Fuzzy-based Description of Computational Complexity of Central Nervous Systems, Journal of Telecommunications and Information Technology, 2020, nr 3

Computational intelligence algorithms are currently capable of dealing with simple cognitive processes, but still remain inefficient compared with the human brain’s ability to learn from few exemplars or to analyze problems that have not been defined in an explicit manner. Generalization and decision-making processes typically require an uncertainty model that is applied to the decision options while relying on the probability approach. Thus, models of such cognitive functions usually interact with reinforcement-based learning to simplify complex problems. Decision-makers are needed to choose from the decision options that are available, in order to ensure that the decision-makers’ choices are rational. They maximize the subjective overall utility expected, given by the outcomes in different states and weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using the Bayes’ law. Fuzzy-based models described in this paper propose a different – they may serve as a point of departure for a family of novel methods enabling more effective and neurobiologically reliable brain simulation that is based on fuzzy logic techniques and that turns out to be useful in both basic and applied sciences. The approach presented provides a valuable insight into understanding the aforementioned processes, doing that in a descriptive, fuzzy-based manner, without presenting a complex analysi

Annual Research Report, 2009-2010

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and governmenthttps://digitalcommons.odu.edu/or_researchreports/1001/thumbnail.jp

Dispersal and eco-evolutionary dynamics in response to global change

nrpages: 171status: publishe

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

Monitoring of Honey Bee Colony Losses

In recent decades, independent national and international research programs have revealed possible reasons behind the death of managed honey bee colonies worldwide. Such losses are not due to a single factor, but instead are due to highly complex interactions between various internal and external influences, including pests, pathogens, honey bee stock diversity, and environmental changes. Reduced honey bee vitality and nutrition, exposure to agrochemicals, and the quality of colony management contribute to reduced colony survival in beekeeping operations. Our Special Issue (SI) on ‘’Monitoring of Honey Bee Colony Losses” aims to address the specific challenges that honey bee researchers and beekeepers face. This SI includes four reviews, with one being a meta-analysis that identifies gaps in the current and future directions for research into honey bee colonies’ mortalities. Other review articles include studies regarding the impact of numerous factors on honey bee mortality, including external abiotic factors (e.g., winter conditions and colony management) as well as biotic factors such as attacks by Vespa velutina and Varroa destructor