The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow

This paper describes the synthesis of ?- Fe2O3 nanoparticles by sol-gel route using carboxylic acid(2-hydroxy benzoic acid) as gelatin media and its photo activity for degradation of cibacron red dye . Hematite samples are synthesized at different temperatures: 400, 500, 600, 700, 800 and 900 ?C at 700 ?C the ?-Fe2O3 nanoparticles are formed with particle size 71.93 nm. The nanoparticles are characterized by XRD , SEM, AFM and FTIR . The 0.046 g /l of the catalyst sample shows high photo activity at 3x10-5M dye concentration in acidic medium at pH 3.

The chemical bath deposition technique (CBD) is considered the cheapest and easiest compared with other deposition techniques. However, it is highly sensitive to effective parameter deposition values such as pH, temperature, and so on. The pH value of the reaction solution has a direct impact on both the nucleation and growth rate of the film. Consequently, this study presents a novel investigation into the effect of a precise change. in the pH reaction solution value on the structural, morphological, and photoresponse characteristics of tin monosulphide (SnS) films. The films were grown on a flexible polyester substrate with pH values of 7.1, 7.4, and 7.7. The X-ray diffraction patterns of the grown films at pH 7.1 and 7.4 confirmed

In the present study, magnet silica-coated Ag2WO4/Ag2S nanocomposites (FOSOAWAS) were fabricated via a multistep method to address the drawbacks related to single photocatalysts (pure Ag2WO4 and pure Ag2S) and to clarify the significant influence of semiconductor heterojunction on the enhancement of visible-light-driven organic degradation. Different techniques were performed to investigate the elemental composition, morphology, magnetic and photoelectrochemical properties of the fabricated FOSOAWAS photocatalyst. The FOSOAWAS photocatalyst (1 g/L) exhibited excellent photodegradation efficiency (99.5%) against Congo red dye (CR = 20 ppm) after 140 min of visible-light illumination. This result confirmed the ability of the heterojunction be

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification