scholarly journals LOCATION BASED ADVERTISING FOR MASS MARKETING

2018 ◽  
Vol XLII-5 ◽  
pp. 189-192
Author(s):  
S. Vignesh Kandasamy ◽  
A. Madhu ◽  
P. K. Gupta ◽  
A. Niveditha ◽  
K. Bordoloi
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Retail Industry ◽  
Pricing Strategies ◽  
Web Based ◽  
Best Value ◽  
Open Source Gis ◽  
Ict Tools ◽  
Python Programming ◽  
Mass Marketing

<p><strong>Abstract.</strong> GIS and machine learning (ML) are powerful ICT tools in retail industry which helps the sellers understand their markets. For the consumers, however, there always lies an ambiguity with respect to the quality and quantity of the product to be purchased, vis-à-vis the price paid for it. Most retail businesses today adopt “Discount Pricing Strategies” or “Offers” to make new customers and increase sales. Owing to several establishments selling the same product and offering a variety of offers, the process of identifying the shops where the consumer can get the best value for his money, requires a lot of manual effort. A prototype has been developed in this study to allow the consumers to locate such prospective shops based on advertisements in newspapers. This solution has a two-pronged approach. First, all the offers advertised in the newspaper are pre-processed and text extraction is performed using a ML algorithm named Tesseract OCR. Second the location of shops is collected and stored in a geodatabase. Finally, the advertisement is matched to the respective geo-located shop based on its name and location. Further based on the location of the consumer and his purchase choice, shops offering discounts are shown on a web based map. This prototype provides the consumer, a platform for geo-discovery of establishments of interest through the clutter of unrelated endorsements, by the use of Open Source GIS, Python programming and ML techniques.</p>

scholarly journals Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

2021 ◽  
Author(s):  
Furkan M. Torun ◽  
Sebastian Virreira Winter ◽  
Sophia Doll ◽  
Felix M. Riese ◽  
Artem Vorobyev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Biomarker Discovery ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Open Science ◽  
Easy Access ◽  
Health State ◽  
List Type ◽  
Web Based

AbstractBiomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.Graphical AbstractHighlightsOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discoveryThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledgeOmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the datasetSeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integratedAll results, settings and method text can be exported in publication-ready formats

scholarly journals AN ONLINE REPOSITORY OF PYTHON RESOURCES FOR TEACHING MACHINE TRANSLATION TO TRANSLATION STUDENTS

2021 ◽  
Vol 8 ◽  
pp. 4-30
Author(s):  
Ralph Krüger
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Language Processing ◽  
Training Data ◽  
Teaching Resources ◽  
Web Based ◽  
Training Institutions ◽  
Python Programming ◽  
Translation Systems ◽  
Modern Machine

This paper presents an online repository of Python resources aimed at teaching the technical dimension of machine translation to students of translation studies programmes. The Python resources provided in this repository are Jupyter notebooks. These are web-based computational environments in which students can run commented blocks of code in order to perform MT-related tasks such as exploring word embeddings, preparing MT training data, training open- source machine translation systems or calculating automatic MT quality metrics such as BLEU, METEOR, BERTScore or COMET. The notebooks are prepared in such a way that students can interact with them even if they have had little to no prior exposure to the Python programming language. The notebooks are provided as open-source resources under the MIT License and can be used and modified by translator training institutions which intend to make their students familiar with the more technical aspects of modern machine translation technology. Institutions who would like to contribute their own Python-based teaching resources to the repository are welcome to do so. Keywords: translation technology, machine translation, natural language processing, translation didactics, Jupyter notebooks, Python programming

scholarly journals On evaluation metrics for medical applications of artificial intelligence

2021 ◽  
Author(s):  
Steven Hicks ◽  
Inga Strüke ◽  
Vajira Thambawita ◽  
Malek Hammou ◽  
Pål Halvorsen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Care ◽  
Open Source ◽  
Performance Metrics ◽  
Improve Patient Care ◽  
Evaluation Metrics ◽  
Medical Applications ◽  
Web Based ◽  
Improve Patient

Clinicians and model developers need to understand how proposed machine learning (ML) models could improve patient care. In fact, no single metric captures all the desirable properties of a model and several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

scholarly journals AN ONLINE REPOSITORY OF PYTHON RESOURCE FOR TEACHING MACHINE TRANSLATION TO TRANSLATION STUDENTS

2021 ◽  
Vol 8 ◽  
pp. 4-30
Author(s):  
Ralph Krüger
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Language Processing ◽  
Training Data ◽  
Teaching Resources ◽  
Web Based ◽  
Training Institutions ◽  
Python Programming ◽  
Translation Systems ◽  
Modern Machine

This paper presents an online repository of Python resources aimed at teaching the technical dimension of machine translation to students of translation studies programmes. The Python resources provided in this repository are Jupyter notebooks. These are web-based computational environments in which students can run commented blocks of code in order to perform MT-related tasks such as exploring word embeddings, preparing MT training data, training open-source machine translation systems or calculating automatic MT quality metrics such as BLEU, METEOR, BERTScore or COMET. The notebooks are prepared in such a way that students can interact with them even if they have had little to no prior exposure to the Python programming language. The notebooks are provided as open-source resources under the MIT License and can be used and modified by translator training institutions which intend to make their students familiar with the more technical aspects of modern machine translation technology. Institutions who would like to contribute their own Python-based teaching resources to the repository are welcome to do so. Keywords: translation technology, machine translation, natural language processing, translation didactics, Jupyter notebooks, Python programming

scholarly journals RWRE–A WEB BASED OPEN SOURCE GIS TOOL FOR ROOF WATER RUNOFF ESTIMATION

2018 ◽  
Vol 37 (2) ◽  
pp. 235
Author(s):  
Chaure Shailesh ◽  
Gupta D. C. ◽  
Solanki H. K. ◽  
Dubey Pramod
Keyword(s):  
Open Source ◽  
Water Runoff ◽  
Web Based ◽  
Open Source Gis

scholarly journals Feature Selection Algorithms as One of the Python Data Analytical Tools

2020 ◽  
Vol 12 (3) ◽  
pp. 54
Author(s):  
Nikita Pilnenskiy ◽  
Ivan Smetannikov
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Source ◽  
Information Technologies ◽  
Current Trend ◽  
Modern State ◽  
Machine Learning Applications ◽  
Selection Algorithms ◽  
Python Programming ◽  
Analytical Tools

With the current trend of rapidly growing popularity of the Python programming language for machine learning applications, the gap between machine learning engineer needs and existing Python tools increases. Especially, it is noticeable for more classical machine learning fields, namely, feature selection, as the community attention in the last decade has mainly shifted to neural networks. This paper has two main purposes. First, we perform an overview of existing open-source Python and Python-compatible feature selection libraries, show their problems, if any, and demonstrate the gap between these libraries and the modern state of feature selection field. Then, we present new open-source scikit-learn compatible ITMO FS (Information Technologies, Mechanics and Optics University feature selection) library that is currently under development, explain how its architecture covers modern views on feature selection, and provide some code examples on how to use it with Python and its performance compared with other Python feature selection libraries.

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