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.

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.

Electricity purchase price variation can influence the economic feasibility of residential distributed generation, particularly in regulated markets where grid electricity prices and export compensation conditions affect investment decisions. This study evaluates the impact of flat electricity purchase price scenarios on the techno-economic viability of residential grid-connected energy systems in Santo Domingo Tehuantepec, Oaxaca, Mexico, using HOMER Pro. The analysis considers PV, wind generation, diesel generation, converter, and grid connection as candidate components, while evaluating three residential demand profiles of 11.26, 30.00, and 83.30 kWh/day and 10 electricity purchase price scenarios ranging from 3.45 to 5.00 MXN/kWh. The objective is to identify the electricity purchase price values at which the optimal architecture changes from conventional grid-only supply to PV/converter/grid adoption under the evaluated case study assumptions. The results show that grid-only supply remains the least-cost option from 3.45 to 4.20 MXN/kWh for all demand profiles. At 4.25 MXN/kWh, HOMER Pro selects PV/converter/grid configurations for the medium- and high-demand profiles, while the low-demand profile remains grid-only. At 4.30 MXN/kWh, PV/converter/grid also becomes optimal for the low-demand profile. At 5.00 MXN/kWh, ROI reaches 11.0% for the three residential demand profiles, while payback decreases to 6.5 years for the low- and medium-demand profiles and 6.4 years for the high-demand profile. The wind turbine and diesel generator were not selected in the optimal configurations, despite being included as candidate technologies. These findings provide a practical case study indicator of the electricity purchase price levels at which residential PV-grid adoption becomes economically competitive under flat purchase price scenarios and zero export compensation.