Survey of artificial intelligence approaches in the study of anthropogenic impacts on symbiotic organisms – a holistic view

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

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Changes in Ciliate Communities Reveal Modification of Lake Functioning Over the Last Century

10.21203/rs.3.rs-1126972/v1 ◽

2021 ◽

Author(s):

Barouillet Cécilia ◽

Valentin Vasselon ◽

François Keck ◽

Laurent Millet ◽

David Etienne ◽

...

Keyword(s):

Environmental Changes ◽

Anthropogenic Impacts ◽

Elevation Gradient ◽

Holistic View ◽

Β Diversity ◽

Functional Community ◽

Littoral Zones ◽

Benthic Food ◽

Lowland Lakes

Abstract Ciliates are unicellular heterotrophic organisms that play a key role in the planktonic and benthic food webs of lakes, and represent a great potential as bioindicator. In this study, we used the top-bottom paleolimnological approach to compare the recent and past (i.e. prior to major anthropogenic impacts) ciliate communities of 48 lakes located along an elevation gradient using metabarcoding techniques applied on sedimentary DNA (sed-DNA). Our results show an overall decline in the β-diversity in recent time, especially in lowland lakes which are more strongly expose to local human pressure. Analyses of the functional groups indicate important restructuration of the trophic food web and changes that are consistent with several well documented environmental changes such as the widespread increase in deep water anoxia, changes in thermal stability and nutrient cycling. Our study demonstrates the potential offered by sed-DNA to uncover information about past ciliate communities on a wide variety of lakes and the potential of using ciliates as valuable indicators, integrating information from the pelagic to the benthic profundal (and littoral) zones. Through trait-based functional community approach, the ciliates provide additional valuable information on ecosystem functioning, thus offering more a holistic view on the long-term changes of aquatic ecosystems.

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Electricity Market Empowered by Artificial Intelligence: A Platform Approach

Energies ◽

10.3390/en12214128 ◽

2019 ◽

Vol 12 (21) ◽

pp. 4128 ◽

Cited By ~ 2

Author(s):

Yueqiang Xu ◽

Petri Ahokangas ◽

Jean-Nicolas Louis ◽

Eva Pongrácz

Keyword(s):

Artificial Intelligence ◽

Electricity Market ◽

Energy System ◽

Holistic View ◽

Aggregated Data ◽

Horizon 2020 ◽

Distributed Energy System ◽

Innovation Project ◽

Energy Domain ◽

High Level

Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform.

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A new approach for association rules mining using computational and artificial intelligence

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200707 ◽

2020 ◽

Vol 39 (5) ◽

pp. 7233-7246

Author(s):

Fahed Yoseph ◽

Markku Heikkilä

Keyword(s):

Artificial Intelligence ◽

Association Rules ◽

Market Basket Analysis ◽

Holistic View ◽

Association Rules Mining ◽

Point Of Sale ◽

Market Basket ◽

Market Intelligence ◽

Product Assortment ◽

Analysis Process

Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment.

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Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic—A South American Perspective

Frontiers in Public Health ◽

10.3389/fpubh.2020.600213 ◽

2020 ◽

Vol 8 ◽

Author(s):

Parag Chatterjee ◽

Andreína Tesis ◽

Leandro J. Cymberknop ◽

Ricardo L. Armentano

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Mobile Apps ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Healthcare Management ◽

Healthcare Personnel ◽

South American ◽

Risk Modeling ◽

Holistic View

The shudders of the COVID-19 pandemic have projected newer challenges in the healthcare domain across the world. In South American scenario, severe issues and difficulties have been noticed in areas like patient consultations, remote monitoring, medical resources, healthcare personnel etc. This work is aimed at providing a holistic view to the digital healthcare during the times of COVID-19 pandemic in South America. It includes different initiatives like mobile apps, web-platforms and intelligent analyses toward early detection and overall healthcare management. In addition to discussing briefly the key issues toward extensive implementation of eHealth paradigms, this work also sheds light on some key aspects of Artificial Intelligence and the Internet of Things along their potential applications like clinical decision support systems and predictive risk modeling, especially in the direction of combating the emergent challenges due to the COVID-19 pandemic.

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Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Journal of Modelling in Management ◽

10.1108/jm2-12-2020-0322 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hassan Younis ◽

Balan Sundarakani ◽

Malek Alsharairi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Supply Chain ◽

Future Research ◽

Unique Contribution ◽

Research Directions ◽

Holistic View ◽

Content Type ◽

Future Research Directions ◽

Research Studies

Purpose The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof. Design/methodology/approach Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM). Findings AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness. Research limitations/implications This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue. Practical implications This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore. Originality/value Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

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A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

Machines ◽

10.3390/machines9120351 ◽

2021 ◽

Vol 9 (12) ◽

pp. 351

Author(s):

Lorenzo Colantonio ◽

Lucas Equeter ◽

Pierre Dehombreux ◽

François Ducobu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Literature Review ◽

Systematic Literature Review ◽

Research Question ◽

Support Vector ◽

Self Organizing Map ◽

Holistic View ◽

Benchmark Database ◽

Depth Analysis

In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

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