scholarly journals Articles with impact: insights into 10 years of research with machine learning

2020 ◽  
Vol 129 (4) ◽  
pp. 967-979
Author(s):  
Stephan van der Zwaard ◽  
Arie-Willem de Leeuw ◽  
L. (Rens) A. Meerhoff ◽  
Sue C. Bodine ◽  
Arno Knobbe
Keyword(s):  
Machine Learning ◽  
High Impact ◽  
Research Articles ◽  
Comprehensive Collection ◽  
Number Of Citations ◽  
Article Impact

Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.

scholarly journals Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

scholarly journals Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches

2020 ◽  
Vol 12 (4) ◽  
pp. 739
Author(s):  
Keiller Nogueira ◽  
Gabriel L. S. Machado ◽  
Pedro H. T. Gama ◽  
Caio C. V. da Silva ◽  
Remis Balaniuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Impact ◽  
Automatic Machine ◽  
Feature Representation ◽  
Data Driven ◽  
Maintenance Costs ◽  
Crucial Step ◽  
Machine Learning Methods ◽  
High Resolution Images

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2000 high-resolution images.

A Bird’s Eye View of 100 years of research on Hypertension: Machine Learning Classifications of Topics (Preprint)

2021 ◽  
Author(s):  
Mustapha Abba ◽  
Chidozie Nduka ◽  
Seun Anjorin ◽  
Shukri Mohamed ◽  
Emmanuel Agogo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Latent Dirichlet Allocation ◽  
Learning Algorithm ◽  
Temporal Trends ◽  
Relevant Information ◽  
Research Articles ◽  
Major Cardiovascular Events ◽  
And Shifts ◽  
Negative Sentiment ◽  
Over Time

BACKGROUND Due to scientific and technical advancements in the field, published hypertension research has developed during the last decade. Given the huge amount of scientific material published in this field, identifying the relevant information is difficult. We employed topic modelling, which is a strong approach for extracting useful information from enormous amounts of unstructured text. OBJECTIVE To utilize a machine learning algorithm to uncover hidden topics and subtopics from 100 years of peer-reviewed hypertension publications and identify temporal trends. METHODS The titles and abstracts of hypertension papers indexed in PubMed were examined. We used the Latent Dirichlet Allocation (LDA) model to select 20 primary subjects and then ran a trend analysis to see how popular they were over time. RESULTS We gathered 581,750 hypertension-related research articles from 1900 to 2018 and divided them into 20 categories. Preclinical, risk factors, complications, and therapy studies were the categories used to categorise the publications. We discovered themes that were becoming increasingly ‘hot,' becoming less ‘cold,' and being published seldom. Risk variables and major cardiovascular events subjects displayed very dynamic patterns over time (how? – briefly detail here). The majority of the articles (71.2%) had a negative valency, followed by positive (20.6%) and neutral valencies (8.2 percent). Between 1980 and 2000, negative sentiment articles fell somewhat, while positive and neutral sentiment articles climbed significantly. CONCLUSIONS This unique machine learning methodology provided fascinating insights on current hypertension research trends. This method allows researchers to discover study subjects and shifts in study focus, and in the end, it captures the broader picture of the primary concepts in current hypertension research articles. CLINICALTRIAL Not applicable

Citations, Open Access and University Rankings

2017 ◽  
pp. 129-139
Author(s):  
Clive Baldock
Keyword(s):  
Open Access ◽  
Research Performance ◽  
Citation Impact ◽  
Publishing Research ◽  
Research Articles ◽  
University Rankings ◽  
Highly Cited Papers ◽  
Ranking Systems ◽  
Number Of Citations ◽  
Highly Cited

The citation impact of research articles contributes to the assessment of the research performance of universities in some international university ranking systems either as the number of citations per paper, number of citations per faculty, total number of citations, number of highly cited papers or percentage of highly cited papers. Publishing research articles in Open Access (OA) journals has the potential for increasing the citation impact of research articles and in so doing improve an institutions position in university rankings. This chapter reviews the evidence for an increase in citations through publishing in Open Access publications.

scholarly journals A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 551-604
Author(s):  
Damien Warren Fernando ◽  
Nikos Komninos ◽  
Thomas Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Learning Techniques ◽  
New Directions ◽  
Encryption Schemes ◽  
Malware Evolution ◽  
Number Of Citations ◽  
Research Studies

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

scholarly journals Coupling physics in machine learning to predict properties of high-temperatures alloys

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jian Peng ◽  
Yukinori Yamamoto ◽  
Jeffrey A. Hawk ◽  
Edgar Lara-Curzio ◽  
Dongwon Shin
Keyword(s):  
Machine Learning ◽  
High Temperature ◽  
Yield Strength ◽  
Strengthening Mechanism ◽  
Current Approach ◽  
High Impact ◽  
Data Driven ◽  
High Temperature Alloy ◽  
High Temperature Alloys ◽  
Verification Process

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

scholarly journals Are the most highly cited articles the ones that are the most downloaded? A bibliometric study of IRRODL

2015 ◽  
Vol 16 (3) ◽  
Author(s):  
Raidell Avello Martínez ◽  
Terry Anderson
Keyword(s):  
Positive Relationship ◽  
Research Articles ◽  
Bibliometric Indicators ◽  
Academic Journal ◽  
Effective Distance ◽  
Open And Distance Learning ◽  
Number Of Citations ◽  
The Impact ◽  
Highly Cited Articles ◽  
Highly Cited

<p class="Body">Publication of research, innovation, challenges and successes is of critical importance to the evolution of more effective distance education programming. Publication in peer reviewed journal format is the most prestigious and the most widespread form of dissemination in education and most other disciplines, thus the importance of understanding what is published and its impact on both researchers and practitioners. In this article we identify and classify the leading articles in arguably the leading peer reviewed journals in this discipline.</p><p class="Body">The journal <em>The International Review of Research in Open and Distance Learning</em> (IRRODL) is a peer reviewed academic journal that has been published since 2000. The journal has published between 3 and 6 issues annually with between 50 and 111 research articles per volume. In order to assess the general and the particular impact of highly cited articles this work describes the main bibliometric indicators of the IRRODL journal and these are compared with the total galley views in all formats, PDF, HTML, EPUB and MP3, that IRRODL publishes. In addition to identifying characteristics of the most widely cited articles this research determines if there is a correlation between the articles most highly cited by other publishing researchers and the number of views, indicating interest from both practitioners and research communities. The results show a significant and positive relationship between the total number of citations and the number of views received by articles published in the journal, indicating the impact of the journal extends beyond active publishers to practitioner consumers.</p>

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