Author: basma mohammed khalil

The smart grid (SG) significantly enhances the conventional power grids with information and communication technologies, control decision-making systems, simulation analysis, advanced measurement and sensing. In contrast to conventional power grids, the SG has further benefits in stability, situational awareness, self-healing, information interaction, and renewable energy consumption. But the precise prediction of long-term electric energy consumption remains a challenge. Recently, machine learning (ML), particularly deep learning, has rapidly advanced and has demonstrated outstanding performance in various tasks of the SG fields. The representation capability of ML approaches is considerably enhanced. The SG is a curial framework area, hence ML algorithms including it should be interpretable for improving reliability of the system and increasing user trust. This study develops a new Planet Optimization with Machine Learning Enabled Power Usage Forecasting Modeling (POML-PUFM) in SG environment. The presented POML-PUFM technique forecast the utilization of power in the SGs for smart city applications. In the presented POML-PUFM technique, data pre-processing takes place to transform them into compatible format. For forecasting purposes, the POML-PUFM technique employs twin-support vector machine (TWSVM) model. In addition, the study used PO model to adjust the parameter related to the TWSVM method. The prediction performance of the presented POML-PUFM algorithm is examined and the outcomes are examined under several measures. The study verified the improvements of the POML-PUFM technique over other ML models. DOI link: title

In smart grid, energy management will be an essential one to reduce energy costs of users while maximizing the comfort of users and lessening the peak to-average ratio and carbon emission in realtime pricing techniques. Conversely, the advent of bidirectional transmission and power transfer technologies allows Electric Vehicle (EV) charging or discharging scheduling, load scheduling or shifting, and optimum energy distribution, making the smart power grids. SGs will enable the users to schedule home appliances concerning the Demand Response program (DR) provided by Distribution System Operator (DSO). In this regard, not only the consumers save the cost of using energy, and also it will be very comfortable, but the utility companies also control peakhour demand and diminish Carbon Emissions (CEs). This article introduces a new Heap Based Optimization with Deep Learning Based Energy Forecasting in Smart Grid (HBODL-EFSG) with the consideration of DR. The presented HBODL-EFSG technique majorly focuses on the prediction of energy in SGs by the consideration of DR. To accomplish this, the presented HBODL-EFSG technique applies data standardization process to normalize the input data into a uniform format. For energy-level forecasting, the presented HBODL-EFSG technique uses Deep Variational Autoencoder (DVAE) model. At last, the hyperparameter tuning of the DVAE method was optimally adjusted using the HBO technique. A series of simulation analyses take place to highlight the enhanced forecasting performance of the HBODL-EFSG approach. A comprehensive comparison analysis portrays the precipitated results of the HBODL-EFSG procedure over other techniques. DOI link : title

Unmanned Aerial Vehicles (UAVs) are broadly utilized in civilian and military fields with the continuous growth of (UAVs) technology. Multi-UAV networks were commonly termed as flying ad hoc networks (FANETs). Segregating numerous UAVs into clusters for management could enhance network scalability, minimize energy consumption, and maximize network lifetime to some extent, thus UAV clustering will be a crucial direction for drone network applications. But UAV have the features of high mobility and limited energy resources, which put forth difficulties in UAVs cluster transmission networking. Therefore, this article develops a Political Optimizer based Robust Energy Aware Clustering Scheme (PO-REACS) for FANET. The proposed PO-REACS technique aims to cluster UAVs for maximum network efficiency. In the presented PO-REACS technique, the major concepts of multi-staged procedure of politics in human society are used. For an effective selection of cluster heads (CHs), the PO-REACS technique computes multiple parameter fitness functions. For highlighting the superior performances of the PO-REACS system, a widespread simulation studies are made. The comparison study stated the betterment of the PO-REACS technique over existing methodologies. DOI link: title

Religious mass gathering at religious places presents a great difficulty in crowd management, providing the necessities of basic amenities, and addressing their healthcare requirements. Certain ritual practices may increase the risk of transferring respiratory pathogens. The COVID19 pandemic was an unexpected healthcare crisis with impacts reflected in every sphere of human lives. To limit the spread of the virus, the World Health Organization (WHO) declared wearing face masks is imperative manner. But manual assessment, whether an individual wears face masks or not in a public place, will be a difficult task. The need to monitor people wearing face masks is required to construct an automatic method. This study introduces a Computer Vision based Face Mask Recognition in Religious Mass Gatherings and COVID-19 Infection, named the CVFMR-RMG technique. The presented CVFMR-RMG technique aims to recognize the faces with and without masks via CV and image processing techniques. Initially, the input images are pre-processed via the adaptive Weiner filtering (WF) model. In the presented CVFMR-RMG technique, the face detection process takes place using Faster Region Convolutional Neural Network (Faster RCNN) approach. For facemask detection and classification, the sparse stacked auto encoder (SSAE) technique was employed. To improve the facemask detection performance of the SSAE model, a modified artificial fish swarm algorithm (MAFSA) is used for hyperparameter tuning. The performance assessment of the CVFMR-RMG system can be tested using the facemask database. The simulation outcomes illustrate the improved performance of the CVFMR-RMG system over other models. DOI link: title