scholarly journals Approach to Kirana Store Product Arrangement Using Machine Learning

2021 ◽  
pp. 72-83
Author(s):  
Ajita Patel ◽  
Krishna Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Domain Knowledge ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Detection Techniques ◽  
Store Product ◽  
Shopping Basket ◽  
Objective Detection ◽  
Better Than

Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.

scholarly journals Optimization of Store Layout using Market Basket Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 6459-6463
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Product Placement ◽  
Retail Store ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Arduous Task ◽  
History Of ◽  
Shopping Basket ◽  
Grid Layout

Store layout is a crucial factor for attracting customers in a retail store. Use of appropriate store layout results in an increase in sales of the store. Grid layout, free flow layout, spine layout is a few commonly used store layouts in the retail store. The grid layout is used for supermarkets but the placement of different products as per the preference of the customer is quite an arduous task there. Purchase history of a supermarket can be utilized to predict the preferences of the customers and can be utilized as an aid for designing a better store layout. Market basket analysis is employed to get insights from the POS data of the supermarket. Market basket analysis (MBA) helps to extract the various association rules from the purchase data of the shoppers. A customer can pick different items identified with the items that the person has just put in his or her shopping basket or cart which frames an association rule. The extraction of such rules can help in the appropriate product placement in the store as per the shopper’s preference.

Intelligent Slotting for the Warehouse

2016 ◽  
pp. 326-342
Author(s):  
Narayanan Padmanabahan Iyer ◽  
Ramesh Jayal
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Business Growth ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Rapid Changes ◽  
Future Supply

In the current and future Supply Chain landscape, we need to ensure that we keep the warehouse ship sailing amidst the turbulent waters of dynamic business growth and rapid changes in technology. There are several challenges to be overcome, and many opportunities to be embraced on the path to achieving this. In this chapter, the authors detail one of the key problems facing the warehouse and that is Slotting. They look at the various business drivers, and technological drivers impacting Slotting. They propose a solution to tackle this problem by using Market Basket Analysis and Machine Learning.

scholarly journals Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data

2020 ◽  
Vol 9 (11) ◽  
pp. 638
Author(s):  
Sina Keller ◽  
Raoul Gabriel ◽  
Johanna Guth
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Road Network ◽  
Reference Data ◽  
Coefficient Of Determination ◽  
Rural Roads ◽  
Average Speed ◽  
Learning Framework ◽  
Input Feature ◽  
Better Than

Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination R2=80.43%, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. The implementation of our estimation framework for an exemplary dataset is provided on GitHub.

Market Basket Analysis Approach to Machine Learning

2021 ◽  
Author(s):  
Abu Hasnat Patwary ◽  
Md Tamim Eshan ◽  
Prazzal Debnath ◽  
Abdus Sattar
Keyword(s):  
Machine Learning ◽  
Analysis Approach ◽  
Market Basket Analysis ◽  
Market Basket

MODEL SISTEM INFORMASI PEMASARAN SEBAGAI PENUNJANG KEPUTUSAN PROMOSI PRODUK MENGGUNAKAN ALGORITMA MAX -MINER

ICIT Journal ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 94-104
Author(s):  
Fernando Siboro ◽  
Capri Eriansyah ◽  
Muhammad Adi Sofyan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Market Basket Analysis ◽  
Market Basket

Teknologi informasi saat ini terus berkembang semakin cepat, membuat pola berfikir manusia berubah, dengan proses pertumbuhan yang seperti ini, generasi akan datang diharuskan mempunyai keahlian yang lebih baik di bidang pemanfaatan teknologi informasi. Kebutuhan adanya kemudahan dari segi pemasaran, saat ini dirasa sangat penting, terutama bagi perusahaan yang bergerak dibidang penjulan atau distributor guna menunjang meningkatkan akurasi dan kualitas pemasaran itu sendiri. Namun pada kenyataanya, sistem yang berjalan masih tergolong kurang efektif dan efesien dalam melayani kebutuhan pelanggan, hal ini dikarenakan sistem pemasaran produk hanya bisa diakses secara manual, dan belum adanya media informasi seputar produk yang ditawarkan, oleh sebab itu dibuatlah suatu perancangan sistem informasi yang mengatur pemasaran produk dan dapat menjadi bahan dalam pembuatan laporan sistem penunjang keputusan. Dalam perancangan ini menggunakan metode data mining market basket analysis dan Max-Miner sebagai algoritma. Serta menggunakan metode penerapan sistem waterfall atau sering dinamakan siklus hidup klasik (classic life cycle). Dengan demikian rancang bangun sistem informasi ini, mengacu kepada bagaimana cara agar pemasaran produk dapat di akses dengan mudah, cepat, dan akurat dimanapun dan kapanpun, calon customer dapat mengakses tanpa terkendala waktu dan tempat, serta menjadi wadah dalam pengambilan keputusan oleh perusahaan. Metodologi desain menggunakan uml yang melimuti usecase, activity, squence dan untuk pengelolaan basis data menggunakan mysql. Sistem ini diharapkan mampu dijadikan salah satu penunjang keputusan untuk kebutuhan promosi produk. Kata Kunci: Penunjang pemasaran, promosi produk, algoritma Max-Miner

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

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