METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

Author(s):  
A.E. Semenov

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

2015 ◽  
Vol 5 (1) ◽  
pp. 160
Author(s):  
Yuli Kurniyati ◽  
Bening Hadilinatih

<p>Areas Based Economic Empowerment Program (Program Ekonomi Berbasis Kewilayahan/PEW) is a program designed to focus on the learning process and empowercommunities through local economic institutions to shore up the economy of thecommunity itself. This study aims to: 1). Knowing the PEW Group self-reliance inorganizing services to members in order to regionally based economic empowerment.2). Identifying the factors management, member participation and partnership thathinder or support the PEW Group self-reliance and self-reliance opportunities for effortsto develop a support group for regionally based economic empowerment, 3). Formula tepolicy recommendations for the city authorities to develop and implement a model ofselfreliance development PEW group as a regionally based economic empowerment strategy in the city of Yogyakarta. This research is qualitative research, the research took place in the townYogyakarta. The collecting data techniques used were: study documentation, participant, observation, in-depth interviews, and focus group discussion (FGD). In the first studywere: 1). Evaluating Performance PEW Group 2). Identify factors inhibiting andsupporting self-sufficiency Group 3). Self-Supporting analyze PEW Group 4). Early formulation compile policy recommendations group. The research development model of self-reliance. Year II study is 1). Self-Supporting Group to develop a model based onthe results of Phase I study 2). Validation conduct joint FGD Stakeholder Model through3). Develop Model Self-Supporting Implementation Handbook. Results showed that the level of self-reliance menilitian PEW group is still low. This isreflected in the level of independence that is still low, both in terms of independence inthe administration, self-reliance and independence in the management of the assets. PEW group of selfsufficiency level is still low, due to several factors, namely: (1) Capacity Board PEW Group is still low (2) The lack of participation of members of the Group, and (3) lack of stakeholder support. Another factor that still require serious treatment that can increase self-reliance PEW Group is a factor Assistance Group Implementation and Monitoring and Evaluation during implementation is still lacking.</p>


2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.


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