Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

  Foot and ankle movements are essential in various activities like walking, running, and balance, where the mechanics of these movements are affected by the muscles around the ankle joint [1,2]. In this study, the correlations between isometric ankle torque and muscle activity of the tibialis anterior (TA) and gastrocnemius (GAS) in the course of dorsiflexion and plantarflexion was investigated. Eight healthy participants were enrolled for the study, where the ankle torque and surface Electromyography (sEMG) of the main flexors were measured and analyzed. The results showed that ankle torque is higher in plantarflexion than dorsiflexion. In addition, the TA has greater muscle activity during dorsiflexion, while the GAS presents higher ac

New substituted coumarins derivatives were synthesized by using nitration reaction to produce different nitro coumarin isomers which were separated from these isomers by using different solvent, and the reduction of nitro compounds was done to give corresponding amino coumarins. Temperature and reaction time of reaction were very important factors in determining the most productive nitro isotopes. A low temperature for three hours was sufficient to give a high product of a compound 6-nitro coumarin while increasing the temperature for a period of twenty-four hours that gave a high product of 8-nitro-coumarin. The synthesized compounds were confirmed by FT-IR,1 H-NMR, and13 C-NMR spectroscopy and all final compounds were tested for their ant