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In this paper novel making use of a CNN to forecast the spread of COVID-19 and comparing its accuracy with Logistic Regression is the main objective. Applying Logistic Regression and Convolutional Neural Networks to Regression are two groups used each with twenty samples to assess the accuracy of COVID-19 forecast. We used G power with an 80% pretest power to decide on the sample size. The CNN and LR algorithms were trained using data models that included both normal and COVID-19 chest X-rays. The accuracy of CNN is 98.5 % and LR has an accuracy of 77.7 % with a statistical significance of 0.496 (p>0.05). Compared to other methods, Convolutional Neural Networks use a to the Logistic Regression. [ABSTRACT FROM AUTHOR]

This study investigates a new method for COVID-19 outbreak prediction by means of an Artificial neural network (ANN) using convolutional layers (CNN). Compared to with an Adaptive boosting classifier. In this method, two groups are used. With the help of CNNs, one group and two more groups with Ada boost method employed a 20-person sample for each research to evaluate the precision of COVID-19 predictions. Applying G power and a pretest power of 80%, we determined the sample size. The CNN and Ada boost algorithms were trained using chest X-rays of both healthy and COVID-19 afflicted people. The results show that CNN achieved 98.5% accuracy and Ada boosted 79% accuracy, with a p-value of less than 0.05 indicating statistical significance. To review: Deep Learning Network algorithm has achieved better accuracy compared to the Ada boost classifier. [ABSTRACT FROM AUTHOR]

The goal of current study is to effectively predict COVID-19 (+) using lung X-rays and cutting-edge artificial intelligence approaches. This study offers the promising VGG16 transfer learning based model for the quicker and more precise diagnosis of COVID-19. The accuracy, precision, recall, and f1 score of performance analysis are used to assess the effectiveness of the system under evaluation. 800 X-ray specimens were used in the experiments. The suggested method for detecting the COVID-19 X-Ray images is based on the VGG16 model with transfer learning and examined with various optimizers. Stochastic Gradient Descent (SGD), RMSProp, Adam, Adagrad, AdaDelta, Adamax, and NAdam are the optimizers. The suggested method uses chest X-rays to more effectively and precisely determine if COVID-19 is positive or negative. [ABSTRACT FROM AUTHOR]

Pneumonia is the leading cause of death in children and one of the leading causes of death worldwide. Early detection of pneumonia in pediatric is needed so that children get proper treatment and avoid wider transmission. This study aims to detect pneumonia in children based on chest X-ray (CXR) images by comparing the Vision Transformer (ViT), InceptionV3, and Xception models. Vision Transformer (ViT) is a new transformer model that trains neural networks on image data. Meanwhile, the InceptionV3 and Xception models are transfer learning models in a convolutional neural network. This study uses a dataset called the Pediatric Chest X-ray Pneumonia, which contains two classes, namely normal and pneumonia. Vision Transformer achieved the best accuracy, precision, recall, and F1-measure values of 97.78, 97.46, 97.88, and 97.16. Furthermore, the second model, namely InceptionV3, obtained accuracy, precision, recall, and F1-measure values of 97.18, 96.06, 96.87, and 96.45. Finally, the Xception model obtained accuracy, precision, recall, and F1-measure values of 97.01, 95.85, 96.65, and 96.24. So, the conclusion of this research is that the Vision Transformer model can generate good performance for pediatric pneumonia detection based on CXR. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has created a global health crisis and an urgent need for accurate and efficient diagnostic tools. This study aims to investigate the potential of deep radiomic analysis in predicting COVID-19 using CT and X-ray images. The study utilizes a large dataset of CT and X-ray images from COVID-19 patients, as well as healthy controls. Deep radiomic analysis is performed on the images using various deep learning algorithms, including convolutional neural networks and autoencoders. The results demonstrate that deep radiomic analysis can accurately distinguish COVID-19 patients from healthy controls with high sensitivity and specificity. The study concludes that deep radiomic analysis has the potential to be a valuabletool in the diagnosis and management of COVID-19, particularly in settings where molecular testing may not be readily available. Our findings suggest that deep radiomic analysis may provide a useful tool for screening and diagnosing COVID-19, especially in areas with limited access to PCR testing. This approach has the potential to facilitate rapid and accurate diagnosis, allowing for earlier treatment and better outcomes for patients. [ABSTRACT FROM AUTHOR]

The development of aberrant cell proliferation in the lungs is a problematic condition that has the potential to result in death. On the list of diseases that most frequently result in mortality, lung cancer takes first place. The early stages of lung cancer are notoriously difficult to diagnose due to the fact that cancer cells with dimensions less than very small are notoriously difficult to spot by imaging. If the cell abnormalities are discovered in the early stages, it will be possible to begin therapy sooner, which will result in an improved chance of the patient surviving the illness. Several different image processing strategies can be utilized in the diagnostic phase of patient care to help spot signs of disease. In this paper, classification of Lung Cancer from chest X-ray images has been done using optimized Convolutional Neural Network (OCNN) and Ant Lion Optimization (ALO) algorithm. In pre-processing step, the contrast of all images are enhanced using Histogram Equalization (HE) method and the noises are removed from all images using Median Filtering. After the pre-processing step, feature extraction is performed using Gray Level Spatial Dependence (GLSD) to extract the statistical features. The feature vector is then trained and classified using OCNN-ALO algorithm. The ALO algorithm is used to optimize the hyper parameters of CNN layers. It classifies the lung images into normal and lung tumor affected. Performance results have indicated that OCNN-ALO attains the superior performance with 95.15% accuracy, 85.43% sensitivity, 93.4% specificity and 76.43% F1-score. [ABSTRACT FROM AUTHOR]

Covid19 and Pneumonia is a life-threatening disease that affects lungs in a person causing infection and is often caused by a bacteria known as Streptococcus pneumoniae. According to the World Health Organization (WHO), every one in ten people die in India is due to pneumonia. For COVID-19 detectionand deep learning model have been developed that can accurately detect pneumonia and COVID-19 caseswith the help of frontal chest X-rays. These models use similar architectures to pneumonia detection models, but are trained specifically on COVID-19 cases. One example of such a model is COVID-Net, which was developed specifically for the COVID-19 detection from using chest X-rays. Therefore, establishing an autonomous pneumonia detection system will provide benefit for the purpose of treating the disease with efficiency, especially for rural area. Deep Neural Network had received too much attentionfor the purpose of disease classification. The success of this approach is due to deep learning, in the field of dangerous disease prediction. Moreover, features learned by deep learning model pretrained on vast dataset is of great value in for image segregation tasks. In the study, our evaluation of performance with a pretrained deep learning model used a features extractor, follow with differently classified with classifying pneumonia, normal and covid chest radiographs. increase. there are several deep learning models that had been developed for the pneumonia detection, and most optimal model may depend on various factors. CheXNet, DenseNet, and COVID-Net are some examples of deep learning models that have shown promising results for pneumonia and COVID-19 detection from medical images [ABSTRACT FROM AUTHOR]

Each metal has different properties with another and mechanical properties were important in the construction. Dissimilar welding still has problem because of thermal properties from both base metal. This study discussed performance from a Stainless Steel 304 and ST 37 plate joined by the MIG welding and Inconel as a filler wire with thin of both plate less than 1 mm. Performance of a dissimilar weld join was analyzed with tensile performance, macrostructure, and weld joint condition scanned by X-ray. The lowest tensile performance was around 1.5 kN which could be represented by specimen 2a, and the second tensile performance was not more than 2.5 kN, it could be shown specimen 7a. Specimen 1 of macrostructure appeared a crack at base metal with filler, Specimen 2 of macrostructure shown that a weld join change, a weld join of specimen 7 was not complete. Specimen 3 and 4 of X-ray analysis was complete weld join although specimen 4 was better than specimen 3. [ABSTRACT FROM AUTHOR]

The current (mA) control is necessary to controlling the number of electrons and x-ray intensity at x-ray machine. Radiographers are likewise impacted by the presence of high radiation levels. This study aimed to improve performance the x-ray machine with wireless mA control system prototype. One of the important benefits of the proposed study is the simplicity to controlling intensity of x-ray based on wireless mA control system. This research method uses rheostat as current selection (mA). The current settings that can be selected are 20 mA, 30 mA, 40 mA, 50 mA and 60 mA, which can be done manually and via the mobile phone (cellphone) connected to this device via wireless using Wemos D1Mini. Tool testing is carried out by comparing the results of measuring the output voltage of the filament transformer using AVOmeter with the results of voltage calculation through formula analysis. In addition, tests are carried out for the distance of the tool control connection in the presence of obstacles and no obstacles. Based on the test results obtained, it is known that the smallest correction value is 0,01 VAC for the mA option of 30 mA. The x-ray device can be controlled wirelessly using Android up to a distance of 20 meters with a 14 cm thick wall obstructed condition. [ABSTRACT FROM AUTHOR]

In medicine, X-ray images are important, on the basis of which medical professionals can obtain necessary information about the internal structures of patients. The diagnosis is determined using the information that has been received. In some cases, it will not be possible to obtain information sufficient for diagnosis from X-ray image. For example, if the radiation is not delivered to the patient in sufficient quantity, the contrast of the medical X-ray image will not meet the requirement, which means that the analysis of the image will be complicated. This does not allow for making an accurate diagnosis. So, when fine-tuning the contrast of the X-ray image, it's important to guide the patient through the initial processing of the X-ray image, sparing them from undergoing a re-examination. The automation of this process requires the use of objective evaluation indicators. The primary objective of this research is to enhance the contrast of X-ray images through the utilization of an algorithm. Additionally, it aims to evaluate the resulting images using the widely recognized RMS (Root Mean Square) evaluation indicator. Furthermore, the research seeks to identify the most effective algorithm or sequence of algorithms based on this evaluation indicator. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common causes of death according to WHO in 2020. One of the ways to detect lung cancer is through X-rays. Because the examination is generally carried out manually through the naked eye of experts, it is advisable to have an alternative to detect the disease as early as possible so that the patient can get the right treatment. This study aims to detect the presence of lung cancer from chest x-ray images with the application of deep learning, namely Convolutional Neural Network (CNN). In this study, the authors used 90 cancer images and 90 normal images. The research design stage consisted of pre-processing on x-ray images and identification using CNN. The research obtained an accuracy rate of 87%. [ABSTRACT FROM AUTHOR]

Evaluation for Lα, Lβ, Lγ, X-ray photo cross-section have been done for 51Sb and 52Te at excitation energy 6 KeV and 8keV. The theoretical values of the cross-sections were calculated using tabulated data sets of different physical parameters, such as L subshell photoionization cross-sections (PCS) σLi (i= α, β, γ), fluorescence yields (ωi), coster-kronig transition probabilities (fij) and radiative decay rates (Fij). DHS model with data set of Campbell and Puri was used to calculate L XRF cross sections. Theoretical data of these elements at this excitation energy are highly desirable in order to check the reliability of experimental results. [ABSTRACT FROM AUTHOR]

Novel Corona-virus (COVID-19) must be recognized immediately and precisely to avoid or contain a possible pandemic by immediate quarantine and appropriate medical treatment. Detecting a disease will be challenging due to the increased number of COVID-19 patients and the virus' mutation. However, computer-assisted medical diagnosis has attained cutting-edge outcomes using artificial intelligence and deep learning methodologies. This study uses current resources and powerful deep learning methods to present an alternative diagnostic tool for COVID-19 instances. This work aims to investigate the feasibility of combining state-of-the-art classifiers for the most accurate detection of COVID-19 from chest X-ray images. The model utilizes the IEEE covid chest X-ray dataset (https://github.com/ieee8023/covid-chestxray-dataset) and the EfficientNet architecture to predict an accuracy of 99 percent. As a result, a model such as this one can assist medical practitioners in diagnosing COVID-19 cases more quickly than a radiologist reading through each scan one by one, mainly when many patients must be evaluated in short period. [ABSTRACT FROM AUTHOR]

Serious disease known as pneumonia, which can occur in one or both lungs, is frequently brought on by bacteria, fungi, or viruses. Based on the x-rays we have, we will be able to identify this lung illness. In this study we conduct investigation and comparison of the identification of lung illness with the use of several deep learning method. This study uses four adaptable and effective deep learning methodologies, and a chest X-beam image to predict and identify a patient with and without the condition. VGG-16, Res-Net, Inception-Net, and Dense-Net. In this study models CNN(5 layes), Dense-Net, VGG-16, Res-Net, Inception-Net achieve the validation accuracy of 92.5, 90, 80, 85 and 83.3. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

An assessment of the Convolutional Neural Network (CNN) is presented in this study, which makes the test faster and more reliable in recognizing COVID-19 from chest X-Ray images. In light of the large number of studies already conducted, the proposed model strives to improve accuracy and metrics by incorporating new methodologies. CNN models such as VGG16 have been used to achieve better outcomes. Order metrics were used to estimate the exhibition's size in this assessment. There is a strong correlation between this research and the ability to detect SARS-CoV-2 from CXR images of the lungs. In terms of accuracy, a model is the best option. VGG-16 may be used to train a CNN network to determine if a person has COVID-19 just by looking at a chest X-ray images, improving the radiography dataset's success rate. [ABSTRACT FROM AUTHOR]

X-ray machines have been used for a long time in industries for non-destructive testing. X-ray imaging can detect surface and subsurface defects in the materials. An X-ray machine consists of some components that work together to produce adequate amounts of the x-ray beam to produce a clear image of the materials. If one or more components are damaged, it will harm the worker. The X-ray failure will cause unrestraint x-ray production which means we cannot control the radiation exposure. The failure and damage of the X-ray machine are also will cost a lot of money for a replacement. Due to prevent this risk from happening, the X-Ray Laboratory team National Research and Innovation Agency (BRIN) has identified and analyzed the risk using a systematic risk assessment framework. The first step was to identify some critical components in the X-ray machine, then analyzed all the hazards associated with Failure Mode Effect Analysis (FMEA). The FMEA method is combining technology and experience to identify and eliminate potential failure modes. The result of the FMEA analysis will be guidance for the maintenance and safety procedures for an X-Ray machine personnel. Following these procedures, it is expected can prolong the life of the X-ray machine. [ABSTRACT FROM AUTHOR]

Anthropogenic activities such as industry are primary sources of heavy metals and other elements, which can contaminate the surrounding environment. One of the most significant problems is soil and air pollution, which is harmful to humans and animals because it accumulates hazardous metals in plant tissue when growing in contaminated environments. This study identified and evaluated the magnitude of heavy metal contamination in leaves of plant samples at 22 locations, which consist of radii of 0.5-1; 3; and 5 KM from the industrial site in Gresik, East Java. The level of elements in the plant has been determined using Total X-Ray Fluorescence. An estimation of human health risks from consuming the plants was also conducted. The analysis result of the element content in plant samples was in the following order: Pb < Cu < Zn < Mn < Fe. The range of concentrations for each element in the samples was obtained and compared with the permissible levels set by the Food and Agricultural Organization (FAO) and World Health Organization (WHO) standards. The results obtained from this analysis revealed that Mn, Fe, Cu, Zn, and Pb are in general accordance with the recommended standard, and the hazard quotient (HQ) was lower than 1 for a crop, indicating no appreciable health risk due to the consumption of these samples in the study area. [ABSTRACT FROM AUTHOR]

Pneumonia is one amongst the most happening diseases all around and requires proper diagnosis at an early stage. A huge number of children die each year because of pneumonia all over the world. According to the latest estimation Pneumonia kills many children than any other infectious disease, claiming the lives of over 8,00,000 children under the age of five. The image processing would help better for prior diagnosis of diseases such as cancer, pneumonia etc. Convolutional neural networks (CNN) are widely used in image processing. The diagnosis of pneumonia can be achieved by using the X-ray image of the lungs. This diagnosis was done by considering the factor of having cloud kind of formation on X-ray images. Hence, artificial intelligences-based frameworks are required. [ABSTRACT FROM AUTHOR]

COVID-19 is an extremely serious disease caused by COVID-2 (SARS-CoV-2), an extremely severe respiratory condition. COVID-19 was discovered in Wuhan City, China, in December of 2019 before spreading around the world and becoming a pandemic. It has had a significant impact on daily life, general well-being, and the global economy. Distinguishing the effective positive cases at the right time is critical in the early stages of treatment. Detecting this virus necessitates a large number of tests, which takes time, and no other automated tool kits are currently available. X-ray images of the chest obtained through radiology imaging methods reveal important information about the COVID-19 infection. Deep learning methods on radiological images using advanced technologies such as artificial intelligence provide precise analysis of the infection and can be useful in treating patients in remote locations where medical facilities are not immediately available. Convolution neural networks, one of the deep learning methods used in the proposed model, are used to classify the person as Covid positive or negative by analysing images of chest X-rays. [ABSTRACT FROM AUTHOR]

The objective of the study is to detect the covid-19 positive patient with the help of Chest X-ray image dataset by using machine learning based on Multi-dimensional Convolutional Neural Network Algorithm. To achieve accuracy a novel augmented dataset classification is used. Accuracy and Loss of Covid-19 detection are Performed from kaggle library. Dataset of 2400 chest image dataset with total sample size 30. The two groups Multi-dimensional Convolutional Neural Network(N=15) and Convolutional Neural Network(N=15). The study proved that Multi-dimensional Convolutional Neural Network achieved better accuracy as 99.66% which is higher, compared to Convolutional Neural Network accuracy as 99.13%. Finally, Multi-dimensional CNN appears significantly better than CNN. The two algorithm Multi-dimensional CNN and CNN are statistically satisfied with the independent sample T-test value(p<0.001) with confidence level of 95%. Study shows Multidimensional CNN seems better (Std. Error Mean= 1.96525) than CNN(Std. Error Mean= 6.13974). [ABSTRACT FROM AUTHOR]

A globally affected pandemic SARS-CoV-2 created health emergencies the world all over. After originating in China in 2019, COVID-19 viruses obtained various mutations in the past two years and created severe impacts on the quality of human life with a high mortality rate. Early diagnosis and rapid isolation is the best solution to prevent the spread of the viruses. Among various diagnosis tests, a chest X-ray scan shows better results. Since manual analysis of X-ray images of the chest is burdensome, many deep learning-based methods have evolved in the past two years. However, the methods developed so far still need improvements for successful implementation in clinical settings. This piece of writing proposes an avant-garde deep feature fusion model for the effective detection of COVID-19. The experiments in this work are arranged in two stages. In the first phase, the images of chest X-ray are tried to classify using three robust deep learning networks such as ResNet-50,Inception-v3, and DenseNet. We have combined the features from the highly performed deep learning networks and designed a deep feature fusion model in the second stage. The promising results in a combined dataset (combination of two publically available data sets) of X-ray images of the chest show the competence of the schemed model. We have also used four various data augmentation methods to generate more COVID-19-infected samples in this work. [ABSTRACT FROM AUTHOR]