Protection of Works Generated by Machine Learning Software

2020 ◽  
Vol 69 (7) ◽  
pp. 763-767
Keyword(s):  
Machine Learning ◽  
Learning Software

scholarly journals DECODING VIEWS AND SENTIMENTS OF PROGRAM HEADS TOWARDS THE SUPERVISION OF INSTRUCTION DURING THE COVID-19 PANDEMIC

2021 ◽  
pp. 323-329
Author(s):  
Jesus M. Meneses ◽  
Karen W. Cantilang ◽  
Delbert A. Dala ◽  
Jovito B. Madeja
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Latent Semantic Indexing ◽  
State University ◽  
Written Responses ◽  
Strategic Role ◽  
To Come ◽  
Learning Software ◽  
Supervision Of Instruction

The purpose of this study was to decode the hidden views and sentiments from the collated written responses of Eastern Samar State University’s Program Heads regarding supervision of instructions amidst the COVID-19 pandemic. This study utilized Exploratory Sequential Mixed Method to explore and understand the perspective or sentiments of Eastern Samar State University program heads towards supervision of instruction in the midst of the COVID-19 pandemic. Data were collected/collated from the participants indirectly using an interview questionnaire containing an open-ended question. The same were processed and analyzed using an open-source machine learning software called Orange toolbox (Demsar et al., 2013) wherein pre-processing, sentiment analysis and topic modelling built-in tools were utilized. The results showed that the most prominent words generated by the machine learning tool from the text file of responses are the words pandemic, performance, program, learning, difficult, supervision, instruction, internet, faculty, online students, teaching, delivery confusing, challenging, poor and connectivity. The dominant sentiment associated thereof lean towards negative polarity which implicate negative sentiments. Hidden topics were automatically generated by the machine which allowed the researchers to come up with the following related themes: “Impact of pandemic in the supervision of instruction of faculty and learning of students”, “Challenges in the delivery of instruction and supervision due to poor internet connectivity”, and “Strategic role of online modalities and connectivity in supervision and delivery of instruction”. There are limited researches navigating in text mining and sentiment analysis with the use of Orange toolbox particularly those that deals with supervision of instruction in a Philippine State University. There are related studies using machine learning software, but nothing like this study directed towards a specific gap in specific locale. KEYWORDS: Pandemic, Latent Semantic Indexing, Orange Toolbox, Sentiment Analysis, Thematic Analysis.

scholarly journals The challenge of clinical adoption—the insurmountable obstacle that will stop machine learning?

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20180017
Author(s):  
Jonathan Taylor ◽  
John Fenner
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Health Economics ◽  
Clinical Environment ◽  
New Approach ◽  
Representative Data ◽  
Potential Benefits ◽  
Insurmountable Obstacle ◽  
Learning Software ◽  
Clinical Adoption

Machine learning promises much in the field of radiology, both in terms of software that can directly analyse patient data and algorithms that can automatically perform other processes in the reporting pipeline. However, clinical practice remains largely untouched by such technology. This article highlights what we consider to be the major obstacles to widespread clinical adoption of machine learning software, namely: representative data and evidence, regulations, health economics, heterogeneity of the clinical environment and support and promotion. We argue that these issues are currently so substantial that machine learning will struggle to find acceptance beyond the narrow group of applications where the potential benefits are readily evident. In order that machine learning can fulfil its potential in radiology, a radical new approach is needed, where significant resources are directed at reducing impediments to translation rather than always being focused solely on development of the technology itself.

Using Data Mining to Predict Possible Future Depression Cases

2014 ◽  
Vol 3 (4) ◽  
pp. 231
Author(s):  
Kevin Daimi ◽  
Shadi Banitaan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Synthetic Data ◽  
Considerable Difficulty ◽  
Symptoms Of Depression ◽  
Using Data ◽  
Learning Software

Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of machine learning software, WEKA, is used.

scholarly journals Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0225841 ◽  
Author(s):  
Seán Fitzgerald ◽  
Shunli Wang ◽  
Daying Dai ◽  
Dennis H. Murphree ◽  
Abhay Pandit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Blood Clots ◽  
Learning Software

scholarly journals Using Data Science Software to Address Health Disparities

2021 ◽  
Vol 6 (2) ◽  
pp. 45-58
Author(s):  
Jose O. Huerta ◽  
Gayle L. Prybutok ◽  
Victor R. Prybutok
Keyword(s):  
Machine Learning ◽  
Health Disparities ◽  
Data Science ◽  
Science Technology ◽  
Science Operations ◽  
Using Data ◽  
Learning Software ◽  
Computational Processes

The article assesses data science software to evaluate the usefulness of data science technology in addressing concerns such as health disparities. Data science software was analyzed using KDnuggets data related to analytics, data science, and machine learning software. Data science functionalities include computational processes and frameworks that are relevant for healthcare. This study demonstrates the importance of leading applications for conducting data science operations that can improve care in healthcare networks by addressing such factors as health disparities.

scholarly journals Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths

2019 ◽  
Vol 3 (1) ◽  
pp. 1-4
Author(s):  
Simon Hawatichke Chiwamba ◽  
Jackson Phiri ◽  
Philip O. Y. Nkunika ◽  
Mayumbo Nyirenda ◽  
Monica M. Kabemba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
Fall Armyworm ◽  
Machine Learning Algorithms ◽  
Training Time ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Learning Technique ◽  
Computer Scientists ◽  
Learning Software

Automated entomology is one of the field that has received a fair attention from the computer scientists and its support disciplines. This can further be confirmed by the recent attention that the Fall Armyworm (FAW) (Spodoptera frugiperda) has received in Africa particularly the Southern African Development Community (SADC). As the FAW is known for its devastating effects, stakeholders such as the Food and Agriculture Organization (FAO), SADC and University of Zambia (UNZA) have agreed to develop robust early monitoring and warning system. To supplement the stakeholders’ efforts, we choose a branch of artificial intelligence that employs deep neural network architectures known as Google TensorFlow. It is an advanced state-of-the-art machine learning technique that can be used to identify the FAW moths. In this paper, we use Google TensorFlow, an open source deep learning software library for defining, training and deploying machine learning models. We use the transfer learning technique to retrain the Inception v3 model in TensorFlow on the insect dataset, which reduces the training time and improve the accuracy of FAW moth identification. Our retrained model achieves a train accuracy of 57 – 60 %, cross entropy of 65 – 70% and validation accuracy of 

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