translation), analytics services (e.g., product recommendations or knowledge inference from big data), speech services (e.g., text-to-speech, speech-to-text), or computer vision services (e.g., analyzing of images and videos in order to find and identify objects, text, and labels) (Javadi et al 2020;Pandl et al 2021). It is easy for developers of all skill levels to use machine learning technology by relying on pre-trained models (Ramesh 2017). Users with limited knowledge and related expertise do not have to engage in the time-consuming and laborintensive aggregation of large amounts of data but can rely on the knowledge representation in the pre-trained AI models.…”

Section: Ai Software Servicesmentioning

confidence: 99%

Artificial Intelligence as a Service

et al. 2021

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is undoubtedly one of the most actively debated technologies, providing auspicious opportunities to contribute to individuals' well-being, the success and innovativeness of organizations, and societies' prosperity and advancement (Thiebes et al. 2020). The McKinsey Global Institute predicts that the utilization of AI could yield an additional worldwide economic output of USD 13 trillion by 2030 (Bughin et al. 2018).

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Section: Ai Software Servicesmentioning

confidence: 99%

Artificial Intelligence as a Service

et al. 2021

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“…Along with a functional AI infrastructure with different services that support all the phases of the AI infrastructure implementation, these services offer several trained solutions in areas such as image, speech, and text inference in different industries and lines of business. Some of the most recognized offers for AI platform-as-a-service (PaaS) in the market are SAP Leonardo ML [34,69], Amazon AWS Machine Learning [70][71][72][73], and Microsoft Azure Cognitive Services [74,75], to name a few.…”

Section: Edge Artificial Intelligencementioning

confidence: 99%

Large Geographical Area Aerial Surveillance Systems Data Network Infrastructure Managed by Artificial Intelligence and Certified over Blockchain: A Review

Batista

Melício

Santos

2021

Network

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This paper proposes an aerial data network infrastructure for Large Geographical Area Surveillance Systems. The work presents a review of previous works from the authors, existing technologies in the market, and other scientific work, with the goal of creating a data network supported by Autonomous Tethered Aerostat Airships used for sensor fixing, a drones deployment base, and meshed data network nodes installation. The proposed approach for data network infrastructure supports several independent and heterogeneous services from independent, private, and public companies. The presented solution employs Edge Artificial Intelligence (AI) systems for autonomous infrastructure management. The Edge AI used in the presented solution enables the AI management solution to work without the need for a permanent connection to cloud services and is constantly fed by the locally generated sensor data. These systems interact with other network AI services to accomplish coordinated tasks. Blockchain technology services are deployed to ensure secure and auditable decisions and operations, which are validated by the different involved ledgers.

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“…Amazon Machine Learning powerful algorithms create machine learning (ML) models by finding patterns in our existing data. Then, the service uses these models to process new data and generate predictions for your application [13]. To create a model various steps was used in AWS Machine Learning.…”

Section: J Hardware For Machine Learning: Challenges and Opportunitiesmentioning

confidence: 99%

“…Data set is used to create a binary classification model using Amazon Web Service (AWS) Machine Learning platform. 70 % of the data is used to train the binary classification model and 30 % of the dataset is used to test the model [13]. The model tested by using two features in AWS Machine Learning.…”

Section: J Hardware For Machine Learning: Challenges and Opportunitiesmentioning

confidence: 99%

“…This paper also compared the energy and accuracy of hand crafted and DNN in which hand crafted gives higher energy efficiency as compared to DNN. Ramesh[2017]developed a Machine Learning model to perform predictive analytics on the banking dataset. Data set is used to create a binary classification model using Amazon Web Service (AWS) Machine Learning platform.…”

Section: J Hardware For Machine Learning: Challenges and Opportunitiesmentioning

confidence: 99%

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Exterminating Computational Limits of Machine Learning with Merits of Serverless

Kaur¹,

Kaur²

2018

IJERT

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-The trend of determining patterns using the various machine-learning, data mining, deep-learning or neural networks are limited due to the computational power of machines. Sometimes the cost estimation exceeds heavily from the estimates, or the cost is not sufficient for the enterprises to get the desired infrastructure. Unexpected computational resources at times limit the accuracy and increase the cost of infrastructure to find the perfect patterns. A serverless compute may give an optimal solution to the computational resources provided by the cloud. Resources are elastic, which can expand and shrink according to the input data sets, resulting in lesser computational costs. Index Terms -AWS (Amazon

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Predictive analytics for banking user data using AWS Machine Learning cloud service

Cited by 14 publications

References 1 publication

Artificial Intelligence as a Service

Artificial Intelligence as a Service

Large Geographical Area Aerial Surveillance Systems Data Network Infrastructure Managed by Artificial Intelligence and Certified over Blockchain: A Review

Exterminating Computational Limits of Machine Learning with Merits of Serverless

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