Prospects of Artificial Intelligence (AI) Towards the Circular Economy

2021 ◽  
pp. 223-237
Author(s):  
K. Pallavi ◽  
Hergovind Singh
Keyword(s):  
Artificial Intelligence ◽  
Water Pollution ◽  
Air Pollution ◽  
Decision Support ◽  
Everyday Life ◽  
Soil Pollution ◽  
Circular Economy ◽  
State Of The Art ◽  
Technological Support ◽  
The World

Artificial intelligence has become a large part of everyday life. The world is heading towards new heights of adaption of various decision support technologies. In the present era, the rate at which we are consuming natural finite resources and depleting them, through producing chemicals, soil pollution, water pollution, air pollution, etc., is destroying our ecosystem. We have tried several recycling methods to minimize wastage, but it is insignificant. Now there is a need to think about state-of-the-art technological support like artificial intelligence (AI). This chapter explores the prospects of artificial intelligence in the circular economy.

scholarly journals Economical evaluation of the living environment polution

2002 ◽  
Vol 50 ◽  
Author(s):  
ADRIANA CSIKÓSOVÁ ◽  
KATARÍNA KAMENÍKOVÁ
Keyword(s):  
Water Pollution ◽  
Air Pollution ◽  
Soil Pollution ◽  
Living Environment ◽  
Economical Evaluation ◽  
The World ◽  
Ecological Behaviour

The present paper analyses a magnesite firm and its influence on the living environment due to air pollution, water pollution and soil pollution. The analysed firm is a producer of clinker and magnesite products that are exported to some big countries of the world. It is a lucrative firm with a profit achievement during the following period. But it must pay considerable fees for polluting the living environment and therefore it must accept several measures for improving ecological behaviour of the firm.

scholarly journals Debris and Eutrophication Control for Industrial Environmental Monitoring Using IoT

2018 ◽  
Vol 7 (2.24) ◽  
pp. 464
Author(s):  
S Subramanian ◽  
M Vinodhini
Keyword(s):  
Water Pollution ◽  
Air Pollution ◽  
Developing Countries ◽  
Environmental Monitoring ◽  
Ph Value ◽  
Water Parameters ◽  
Automobile Exhaust ◽  
Eutrophication Control ◽  
The World

Pollution is the major concern of all the nations of the world, especially developing countries. From that air pollution and water pollution is due to industrial and automobile exhaust. multitude sensors that can be set in microcontroller. It can be monitoring the water parameters and gas range in industries. This presentation based on not only monitoring the range of ph value and gas range that release from industry and also control the pollution in order to shutdown the power in industry.   

scholarly journals Five takeaways from Mitchell's Artificial Intelligence: A guide for to thinking humans

2020 ◽  
Author(s):  
AISDL
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Human Intelligence ◽  
Santa Fe ◽  
The World

In Artificial Intelligence: A guide for to thinking humans, Melanie Mitchell, an AI researcher in the Santa Fe Institute, provides an accessible review of the state-of-the-art AI systems around the world and highlights how these advanced systems differ from human intelligence.

https://journals.vgtu.lt/index.php/MMA/article/download/9887/8590

2001 ◽  
Vol 6 (1) ◽  
pp. 97-105
Author(s):  
A. Kolesnikov ◽  
A. Yashin
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Support Systems ◽  
Hybrid Intelligent Systems ◽  
The World ◽  
Human Intellect ◽  
Modelling Approaches

The paper studies the basic problems of Artificial Intelligence, such as integration of difference attributes of human intellect. For this purpose we have been created synergetic systems that are hybrid intelligent systems (HYIS). The paper shows the world of decision support problems and the world of modelling approaches evolution. The term ‘heterogeneous problem’ for decision support systems is discussed. Two models of interaction between the problems world and the methods world also the results of HYIS creating are discussed. The formalism of HYIS is introduced.

Understanding the Legacy of Herbert Simon to Decision Support Systems

2011 ◽  
pp. 930-938
Author(s):  
Jean-Charles Pomerol ◽  
Frédéric Adam
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Management Practice ◽  
Herbert Simon ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
The World ◽  
New Ideas ◽  
Contemporary Author

Herbert Simon is unique in our discipline in terms of the far-reaching impact which his work has had on management and the understanding of managerial decision making, especially when his further work with James March is considered. Mintzberg himself, who considerably advanced our ideas on management practice, noted that he always considered Simon to be the most influential and important contemporary author in terms of organizational theory (1990, p. 94). Jared Cohon, president of Carnegie Mellon University, where Simon was a fixture for 52 years said “few if any scientists and scholars around the world have had as great an influence as had Simon across so many fields, economics, computer science, psychology, and artificial intelligence amongst them.” Indeed, Herbert Simon’s contribution to management and DSS is such that the science and practice of management and decision making has been durably changed under his influence. This article considers the new ideas brought by Simon in management theory and looks at his contribution to our understanding of managerial decision making and DSSs.

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

scholarly journals Trash Boat for Floating Waste Removal

2020 ◽  
Vol 9 (6) ◽  
pp. 690-693
Keyword(s):  
Artificial Intelligence ◽  
Water Pollution ◽  
Major Part ◽  
Indian Population ◽  
Clean Water ◽  
Indian Water ◽  
Sanitation And Hygiene ◽  
The World ◽  
The Status ◽  
Living Organisms

Pollution plays a major part in global warming and water pollution stands alarming for all living organisms in the world. Water pollution is killing tens of millions of Indians and nearly 19.3% of Indian population does no longer have access to clean water. The toxicity of Indian water our bodies are increasing except India’s boom and urbanization. It is located that out of general water available, round 70% of surface water in India is not worthy for consumption. India has a mortality fee of 400000 lives according to year because of lack of sanitation and hygiene. Researchers have found that the closing purpose of water pollution is the plastics. In order to overcome these issues, a smart trash boat is designed in this paper which can accumulate all the floating and semi floating wastes specifically plastics from the urban drain from any water bodies. This system is enabled with Artificial Intelligence and image processing which is capable of classifying, managing, accumulating and indicating the status of trashes along with its statistics.

scholarly journals To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

2021 ◽  
Vol 7 ◽  
pp. e479
Author(s):  
Elvio Amparore ◽  
Alan Perotti ◽  
Paolo Bajardi
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
State Of The Art ◽  
Black Box ◽  
Evaluation Procedures ◽  
Local Linear ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
The Many ◽  
Black Box Models

The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective explanations for black-box classifiers. The existing literature lists many desirable properties for explanations to be useful, but there is a scarce consensus on how to quantitatively evaluate explanations in practice. Moreover, explanations are typically used only to inspect black-box models, and the proactive use of explanations as a decision support is generally overlooked. Among the many approaches to XAI, a widely adopted paradigm is Local Linear Explanations—with LIME and SHAP emerging as state-of-the-art methods. We show that these methods are plagued by many defects including unstable explanations, divergence of actual implementations from the promised theoretical properties, and explanations for the wrong label. This highlights the need to have standard and unbiased evaluation procedures for Local Linear Explanations in the XAI field. In this paper we address the problem of identifying a clear and unambiguous set of metrics for the evaluation of Local Linear Explanations. This set includes both existing and novel metrics defined specifically for this class of explanations. All metrics have been included in an open Python framework, named LEAF. The purpose of LEAF is to provide a reference for end users to evaluate explanations in a standardised and unbiased way, and to guide researchers towards developing improved explainable techniques.

scholarly journals Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

2020 ◽  
Author(s):  
Quoc-Viet Pham ◽  
Dinh C. Nguyen ◽  
Thien Huynh-The ◽  
Won-Joo Hwang ◽  
Pubudu N. Pathirana
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
State Of The Art ◽  
The State ◽  
Daily Lives ◽  
Recent Advances ◽  
The Arts ◽  
The World ◽  
Novel Coronavirus

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 215 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 14 April 2020, a cumulative total of 1,853,265 (118,854) infected (dead) COVID-19 cases were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify their applications in fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

scholarly journals Land Pollution in Kenya

2020 ◽  
Vol 3 (2) ◽  
pp. 1-11
Author(s):  
Joseph Mwangi Munyua
Keyword(s):  
Water Pollution ◽  
Air Pollution ◽  
Soil Erosion ◽  
Public Participation ◽  
Relevant Literature ◽  
Christian Doctrine ◽  
Agriculture Industry ◽  
The World ◽  
The Subject ◽  
Doctrine Of Creation

Article 69 (d) of Kenya’s Constitution (2010) encourages public participation in the management, protection, and conservation of the environment. In the context of eco-theology, this article seeks to explore the efficacy of the Christian doctrine of creation in to curbing the evidential land pollution in Kenya. Basically, air pollution, water pollution, and land pollution are the three major kinds of environmental pollution in the world. The term land pollution means the degradation (destruction) of the earth’s surface and soil via human activities. Land pollution is a major problem in Kenya that is caused by various factors such as deforestation and soil erosion, agriculture, industry, mining, landfills, illegal dumping of waste, and construction activities. Some of its devastating effects in Kenya include: water pollution, soil pollution, air pollution, human health problems, decline of tourism, and so forth. Thus, land pollution poses a serious threat to all Kenyans, a phenomenon that serves to justify the necessity of this article. As a doctrinal response, this article endeavours to unveil the Christian doctrine of creation and how it can be utilised to curb the ongoing land pollution in Kenya. In its methodology, this article reviews the appropriate and relevant literature on pollution and eco-theological approach, the exegetical method, the legal-constitutional basis of addressing the subject, and the use of archival resources.

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