Prediction of crops yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study the performance of artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction are evaluated. According to the results, ANN-GWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R ² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

This article deals with components (that effectuate the electronic protection) of property protection systems. Within this, mostly with to delineate the electronic outdoor protection, the intrusion detection system and some possible additional functions of the access control system. The solutions presented here are aimed to keep the cost low from the viewpoint of hardware requirements, together with to take advantage of the software opportunities.

In civil and industrial areas often a task to log the data of an embedded system. In some applications the redundant data storage and archiving is required. In the field it can be occur, that the device suffers some electromagnetic impact. It can cause for example, some high current industrial device, a near lightning strike, a transient electromagnetic disturbance, EMP jamming, etc. It can cause damage in the data measured by the embedded system. In this paper shows an architectural solution to keep the data safe, or flag the exact part of the data, which can contain errors.

The aim of the study was to create a model environment for an UAV and to run simulations for dynamic obstacle avoidance in this environment in order to make an optimum method for real flight later. This paper presents the steps for building a simulation environment for UAV flight, the program codes and map creation. The ultimate goal of this project is to develop a working method for automated UAV flight in any real world environment.