Prediction of building energy consumption plays an important role in energy conservation, management, and planning. Continuously improving and enhancing the performance of forecasting models is the key to ensuring the performance sustainability of energy systems. In this connection, the current paper presented a new improved hybrid model of machine learning application for forecasting the cooling load (CL) and the heating load (HL) of residential buildings after studying and analyzing various types of CL and HL forecasting models. The proposed hybrid model, called group support vector regression (GSVR), is a combination of group method of data handling (GMDH) and support vector regression (SVR) models. To forecast CL and HL, this study also made use of base methods such as back-propagation neural network (BPNN), elastic-net regression (ENR), general regression neural network (GRNN), k-nearest neighbors (kNN), partial least squares regression (PLSR), GMDH, and SVR. The technical parameters of the building were utilized as input variables of the forecasting models, and the CL and HL were adopted as the output variables of each network. All models were saved in the form of black box after training and initial testing. Finally, comparative analysis was performed to assess the predictive performance of the suggested model and the well-known basic models. Based on the results, the proposed hybrid method with high correlation coefficient (R) (e.g. R=99.92% for CL forecasting and R=99.99% for HL forecasting) and minimal statistical error values provided the most optimal prediction performance.

In today’s world, phishing attacks are gradually increasing, resulting in individuals losing valuables, assets, personal information, etc., to unauthorized parties. In phishing, attackers craft malicious websites disguised as well-known, legitimate sites and send them to individuals to steal personal information and other related private details. Therefore, an efficient and accurate method is required to determine whether a website is malicious. Numerous methods have been proposed for detecting malicious uniform resource locators (URLs) using deep learning, machine learning, and other approaches. In this study, we have used malicious and benign URLs datasets and have proposed a detection mechanism for detecting malicious URLs using recurrent neural network models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and the gated recurrent unit (GRU). Experimental results have shown that the proposed mechanism achieved an accuracy of 97.0% for LSTM, 99.0% for Bi-LSTM, and 97.5% for GRU, respectively.