scholarly journals Overview of current state of research on the application of artificial intelligence techniques for COVID-19

2021 ◽  
Vol 7 ◽  
pp. e564
Author(s):  
Vijay Kumar ◽  
Dilbag Singh ◽  
Manjit Kaur ◽  
Robertas Damaševičius
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Interaction ◽  
Future Research ◽  
Rapid Testing ◽  
Learning Models ◽  
Open Area ◽  
Current State ◽  
State Of Research ◽  
Novel Drug

Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

scholarly journals A Survey on the Applications of Machine Learning in the Early Phases of Product Development

2019 ◽  
Vol 1 (1) ◽  
pp. 2437-2446 ◽  
Author(s):  
Seyed Sina Shabestari ◽  
Michael Herzog ◽  
Beate Bender
Keyword(s):  
Machine Learning ◽  
Product Development ◽  
Product Development Process ◽  
Future Research ◽  
Concept Design ◽  
Survey Techniques ◽  
Current State ◽  
State Of Research ◽  
Applications Of Machine Learning ◽  
Early Phases

AbstractMachine learning has shown its potential to support the knowledge extraction within the development processes and particularly in the early phases where critical decisions have to be made. However, the current state of the research in the applications of the machine learning in the product development are fragmented. A holistic overall view provides the opportunity to analyze the current state of research and is the basis for the strategic planning of future research and the actions needed. Hence, implementing the systematic literature survey techniques, the state of the applications of machine learning in the early phases of the product development process namely the Requirements, functional modelling and principal concept design is reviewed and discussed.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

Second language structural priming: A critical review and directions for future research

2017 ◽  
Vol 34 (4) ◽  
pp. 539-552 ◽  
Author(s):  
Carrie N. Jackson
Keyword(s):  
Second Language ◽  
Future Research ◽  
Structural Priming ◽  
Cognitive Mechanisms ◽  
L2 Acquisition ◽  
Current State ◽  
Current Review ◽  
Open Questions ◽  
Key Issues ◽  
State Of Research

The last 15 years has seen a tremendous growth in research on structural priming among second language (L2) speakers. Structural priming is the phenomenon whereby speakers are more likely to repeat a structure they have recently heard or produced. Research on L2 structural priming speaks to key issues regarding the underlying linguistic and cognitive mechanisms that support L2 acquisition and use, and the extent to which lexical and grammatical information are shared across an L2 speaker’s languages. As the number of researchers investigating L2 priming and its implications for L2 learning continues to grow, it is important to assess the current state of research in this area and establish directions for continued inquiry. The goal of the current review is to provide an overview of recent research on within-language L2 structural priming, with an eye towards the open questions that remain.

scholarly journals Agile Transformation at Scale: A Tertiary Study

2021 ◽  
pp. 3-11
Author(s):  
Suddhasvatta Das ◽  
Kevin Gary
Keyword(s):  
Large Scale ◽  
Software Industry ◽  
Future Research ◽  
Small Scale ◽  
Agile Processes ◽  
Current State ◽  
Research Findings ◽  
State Of Research ◽  
Agile Software ◽  
Tertiary Study

AbstractDue to the fast-paced nature of the software industry and the success of small agile projects, researchers and practitioners are interested in scaling agile processes to larger projects. Agile software development (ASD) has been growing in popularity for over two decades. With the success of small-scale agile transformation, organizations started to focus on scaling agile. There is a scarcity of literature in this field making it harder to find plausible evidence to identify the science behind large scale agile transformation. The objective of this paper is to present a better understanding of the current state of research in the field of scaled agile transformation and explore research gaps. This tertiary study identifies seven relevant peer reviewed studies and reports research findings and future research avenues.

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

scholarly journals Biomarkers in Posttraumatic Stress Disorder: Overview and Implications for Future Research

2013 ◽  
Vol 35 ◽  
pp. 43-54 ◽  
Author(s):  
Ulrike Schmidt ◽  
Sebastian F. Kaltwasser ◽  
Carsten T. Wotjak
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Sexual Abuse ◽  
Anxiety Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Future Research ◽  
Clinical Use ◽  
Current State ◽  
Life Threatening ◽  
State Of Research

PTSD can develop in the aftermath of traumatic incidents like combat, sexual abuse, or life threatening accidents. Unfortunately, there are still no biomarkers for this debilitating anxiety disorder in clinical use. Anyhow, there are numerous studies describing potential PTSD biomarkers, some of which might progress to the point of practical use in the future. Here, we outline and comment on some of the most prominent findings on potential imaging, psychological, endocrine, and molecular PTSD biomarkers and classify them into risk, disease, and therapy markers. Since for most of these potential PTSD markers a causal role in PTSD has been demonstrated or at least postulated, this review also gives an overview on the current state of research on PTSD pathobiology.

Sport in business studies: a state-of-the-art literature review

2018 ◽  
Vol 8 (5) ◽  
pp. 529-546
Author(s):  
Christofer Laurell ◽  
Sten Soderman
Keyword(s):  
Systematic Review ◽  
Literature Review ◽  
Marketing Strategy ◽  
Design Methodology ◽  
State Of The Art ◽  
Future Research ◽  
Content Type ◽  
Business Studies ◽  
Current State ◽  
State Of Research

PurposeThe purpose of this paper is to provide a systematic review of articles on sport published in leading business studies journals within marketing, organisational studies and strategy.Design/methodology/approachBased on a review of 38 identified articles within the subfields of marketing, strategy and organisation studies published between 2000 and 2015, the articles’ topical, theoretical and methodological orientation within the studied subfields were analysed followed by a cross-subfield analysis.FindingsThe authors identify considerable differences in topical, theoretical and methodological orientation among the studied subfields’ associated articles. Overall, the authors also find that articles across all subfields tend to be focussed on contributing to mature theory, even though the subfield of marketing in particular exhibits contributions to nascent theory in contrast to organisation studies and strategy.Originality/valueThis paper contributes by illustrating the current state of research that is devoted or related to the phenomenon of sport within three subfields in business studies. Furthermore, the authors discuss the role played by leading business studies journalsvis-à-vissport sector-specific journals and offer avenues for future research.

Train Deep Learning Models using subsurface geological images datasets

2021 ◽  
Author(s):  
Ramy Abdallah ◽  
Clare E. Bond ◽  
Robert W.H. Butler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Models ◽  
Structural Interpretation ◽  
Performance Accuracy ◽  
Learning Techniques ◽  
Machine Learning Models

&lt;p&gt;Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model&amp;#8217;s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.&lt;/p&gt;

Recent Progress in Quantum Machine Learning

2021 ◽  
pp. 232-256
Author(s):  
Amandeep Singh Bhatia ◽  
Renata Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantum Computing ◽  
Recent Progress ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Exciting Field ◽  
Quantum Machine Learning ◽  
The Subject ◽  
Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

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