scholarly journals Detection of an Autism EEG Signature Through a New Processing Method Based on a Topological Approach

2021 ◽  
Author(s):  
ENZO GROSSI ◽  
Rebecca White ◽  
Ronald Swatzyna
Keyword(s):  
Machine Learning ◽  
Psychiatric Disorders ◽  
Minimum Spanning Tree ◽  
Triangular Matrix ◽  
Learning Systems ◽  
Learning System ◽  
Eeg Data ◽  
New Processing ◽  
Global Accuracy ◽  
The One

Abstract A new pre-processing approach of EEG data to detect topological EEG features has been applied to a continuous segment of artifact-free EEG data lasting 10 minutes in ASCII format derived from 50 ASD children and 50 children with other Neuro-psychiatric disorders, matched for age and male/female ratios. Each EEG was manipulated using a Cin-Cin algorithm, based on an input vector characterized by a linear composition of city-block matrix distances among19 electrodes. From the resulting triangular matrix of 171 numbers expressing all of the one-by-one distances among the 19 electrodes a minimum spanning tree(MST) is calculated. Electrode identification serial codes, sorted according to the decreasing number of links in MST, and the number of links in MST are taken as input vectors for machine learning systems. With this method all the content of an EEG is transformed in 38 numbers which represent the input vectors for machine learning systems classifiers. Machine learning systems have been applied to build up a predictive model to distinguish between the two diagnostic classes. The best machine learning system (KNN algorithm) obtained a global accuracy of 93.2% (92.37 % sensitivity and 94.03 % specificity) in differentiating ASD subjects from NPD subjects. In conclusion the results obtained in this study suggest that the two new pre-processing methods introduced, in particular the MST algorithm, have great potential to allow a machine learning system to discriminate EEGs obtained from subjects with autism from EEGs obtained from subjects affected by other psychiatric disorders.

scholarly journals Machine learning for human learners: opportunities, issues, tensions and threats

2020 ◽  
Author(s):  
Mary E. Webb ◽  
Andrew Fluck ◽  
Johannes Magenheim ◽  
Joyce Malyn-Smith ◽  
Juliet Waters ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ethical Issues ◽  
Practical Experience ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Behaviour ◽  
Recent Developments ◽  
Key Aspects ◽  
School Curricula

AbstractMachine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

scholarly journals An empirical study of authentication methods to secure e-learning system activities against impersonation fraud

2016 ◽  
Vol 4 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Shauna Beaudin ◽  
Yar Levy ◽  
James Parrish ◽  
Theon Danet
Keyword(s):  
Exploratory Study ◽  
Learning Systems ◽  
Learning System ◽  
Descriptive Statistics ◽  
Learning Activities ◽  
Web Based ◽  
Academic Organizations ◽  
E Learning ◽  
The One ◽  
High Level

The demand for e-learning systems in both academic and non-academic organizations has increased the need to improve security against impersonation fraud. Although there are a number of studies focused on securing Web-based systems from Information Systems (IS) misuse, research has recognized the importance of identifying suitable levels of authenticating strength for various activities. In e-learning systems, it is evident that due to the variation in authentication strength among controls, a ‘one size fits all’ solution is not suitable for securing diverse e-learning activities against impersonation fraud. The focus of this exploratory study was to investigate what levels of authentication strength users perceive to be most suitable for activities in e-learning systems against impersonation fraud and aimed to assess if the ‘one size fits all’ approach that is mainly used is valid when it comes to securing e-learning activities from impersonation fraud. A sample of 1,070 e-learners was analyzed using descriptive statistics and exploratory factor analysis to uncover suitable levels of authentication strength to secure elearning activities against impersonation fraud. The findings determined that there is a specific set of e-learning activities that have high potential for impersonation and need a moderate to high level of authentication strength to reduce the threat.

Intelligent Big Data Analytics in Health

2022 ◽  
pp. 1663-1702
Author(s):  
Ebru Aydindag Bayrak ◽  
Pinar Kirci
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Early Diagnosis ◽  
Neurological Disorders ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Learning Systems ◽  
Learning System ◽  
Definition Of

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

scholarly journals A Systematic Literature Review on Federated Machine Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-39
Author(s):  
Sin Kit Lo ◽  
Qinghua Lu ◽  
Chen Wang ◽  
Hye-Young Paik ◽  
Liming Zhu
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
System Development ◽  
Learning Systems ◽  
Learning System ◽  
Requirement Analysis ◽  
Future Trends ◽  
Data Synthesis

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.

scholarly journals Towards Knowledgeable Supervised Lifelong Learning Systems

2020 ◽  
Vol 68 ◽  
pp. 159-224
Author(s):  
Diana Benavides-Prado ◽  
Yun Sing Koh ◽  
Patricia Riddle
Keyword(s):  
Machine Learning ◽  
Multitask Learning ◽  
Learning Systems ◽  
Learning System ◽  
Support Vector ◽  
Vector Machines ◽  
Real World Datasets ◽  
First Time ◽  
Continual Learning

Learning a sequence of tasks is a long-standing challenge in machine learning. This setting applies to learning systems that observe examples of a range of tasks at different points in time. A learning system should become more knowledgeable as more related tasks are learned. Although the problem of learning sequentially was acknowledged for the first time decades ago, the research in this area has been rather limited. Research in transfer learning, multitask learning, metalearning and deep learning has studied some challenges of these kinds of systems. Recent research in lifelong machine learning and continual learning has revived interest in this problem. We propose Proficiente, a full framework for long-term learning systems. Proficiente relies on knowledge transferred between hypotheses learned with Support Vector Machines. The first component of the framework is focused on transferring forward selectively from a set of existing hypotheses or functions representing knowledge acquired during previous tasks to a new target task. A second component of Proficiente is focused on transferring backward, a novel ability of long-term learning systems that aim to exploit knowledge derived from recent tasks to encourage refinement of existing knowledge. We propose a method that transfers selectively from a task learned recently to existing hypotheses representing previous tasks. The method encourages retention of existing knowledge whilst refining. We analyse the theoretical properties of the proposed framework. Proficiente is accompanied by an agnostic metric that can be used to determine if a long-term learning system is becoming more knowledgeable. We evaluate Proficiente in both synthetic and real-world datasets, and demonstrate scenarios where knowledgeable supervised learning systems can be achieved by means of transfer.

scholarly journals MLPM: Machine Learning Package Manager

2020 ◽  
Author(s):  
Xiaozhe Yao
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Community ◽  
Source Code ◽  
Learning Systems ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Third Party ◽  
Small Companies ◽  
Run Test

In recent years, the machine learning community has witnessed rapid growth in the development of deep learning and its application. Unlike other software that can be installed through the package manager, developing machine learning systems usually need to search for the source code or start from scratch, debug and then deploy to production. It usually costs much for small companies and research institutions to run, test, evaluate, deploy and monitor machine learning system. In this paper, we proposed a machine learning package manager aiming to assist users1) find potentially useful models,2) resolve dependencies,3) deploy as HTTP service. By using the MLPM, users are enabled to easily adopt existing and well-established machine learning algorithms and libraries to their project within few steps. MLPM also allows third-party extensions to be installed, which makes the system customizable according to users’ workflow.rpose

Intelligent Big Data Analytics in Health

2019 ◽  
pp. 252-291
Author(s):  
Ebru Aydindag Bayrak ◽  
Pinar Kirci
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Early Diagnosis ◽  
Neurological Disorders ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Learning Systems ◽  
Learning System ◽  
Definition Of

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

RECENT ADVANCES IN MACHINE LEARNING

1992 ◽  
Vol 06 (04) ◽  
pp. 469-511 ◽  
Author(s):  
YVES KODRATOFF
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Point Of View ◽  
Learning Systems ◽  
Learning System ◽  
Reasoning Process ◽  
Body Of Knowledge ◽  
Recent Advances ◽  
Induction Technique ◽  
Flow Of Information

Inference is a very general reasoning process that allows us to draw consequences from some body of knowledge. Machine learning (ML) uses the three kinds of possible inferences, deductive, inductive, and analogical. We describe here different methods, using these inferences, that have been created during the last decade to improve the way machines can learn. We have already presented the most classical approaches in a book (Kodratoff, 1988), and in several review papers (Kodratoff, 1989, 1990a, 1992). These results will be described here very briefly, in order to leave room for newer results. We include also genetic algorithms as an induction technique. We restrict our presentation to the symbolic aspects of connectionism. A learning system can also be viewed as a mechanism skimming interesting knowledge out of the flow of information that runs through it. We present several existing learning systems from this point of view.

Detection of an Autism EEG Signature From Only Two EEG Channels Through Features Extraction and Advanced Machine Learning Analysis

2020 ◽  
pp. 155005942098242
Author(s):  
Enzo Grossi ◽  
Giovanni Valbusa ◽  
Massimo Buscema
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Autism Spectrum ◽  
Features Extraction ◽  
Learning Systems ◽  
Data Set ◽  
Input Selection ◽  
Eeg Data ◽  
A Minor

Background and Objective In 2 previous studies, we have shown the ability of special machine learning systems applied to standard EEG data in distinguishing children with autism spectrum disorder (ASD) from non-ASD children with an overall accuracy rate of 100% and 98.4%, respectively. Since the equipment routinely available in neonatology units employ few derivations, we were curious to check if just 2 derivations were enough to allow good performance in the same cases of the above-mentioned studies. Methods A continuous segment of artifact-free EEG data lasting 1 minute in ASCCI format from C3 and C4 EEG channels present in 2 previous studies, was used for features extraction and subsequent analyses with advanced machine learning systems. A features extraction software package (Python tsfresh) applied to time-series raw data derived 1588 quantitative features. A special hybrid system called TWIST (Training with Input Selection and Testing), coupling an evolutionary algorithm named Gen-D and a backpropagation neural network, was used to subdivide the data set into training and testing sets as well as to select features yielding the maximum amount of information after a first variable selection performed with linear correlation index threshold. Results After this intelligent preprocessing, 12 features were extracted from C3-C4 time-series of study 1 and 36 C3-C4 time-series of study 2 representing the EEG signature. Acting on these features the overall accuracy predictive capability of the best artificial neural network acting as a classifier in deciphering autistic cases from typicals (study 1) and other neuropsychiatric disorders (study 2) resulted in 100 % for study 1 and 94.95 % for study 2. Conclusions The results of this study suggest that also a minor part of EEG contains precious information useful to detect autism if treated with advanced computational algorithms. This could allow in the future to use standard EEG from newborns to check if the ASD signature is already present at birth.

scholarly journals Methods for the Explanation of Machine Learning Processes and Results for Non-Experts

2018 ◽  
Author(s):  
Joachim Diederich
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Learning System ◽  
Support Vector ◽  
Domain Expert ◽  
First Order ◽  
Science Technology ◽  
Expert User ◽  
Vector Machines ◽  
Multi Media

Machine learning systems such as artificial neural networks (ANNs) and support vector machines (SVMs) are commonplace in science, technology, business and health. However, the lack of an explanation capability is an impediment to the more wide-spread use of machine learning systems. For more than 20 years, the field of rule extraction from ANNs and SVMs has tried to generate explanations for machine learning based decisions in the form of propositional, probabilistic or first-order rules. However, there is no generally accepted technique that works for all machine learning systems. In addition, there is no method that is tailored to the needs of a user who may not be a domain expert and may not have a technology background. This paper introduces a number of techniques for the generation of multi-media clips that explain the behavior of a machine learning system to a non-expert user.

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