scholarly journals Automatic Learning in Agriculture: A Survey

2021 ◽  
Vol 10 (1) ◽  
pp. 1325-1345
Author(s):  
Alia AlKameli ◽  
Mustafa Hammad
Keyword(s):  
Automatic Learning

On the Automatic Learning of Sentiment Lexicons

2015 ◽  
Author(s):  
Aliaksei Severyn ◽  
Alessandro Moschitti
Keyword(s):  
Automatic Learning

ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases

2021 ◽  
Author(s):  
Jinze Bai ◽  
Jialin Wang ◽  
Zhao Li ◽  
Donghui Ding ◽  
Ji Zhang ◽  
...  
Keyword(s):  
Relational Databases ◽  
Automatic Learning

scholarly journals MLatom 2: An Integrative Platform for Atomistic Machine Learning

2021 ◽  
Vol 379 (4) ◽  
Author(s):  
Pavlo O. Dral ◽  
Fuchun Ge ◽  
Bao-Xin Xue ◽  
Yi-Fan Hou ◽  
Max Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Package ◽  
State Of The Art ◽  
Kernel Method ◽  
Automatic Learning ◽  
Hyperparameter Optimization ◽  
Input And Output ◽  
Spectrum Simulation ◽  
Farthest Point ◽  
Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

Automatic Learning of Cognitive Exercises for Socially Assistive Robotics

2021 ◽  
Author(s):  
Alejandro Suarez-Hernandez ◽  
Antonio Andriella ◽  
Aleksandar Taranovic ◽  
Javier Segovia-Aguas ◽  
Carme Torras ◽  
...  
Keyword(s):  
Assistive Robotics ◽  
Automatic Learning ◽  
Socially Assistive Robotics

GIS Based Multi-Criteria decision support system and machine learning for Hospital Site Selection case study Oran - Algeria

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Site Selection ◽  
Decision Makers ◽  
Analysis Method ◽  
Automatic Learning ◽  
Hospital Site ◽  
Set Up ◽  
High Level ◽  
Selection Of ◽  
The Ideal

The selection of hospital sites is one of the most important choice a decision maker has to take so as to resist the pandemic. The decision may considerably affect the outbreak transmission in terms of efficiency , budget, etc. The main targeted objective of this study is to find the ideal location where to set up a hospital in the willaya of Oran Alg. For this reason, we have used a geographic information system coupled to the multi-criteria analysis method AHP in order to evaluate diverse criteria of physiological positioning , environmental and economical. Another objective of this study is to evaluate the advanced techniques of the automatic learning . the method of the random forest (RF) for the patterning of the hospital site selection in the willaya of Oran. The result of our study may be useful to decision makers to know the suitability of the sites as it provides a high level of confidence and consequently accelerate the power to control the COVID19 pandemic.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

scholarly journals System to assist in the Diagnosis of Diabetes using Ontology and Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9971-9975
Keyword(s):  
Machine Learning ◽  
Public Health Problem ◽  
Medical Data ◽  
The Body ◽  
Machine Learning Method ◽  
Learning Method ◽  
Automatic Learning ◽  
Early Diabetes ◽  
Data Set ◽  
Medical Tests

Diabetes mellitus has become a public health problem in both developed and developing countries. If it is not treated early, diabetes-related complications in many vital organs of the body can become fatal. Its early detection is very important for early treatment that can prevent the disease from progressing to such complications. This article focuses on designing a system to assist in the diagnosis of diabetes disease based on medical ontology and automatic learning. The proposed method uses automatic learning algorithms as a classifier for the diagnosis of diabetes based on a medical data set. The ontology suggests a pre-processing of a coherent, consistent, interoperable and shareable knowledge basis of data and the machine learning method focuses on classification based on symptoms and medical tests. Based on the experimental results, DDAS not only offers better performance in predicting and diagnosing diabetes in individuals, but also has better accuracy in recommending useful treatment to patients.

scholarly journals A Markov-Based Intrusion Tolerance Finite Automaton

2021 ◽  
Vol 28 (2) ◽  
pp. 89-100
Keyword(s):  
Network Security ◽  
Markov Model ◽  
Finite Automaton ◽  
Network Services ◽  
Intrusion Tolerance ◽  
Security Strategy ◽  
Automatic Learning ◽  
Learning Mechanism ◽  
Proposed Model ◽  
Tolerance Model

It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton.

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