A Robust Approach with Text Analytics for Bengali Digit Recognition Using Machine Learning

2020 ◽  
pp. 157-169
Author(s):  
Dipam Paul ◽  
Prasant Kumar Pattnaik ◽  
Proshikshya Mukherjee
Keyword(s):  
Machine Learning ◽  
Text Analytics ◽  
Robust Approach ◽  
Digit Recognition

scholarly journals Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach

2020 ◽  
Vol 12 (11) ◽  
pp. 4753
Author(s):  
Viju Raghupathi ◽  
Jie Ren ◽  
Wullianallur Raghupathi
Keyword(s):  
Machine Learning ◽  
Corporate Sustainability ◽  
Primary Source ◽  
Text Analytics ◽  
Useful Knowledge ◽  
Word Cloud ◽  
Textual Data ◽  
Shareholder Resolution ◽  
The Impact ◽  
Insight Into

Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders’ perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience.

scholarly journals Handwritten Digit Recognition Using Machine Learning Algorithms

2018 ◽  
Vol 3 (1) ◽  
pp. 29 ◽  
Author(s):  
S M Shamim ◽  
Mohammad Badrul Alam Miah ◽  
Angona Sarker ◽  
Masud Rana ◽  
Abdullah Al Jobair
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Data Entry ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.

scholarly journals Iterative Guided Machine Learning-Assisted Systematic Literature Reviews: A Diabetes Case Study

2021 ◽  
Author(s):  
John Zimmerman ◽  
Robin Soler ◽  
Lavinder James ◽  
Murphy Sarah ◽  
Atkins Charisma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Literature ◽  
Relevant Information ◽  
Text Analytics ◽  
Inclusion Criteria ◽  
Literature Reviews ◽  
Study Test ◽  
Model Training

Abstract Background: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and Machine Learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.Methods: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance. Results: the case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes. Conclusions: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.

scholarly journals Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition

2021 ◽  
Vol 21 (1) ◽  
pp. 574
Author(s):  
Owais Mujtaba Khandy ◽  
Samad Dadvandipour
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Character Recognition ◽  
Machine Learning Algorithms ◽  
Maximum Efficiency ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Work Done

<p><span>This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and Random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods. </span></p>

Sign in / Sign up

Export Citation Format

Share Document