Location based ATM locator system using OpenStreetMap

Das, Rajib; Purohit, Parijat Prashun; Alam, Tauhidul; Chowdhury, Mahfuzulhoq

doi:10.1109/skima.2014.7083518

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…In Mahmoud & Akkari [21], user location served as a yardstick to suggest the nearest location based on this algorithm. Other similar research (e.g., [23], [24], and [25]) show that the provision of accurate location information, judging by a significant reduction in navigation time, is hinged on two essential parameters -latitude and longitude coordinates -in the algorithm. Based on a Desk-based Literature Search (DLS), existing L-aS are implemented based on either functional or non-functional requirements.…”

Section: Shortest Path Computation and Functional And Non-functional ...mentioning

confidence: 84%

A Mobile Based Pharmacy Store Location-aware System

Akhigbe

Ososanya

Olajubu

2022

JIOS

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This paper presents a formulated mobile-based location-awareness model that was implemented into a Location-awareness System (L-aS hereafter) for finding the pharmacy location where prescribed drugs and their prices are available for sale. The scenario that inspired the model formulation was formalized using the unified modeling language. The model was implemented within the android studio integrated development environment with the L-aS database created through SQL lite database. The system was tested using user experience based testing technique. Based on core system performance testing, the system demonstrated a normal response time, resource utilization (i.e. storage and memory usage), and data use potentials of 414.6ms, 4.964mb and 1.9116 kb/secs, and 3.0296mb respectively. Therefore, the system performed well under ordinary conditions as an android application running on small memory devices. The study concluded that the developed mobile based pharmacy store location-aware system was useful to provide information to purchase prescribed drugs especially in perplexed situation(s).

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Section: Shortest Path Computation and Functional And Non-functional ...mentioning

confidence: 84%

A Mobile Based Pharmacy Store Location-aware System

Akhigbe

Ososanya

Olajubu

2022

JIOS

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“…When calculating spherical distance, the great‐circle distance or Haversine formula is generally used (Dordevic & Whitmeyer, 2015; Mahmoud & Akkari, 2016). Compared to the great‐circle distance, the Haversine formula uses a sinusoidal function to maintain enough effective numbers even at short distances (Das, Purohit, Alam, & Chowdhury, 2014). Considering that the distance between two POIs may be only a few meters or less, we use the Haversine formula written as:

h = hav (φ_{2} ‐ φ_{1}) + \cos (φ_{1}) \cos (φ_{2}) hav (λ_{2} ‐ λ_{1})

where

h

is the great‐circle distance in radians,

φ_{1}

and

φ_{2}

represent the latitudes of two points,

λ_{1}

and

λ_{2}

represents the longitudes of the two points, and the Haversine function is calculated as:

hav (θ) = \sin^{2} (θ / 2) = (1 ‐ \cos (θ)) / 2

…”

Section: Methodsmentioning

confidence: 99%

A points of interest matching method using a multivariate weighting function with gradient descent optimization

Yang

Wang

Zhang

et al. 2020

Transactions in GIS

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Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning.

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“…The application offers services in selected countries, but there are instances where the ATM may not be available, the database is outdated, and the information provided is incorrect [16], [17], [18].…”

Section: Literature Reviewmentioning

confidence: 99%

User-Centric Context-Aware Location-Based Service for ATM’s Users

Abdul Rehman Baloch,

Kamran Taj Pathan,

Azhar Ali Shah

et al. 2023

VAWKUM trans. comput. sci.

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The article discusses a context-aware system designed to help Automated Teller Machine (ATM) users quickly locate a working ATM with cash. Many people rely on ATMs for quick cash withdrawals, but often waste time searching for a working machine. The proposed system takes into account the user’s environmental context, such as their activity, the availability of cash in the ATM, the on/off status of the machine, and the presence of a line or crowd at the ATM booth. The objective of the system is to enhance the ATM locator according to the user’s specific needs, utilizing advanced features to recommend the best option for ATM customers based on their current situation. This user-centric approach aims to provide a more efficient and effective system for ATM users.

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Location based ATM locator system using OpenStreetMap

Cited by 9 publications

References 8 publications

A Mobile Based Pharmacy Store Location-aware System

A Mobile Based Pharmacy Store Location-aware System

A points of interest matching method using a multivariate weighting function with gradient descent optimization

User-Centric Context-Aware Location-Based Service for ATM’s Users

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