adaptive neural fuzzy inference system(anfis)

In recent years, the digital revolution in education has had a transformative effect on learning management systems in many countries. The personalisation of education, driven by digital technologies, has led to a significant increase in the importance of creating a strategic roadmap for the effective use of these technologies in both education and agriculture in Iran. It is imperative for policymakers to assess and predict the significance of the dimensions of digital transformation when developing a roadmap. The present research identifies four key dimensions affecting digital transformation: cultural, economic, institutional, and educational. Each dimension, along with its components, was evaluated using ANFIS modelling as input. The study gathered the opinions of 96 participants through targeted sampling, which included educational policymakers, school principals, senior users of digital technology, and managers of knowledge-based companies involved in digital technology for education. The data analysis and calculation results indicated that the cultural dimension ranked first, with an importance score of 4.23. This finding underscores the importance of this dimension and its components in shaping the roadmap for digital transformation in Iran's education system. The model's validity was further confirmed by an error percentage of 0.00015, which was determined through analysis.

The energy consumption of a residential building is considered in terms of energy use and efficiency. Therefore, forecasting the energy consumption of buildings has been raised as a challenge in recent decades. In a residential home, electricity consumption can have recognizable patterns daily, monthly, or yearly depending on living conditions and daily habits and events. In this research, artificial neural network (ANN) and adaptive fuzzy-neural inference system (ANFIS) have been performed using MATLAB software to predict building energy consumption. Also, random data collected based on the criteria obtained from the hourly electricity consumption of conventional residential buildings in Tehran has been used. In order to evaluate and measure the performance of this model, statistical indicators have been used. According to the applied settings (type of learning, number of steps, and error tolerance), the system error rate is calculated based on MSE, RMSE, μ, σ, and R statistical indicators and the results of energy consumption forecast in three buildings with high accuracy and correlation coefficient. R is more than 98%. The output of this research is an intelligent combined system of ANN and ANFIS. The obtained values well show the ability of this model to estimate energy consumption in the mentioned buildings with high accuracy.

The adaptive fuzzy neural inference system (ANFIS) is an efficient estimation model not only among fuzzy neural systems but also among other types of machine learning techniques. Despite its acceptance among researchers, ANFIS cited limitations such as inefficiencies in large data and data problems, cost of computation, processing time and optimization, and error training. The ANFIS structural design is a complex optimization problem that can be improved using meta-heuristic algorithms. In this study, to optimize and reduce errors, a new meta-heuristic algorithm inspired by nomadic migration was designed and used to design an adaptive fuzzy neural system called the Qashqai nomadic meta-heuristic algorithm. The results of the hypothesis test showed that the Qashqai optimization algorithm is not defeated by the genetic algorithm and particle swarm and works well in terms of convergence to the optimal answer. In this hybrid algorithm, random data set are first generated and then trained by designing a basic fuzzy neural system. Subsequently, the parameters of the basic fuzzy system were adjusted according to the modeling error using the meta-heuristic optimization algorithm of Qashqai nomads. The fuzzy nervous system with the best values was obtained as the final result.The main achievements of the study are:• Improving ANFIS accuracy using a novel meta-heuristic algorithm.• Fix and remove some problems and Limitations in the Anfis model, such as inefficiencies in large data, cost of computation, Answer accuracy, and reduce errors.• Comparing the proposed ANFIS+QA with some recent related work such as ANFIS+QA and ANFIS+Pso.