Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Detection of Web Cross-Site Scripting (XSS) Attacks

Most applications looking for XSS vulnerabilities have a variety of weaknesses related to the nature of constructing internet applications. Existing XSS vulnerability packages solely scan public net resources, which negatively influences the safety of internet resources. Threats may be in non-public sections of internet resources that can only be accessed by approved users. The aim of this work is to improve available internet functions for preventing XSS assaults by creating a programme that detects XSS vulnerabilities by completely mapping internet applications. The innovation of this work lies in its use of environment-friendly algorithms for locating extraordinary XSS vulnerabilities in addition to encompassing pre-approved XSS vulnerability scanning in examined internet functions to generate a complete internet resource map. Using the developed programme to discover XSS vulnerabilities increases the effectiveness of internet utility protection. This programme also simplifies the use of internet applications. Even customers unfamiliar with the fundamentals of internet security can use this programme due to its capability to generate a document with suggestions for rectifying detected XSS vulnerabilities

Performance Improvement of npn Solar Cell Microstructure by TCAD Simulation: Role of Emitter Contact and ARC

In the current study, the performance of the npn solar cell (SC) microstructure is improved by inspecting some modifications to provide possible paths for fabrication techniques of the structure. The npn microstructure is simulated by applying a process simulator by starting with a heavily doped p-type substrate which could be based on low-cost Si wafers. After etching deep notches through the substrate and forming the emitter by n-type diffusion, an aluminum layer is deposited to form the emitter electrode with about 0.1 µm thickness; thereby, the notches are partially filled. This nearly-open-notches microstructure, using thin metal instead of filling the notch completely with Al, gives an efficiency of 15.3%, which is higher than the conventional structure by 0.8%. Moreover, as antireflection coating (ARC) techniques play a crucial role in decreasing the front surface reflectivity, we apply different ARC schemes to inspect their influence on the optical performance. The influence of utilizing single layer (ZnO), double (Si3N4/ZnO), and triple (SiO2/Si3N/ZnO) ARC systems is investigated, and the simulation results are compared. The improvement in the structure performance because of the inclusion of ARC is evaluated by the relative change in the efficiency (Δη). In the single, double, and triple ARC, Δη is found to be 12.5%, 15.4%, and 17%, respectively. All simulations are performed by using a full TCAD process and device simulators under AM1.5 illumination

Performance Improvement of npn Solar Cell Microstructure by TCAD Simulation: Role of Emitter Contact and ARC

In the current study, the performance of the npn solar cell (SC) microstructure is improved by inspecting some modifications to provide possible paths for fabrication techniques of the structure. The npn microstructure is simulated by applying a process simulator by starting with a heavily doped p-type substrate which could be based on low-cost Si wafers. After etching deep notches through the substrate and forming the emitter by n-type diffusion, an aluminum layer is deposited to form the emitter electrode with about 0.1 µm thickness; thereby, the notches are partially filled. This nearly-open-notches microstructure, using thin metal instead of filling the notch completely with Al, gives an efficiency of 15.3%, which is higher than the conventional structure by 0.8%. Moreover, as antireflection coating (ARC) techniques play a crucial role in decreasing the front surface reflectivity, we apply different ARC schemes to inspect their influence on the optical performance. The influence of utilizing single layer (ZnO), double (Si3N4/ZnO), and triple (SiO2/Si3N/ZnO) ARC systems is investigated, and the simulation results are compared. The improvement in the structure performance because of the inclusion of ARC is evaluated by the relative change in the efficiency (Δη). In the single, double, and triple ARC, Δη is found to be 12.5%, 15.4%, and 17%, respectively. All simulations are performed by using a full TCAD process and device simulators under AM1.5 illumination

Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases

The immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of WBCs. The characteristics of WBCs are determined based on the chromatic, geometric, and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and differing opinions of experts and takes a long time; however, artificial intelligence techniques can help to solve all these challenges. Determining the type of WBC using automatic diagnosis helps hematologists to identify different types of blood diseases. This work aims to overcome manual diagnosis by developing automated systems for classifying microscopic blood sample datasets for the early detection of diseases in WBCs. Several proposed systems were used: first, neural network algorithms, such as artificial neural networks (ANNs) and feed-forward neural networks (FFNNs), were applied to diagnose the dataset based on the features extracted using the hybrid method between two algorithms, the local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM). All algorithms attained superior accuracy for WBC diagnosis. Second, the pre-trained convolutional neural network (CNN) models AlexNet, ResNet-50, GoogLeNet, and ResNet-18 were applied for the early detection of WBC diseases. All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN models block for extracting deep features and the SVM algorithm block for the classification of deep features with superior accuracy and efficiency. These hybrid techniques are named AlexNet with SVM, ResNet-50 with SVM, GoogLeNet with SVM, and ResNet-18 with SVM. All techniques achieved promising results when diagnosing the dataset for the early detection of WBC diseases. The ResNet-50 model achieved an accuracy of 99.3%, a precision of 99.5%, a sensitivity of 99.25%, a specificity of 99.75%, and an AUC of 99.99%

Performance Investigation of a Proposed Flipped npn Microstructure Silicon Solar Cell Using TCAD Simulation

This work aims at inspecting the device operation and performance of a novel flipped npn microstructure solar cell based on low-cost heavily doped silicon wafers. The flipped structure was designed to eliminate the shadowing effect as applied in the conventional silicon-based interdigitated back-contact cell (IBC). Due to the disappearance of the shadowing impact, the optical performance and short-circuit current density of the structure have been improved. Accordingly, the cell power conversion efficiency (PCE) has been improved in comparison to the conventional npn solar cell microstructure. A detailed analysis of the flipped npn structure was carried out in which we performed TCAD simulations for the electrical and optical performance of the flipped cell. Additionally, a comparison between the presented flipped microstructure and the conventional npn solar cell was accomplished. The PCE of the conventional npn structure was found to be 14.5%, while it was about 15% for the flipped structure when using the same cell physical parameters. Furthermore, the surface recombination velocity and base bulk lifetime, which are the most important recombination parameters, were studied to investigate their influence on the flipped microstructure performance. An efficiency of up to 16% could be reached when some design parameters were properly fine-tuned. Moreover, the impact of the different physical models on the performance of the proposed cell was studied, and it was revealed that band gap narrowing effect was the most significant factor limiting the open-circuit voltage. All the simulations accomplished in this analysis were carried out using the SILVACO TCAD process and device simulators