Technological advancements in the cloud field are becoming widely used on a large scale in increasing activity sectors. Agriculture is an important domain in everyday life, central to human existence. This research comparatively analyzes two proposed types of infrastructures that optimize the growth flow of plants in a hydroponic system for continuous monitoring, one full-cloud and one full-local. The study’s main objective is to determine which of the two infrastructures is more suitable for the hydroponic scenario by conducting seven types of tests. This research aims to fill a gap in the specialized literature through a detailed analysis of the configuration, implementation methods, and all implications of the two approaches from the perspective of the seven indicators. The seven indicators are response time, operational reliability, implementation costs, operational costs, configuration scalability, data accessibility, and data security. The cloud infrastructure uses Microsoft Azure technologies, while the local variant uses custom-made scripts and locally installed services. For both software infrastructures, the hardware components are identical, including an M5Stack module with sensors for monitoring temperature, humidity, electrical conductivity, and liquid level in the hydroponic container. The test results highlight that the local infrastructure offers a shorter response time (200 ms compared to 300 ms for the cloud infrastructure). The results also showed lower operational costs for the local infrastructure, making it more suitable for autonomous hydroponic systems. On the other hand, the results showed that cloud infrastructure has greater data accessibility than local infrastructure, and the security measures are advanced. These advantages of cloud infrastructure involve recurring costs of USD 82.57/month. The limitations of this research are associated with the exclusion of human errors and cybernetics simulations from the analysis. Another limitation concerns the real analysis of short-term costs. Future research will explore the real fluctuations of long-term costs. Additionally, infrastructure studies on different plant species and hydroponic farms will also be considered.

The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM_2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM_2.5 hourly measurements. The main statistical parameters that were computed are root mean square error (RMSE) and mean absolute error (MAE).

A controller is the most important part of an automatic control system. Its main purpose is to receive the signals from a transmitter and to compare it with a reference value. Depending on the error the controller sends a control signal to an actuator, so that the controlled variable to be equal with the reference value. The most used algorithm by a controller is the PID algorithm. In this paper, two methods of determining the optimum values of the tuning parameters for the PID controller will be presented, using case studies. The optimization criteria are the integral criteria, the square of the error between reference and controlled value being taken into consideration. The results consisted in the optimal values of the PID tuning parameters (gain and reset) for the analyzed cases associated to a temperature control system.

Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27]a, in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM2.5) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient.

Recent studies on air pollution emphasized particulate matter impact on human health and climate changes. This impact generated a trend for developing research projects which deal with monitoring and forecasting air quality. This paper fits into this trend and presents an ANFIS (adaptive neuro-fuzzy inference system) modelling approach to predict particulate matter concentration for short terms. The ANFIS technique was tested for three data sets covering all seasons specific to urban areas from Romania. The data sets type imposed the fuzzy inference system generating method and the optimization method. The resulted prediction model can be used to warn the population when the PM concentration exceeds standard limits, and also to extract useful data for knowledge-based modelling.

Accurate forecasting of fine particulate matter concentration in cities is an important problem that can be solved with efficient methods as those provided by computational intelligence, which apply a data driven approach. An example of such method is given by artificial neural networks that are universal approximators, providing very good solutions to time series forecasting. The paper presents a neural network model for the prediction of PM _2.5 air pollutant concentration, based on feed-forward neural networks and recurrent neural networks, which was developed by experiment, starting with different settings of the artificial neural network architecture. A case study of short-term PM _2.5 forecasting based on the proposed neural model is detailed in the paper.