Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

Abstract’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.

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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

10.36227/techrxiv.12743933.v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Thanh Thi Nguyen

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Medical Image ◽

Current Status ◽

Future Research ◽

Research Directions ◽

Daily Lives ◽

Related Data ◽

Future Research Directions ◽

The Internet Of Things

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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SIGNIFICANT ROLE OF AI & ML TECHNIQUES USED IN IMPROVEMENT OF CYBER SECURITY

BSSS Journal of Computer ◽

10.51767/jc1209 ◽

2021 ◽

Vol 12 (1) ◽

pp. 80-89

Author(s):

Muskan Kumari ◽

Keyword(s):

Artificial Intelligence ◽

Cyber Security ◽

Cyber Attacks ◽

Current Status ◽

Future Research ◽

Engineering Managers ◽

Network Protection ◽

Machine Leaning ◽

Expert Community

Cyber Security has become an arising challenge for business information system in current era. AI (Artificial Intelligence) is broadly utilized in various field, however it is still generally new in cyber security. Nonetheless, the applications in network protection are significant for everybody`s day by day life. In this paper, we present the current status of AI in cyber security field, and afterward portray a few contextual investigations and uses of AI to help the community including engineering managers, teachers, educators, business people, and understudies to more readily comprehend this field, for example, the difficulties and uncertain issues of AI in online protection. According to the new challenges, the expert community has two main approaches: to adopt the philosophy and methods of Military Intelligence, and to use Artificial Intelligence methods for counteraction of Cyber Attacks. Cyber security is a vital danger for any business as the quantity of attacks is expanding. Developing of attacks on cyber security is undermining our reality. AI (Artificial Intelligence) and ML (Machine Leaning) can help identify dangers and give proposals to cyber Analyst. Advancement of appropriation of AI/ML applied to cyber security requires banding together of industry, the scholarly community, and government on a worldwide scale. We also discuss future research opportunities associated with the development of AI techniques in the cyber security ?eld across a scope of utilization areas.

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A Systematic Review of Artificial Intelligence and Robots in Value Co-creation: Current Status and Future Research Avenues

Journal of Creating Value ◽

10.1177/2394964318805625 ◽

2018 ◽

Vol 4 (2) ◽

pp. 211-228 ◽

Cited By ~ 15

Author(s):

Valtteri Kaartemo ◽

Anu Helkkula

Keyword(s):

Artificial Intelligence ◽

Science And Technology Studies ◽

Service Providers ◽

Actor Network Theory ◽

Well Being ◽

Customer Engagement ◽

Current Status ◽

Service Research ◽

Future Research ◽

Resource Integration

As artificial intelligence (AI) and robots are increasingly taking place in practical service solutions, it is necessary to understand technology in value co-creation. We conducted a systematic literature review on the topic to advance theoretical analysis of AI and robots in value co-creation. By systematically reviewing 61 AI and robotics articles, which have been published in top marketing and service research journals, we identified four themes in literature, namely, generic field advancement, supporting service providers, enabling resource integration between service providers and beneficiaries, and supporting beneficiaries’ well-being. With the identification of the first set of literature on AI and robots in value co-creation, we push forward an important sub-field of value co-creation literature. In addition, to advance the field, we suggest building on actor–network theory and science and technology studies to understand the agency of technology in value co-creation. Considering that technology has agency, it opens new interesting research avenues around shopping bots and human-to-non-human frontline interaction that are likely to influence resource integration, customer engagement and value co-creation in the future. We also encourage our colleagues to conduct postphenomenological research to be better geared for analysing how technology (including AI and robots) mediates the individual experience of value.

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Graphene Oxide Membrane: Recent Advancement in Waste Water Treatment and its Applications

Current Nanomaterials ◽

10.2174/2405461505999200527140938 ◽

2020 ◽

Vol 5 (2) ◽

pp. 111-157

Author(s):

Nusrat Sahiba ◽

Pankaj Teli ◽

Prakash Prajapat ◽

Shikha Agarwal

Keyword(s):

Graphene Oxide ◽

Water Treatment ◽

Waste Water Treatment ◽

Current Status ◽

Future Research ◽

Wastewater Management ◽

Challenges And Opportunities ◽

Future Challenges ◽

Potential Applications ◽

Oxide Membrane

World water resources are barely alive due to various factors such as rise in population, adverse changes in the environment and the effects of pollutants, which increase the demand for fresh-water. Numerous techniques have been developed to solve the problem of water inadequacy, but most of them are adverse with respect to the environment and economy. Graphene-oxide (GO) nanopore materials may be an effective solution for water-purification due to its properties of easy fabrication and modification. This next-generation membrane has high waterflux, selectivity, and permeability to selected molecules. In this discussion, we have covered the latest technologies and potential applications of GO for waterpurification, which shall help researchers to get quick ideas for future research to design and fabricate multi-layered GO membranes. This article gives a snapshot of current status and proposed strategies of graphene-membranes for water treatment with earlier information to wastewater management and stimulated progress in this area from 2017 to date. The future challenges and opportunities in this field have also been highlighted.

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Role of Emerging Technologies in COVID 19: Analyses, Predictions, and Future Countermeasures (Preprint)

10.2196/preprints.20038 ◽

2020 ◽

Author(s):

Dr. Rekha G

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Internet Of Things ◽

Language Processing ◽

Emerging Technologies ◽

Point Of Care ◽

Cost Effective ◽

Current Status ◽

Future Research ◽

Smart Healthcare

UNSTRUCTURED In the resent decade, emerging technologies like Artificial Intelligence, Blockchain Technology, Cloud Computing , Internet of Things (IoT), etc., have changed people life a lot (in terms of living). Artificial Intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which is currently happening around the globe.We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. For this, a summary of COVID-19 related data sources that are available for research purposes (for future researchers) is also presented.For that, all the tools, resources and datasets needed to facilitate AI research are also been reviewed. Also discussed about Machine Learning use cases for Drug Formulations, Treatment of Patients Suffering with COVID-19, how Artificial Intelligence and internet of things can be useful to develop Cost- effective and Rapid Point-of-Care Diagnostics. For example, uses of Internet of Medical Things for Smart Healthcare (primary focus on detecting COVID-19 symptoms, and alerts for other users) have been discussed in this work. In summary, this work providesuseful information about (potential of) AI methods, machine learning, internet of things, used in many applications like Medicare, COVID-19 outbreak and summarizes several critical roles of Artificial Intelligence (including machine learning and internet of things) research in this unprecedented battle.We also discuss several future Research directions, global impact of corona on internet of things and many applications. It is envisaged that this work will provide AI, and ML researchers and the wider community an overview of the current status of AI and ML applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Artificial Intelligence-driven drug repurposing and structural biology for SARS-CoV-2

10.21203/rs.3.rs-370041/v1 ◽

2021 ◽

Author(s):

Kartikay Prasad ◽

Vijay Kumar

Keyword(s):

Artificial Intelligence ◽

Drug Development ◽

Structural Biology ◽

Drug Repurposing ◽

Current Status ◽

Full Potential ◽

Potential Applications ◽

Infectious Proteins ◽

Applications Of Ai

Abstract It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing and development and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

10.36227/techrxiv.12743933 ◽

2020 ◽

Cited By ~ 1

Author(s):

Thanh Thi Nguyen

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Medical Image ◽

Current Status ◽

Future Research ◽

Research Directions ◽

Daily Lives ◽

Related Data ◽

Future Research Directions ◽

The Internet Of Things

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Semantic segmentation with deep learning: detection of cracks at the cut edge of glass

Glass Structures & Engineering ◽

10.1007/s40940-020-00133-7 ◽

2020 ◽

Author(s):

Michael Drass ◽

Hagen Berthold ◽

Michael A. Kraus ◽

Steffen Müller-Braun

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Classical Problem ◽

Semantic Segmentation ◽

Neural Net ◽

Cut Edge ◽

Glass Processing ◽

Time Savings ◽

First Time ◽

Glass Edge

Abstract In this paper, artificial intelligence (AI) will be applied for the first time in the context of glass processing. The goal is to use an algorithm based on artificial intelligence to detect the fractured edge of a cut glass in order to generate a so-called mask image by AI. In the context of AI, this is a classical problem of semantic segmentation, in which objects (here the cut-edge of the cut glass) are automatically surrounded by the power of AI or detected and drawn. An original image of a cut glass edge is implemented into a deep neural net and processed in such a way that a mask image, i.e. an image of the cut edge, is automatically generated. Currently, this is only possible by manual tracing the cut-edge due to the fact that the crack contour of glass can sometimes only be recognized roughly. After manually marking the crack using an image processing program, the contour is then automatically evaluated further. AI and deep learning may provide the potential to automate the step of manual detection of the cut-edge of cut glass to great extent. In addition to the enormous time savings, the objectivity and reproducibility of detection is an important aspect, which will be addressed in this paper.

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Review of Artificial Intelligence Techniques inGreen/Smart Buildings.

10.21203/rs.3.rs-288182/v1 ◽

2021 ◽

Author(s):

Diego Rodriguez-Gracia ◽

Maria de las Mercedes Capobianco-Uriarte ◽

Eduardo Teran-Yepez ◽

Jose Piedra-Fernandez ◽

Luis Iribarne ◽

...

Keyword(s):

Artificial Intelligence ◽

Rapid Evolution ◽

Environmental Benefits ◽

Current Status ◽

Future Research ◽

Smart Buildings ◽

The Past ◽

Intelligent Buildings ◽

The Many ◽

The Relationship

Abstract The many benefits offered by green or smart buildings have led to an increase in their construction. In turn, this growth has been accompanied by a rapid evolution of research on this topic. Thus, given the specialist interest, research on the use of artificial intelligence in this type of construction has been gaining space. This topic, although still novel, due to its current and future importance requires a literature review to identify the main actors, evaluate the past and establish future lines of research. The results based on 174 manuscripts detected in Web of Science and Scopus databases allow us to establish the main authors, institutions, countries and journals as well as the seminal papers in this field. Furthermore, through a keywords co-occurrence analysis this study identifies some of the topics that have received most interest in the past as well as some promising future research trends. This bibliometric study analyzes the relationship between the main clusters DML&B (Deep - Machine Learning and Building Constructions) by means of a detailed description of the fundamental concepts identified in the content analysis. It is complemented by a temporal keyword analysis focusing on the economic, social and environmental benefits obtained through green or intelligent buildings. Consequently, this research contributes to the literature by providing an overview of the past and current status of this field, as well as by opening future research lines.

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Artificial Intelligence in Education: A Panoramic Review

10.35542/osf.io/zvu2n ◽

2020 ◽

Author(s):

Kashif Ahmad ◽

Junaid Qadir ◽

Ala Al-Fuqaha ◽

Waleed Iqbal ◽

Ammar El-Hassan ◽

...

Keyword(s):

Artificial Intelligence ◽

Research Work ◽

Educational Data Mining ◽

Research Trends ◽

Drop Out ◽

Future Research ◽

Artificial Intelligence In Education ◽

Potential Applications ◽

Market Opportunities ◽

Applications Of Ai

Motivated by the importance of education in an individual's and a society's development, researchers have been exploring the use of Artificial Intelligence (AI) in the domain and have come up with myriad potential applications. This paper pays particular attention to this issue by highlighting the future scope and market opportunities for AI in education, the existing tools and applications deployed in several applications of AI in education, research trends, current limitations and pitfalls of AI in education. In particular, the paper reviews the various applications of AI in education including student grading and evaluations, students' retention and drop out prediction, sentiment analysis, intelligent tutoring, classrooms' monitoring and recommendation systems. The paper also provides a detailed bibliometric analysis to highlight the research trends in the domain over six years (2014--2019). For this study, we analyze research publications in various related sub-domains such as learning analytics, educational data mining (EDM), and big data in education. The paper analyzes educational applications from different perspectives. On the one hand, it provides a detailed description of the tools and platforms developed as the outcome of the research work achieved in these applications. On the other side, it identifies the potential challenges, current limitations and hints for further improvement. We also provide important insights into the use and pitfalls of AI in education. We believe such rigorous analysis will provide a baseline for future research in the domain.

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