Knowledge acquisition via interactive Distributed Cognitive skill Modules

The human's cognitive capacity for problem solving is always limited to his/her educational background, skills, experiences, etc. Hence, it is often insufficient to bring solution to extraordinary problems especially when there is a time restriction. Nowadays this sort of personal cognitive limitations are overcome at some extend by the computational utilities (e.g. program packages, internet, etc.) where each one provides a specific background skill to the individual to solve a particular problem. Nevertheless these models are all based on already available conventional tools or knowledge and unable to solve spontaneous unique problems, except human's procedural cognitive skills. But unfortunately such low-level skills can not be modelled and stored in a conventional way like classical models and knowledge. This work aims to introduce an early stage of a modular approach to procedural skill acquisition and storage via distributed cognitive skill modules which provide unique opportunity to extend the limits of its exploitation.

Supervised laser-speckle image sampling of skin tissue to detect very early stage of diabetes by its effects on skin subcellular properties

This paper investigates the effectiveness of an expert system based on K-nearest neighbors algorithm for laser speckle image sampling applied to the early detection of diabetes. With the latest developments in artificial intelligent guided laser speckle imaging technologies, it may be possible to optimise laser parameters, such as wavelength, energy level and image texture measures in association with a suitable AI technique to interact effectively with the subcellular properties of a skin tissue to detect early signs of diabetes. The new approach is potentially more effective than the classical skin glucose level observation because of its optimised combination of laser physics and AI techniques, and additionally, it allows non-expert individuals to perform more frequent skin tissue tests for an early detection of diabetes.

Progressive observation of Covid-19 vaccination effects on skin-cellular structures by use of Intelligent Laser Speckle Classification (ILSC)

We have made a progressive observation of Covid-19 Astra Zeneca Vaccination effect on Skin cellular network and properties by use of well established Intelligent Laser Speckle Classification (ILSC) image based technique and managed to distinguish between three different subjects groups via their laser speckle skin image samplings such as early-vaccinated, late-vaccinated and non-vaccinated individuals. The results have proven that the ILSC technique in association with the optimised Bayesian network is capable of classifying skin changes of vaccinated and non-vaccinated individuals and also of detecting progressive development made on skin cellular properties for a month period.

Detection of sub-cellular changes by use of $λ=6.5$ micron laser light interaction in association with Intelligent Laser Speckle Classification (ILSC) technique

The study is based on a principle of laser physics so that a (coherent) laser light whose wavelength is shorter than a feature under inspection (like sub-cellular component) can interact with such specific feature (or textural features) and generates laser speckle patterns which can characterize those specific features. By the method we have managed to detect differences at sub-cellular scales such as genetic modification, cellular shape deformation, etc. with 87% accuracy. In this study red laser is used whose wavelength (6.5 microns) is shorter than a plant cell (~60 microns) that is suitable to interact with sub-cellular features. The work is assumed to be an initial stage of further application on human cellular changes observation that would be utilized for development of more accurate methods such as better drug delivery assessments, systemic diseases early diagnosis, etc.

Low-level cognitive skill transfer between two individuals' minds via computer game-based framework

The novel technique introduced here aims to accomplish the first stage of transferring low-level cognitive skills between two individuals (e.g. from expert to learner) to ease the consecutive higher level declarative learning process for the target "learner" individual in a game environment. Such low-level cognitive skill is associated with the procedural knowledge and established at low-level of mind which can be unveiled and transferred by only a novel technique (rather than by a traditional educational environment ) like a highly interactive computer game domain in which a user exposes his/her unconscious mind behaviors via the game-hero non-deliberately during the game sessions. The cognitive data exposed by the game-hero would be recorded, and then be modelled by the artificial intelligence technique like Bayesian networks for an early stage of cognitive skill transfer and the cognitive stimuli are also generated to be used as game agents to train the learner.