scholarly journals Comparison of Automated Machine Learning Tools for SMS Spam Message Filtering

2021 ◽  
pp. 307-316
Author(s):  
Waddah Saeed
Keyword(s):  
Machine Learning ◽  
Learning Tools ◽  
Automated Machine Learning

scholarly journals Machine Learning Tools For off-Target Early Safety Assessment of Small Molecules In Drug Discovery (Single Task Neural Networks Vs Automated Machine Learning)

2021 ◽  
Author(s):  
Doha Naga ◽  
Wolfgang Muster ◽  
Eunice Musvasva ◽  
Gerhard F. Ecker
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Pharmaceutical Companies ◽  
Learning Tools ◽  
Safety Issues ◽  
Learning Framework ◽  
Automated Machine Learning ◽  
And Performance ◽  
Preclinical Safety

Abstract Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies[1-3]. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies including Roche, routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies.Hereby we present a machine learning framework aiming at the prediction of our in-house 50 off-target panel[4] activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and accelerate drug discovery. It incorporates different ML approaches such as deep learning and automated machine learning. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.

scholarly journals Automated curation of CNMF-E-extracted ROI spatial footprints and calcium traces using open-source AutoML tools

2020 ◽  
Author(s):  
LM Tran ◽  
AJ Mocle ◽  
AI Ramsaran ◽  
AD Jacob ◽  
PW Frankland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Calcium Imaging ◽  
Ground Truth ◽  
Learning Tools ◽  
Test Dataset ◽  
Manual Curation ◽  
Data Points ◽  
Roi Extraction ◽  
Automated Machine Learning

AbstractIn vivo 1-photon calcium imaging is an increasingly prevalent method in behavioural neuroscience. Numerous analysis pipelines have been developed to improve the reliability and scalability of pre-processing and ROI extraction for these large calcium imaging datasets. Despite these advancements in pre-processing methods, manual curation of the extracted spatial footprints and calcium traces of neurons remains important for quality control. Here, we propose an additional semi-automated curation step for sorting spatial footprints and calcium traces from putative neurons extracted using the popular CNMF-E algorithm. We used the automated machine learning tools TPOT and AutoSklearn to generate classifiers to curate the extracted ROIs trained on a subset of human-labeled data. AutoSklearn produced the best performing classifier, achieving an F1 score > 92% on the ground truth test dataset. This automated approach is a useful strategy for filtering ROIs with relatively few labeled data points, and can be easily added to pre-existing pipelines currently using CNMF-E for ROI extraction.

scholarly journals AutoML to Date and Beyond: Challenges and Opportunities

2022 ◽  
Vol 54 (8) ◽  
pp. 1-36
Author(s):  
Shubhra Kanti Karmaker (“Santu”) ◽  
Md. Mahadi Hassan ◽  
Micah J. Smith ◽  
Lei Xu ◽  
Chengxiang Zhai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Training Dataset ◽  
Learning Tools ◽  
Specific Data ◽  
Domain Experts ◽  
Domain Specific ◽  
Challenges And Opportunities ◽  
Automated Machine Learning ◽  
End To End ◽  
Prediction Problems

As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML’s main selling points, the process still requires human involvement at a number of vital steps, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training dataset, and selecting a promising machine learning technique. These steps often require a prolonged back-and-forth that makes this process inefficient for domain experts and data scientists alike and keeps so-called AutoML systems from being truly automatic. In this review article, we introduce a new classification system for AutoML systems, using a seven-tiered schematic to distinguish these systems based on their level of autonomy. We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far. We highlight those subtasks that are still done manually—generally by a data scientist—and explain how this limits domain experts’ access to machine learning. Next, we introduce our novel level-based taxonomy for AutoML systems and define each level according to the scope of automation support provided. Finally, we lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline and discussing important challenges that stand in the way of this ambitious goal.

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

Bandit-Based Automated Machine Learning

2018 ◽  
Author(s):  
Silvia Cristina Nunes das Dores ◽  
Carlos Soares ◽  
Duncan Ruiz
Keyword(s):  
Machine Learning ◽  
Automated Machine Learning

Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency

2021 ◽  
Vol 192 ◽  
pp. 103181
Author(s):  
Jagadish Timsina ◽  
Sudarshan Dutta ◽  
Krishna Prasad Devkota ◽  
Somsubhra Chakraborty ◽  
Ram Krishna Neupane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Learning Tools ◽  
Nutrient Use ◽  
Use Efficiency

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Automated Machine-Learning Radiotherapy Planning for Pediatric and Adult Brain Tumours

2021 ◽  
Vol 52 (2) ◽  
pp. S3
Author(s):  
Grace Tsui ◽  
Derek S. Tsang ◽  
Chris McIntosh ◽  
Thomas G. Purdie ◽  
Glenn Bauman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumours ◽  
Adult Brain ◽  
Radiotherapy Planning ◽  
Automated Machine Learning
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