DEVELOPING PREDICTIVE MODEL FOR VACANT HOUSING DISTRIBUTION USING MUNICIPALITY-OWNED DATA —CASE STUDY IN MAEBASHI CITY—

2021 ◽  
Vol 77 (2) ◽  
pp. 62-71
Author(s):  
Hiroki BABA ◽  
Yuki AKIYAMA ◽  
Osamu YACHIDA
Keyword(s):  
Predictive Model ◽  
Vacant Housing

Towards a predictive model for polymer solubility using the noncovalent interaction index: polyethylene as a case study

2021 ◽  
Author(s):  
Mats Denayer ◽  
Jelle Vekeman ◽  
Frederik Tielens ◽  
Frank De Proft
Keyword(s):  
Predictive Model ◽  
Noncovalent Interaction ◽  
Intramolecular Interactions ◽  
Interaction Index ◽  
Covalent Interaction ◽  
Polymer Solubility

A novel solubility descriptor is presented based on the non-covalent interaction index, providing information on the solute’s inter- and intramolecular interactions, and its conformation. Polyethylene in (anti)solvent is used as a case-study.

An AHP–TOPSIS Predictive Model for District-Scale Mapping of Porphyry Cu–Au Potential: A Case Study from Salafchegan Area (Central Iran)

2016 ◽  
Vol 25 (4) ◽  
pp. 417-429 ◽  
Author(s):  
Hooshang H. Asadi ◽  
Atefeh Sansoleimani ◽  
Moslem Fatehi ◽  
Emmanuel John M. Carranza
Keyword(s):  
Predictive Model ◽  
Central Iran ◽  
Porphyry Cu

Predictive Modeling of Product Returns for Remanufacturing

2015 ◽  
Author(s):  
Jungmok Ma ◽  
Harrison M. Kim
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Mixed Model ◽  
Moving Average ◽  
Predictive Performance ◽  
Performance Measure ◽  
Selection Algorithm ◽  
Product Returns ◽  
Distributed Lag

As awareness of environmental issues increases, the pressures from the public and policy makers have forced OEMs to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical with regards to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying better predictive models. Unlike other major approaches in literature (distributed lag model or DLM), the predictive model selection algorithm focuses on the predictive power over new or future returns. The proposed algorithm extends the set of candidate models that should be considered: autoregressive integrated moving average or ARIMA (previous returns for future returns), DLM (previous sales for future returns), and mixed model (both previous sales and returns for future returns). The prediction performance measure from holdout samples is used to find a better model among them. The case study of reusable bottles shows that one of the candidate models, ARIMA, can predict better than the DLM depending on the relationships between returns and sales. The univariate model is widely unexplored due to the criticism that the model cannot utilize the previous sales. Another candidate model, mixed model, can provide a chance to find a better predictive model by combining the ARIMA and DLM. The case study also shows that the DLM in the predictive model selection algorithm can provide a good predictive performance when there are relatively strong and static relationships between returns and sales.

scholarly journals VALUE ADDED MODEL OF COCONUT PROCESSING INDUSTRY (CASE STUDY)

2021 ◽  
Vol 6 (2) ◽  
pp. 11-16
Author(s):  
Nurfajriah Zhia ◽  
Halim Mahfud ◽  
Rudhy Ho Purabaya
Keyword(s):  
Interest Rates ◽  
Predictive Model ◽  
Value Added ◽  
Creative Industries ◽  
Added Value ◽  
Balance Model ◽  
Processing Industry ◽  
Financial Feasibility ◽  
Coconut Coir

Coconut commodity has a strategic value because it has an important role in the economy, society and culture of Indonesian society. Coconut plant is a multipurpose plant where all parts of the plant have economic value, one of which is coconut husk. The potential of coconut coir is very large and has not been used optimally. Whereas coconut coir, when processed, will produce various products such as home industries, furniture, geotextiles, boards, and creative industries. This study is aimed at analyzing the potential development of the coconut coir processing industry and the added value that will be generated using the case study method and the location selection is done deliberately (purposive sampling). The business financial feasibility model obtained is a predictive model for analysis and planning of business financial feasibility through the NPV, IRR, PBP, BCR criteria with various scenarios of changes in prices, interest rates, and production scale.The business balance model obtained is a predictive model that can be used to analyze the price gap level to plan the price level that will provide proportional profit to produce coconut coir processing factory business. Based on the results of the verification of the model with input using the assumption of parameter values, it shows that the coconut coir processing industry is feasible to run.

A new predictive model for the cyanotoxin content from experimental cyanobacteria concentrations in a reservoir based on the ABC optimized support vector machine approach: A case study in Northern Spain

2015 ◽  
Vol 30 ◽  
pp. 49-59
Author(s):  
P.J. García Nieto ◽  
J.R. Alonso Fernández ◽  
E. García-Gonzalo ◽  
C. Díaz Muñiz ◽  
R. Mayo Bayón ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Predictive Model ◽  
Support Vector ◽  
Northern Spain
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