Prediction of diabetic retinopathy using machine learning techniques

Diabetic Retinopathy (DR) is the complicatedness of diabetes that happens due to macular degeneration among Type II diabetic patients. The early symptom of this disease is predicted through annual eye checkups. Hence, one can save their vision at an early stage. Later on, it prompts retinal detachment. There is a requirement for awareness among diabetic patients about this disease to prevent their life from vision misfortune. Along these lines, there is a need for a computer-assisted method to analyze the disease. The proposed system used Adaptive Histogram Equalization (AHE) technique for image enhancement, Hop Field Neural Network for blood vessel segmentation, and Adaptive Resonance Theory (ART) for blood vessel classification. The proposed system analyzes the disease and classifies the disease level effectively with high accuracy. Also, the system notifies the users about the stages of the disease. The proposed system is evaluated with the clinical as well as open fundus image data sets like DRIVE, STARE, MESSIDOR, HRF, DRIONS, and REVIEW for diabetic retinopathy prediction. Also, physicians evaluated the system and concluded that the proposed system does not deviate from the quality of disease analysis and grading. The proposed techniques accomplished 99.99% accuracy. The system is evaluated by the ophthalmologists and witnesses that the proposed system has not veered off as far as quality.

An Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data

The groundwater for aquatic purposes must be assessed prior to its consumption. Huge number of conventional methods are existing for assessing the quality of groundwater. The water quality index is one of the important conventional methods to assess the groundwater quality. But the conventional methods alone are not enough to assess groundwater quality as well as classify based on its purity. In this paper, we propose an enhanced weight update method for Simplified Fuzzy Adaptive Resonance Theory model to classify the groundwater quality depending on the relative weights of the groundwater quality parameters. Finding the optimal weights is the key to achieve better accuracy of the model, most of the nonlinear models fails to exhibit good accuracy if they fail to learn the optimal weights in the learning process. The aim of the work is to find the good fit between the predicted and the actual groundwater quality grades by identifying the optimal weights of the network by the enhanced weight update method. The Simplified Fuzzy Adaptive Resonance Theory map with the enhanced weight update method performance is justified by comparing it with the Simplified Fuzzy Adaptive Resonance Theory Map. The enhanced weight update method improves the accuracy of the Simplified Fuzzy Adaptive Resonance Theory Map in classifying and predicting the groundwater quality.

An environmental approach to developing and applying smart complexes of academic disciplines in professional training of future specialists

The paper discloses modern approaches to creating integrated information environments of SMART complexes of academic disciplines through integrating creative, authorial, non-verbal, encyclopaedic, information-and-communication, self-realization, self-assessment components in professional (vocational) and pre-university professional education. It lists the advantages of such complexes compared to e-textbooks and reveals the requirements for developing them. It highlights the ways of considering future specialists’ psycho-physiological development when selecting and structuring educational information. It recommends applying constructive equalization of students’ cognitive activity based on the Kosko’s quasi-neural network model in their designing an educational trajectory. It shows conditions for ensuring equal opportunities for students’ learning within such complexes. Both selection and structurization of such complexes’ educational information follow students’ psychological development in perceiving it and focus on critical feature patterns of the adaptive resonance theory and the Hopfield model for associative memory. The paper suggests evaluating students’ activities within such complexes by comparing each participant’s achievements with the parameters of completed projects, using the index method based on qualimetric measurements. It specifies the features of an environmental approach in developing such complexes; elaborating their educational material; determining types of learning tasks; creating means of monitoring students’ knowledge. It justifies the results of experimental work, which involved surveying 442 teachers and analyzes the influence of such complexes on the effectiveness of the educational process. It highlights the importance of introducing heuristic forms, methods and techniques of students’ learning to help students obtain, systematize and consolidate educational information and acquire practical skills in performing creative projects and professional tasks.

Zebrafish behavior feature recognition using three-dimensional tracking and machine learning

In this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type of machine learning algorithm, to identify specific behavioral features. The method was tested based on an experiment in which electric shocks were delivered to zebrafish and zebrafish swimming was tracked in 3D simultaneously to find electric shock-associated behaviors. By processing the obtained data with FuzzyART, we discovered that distinguishing behaviors were statistically linked to the electric shock based on the machine learning algorithm. Moreover, our system could accept user-supplied data for detection and quantitative analysis of the behavior features, such as the behavior features defined by the 3D tracking analysis above. This system could be applied to discover new distinct behavior features in mutant zebrafish and used for drug administration screening and cognitive ability tests of zebrafish in the future.

How Do We See a Changing World?

This chapter begins an analysis of how we see changing visual images and scenes. It explains why moving objects do not create unduly persistent trails, or streaks, of persistent visual images that could interfere with our ability to see what is there after they pass by. It does so by showing how the circuits already described for static visual form perception automatically reset themselves in response to changing visual cues, and thereby prevent undue persistence, when they are augmented with habituative transmitter gates, or MTM traces. The MTM traces gate specific connections among the hypercomplex cells that control completion of static boundaries. These MTM-gated circuits embody gated dipoles whose rebound properties autonomically reset boundaries at appropriate times in response to changing visual inputs. A tradeoff between boundary resonance and reset is clarified by this analysis. This kind of resonance and reset cycle shares many properties with the resonance and reset cycle that controls the learning of recognition categories in Adaptive Resonance Theory. The MTM-gated circuits quantitatively explain the main properties of visual persistence that do occur, including persistence of real and illusory contours, persistence after offset of oriented adapting stimuli, and persistence due to spatial competition. Psychophysical data about afterimages and residual traces are also explained by the same mechanisms.