scholarly journals A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

2021 ◽  
Vol 11 (13) ◽  
pp. 5826
Author(s):  
Evangelos Axiotis ◽  
Andreas Kontogiannis ◽  
Eleftherios Kalpoutzakis ◽  
George Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Coastal Zone ◽  
Extraction Process ◽  
Asia Minor ◽  
Efficiency And Effectiveness ◽  
Effectiveness And Efficiency ◽  
Intelligent Tools ◽  
Southern Balkans

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, and the different quality of language use across sources present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research aimed at the southern Balkans and the coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “expert–apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a machine learning (ML) algorithm, utilizing a combination of active learning (AL) and reinforcement learning (RL), and the expert is the human researcher. ML-powered research improved the effectiveness and efficiency of the domain expert by 3.1 and 5.14 times, respectively, fetching a total number of 420 relevant ethnopharmacological documents in only 7 h versus an estimated 36 h of human-expert effort. Therefore, utilizing artificial intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

scholarly journals A Personalized Machine-Learning-enabled Method for Efficient Research in Ethnopharmacology. The case of Southern Balkans and Coastal zone of Asia Minor

2021 ◽  
Author(s):  
Evangelos Axiotis ◽  
Andreas Kontogiannis ◽  
Eleftherios Kalpoutzakis ◽  
George Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Coastal Zone ◽  
Language Use ◽  
Extraction Process ◽  
Asia Minor ◽  
Efficiency And Effectiveness ◽  
Intelligent Tools ◽  
Southern Balkans

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

scholarly journals A Personalized Machine-Learning-enabled Method for Efficient Research in Ethnopharmacology. The case of Southern Balkans and Coastal zone of Asia Minor

2021 ◽  
Author(s):  
Evangelos Axiotis ◽  
Andreas Kontogiannis ◽  
Eleftherios Kalpoutzakis ◽  
George Giannakopoulos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Coastal Zone ◽  
Language Use ◽  
Asia Minor ◽  
Domain Expert ◽  
Efficiency And Effectiveness ◽  
Intelligent Tools ◽  
Southern Balkans

An ethnopharmacology expert faces several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how the expert can be supported effectively through intelligent tools for the ethnopharmacological research in the Southern Balkans and Coastal zone of Asia Minor. Our work follows an “Expert-Apprentice” paradigm in a crawling process, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

scholarly journals PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

2019 ◽  
Vol 9 (15) ◽  
pp. 3037 ◽  
Author(s):  
Isaac Machorro-Cano ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Uriel Ramos-Deonati ◽  
José Luis Sánchez-Cervantes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Overweight And Obesity ◽  
Process Data ◽  
Associated Conditions ◽  
Smart Health ◽  
Medical Recommendations ◽  
And Control

Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

scholarly journals Improving the Quality of City Cleanliness through Investment Paradiplomacy in Bantaeng, Indonesia

2021 ◽  
Vol 277 ◽  
pp. 02007
Author(s):  
Ahmad Harakan ◽  
Nuryanti Mustari ◽  
Abel Alfred Kinyondo
Keyword(s):  
Local Governments ◽  
Best Practice ◽  
Case Study Method ◽  
Depth Data ◽  
The Reformation ◽  
Management Policies ◽  
Effectiveness And Efficiency ◽  
Learning And Collaboration

The phenomenon of governance after the Reformation was the provision of more excellent opportunities for local governments to explore the capabilities and resources of their respective regions. Autonomy reflects the centralization policy that was previously implemented and is considered to have not been maximal in producing quality governance improvements, especially in cleanliness, which is the main problem in governance. This opportunity can be implemented by conducting learning and collaboration with other parties, both private and local governments abroad, who have a best practice profile of cleanliness governance through paradiplomacy practices. This research wants to obtain in-depth data. Data collection was carried out in three ways, namely interviews, observation, and documentation studies, through a qualitative approach with the case study method. The learning Process and cooperation in the Bantaeng Regency government impact changing people’s views and making hygiene management policies in Bantaeng. Besides, there is technology transfer to support the effectiveness and efficiency of waste management and city cleanliness in Bantaeng Regency.

scholarly journals Iterative Guided Machine Learning-Assisted Systematic Literature Reviews: A Diabetes Case Study

2021 ◽  
Author(s):  
John Zimmerman ◽  
Robin Soler ◽  
Lavinder James ◽  
Murphy Sarah ◽  
Atkins Charisma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Literature ◽  
Relevant Information ◽  
Text Analytics ◽  
Inclusion Criteria ◽  
Literature Reviews ◽  
Study Test ◽  
Model Training

Abstract Background: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and Machine Learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.Methods: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance. Results: the case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes. Conclusions: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.

scholarly journals A QUALIDADE DE VIDA NO TRABALHO EM UMA PERSPECTIVA CONTRA-HEGEMÔNICA

2018 ◽  
Vol 7 (1) ◽  
pp. 70-79
Author(s):  
Mateus de De Freitas Barreiro
Keyword(s):  
Quality Of Life ◽  
Information Technology ◽  
Well Being ◽  
The State ◽  
Organizational Efficiency ◽  
Competitive Markets ◽  
Preventive Approach ◽  
Efficiency And Effectiveness

Este artigo tem o objetivo de apresentar como a Qualidade de Vida no Trabalho (QVT) pode ser inserida em mercados competitivos, como o das organizações de Tecnologia da informação (TI) que comumente tem dificuldades para reter talentos. Quando a QVT é trabalhada sob uma óptica preventiva, que se contrapõem as visões assistencialistas e hegemônicas, a QVT poderá ser uma ferramenta que interfere diretamente na motivação dos colaboradores, levando a uma maior eficiência e eficácia organizacional, sendo um dos diferenciais para o êxito nos negócios e no bem-estar dos colaboradores. Esta pesquisa visa focar especificamente a QVT à luz do método de Walton, a partir de um estudo de caso sobre uma microempresa de TI no interior do Estado de São Paulo.Palavras-Chave: Qualidade de Vida no Trabalho. Método de Walton. Tecnologia da informação. Abstract: This article aims to present itself as the Quality of Life at Work (QVT) can be inserted in competitive markets, such as the Information Technology (IT) organizations that commonly have difficulty retaining talent. When QVT is crafted under a preventive approach, which counteracts the paternalistic and hegemonic visions, QVT can be a tool that directly affects the motivation of employees, leading to greater organizational efficiency and effectiveness, one of the advantages for success in business and well-being of employees. This research aims to focus specifically QVT the light of Walton method, from a case study of an IT microenterprise in the state of São Paulo.Keywords: Quality of Life at Work. Walton method. Information Technology.

scholarly journals Iterative Guided Machine Learning-Assisted Systematic Literature Reviews: A Diabetes Case Study

2020 ◽  
Author(s):  
John Zimmerman ◽  
Robin Soler ◽  
Lavender James ◽  
Murphy Sarah ◽  
Atkins Charisma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Literature ◽  
Relevant Information ◽  
Text Analytics ◽  
Inclusion Criteria ◽  
Literature Reviews ◽  
Study Test ◽  
Model Training

Abstract Background: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and Machine Learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.Methods: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance.Results: The case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes.Conclusions: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.

Learning Algorithms of Sentiment Analysis

2022 ◽  
pp. 176-194
Author(s):  
Suania Acampa ◽  
Ciro Clemente De Falco ◽  
Domenico Trezza
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Scientific Community ◽  
Learning Algorithms ◽  
Learning Approaches ◽  
Italian Language ◽  
Analysis Techniques ◽  
Stratified Sample

The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.

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