Large-Scale Pattern Storage and Retrieval Using Generalized Brain-State-in-a-Box Neural Networks

Oh, Cheolhwan; Żak, Stanisław H.

doi:10.1109/tnn.2010.2040291

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…An example that synergizes two algorithms proven effective for a number of NBCE/ DoD applications, is in image understanding, in conditions when the image is corrupted by noise or damaged in a number of different ways. The two algorithms, proven to be the most effective ones, are: Generalized Brain-State-in-a-Box neural network (gBSBNN) introduced by [37] and the combined discrete Fourier transform and neural network (DFT-NN) algorithm from [22]. The synergy of these two symbiotic algorithms is envisioned for future research as follows: For one set of conditions, gBSBNN performs better and DFT-NN performs better for another set of conditions.…”

Section: Examples/tasks Related To Nbcementioning

confidence: 99%

Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems

Babović,

Bajat,

Đokić

et al. 2023

J Big Data

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This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).

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Section: Examples/tasks Related To Nbcementioning

confidence: 99%

Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems

Babović,

Bajat,

Đokić

et al. 2023

J Big Data

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show abstract

“…Also, it is important to teach students how to synergize two or more algorithms proven effective for a number of applications, like image understanding, especially in conditions when the image is corrupted by noise or damaged in a number of different ways. For example, two algorithms introduced by Stan Zak are: Generalized Brain-State-in-a-Box neural network (gBSBNN) [26] and combined Discrete Fourier Transform and neural network (DFT-NN) [14]. Their synergy envisioned for the research in NBC is as follows: For one set of conditions, gBSBNN performs better, while DFT-NN performs better for another set of conditions.…”

Section: Issues In Nature-based Constructionmentioning

confidence: 99%

Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains

Babović,

Bajat,

Barac

et al. 2023

J Big Data

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This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.

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“…There are many current areas of research within the field of health informatics, including bioinformatics, image informatics, clinical informatics, public health informatics, and translational bio-informatics. Research done in health informatics includes data acquisition, retrieval, storage and analytics [ 17 ] employing data mining techniques and mining web [ 18 ].…”

Section: Background and Objectivesmentioning

confidence: 99%

Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers

Gómez–Pulido

Pulido

Rodríguez‐Puyol

et al. 2021

IJERPH

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A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

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Large-Scale Pattern Storage and Retrieval Using Generalized Brain-State-in-a-Box Neural Networks

Cited by 13 publications

References 28 publications

Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems

Research in computing-intensive simulations for nature-oriented civil-engineering and related scientific fields, using machine learning and big data: an overview of open problems

Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains

Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers

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