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

Classification of Chaotic Behaviors in Jerky Dynamical Systems

2021 ◽  
Vol 30 (1) ◽  
pp. 93-110
Author(s):  
Tianyi Wang ◽  
Keyword(s):  
Machine Learning ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
Learning Algorithm ◽  
Model Systems ◽  
Supervised Machine Learning ◽  
Learning Program ◽  
New Methods ◽  
Over Time

Differential equations are widely used to model systems that change over time, some of which exhibit chaotic behaviors. This paper proposes two new methods to classify these behaviors that are utilized by a supervised machine learning algorithm. Dissipative chaotic systems, in contrast to conservative chaotic systems, seem to follow a certain visual pattern. Also, the machine learning program written in the Wolfram Language is utilized to classify chaotic behavior with an accuracy around 99.1±1.1%.

An Efficient Perpetual Learning Algorithm

2019 ◽  
Vol 28 (07) ◽  
pp. 1950022 ◽  
Author(s):  
Haiou Qin ◽  
Du Zhang ◽  
Xibin Sun ◽  
Jiahua Tang ◽  
Jun Peng
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Efficient Algorithm ◽  
Learning Algorithm ◽  
Small Data ◽  
Computing Systems ◽  
Agent Systems ◽  
Multiple Tasks ◽  
Improved Performance ◽  
Over Time

One of the emerging research opportunities in machine learning is to develop computing systems that learn many tasks continuously and improve the performance of learned tasks incrementally over time. In real world, learners have to adapt to labeled and unlabeled samples from various tasks which arrive randomly. In this paper, we propose an efficient algorithm called Efficient Perpetual Learning Algorithm (EPLA) which is suitable for learning multiple tasks in both offline and online settings. The algorithm, which is an extension of ELLA,4 is part of what we call perpetual learning that can learn new tasks or refine knowledge of learned tasks for improved performance with newly arrived labeled samples in an incremental fashion. Several salient features exist for EPLA. The learning episodes are triggered via either extrinsic or intrinsic stimuli. Agent systems based on the proposed algorithm can be engaged in an open-ended and alternating sequence of learning episodes and working episodes. Unlabeled samples can be used to self-train the learner in small data setting. Compared with ELLA, EPLA shows almost equivalent performance without memorizing any labeled samples learned previously.

scholarly journals UniAssist- Implementation of Machine Learning Based Higher Education University Recommendation System

2020 ◽  
pp. 262-266
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Work Experience ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Relevant Information ◽  
Mobile App ◽  
Admission Process ◽  
Software Application ◽  
Tuition Fee

UniAssist project is implemented to help students who have completed their Bachelorette degree and are looking forward to study abroad to pursue their higher education such as Masters. Machine Learning would help identify appropriate Universities for such students and suggest them accordingly. UniAssist would help such individuals by recommending those Universities according to their preference of course, country and considering their grades, work experience and qualifications. There is a need for students hoping to pursue higher education outside India to get to know about proper universities. Data collected is then converted into relevant information that is currently not easily available such as courses offered by their dream universities, the avg. tuition fee and even the avg. expense of living near the chosen university on single mobile app based software platform. This is the first phase of the admission process for every student. The machine-learning algorithm used is Collaborative filtering memory-based approach using KNN calculated using cosine similarity. A mobile-based software application is implemented in order to help and guide students for their higher education.

The Humanitarian Turn at the UNSC: Explaining the development of international norms through machine learning algorithms

2020 ◽  
pp. 002234332092972
Author(s):  
Richard Hanania
Keyword(s):  
Machine Learning ◽  
Cold War ◽  
Latent Dirichlet Allocation ◽  
Learning Algorithm ◽  
Focal Point ◽  
International Norms ◽  
Machine Learning Algorithms ◽  
Clear Understanding ◽  
Norm Diffusion ◽  
The Cold War

The UN Security Council (UNSC) has transformed from a body almost exclusively focused on conflict to one that addresses a wide variety of issues. Despite a series of powerful works in recent years showing how international norms have developed over time, we still lack clear understanding of why and when international institutions change their missions. This article argues that while international politics is usually characterized by inertia, shocks to the system, or focal point events, can compel rational actors to adopt new logics of appropriateness. Since 1945, the end of the Cold War and the signing of the Helsinki Accords stand out as such events. Through latent Dirichlet allocation, a machine learning algorithm used to classify text, UNSC resolutions between 1946 and 2017 can be divided into the subjects of War, Punitive, and Humanitarian. The topic Humanitarian exploded in frequency after the Cold War, and more refined models show that words related to human rights and elections similarly increased after Helsinki. These changes are rapid and occur in almost the immediate aftermath of focal point events, showing their importance for norm diffusion. The analysis also reveals another shift towards humanitarian topics in the mid-2000s, demonstrating the ability of topic modeling to uncover changes that have been missed by earlier kinds of analysis.

Inconsistency-Induced Learning for Perpetual Learners

2013 ◽  
pp. 70-87 ◽  
Author(s):  
Du Zhang ◽  
Meiliu Lu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Target Function ◽  
Data Sets ◽  
Revision Process ◽  
Learning Agents ◽  
Learning Agent ◽  
The One ◽  
Over Time

One of the long-term research goals in machine learning is how to build never-ending learners. The state-of-the-practice in the field of machine learning thus far is still dominated by the one-time learner paradigm: some learning algorithm is utilized on data sets to produce certain model or target function, and then the learner is put away and the model or function is put to work. Such a learn-once-apply-next (or LOAN) approach may not be adequate in dealing with many real world problems and is in sharp contrast with the human’s lifelong learning process. On the other hand, learning can often be brought on through overcoming some inconsistent circumstances. This paper proposes a framework for perpetual learning agents that are capable of continuously refining or augmenting their knowledge through overcoming inconsistencies encountered during their problem-solving episodes. The never-ending nature of a perpetual learning agent is embodied in the framework as the agent’s continuous inconsistency-induced belief revision process. The framework hinges on the agents recognizing inconsistency in data, information, knowledge, or meta-knowledge, identifying the cause of inconsistency, revising or augmenting beliefs to explain, resolve, or accommodate inconsistency. The authors believe that inconsistency can serve as one of the important learning stimuli toward building perpetual learning agents that incrementally improve their performance over time.

Predicting Variant-Driven Multi-Wave Pattern of COVID-19 via a Machine Learning Analysis of Spike Protein Mutations

2021 ◽  
Author(s):  
Adele de Hoffer ◽  
Shahram Vatani ◽  
Corentin Cot ◽  
Giacomo Cacciapaglia ◽  
Maria Luisa Chiusano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Sequence Data ◽  
Epidemiological Data ◽  
Wave Pattern ◽  
Host Cells ◽  
Spike Protein ◽  
Levenshtein Distance ◽  
Current Case ◽  
Over Time

Abstract Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 (‘Delta plus’) is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group (MeRG) framework.

Talent Acquisition Process Optimization Using Machine Learning in Resumes’ Ranking and Matching to Job Descriptions

2021 ◽  
Author(s):  
Mohammed Alghazal
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Optical Character Recognition ◽  
Computational Models ◽  
Latent Dirichlet Allocation ◽  
Learning Algorithm ◽  
Screening Tools ◽  
Job Descriptions ◽  
Job Description ◽  
Description File

Abstract Employers commonly use time-consuming screening tools or online matching engines that are driven by manual roles and predefined keywords, to search for potential job applicants. Such traditional techniques have not kept pace with the new digital revolution in machine learning and big data analytics. This paper presents advanced artificial intelligent solutions employed for ranking resumes and CV-to-Job Description matching. Open source resumes and job descriptions' documents were used to construct and validate the machine learning models in this paper. Documents were converted to images and processed via Google cloud using Optical Character Recognition algorithm (OCR) to extract text information from all resumes and job descriptions' documents, with more than 97% accuracy. Prior to modeling, the extracted text were processed via a series of Natural Language Processing (NLP) techniques by splitting/tokenizing common words, grouping together inflected form of words, i.e. lemmatization, and removal of stop words and punctuation marks. After text processing, resumes were trained using the unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), for topic modeling and categorization. Given the type of resumes used, the algorithm was able to categorize them into 4 main job sectors: marketing and business, engineering, computer science/IT and health. Scores were assigned to each resume to represent the maximum LDA probability for ranking. Another more advanced deep learning algorithm, called Doc2Vec, was also used to train and match potential resumes to relevant job descriptions. In this model, resumes are represented by unique vectors that can be used to group similar documents, match and retrieve resumes related to a given job description document provided by HR. The similarity is measured between each resume and the given job description file to query the top job candidates. The model was tested against several job description files related to engineering, IT and human resources, and was able to identify the top-ranking resumes from over hundreds of trained resumes. This paper presents an innovative method for processing, categorizing and ranking resumes using advanced computational models empowered by the latest fourth industrial resolution technologies. This solution is beneficial to both job seekers and employers, providing efficient and unbiased data-driven method for finding top applicants for a given job.

scholarly journals Improving Accuracy of The Sentence-Level Lexicon-Based Sentiment Analysis Using Machine Learning

2021 ◽  
pp. 57-69
Author(s):  
Titya Eng ◽  
Md Rashed Ibn Nawab ◽  
Kazi Md Shahiduzzaman
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Research Focus ◽  
Sentence Level ◽  
People’S Attitudes ◽  
Improving Accuracy ◽  
Negative Sentiment

Sentiment Analysis studies people's attitudes, opinions, evaluations, emotions, sentiments toward some entities such as products, topics, individuals, services, issues and classify them whether the opinion or evaluations inclines to that entities or not. It is getting more research focus in recent years due to its benefits for scientific and commercial purposes. This research aims at developing a better approach for sentiment analysis at the sentence level by using a combination of lexicon resources and a machine learning method. Moreover, as reviews data on the internet is unstructured and has much noise, this research uses different preprocessing techniques to clean the data before processing in different algorithms discussed in subsequent sections. Additionally, the lexicon building processes, how the lexicon is handled and combined with the machine learning algorithm for predicting sentiment is also discussed. In sentiment analysis, sentence's sentiment can be classified into three classes: positive sentiment, negative sentiment, or neutral. However, in this research work, we have excluded neutral sentiment for avoiding ambiguity and unnecessary complexity. The experiment results show that the proposed algorithm outperforms compared to the baseline machine learning algorithms. We have used four distinct datasets and different performance measures to check and validate the proposed method's robustness.

scholarly journals Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic (Preprint)

2018 ◽  
Author(s):  
Ravali Mamidi ◽  
Michele Miller ◽  
Tanvi Banerjee ◽  
William Romine ◽  
Amit Sheth
Keyword(s):  
Machine Learning ◽  
Topic Modeling ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
The Public ◽  
Public Sentiment ◽  
Domain Expertise ◽  
Treatment Research ◽  
Negative Sentiment

BACKGROUND To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues. OBJECTIVE The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. METHODS Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. RESULTS A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. CONCLUSIONS Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research.

scholarly journals Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

TEM Journal ◽  
2021 ◽  
pp. 1919-1927
Author(s):  
Lidia Sandra ◽  
Ford Lumbangaol ◽  
Tokuro Matsuo
Keyword(s):  
Machine Learning ◽  
Student Learning ◽  
Learning Algorithm ◽  
Research Articles ◽  
Machine Learning Algorithm ◽  
Inclusion Criteria ◽  
Classified Students ◽  
Student’S Performance ◽  
Using Data ◽  
Students Success

One of the ultimate goals of the learning process is the success of student learning. Using data and students' achievement with machine learning to predict the success of student learning will be a crucial contribution to everyone involved in determining appropriate strategies to help students perform. The selected 11 research articles were chosen using the inclusion criteria from 2753 articles from the IEEE Access and Science Direct database that was dated within 2019-2021 and 285 articles that were research articles. This study found that the classification machine learning algorithm was most often used in predicting the success of students' learning. Four algorithms that were used most often to predict the success of students' learning are ANN, Naïve Bayes, Logistic Regression, SVM and Decision Tree. Meanwhile, the data used in these research articles predominantly classified students' success in learning into two or three categories which are pass/fail; or fail/pass/excellent.

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