Deep Learning Prediction Model for NiCrAlY Diffusion Barrier Thickness for Tungsten Wires

In the last decades,technology has used Copper for IC interconnect and it has been the best material used in the wire downsizing.However,Copper is now showing inefficiency as downscaling is getting deeper.Recent research starts to show Tungsten(W)as a possible replacement,for its better downsizing characteristic.The scaling-down of interconnects dimension has to be augmented with thin diffusion layers.It is crucial to subdue tungsten diffusion in the nickel-based thermal spray Flexicord(NiCrAlY)coating layers.Inappropriately,diffusion barriers with thicknesses less than 4.3 nm do not to execute well.With the introduction of two dimensional layers,hexagonal boron has been recommended as a substitute for Tungsten diffusion barrier layers with thicknesses less than 1.5 Nano meters(nm).Nevertheless,vacancies flaws may develop into a Tungsten dissemination path,which is a problematic issue in the manufacturing of diffusion barriers.The energy layer density,of Tungsten atom diffusion via a di-vacancy in NiCrAlY,is computed by density functions 3D.NiCrAlY has complex energy barrier which is thicker than other materials such as Graphene.This is due to the sturdier contact and charge variance of NI and Cr in NiCrAlY.Also,we utilize the energy barriers of several vacancy constructions and produce a dataset to be employed in the proposed 3-imensional deep learning model(3D-DNN).Our trained deep learning neural model can predict the energy barrier of Tungsten diffusion through arbitrarily configured NiCrAlY with accuracy greater than 98.4%in 5×5 cell.Prediction results generate directors on selecting barriers through energy computation.

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identification as an access authority into these applications.Still,face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spoofing detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spoofing(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

Limbal Stem Cell Deficiency(LSCD)is an eye disease that can cause corneal opacity and vascularization.In its advanced stage it can lead to a degree of visual impairment.It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction.LSCD is hard to be diagnosed at early stages.The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD.Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea.Although several medical approaches exist,most of them requires complicated procedure and medical devices.Therefore,in this paper,we pursued the development of a LSCD detection technique(LDT)utilizing image processing methods.Early diagnosis of LSCD is very crucial for physicians to arrange for effective treatment.In the proposed technique,we developed a method for LSCD detection utilizing frontal eye images.A dataset of 280 eye images of frontal and lateral LSCD and normal patients were used in this research.First,the cornea region of both frontal and lateral images is segmented,and the geometric features are extracted through the automated active contour model and the spline curve.While the texture features are extracted using the feature selection algorithm.The experimental results exhibited that the combined features of the geometric and texture will exhibit accuracy of 95.95%,sensitivity of 97.91% and specificity of 94.05% with the random forest classifier of n=40.As a result,this research developed a Limbal stem cell deficiency detection system utilizing features’fusion using image processing techniques for frontal and lateral digital images of the eyes.

A Deep Real-Time Fire Prediction Parallel D-CNN Model on UDOO BOLT V8

Hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most nations.Fire prediction and classification model from a set of fire images can decrease the risk of losing human lives and assets.Timely promotion of fire emergency can be of great aid.Therefore,construction of these prediction models is relevant and critical.This article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time.A fire image database is improved to enhance the images quality prior to classify them as either fire or nonfire.Our proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group(VGG-16)model(Parallel VGG-16)is an enhanced version of the VGG-16 model,by incorporating parallel convolution layers and a fusion module for better accuracy.The experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%,compared to the compared state-of-the-art models.Moreover,we integrate the prediction module into a UDOO BOLT V8 computer,which precisely controlled the fire alarm so that it can cautious people from fire in real time.In this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded system.Our experiments validate the effectiveness and applicability of the proposed fire model.

Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi Arabia.The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)algorithm.Results:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),2022.In addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach.The achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base models.On the other hand,a statistical analysis is performed to measure the significance of the proposed approach.Conclusions:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.