Eye diseases have been a severe problem worldwide, especially in developing countries where technology and finance are limited. Today, the problem is being resolved thanks to pattern classification that is part of pattern recognition, and its primary goal is to group standard features from any entity, object, phenomenon, or event belonging to the real or abstract world. Convolutional Neural Networks are a type of Artificial Neural Network that is much used in intelligent pattern classification, learning machine, and data mining. Also, these algorithms are applied in medicine and ophthalmology for detecting diseases in the human body. This work presents a novel intelligent pattern classification algorithm based on a convolutional neural network, which is validated through the K-Fold Cross Validation test. Two different groups of retinography images are given: Glaucoma and Diabetic Retinopathy. The result of accuracy percentage was of 99.89%. Numerical metrics: Accuracy, Recall, Specificity Precision and F1score with values close to 1, and ROC curves support the suitable performance of the proposed classifier.

Bidirectional Associative Memories (BAM) are systems that allow to associate pairs of patterns. Once a memory has learned, patterns can be recalled in two directions. BAMs have many applications in pattern recognition and image processing. The aim of this survey is to present several models of BAM throughout time, since Kosko [1988] proposed the first model; followed by those works based on or inspired by it, trying to improve recall capacity; to some recent one-shot models—such as Morphological BAM and Alpha-Beta BAM—which are of particular interest, given their superior performance.

Knowledge society blockchain is one of the most powerful and recent tools to make the internet environment safer and reliable. Manufacturing has traditionally been dominated by standard designs that are mass-produced, due to the fact, that custom production causes additional costs that make it less affordable than mass production. This paper proposes to develop a designer expert system for IoT installation layout designs, using blockchain distributed system based on a machine learning, with users entering data to the expert system by a smart bot software. This expert system will work using extreme learning machine as inference engine; therefore, this is a shell to develop any expert system with fast learning. The whole system is represented by a smart contract with a value linked to the value of the expert system, the more this expert system be quoted on the web, the more the shares of the smart contract will cost.

Background: Ocular diseases have been a severe problem worldwide, specifically in underdeveloped countries that do not have enough technology or economy to treat them. It would be beneficial to have software with low installation complexity and ease of use, allowing high efficacy in diagnosing eye diseases. This study aims to design and implement an algorithm based on deep learning to classify ocular diseases with high precision. Methods: This work describes digital image processing techniques for the easier handling of eye images; in particular, blur filters were used. The Canny filter was also applied to obtain the edges that allow the difference between the analyzed diseases. Once the images were pre-processed, a convolutional neural network of our own design was applied to perform the classification task. The validation algorithm used in this work was the hold-out algorithm (80–20). The metrics used to evaluate our proposal were the confusion matrix, accuracy, recall precision, and F1-score. Results: The dataset has five classes, namely, normal, cataract, diabetic retinopathy, glaucoma, and other retina diseases. The network architecture consists of 11 layers, including three convolutional layers, three max pooling layers, one batch normalization layer, one flattening layer, two hidden layers, and one output layer. This model resulted in 97% efficiency across all metrics. Conclusions: With the individual analysis of each metric, it can be observed that the proposed algorithm is capable of differentiating, first, images of healthy eyes from diseased ones and, second, adequately classifying eye diseases.