Forecasting conflict in africa with automated machine learning systems

Abstract The article discusses the current state of technologies for automated machine learning. The development trends and the nature of the distribution model - MLaaS - are defined. There is highlighted a number of problems of automating the machine learning process, such as: excessive simplification and specialization of tools, vagueness of implemented processes, lack of flexibility in the infrastructure hardware, using closed algorithms. As a partial or complete solution to them, we have proposed the architecture, consisting of separate modules: models, hybridizer, learning algorithms module, testing module, user support module, and a theoretical framework. The main feature of the given architecture is its modularity, transparency and encapsulation of components. Each module is described as a separate element, implemented as an independent microservice. The paper describes the benefits of applying the given approach to the implementation of automated machine learning systems, the need to implement the given or similar standards. For each of the modules, its purposes, the tasks it solves and the implemented functionality, as well as the data necessary for the functioning and their sources are described. A general diagram showing the flows of information exchange between modules is presented. The main scenarios for the resulting system operation, as well as ways of interacting with it and the result of its operation - the generated model - are described.

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Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

OLYMPIADS IN INFORMATICS ◽

10.15388/ioi.2018.07 ◽

2018 ◽

Vol 12 ◽

pp. 85-98

Author(s):

Bojan Kostadinov ◽

Mile Jovanov ◽

Emil STANKOV

Keyword(s):

Machine Learning ◽

Data Collection ◽

Educational Policy ◽

Learning Systems ◽

Data Sources ◽

Or Education ◽

Student Activity ◽

The World ◽

E Learning ◽

Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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Bandit-Based Automated Machine Learning

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) ◽

10.1109/bracis.2018.00029 ◽

2018 ◽

Author(s):

Silvia Cristina Nunes das Dores ◽

Carlos Soares ◽

Duncan Ruiz

Keyword(s):

Machine Learning ◽

Automated Machine Learning

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A Robust Automated Machine Learning System with Pseudoinverse Learning

Cognitive Computation ◽

10.1007/s12559-021-09853-6 ◽

2021 ◽

Author(s):

Ke Wang ◽

Ping Guo

Keyword(s):

Machine Learning ◽

Learning System ◽

Automated Machine Learning

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Automated machine learning to predict the co‐occurrence of isocitrate dehydrogenase mutations and O 6 ‐methylguanine‐DNA methyltransferase promoter methylation in patients with gliomas

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.27498 ◽

2021 ◽

Author(s):

Simin Zhang ◽

Huaiqiang Sun ◽

Xiaorui Su ◽

Xibiao Yang ◽

Weina Wang ◽

...

Keyword(s):

Machine Learning ◽

Promoter Methylation ◽

Isocitrate Dehydrogenase ◽

Dna Methyltransferase ◽

Methylguanine Dna Methyltransferase ◽

Automated Machine Learning

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

Journal of Medical Imaging and Radiation Sciences ◽

10.1016/j.jmir.2021.03.005 ◽

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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Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽

10.3390/ai2010004 ◽

2021 ◽

Vol 2 (1) ◽

pp. 34-47

Author(s):

Borja Espejo-Garcia ◽

Ioannis Malounas ◽

Eleanna Vali ◽

Spyros Fountas

Keyword(s):

Machine Learning ◽

Plant Protection ◽

Crop Protection ◽

Identification Problem ◽

Learning System ◽

Classifier Ensembles ◽

Automated Machine Learning ◽

A New Technique ◽

Plant Seedlings ◽

And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽

10.3390/s21072514 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2514

Author(s):

Tharindu Kaluarachchi ◽

Andrew Reis ◽

Suranga Nanayakkara

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Review Paper ◽

Learning Systems ◽

Learning Approaches ◽

Application Development ◽

Research Gaps ◽

Domain Experts ◽

Working Definition ◽

Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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