scholarly journals Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning

2021 ◽  
Vol 9 (3) ◽  
pp. 52-66
Author(s):  
David OYEWOLA ◽  
Emmanuel DADA ◽  
Juliana NDUNAGU ◽  
Daniel Eneojo EMMANUEL
Keyword(s):  
Machine Learning ◽  
Demand And Supply ◽  
On Demand

Challenges and Opportunities in Global Supply Chain Integration

2011 ◽  
pp. 99-134 ◽  
Author(s):  
Mary Holcomb
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chain Integration ◽  
Review Of The Literature ◽  
Demand And Supply ◽  
Chain Management ◽  
On Demand ◽  
The Past ◽  
Challenges And Opportunities ◽  
Versus Supply

Although the level of sophistication in supply chain management has grown at a dramatic rate over the past decade, many firms are still struggling to eliminate functional boundaries. Some companies, however, have begun the process of evolving to a more integrative state – both internally and across their supply chain. Increasing supply chain complexity and the relentless pressure to reduce costs, has made firms realize that attaining the desired level of performance will only be possible through the end-to-end integration of the supply chain. This chapter examines the challenges and opportunities that firms face in trying to achieve this goal. An in-depth review of the literature related to supply chain integration is presented, culminating in a framework that focuses on demand and supply integration. The chapter concludes with a discussion of the desired end state for integration efforts, which is to create an adaptive supply chain that is capable of competing in the environment of “supply chain versus supply chain.”

scholarly journals Economy-on-demand and the fairness of algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 3-16
Author(s):  
Claudia Schubert ◽  
Marc-Thorsten Hütt
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Legal Standards ◽  
On Demand ◽  
Legal Enforcement ◽  
Digital Agency

Algorithms are the key instrument for the economy-on-demand using platforms for its clients, workers and self-employed. An effective legal enforcement must not be limited to the control of the outcome of the algorithm but should also focus on the algorithm itself. This article assesses the present capacities of computer science to control and certify rule-based and data-centric (machine learning) algorithms. It discusses the legal instruments for the control of algorithms and their enforcement and institutional pre-conditions. It favours a digital agency that concentrates expertise and bureaucracy for the certification and official calibration of algorithms and promotes an international approach to the regulation of legal standards.

Big Mac: A Distributed PaaS Framework for on Demand Big Data Processing Using Machine Learning Techniques

2020 ◽  
Vol 17 (1) ◽  
pp. 92-100
Author(s):  
Balanand Jha ◽  
Kumar Abhishek ◽  
Akshay Deepak ◽  
Prakhar Shrivastav ◽  
Suraj Thakre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Techniques ◽  
Computing Power ◽  
On Demand ◽  
Learning Techniques ◽  
Ease Of Access ◽  
Start Ups

In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

Build up a database to determine the management of drinking water in the Province of Cartago, Costa Rica

2016 ◽  
Vol 6 (4) ◽  
pp. 584-592 ◽  
Author(s):  
Silvia M. Soto Córdoba ◽  
Macario Pino Gómez ◽  
Lilliana Gaviria Montoya
Keyword(s):  
Drinking Water ◽  
Costa Rica ◽  
Water Distribution ◽  
Potable Water ◽  
Public Institutions ◽  
Demand And Supply ◽  
On Demand ◽  
Quality Of Water ◽  
Water Exploitation

In Costa Rica, the majority of people have drinking water in their homes, but because of climate change along with the increase of population and non-existent planning programs, the distribution of this resource could be affected. To ensure sustainability of drinking water, information on demand and supply is required. Unfortunately, the information is outdated and there is no single unified database to which all management related institutions have access. Costa Rica has many public institutions that perform quality control, monitor and provide licenses for water exploitation. Each institution organizes its information according to their own criteria, therefore, making it impossible to compare the data, and difficult to identify the main problems. The authors verified and compared every database available, aiming to consolidate one database to determinate management of water distribution in Cartago. The main results of this research identified distribution of water suppliers throughout the province, the results were: Aqueduct Administration Associations (27.1%), Municipal Aqueducts (60.2%) and National Costa Rica Aqueducts and Sewerage Institute (AyA) Aqueducts (14.1%). One hundred per cent of Municipal and AyA Aqueducts disinfect and provide potable water, but 25% of Aqueduct Administration Rural Associations do not disinfect the water and there is not enough information about the quality of water that they offer.

scholarly journals The effect of waiting times on demand and supply for elective surgery: Evidence from Italy

2017 ◽  
Vol 26 ◽  
pp. 92-105 ◽  
Author(s):  
Andrea Riganti ◽  
Luigi Siciliani ◽  
Carlo V. Fiorio
Keyword(s):  
Elective Surgery ◽  
Waiting Times ◽  
Demand And Supply ◽  
On Demand

scholarly journals Customer Segmentation Using Machine Learning

2021 ◽  
pp. 207-211
Author(s):  
Varad R Thalkar
Keyword(s):  
Machine Learning ◽  
Customer Segmentation ◽  
Business Decisions ◽  
Demand And Supply ◽  
Potential Customer ◽  
Customer Base ◽  
Customer Defection ◽  
Customer Segment

Customer Segmentation is the process of division of customer base into several groups called as customer segments such that each customer segment consists of customers who have similar characteristics. Segmentation is based on the similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.The customer segmentation has the importance as it includes, the ability to modify the programs of market so that it is suitable to each of the customer segment, support in business decisions; identification of products associated with each customer segment and to mange the demand and supply of that product; identifying and targeting the potential customer base, and predicting customer defection, providing directions in finding the solutions.

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