scholarly journals Prediction of Bike Share Demand by Machine Learning

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Industrial Revolution ◽  
Prediction Models ◽  
Sharing Economy ◽  
Customer Demand ◽  
Vehicle Accidents ◽  
Demand Prediction ◽  
External Data ◽  
New Feature ◽  
Fierce Competition ◽  
Bike Share

In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.

A Digital Economy Model for Talent Prediction Data Analytic

2019 ◽  
Vol 2 (4) ◽  
pp. 260-266
Author(s):  
Haru Purnomo Ipung ◽  
Amin Soetomo
Keyword(s):  
Business Model ◽  
Industrial Revolution ◽  
Technology Innovation ◽  
Cost Effective ◽  
Sharing Economy ◽  
Token Economy ◽  
Economy Model ◽  
New Business ◽  
The Cost ◽  
Data Analytic

This research proposed a model to assist the design of the associated data architecture and data analytic to support talent forecast in the current accelerating changes in economy, industry and business change due to the accelerating pace of technological change. The emerging and re-emerging economy model were available, such as Industrial revolution 4.0, platform economy, sharing economy and token economy. Those were driven by new business model and technology innovation. An increase capability of technology to automate more jobs will cause a shift in talent pool and workforce. New business model emerge as the availabilityand the cost effective emerging technology, and as a result of emerging or re-emerging economic models. Both, new business model and technology innovation, create new jobs and works that have not been existed decades ago. The future workers will be faced by jobs that may not exist today. A dynamics model of inter-correlation of economy, industry, business model and talent forecast were proposed. A collection of literature review were conducted to initially validate the model.

scholarly journals Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

2020 ◽  
Vol 16 (4) ◽  
pp. 584-601
Author(s):  
Chunwei Chang ◽  
Shengli Li
Keyword(s):  
Prediction Models ◽  
Interaction Effects ◽  
Sharing Economy ◽  
Tourist Attractions ◽  
Price Prediction ◽  
Price Determinants

This research aims to identify price determinants for sharing economy-based accommodation services and to further use the identified price determinants to predict accommodation prices. A dataset drawn from Airbnb.com, was collected for analysis. We identify price determinants from five categories. The top five price determinants are identified as room type, city, distance to tourist attractions, number of pictures posted, and number of amenities provided. More importantly, we find that interaction effects between variables can also significantly influence price. Finally, a series of price prediction models are built based on the identified price determinants.

The impact of technology on architecture in Iran with focus on saving in energy consumption

2015 ◽  
Vol 16 (SE) ◽  
pp. 97-103
Author(s):  
Allah Bakhsh Kavoosi ◽  
Shahin Heidari ◽  
Hamed Mazaherian
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Industrial Revolution ◽  
Demand Prediction ◽  
Interior Spaces ◽  
Heating And Cooling ◽  
Impact Of Technology ◽  
Contemporary Architecture ◽  
The Impact ◽  
The Given

Growth and development of technology caused enormous transformation and change in the world after Industrial Revolution. The contemporary human has prepared the platform for their realization in many activities that the humans were unable to do it in the past time and struck the dream of their realization in their mind so that today doing many of those activities have been apparently practical by human. This accelerating growth accompanied with consuming a lot of energy where with respect to restriction of the given existing resources, it created energy crises. On the other hand, along with growth in industry and requirement for manpower and immigration from village to city and basic architectural changes in houses, which have emerged due to change in social structure it has led to change in lifestyle and type and quantity of consuming energy in contemporary architecture. Inter alia, with increase in human’s capability, cooling and heating and acoustic and lighting technologies were also changed in architecture and using mechanical system was replaced by traditional systems. Application of modern systems, which resulted from growth of industry and development of technology and it unfortunately, caused further manipulation in nature and destruction of it by human in addition to improving capability and potential of human’s creativity. With respect to growth of population and further need for housing and tendency to the dependent heating and cooling systems to them in this article we may notice that the housing is assumed as the greatest consumer of energy to create balance among the exterior and interior spaces in line with creating welfare conditions for heating and cooling and lighting. The tables of energy demand prediction in Iran show that these costs and energy consumption will be dubbed with energy control smart management in architecture.

Technological unemployment and the business model of sharing economy in conditions of digitalized economy

2021 ◽  
Vol 21 (1) ◽  
pp. 208-225
Author(s):  
Lyudmila Belova
Keyword(s):  
Business Model ◽  
Industrial Revolution ◽  
Negative Impact ◽  
Well Being ◽  
Sharing Economy ◽  
Technological Unemployment ◽  
The One ◽  
Development Paradigm ◽  
The Impact ◽  
Industrial Revolutions

The article traces the impact of innovation on employment and workers income during industrial revolutions. The aim of the study is to identify the business model that contributes to improving the well-being and reducing negative impact of innovative transformations on employees. To achieve this goal, we analyze: the conceptions of industrial revolutions; the “Engels pause”, which arose during the First Industrial Revolution as a “surge” in inequality due to the contradiction between productivity growth and profit, on the one hand, and the stagnation of workers’ real incomes, on the other; the effect of replacing manual labor with automated one; the problems of technological unemployment; the digital business model of sharing economy. The findings report conclusions concerning the change in economic development paradigm as a result of the replacement of classical consumption models by sharing economy business model, on the prospects of the sharing economy business model in the context of its ability to solve employment problems, overcome technological unemployment and increase employees’ income. The achieved results can be useful for policymakers and corporate structures that design innovative development strategies.

scholarly journals Understanding the Usage Patterns of Bicycle-Sharing Systems to Predict Users’ Demand: A Case Study in Wenzhou, China

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaomei Xu ◽  
Zhirui Ye ◽  
Jin Li ◽  
Mingtao Xu
Keyword(s):  
Cluster Analysis ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Transportation Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Demand Prediction ◽  
Usage Patterns ◽  
Accuracy Of Prediction

Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based aspect and station-based aspect to provide some guidance for users’ demand prediction. After that, the methodology combining cluster analysis, a back-propagation neural network (BPNN), and comparative analysis is proposed to predict users’ demand. Cluster analysis is used to identify different service types of stations, the BPNN method is utilized to establish the demand prediction models for different service types of stations, and comparative analysis is employed to determine if the accuracy of the prediction models is improved by making a distinction among stations and working/nonworking days. Finally, a case study is conducted to evaluate the performance of the proposed methodology. Results indicate that making a distinction among stations and working/nonworking days when predicting users’ demand can improve the accuracy of prediction models.

scholarly journals Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Sina Faizollahzadeh Ardabili ◽  
Shahabodin Shamshirband
Keyword(s):  
Machine Learning ◽  
Energy Production ◽  
Production Management ◽  
Prediction Models ◽  
State Of The Art ◽  
Electricity Demand ◽  
Original Research ◽  
Review Of The Literature ◽  
Future Prospects ◽  
Demand Prediction

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

Nomograms Predicting Overall Survival and Cancer Special Survival for Cervical Cancer in stage IIIC1

2021 ◽  
Author(s):  
Yifan Feng ◽  
Ye Wang ◽  
Yangqin Xie ◽  
Shuwei Wu ◽  
Yuyang Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
Tumor Size ◽  
Random Sampling ◽  
Prediction Models ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Training Group ◽  
Data Sets ◽  
External Data

Abstract BackgroundThe purpose of this study is to explore the factors that affect the prognosis of overall survival (OS) and cancer special survival (CSS) in cervical cancer with stage IIIC1 and establish nomogram models to predict this prognosis.MethodsData from The Surveil-lance, Epidemiology, and End Results (SEER) Program meeting the inclusion criterions were classified into training group, and data of validation were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan‐Meier curves, OS and CSS of stage IIIC1 were evaluated according to the training group. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell’s C-index and receiver operating characteristic curve (ROC) were calculated to measure the accuracy of the prediction models. Calibration plots show the relationship between the predicted probability and the actual outcome. Decision-curve analysis (DCA) was applied to evaluate the clinical applicability of the constructed nomogram.ResultsThe incidence of pelvic lymph node metastasis, a high-risk factor for prognosis in cervical cancer, decreased slightly over time. There are eight independent prognostic variables for OS, including age, race, histology, differentiation, extension range, tumor size, radiation recode and surgery, but seven for CSS with age excluded. Nomograms of OS and CSS were established based on the results. The C-index for the nomograms of OS and CSS were 0.692, 0.689 respectively when random sampling of SEER data sets, and 0.706, 0.737 respectively when random sampling of external data sets. AUCs for the nomogram of OS were 0.648, 0.644 respectively, and 0.683, 0.675 for the nomogram of CSS. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also proved the value of the two models.ConclusionAge, race, histology, differentiation, extension range, tumor size, radiation recode and surgery were all independent prognosis factors for OS. Only age excepts in CSS. OS and CSS nomograms were established in our study based on the result of multivariate Cox proportional hazard regression, and both own good predictive and clinical application value after validation.

Ontologies application in the sharing economy domain: a systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ummul Hanan Mohamad ◽  
Mohammad Nazir Ahmad ◽  
Ahmad Mujahid Ubaidillah Zakaria
Keyword(s):  
Domain Knowledge ◽  
Industrial Revolution ◽  
Common Knowledge ◽  
Sharing Economy ◽  
Ontology Development ◽  
Comprehensive Overview ◽  
Content Type ◽  
Digital Platforms ◽  
Application Ontology ◽  
Data Heterogeneity

PurposeThis systematic literature review (SLR) paper presents the overview and analysis of the existing ontologies application in the SE domain. It discusses the main challenges in terms of its ontologies development and highlights the key knowledge areas for subdomains in the SE domain that provides a direction to develop ontologies application for SE systematically. The SE is not as straightforward as the traditional economy. It transforms the existing economy ecosystem through peer-to-peer collaborations mediated by the technology. Hence, the complexity of the SE domain accentuates the need to make the SE domain knowledge more explicit.Design/methodology/approachFor the review, the authors only focus on the journal articles published from 2010 to 2020 and mentioned ontology as a solution to overcome the issues specific for the SE domain. The initial identification process produced 3,326 papers from 10 different databases.FindingsAfter applying the inclusion and exclusion criteria, a final set of 11 articles were then analyzed and classified. In SE, good ontology design and development is essential to manage digital platforms, deal with data heterogeneity and govern the interoperability of the SE systems. Yet the preference to build an application ontology, lack of perdurant design and minimal use of the existing standard for building SE common knowledge are deterring the ontology development in this domain. From this review, an anatomy of the SE key subdomain areas is visualized as a reference to further develop the domain ontology for the SE domain systematically.Originality/valueWith the arrival of the Fourth Industrial Revolution (4IR), the sharing economy (SE) has become one of the important domains whose impact has been explosive, and its domain knowledge is complex. Yet, a comprehensive overview and analysis of the ontology applications in the SE domain is not available or well presented to the research community.

Challenges and Opportunities for Shared Economies

2018 ◽  
pp. 1-11
Author(s):  
Chitrakalpa Sen ◽  
Surya Teja Adury
Keyword(s):  
Market Structure ◽  
Industrial Revolution ◽  
Sharing Economy ◽  
Emerging Nations ◽  
Fourth Industrial Revolution ◽  
Challenges And Opportunities ◽  
Unique Nature ◽  
Factors Of Production ◽  
Information Barrier ◽  
Regulatory Challenges

Shared economy or access economy has ushered in a new revolution. It is often mentioned that the fourth industrial revolution will be propagated by sharing economies. This study reviews the nature of shared economy and its impacts. It focuses on the challenges posed by access economy, especially in context of emerging nations. It discusses the unique nature of trust as a construct, the potential of sharing economy platforms to reduce the information barrier, the changing nature of relationship with the factors of production and most importantly, the regulatory challenges in this particular market structure.

Sign in / Sign up

Export Citation Format

Share Document