Cloud computing survey on services, enhancements and challenges in the era of machine learning and data science

<p>Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.</p>

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Intelligent decision-making support system for manufacturing solution recommendation in a cloud framework

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06389-1 ◽

2020 ◽

Author(s):

Alessandro Simeone ◽

Yunfeng Zeng ◽

Alessandra Caggiano

Keyword(s):

Machine Learning ◽

Decision Making ◽

Support System ◽

Cloud Manufacturing ◽

Cloud Platform ◽

Intelligent Decision Making ◽

System A ◽

Intelligent Decision ◽

Learning Procedure ◽

Decision Making Support

AbstractCloud manufacturing represents a valuable tool to enable wide sharing of manufacturing services and solutions by connecting suppliers and customers in large-scale manufacturing networks through a cloud platform. In this context, with increasing manufacturing network size at global scale, the elevated number of manufacturing solutions offered via cloud platform to connected customers can increase the complexity of decision-making, resulting in poor user experience from a customer perspective. To tackle this issue, in this paper, an intelligent decision-making support tool based on a manufacturing service recommendation system (RS) is designed and developed to provide for tailored manufacturing solution recommendation to customers in a cloud manufacturing system. A machine learning procedure based on neural networks for data regression is employed to process historical data on user manufacturing solution preferences and to carry out the automatic extraction of key features from incoming user instances and compatible manufacturing solutions generated by the cloud platform. In this way, the machine learning procedure is able to perform a customer segmentation and build a recommendation list characterized by a ranking of manufacturing solutions which is tailored to the specific customer profile. With the aim to validate the proposed intelligent decision-making support system, a case study is simulated within the framework of a cloud manufacturing platform delivering dynamic sharing of sheet metal cutting manufacturing solutions. The system capability is discussed in terms of machine learning performance as well as industrial applicability and user selection likelihood.

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Applied Intelligent Decision Making in Machine Learning

10.1201/9781003049548 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Decision Making ◽

Intelligent Decision Making ◽

Intelligent Decision

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Cybersecurity Data Science: An Overview from Machine Learning Perspective

10.20944/preprints202006.0139.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Iqbal H. Sarker

Keyword(s):

Machine Learning ◽

Data Science ◽

Cyber Attacks ◽

Data Driven ◽

Machine Learning Techniques ◽

Research Issues ◽

Security Incident ◽

Intelligent Decision Making ◽

Learning Techniques ◽

Intelligent Decision

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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Cognitive Computing Integrated Methodology For Smart Decision Making And Problem-Solving

10.21203/rs.3.rs-927958/v1 ◽

2021 ◽

Author(s):

Jingyuan Liu

Keyword(s):

Machine Learning ◽

Decision Making ◽

Real World ◽

Computational Study ◽

Learning Approaches ◽

Cognitive Computing ◽

Artificial Intelligent ◽

Intelligent Decision Making ◽

Historical Perspectives ◽

Intelligent Decision

Abstract Cognitive computing is the field of intelligent computational study that imitates the brain process for computational intelligence. Decision-making is part of the cognitive process in which opportunities based on certain criteria are selected for a course of action. The choice is generally made using the intelligent assistance system that can turn human decision-making into Artificial Intelligence, system engineering, machine learning approaches. Many complicated real-world problems have been solved by the desire to replicate human intelligence into robots and progress in artificial intelligent technologies. Autonomous systems with machine cognition continuously develop by using enormous data volume and processing power. The cognitive computing system uses skill and awareness derived from knowledge and intelligent decision-making. In this paper, the cognitive computing-based human speech recognition framework (CC-HSRF) takes advantage of next-generation technologies to assist smart decision-making effectively. The proposed methods overview cognitive calculation and its historical perspectives, followed by several strategies to implement algorithms for intelligent decision-making using machine learning. Methods for effective knowledge processing are explored based on cognitive computing models such as Object-Attribute-Relation (OAR). It offers visual and cognitive analytics information, highlighting the framework of conceptual vision and its difficulties. This framework aims to increase the quality of artificial intelligent decision-making based on human perceptions, comprehensions, and actions to reduce business mistakes in the real world and ensure right, accurate, informed, and timely human decisions.

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