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Structural Design Recommendations in the Early Design Phase using Machine Learning [article]

Spyridon Ampanavos, Mehdi Nourbakhsh, Chin-Yi Cheng
2021 arXiv   pre-print
In order to facilitate an informed exploration in the early design stage, we suggest the automation of fundamental structural engineering tasks and introduce ApproxiFramer, a Machine Learning-based system  ...  Structural engineering knowledge can be of significant importance to the architectural design team during the early design phase.  ...  We would like to express our gratitude to Mohammad Keshavarzi for his help with the synthetic data preparation process.  ... 
arXiv:2107.08567v1 fatcat:6ogk62vnmrhpnmhepv4h66hmqq

A Hybrid Teaching Mode Based on Machine Learning Algorithm

Jinjin Liang, Yong Nie
2020 Open Artificial Intelligence Journal  
Results: This paper proposed a hybrid teaching mode utilizing machine learning algorithms, which uses clustering analysis to analyze the learner's characteristics and introduces a support vector machine  ...  effective teaching process designing by educators.  ...  Sincere thanks are given to the dealing manager and editors for kindly giving the instructive suggestions, which makes the paper more flawless.  ... 
doi:10.2174/1874061802006010022 fatcat:aevorxgf2bfnfiothv3f7k32te

Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond

Misawa, Fukuyoshi, Sengoku
2020 International Journal of Environmental Research and Public Health  
There have been prior attempts to utilize machine learning to address issues in the medical field, particularly in diagnoses using medical images and developing therapeutic regimens.  ...  However, few cases have demonstrated the usefulness of machine learning for enhancing health consciousness of patients or the public in general, which is necessary to cause behavioral changes.  ...  Regarding the treatment phase, machine learning is most used in cancer therapeutics.  ... 
doi:10.3390/ijerph17030790 pmid:32012838 pmcid:PMC7037430 fatcat:jlox5kba6jfr5hcq6amms6paiu

Recommendations to Handle Health-related Small Imbalanced Data in Machine Learning

Maria Rauschenberger, Ricardo Baeza-Yates
2020 Mensch & Computer  
We present a use case of early screening of dyslexia with an imbalanced data set using machine learning classification to explain design decisions and discuss issues for further research.  ...  The reason is the negative impact of wrong results on a person, such as in missing early screening of dyslexia or wrong prediction of cancer.  ...  RELATED WORK As in the beginning of machine learning, today small data is used by machine learning models in spite of the focus in big data [14] .  ... 
doi:10.18420/muc2020-ws111-333 dblp:conf/mc/RauschenbergerB20 fatcat:olbl5fze2bfflfz6fxfpj3udai

Practicing design by analyzingfailure component

Nagaraj Ekabote, Krishnaraja G Kodancha, B BKotturshettar
2020 Procedia Computer Science  
Hence a reappraise is essential to address the issue in teaching core design subjects.As a disruption in design teaching and learning, redesigning of a failed machine component as an activity was introduced  ...  Hence a reappraise is essential to address the issue in teaching core design subjects.As a disruption in design teaching and learning, redesigning of a failed machine component as an activity was introduced  ...  Acknowledgements Authors gratefully acknowledge the support by beloved Students, Faculty and Staff, School of Mechanical Engineering, KLE Technological University, Hubballi, Karnataka.  ... 
doi:10.1016/j.procs.2020.05.058 fatcat:eddi4oenkrb3dlwsfzmcdn2w5e

Learnings [chapter]

Chiara Di Francescomarino, Chiara Ghidini, Mauro Dragoni, Udo Kannengiesser, Richard Heininger, Dennis Majoe, Lubomir Billy, Pavol Terpak, Nicola Flores, Franco Cesaro, Alexandra Totter, David Bonaldi (+2 others)
2016 S-BPM in the Production Industry  
In addition to the case learnings, learnings with respect to sensing human and machine properties are reported. As such the  ...  Aside from the procedural reflection, learnings from the regional consulting partners within the cases are described on a general level.  ...  Especially in the last phase of the project the company had to deal with changes of the staff's structure.  ... 
doi:10.1007/978-3-319-48466-2_7 fatcat:ljpyp75xkfe3xdoxwptcrwaso4

Artificial Intelligence: A New Era in Drug Discovery

Devendra Kumar Mishra, Himani Awasthi
2021 Asian Journal of Pharmaceutical Research and Development  
The rapid growth in life sciences and machine learning algorithms has led to enormous statistical access to the growth of AI-based startups focused on drug innovation in recent years.  ...  risks/side effects in late trials that can be very useful in avoiding traumatic events in clinical trials and ultimately clinical trials.  ...  Due to the strong generative and learning ability, deep learning methods have been used to automatically create new structures with certain desired characteristics [22 ] .  ... 
doi:10.22270/ajprd.v9i5.995 fatcat:zjx5ktqie5f37m3ftsasglsypu

DEPRESSIKA: An Early Risk of Depression Detection through Opinions

Abhusan Chataut, Jyotir Moy Chatterjee, Rabi Shankar Rouniyar
2020 Oriental journal of computer science and technology  
This is a classification problem of the Machine Learning [ML].  ...  The approach here is to help the people suffering from depression with appropriate methodology to use in this work.  ...  Acknowledgement The authors acknowledge Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation) for providing us the opportunity to work in this research.  ... 
doi:10.13005/ojcst13.01.03 fatcat:ee7vzcn5qnatlghuxgij2tk7ti

Constructability improvement in seawater intake structure

Erman Surya Bakti, Muhd Zaimi bin Abdul Majid, Rosli Muhamad Zin, Bambang Trigunarsyah
2011 Engineering Construction and Architectural Management  
Practical implications -A constructability improvement check list using theory and lesson learned for the specific construction project was documented.  ...  Purpose -The purpose of this paper is to explore the process, and analyse the implementation of constructability improvement and innovation result during the planning and design for sea water intake structure  ...  Constructability reviews should be conducted at key points in the project life cycle: in the planning phase, early in the design phase, prior to the procurement phase and again prior to the mobilization  ... 
doi:10.1108/09699981111180908 fatcat:orkfijty4bbdtg6662swpvzr2i

Implementing machine learning in medicine

Amol A. Verma, Joshua Murray, Russell Greiner, Joseph Paul Cohen, Kaveh G. Shojania, Marzyeh Ghassemi, Sharon E. Straus, Chloe Pou-Prom, Muhammad Mamdani
2021 CMAJ - Canadian Medical Association Journal  
Figure 2 : 2 Figure 2: Team structure for each phase of development of an early warning system in the General Internal Medicine (GIM) service at St. Michael's Hospital, Toronto, Ontario.  ...  framework can be used to describe the development and adoption of machine-learned solutions: an exploration phase to understand the problem being addressed and the deployment environment, a solution design  ...  in any medium, provided that the original publication is properly cited, the use is noncommercial (i.e., research or educational use), and no modifications or adaptations are made.  ... 
doi:10.1503/cmaj.202434 pmid:35213323 pmcid:PMC8432320 fatcat:4l4m4anmvre6hitmbjyiuzupde

Wizard of Errors: Introducing and Evaluating Machine Learning Errors in Wizard of Oz Studies

Anniek Jansen, Sara Colombo
2022 CHI Conference on Human Factors in Computing Systems Extended Abstracts  
When designing Machine Learning (ML) enabled solutions, designers often need to simulate ML behavior through the Wizard of Oz (WoZ) approach to test the user experience before the ML model is available  ...  We tested WoE with design students to determine the importance of considering ML errors in design, the relevance of using descriptive error types instead of confusion matrix, and the suitability of manual  ...  CONCLUSION Using ML in prototypes during the early phases of a design process is challenging.  ... 
doi:10.1145/3491101.3519684 fatcat:x5cge47ttjbuhhafszxy7s7uxa

Machine learning guided batched design of a bacterial Ribosome Binding Site [article]

Mengyan Zhang, Maciej B Holowko, Huw Hayman Zumpe, Cheng Soon Ong
2022 bioRxiv   pre-print
We used Gaussian Process Regression for the Learn phase of cycle and the Upper Confidence Bound multi-armed bandit algorithm for the Design of genetic variants to be tested in vivo.  ...  To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to show how small genetic parts can be reliably designed  ...  Acknowledgments The authors would like to acknowlege CSIRO's Machine Learning and Artificial Intelligence, and Synthetic Biology Future Science Platforms for providing funding for this research.  ... 
doi:10.1101/2022.01.05.475140 fatcat:ntg4tf26pndvtg6gb74urpefom

Applications of Machine Learning in Drug Discovery

Mingbo Zhang
2019 Biomedical Journal of Scientific & Technical Research  
Recent trend in drug discovery has been marked for the escalating cost and lowering rates of getting approved. In average,  ...  In recent years, machine learning (ML) technique has gained a rapid development.  ...  In conclusion, machine learning may serve as powerful a tool to speed up target identification and validation.  ... 
doi:10.26717/bjstr.2019.23.003831 fatcat:eanjg36yjnh45irdzxy22ejo3y

A Path for Translation of Machine Learning Products into Healthcare Delivery

2020 European medical journal. Innovations  
This review undertakes the first in-depth study to identify how machine learning models that ingest structured electronic health record data can be applied to clinical decision support tasks and translated  ...  The review highlights the varying approaches taken across each phase by teams building machine learning products and presents a discussion of challenges and opportunities.  ...  systems have fundamentally different considerations that must be accounted for in design and implementation." 9 Recommendations for the design and implementation of machine learning as clinical decision  ... 
doi:10.33590/emjinnov/19-00172 fatcat:noehxgsqwrhjlkjdxr23tylxxq

Robust Machine Learning in Critical Care – Software Engineering and Medical Perspectives [article]

Miroslaw Staron, Helena Odenstedt Hergés, Silvana Naredi, Linda Block, Ali El-Merhi, Richard Vithal, Mikael Elam
2021 arXiv   pre-print
In this paper, we address the problem of how to establish a collaboration where software engineering and medicine meets to design robust machine learning systems to be used in patient care.  ...  Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems.  ...  ACKNOWLEDGMENT We would like to thank the patients for participating in our study.  ... 
arXiv:2103.08291v1 fatcat:khqqrtsfajgudpanc5jdixklcq
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