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In this paper we are going to develop a robust automated web application for transacting money in higher level security purpose with high facial recognition. First we have to register our personal details with our face and pass the liveness detection test. Haar cascade-based algorithm has been applied for fast and simple face detection from the input image. The face image is then being converted into grayscale image. After that, the iris, eyebrows, nose, mouth of candidates are extracted from the intensity valleys from the detected face it will consider as datasets now our system can understand who are all authorized and unauthorized. While login our face will be recognized if we authorized will can move to authorized page else our system won’t allow to login. For higher security purpose we have used face recognition module. During the transaction our face will be recognized it will allow only the authorized account holder to transact a money, it doesn’t allow others to transact a money.

Growing usage of Electric Vehicle calls for the invention of new technologies to increase the effective utilization of the Electric Vehicle and battery technology. The battery is the soul of the electric vehicle, so proper utilization of the battery is very important. This project is decided to predict the Lifecycle of the Lithium Ion Battery with the help of Machine Learning Software. The aging of the battery will increase the operating costs, reduce the service life of the equipment, and affect the safe operation of the equipment. There is no ideal solution for the recycling of Li-ion batteries and it is not environmentally friendly. So, predicting the lifecycle of the battery plays a vital role. With the help of data driven linear regression process, the lifecycle is predicted in a shorter span of time than the conventional methods in practice. This is done with the help of Edge Impulse software which is a development platform for embedded machine learning. Predicting the lifecycle of a battery will be useful for battery manufacturers, people buying used batteries and to operators who own a large fleet of electric vehicles. By knowing the lifecycle of a battery, they can be effectively used for the required application.

Background Perforated peptic ulcer (PPU) remains a common condition globally with significant morbidity and mortality. Previous work has demonstrated variation in reporting of patient characteristics in PPU studies, making comparison of studies and outcomes difficult. The aim of this study was to standardize the reporting of patient characteristics, by creating a core descriptor set (CDS) of important descriptors that should be consistently reported in PPU research. Methods Candidate descriptors were identified through systematic review and stakeholder proposals. An international Delphi exercise involving three survey rounds was undertaken to obtain consensus on key patient characteristics for future research. Participants rated items on a scale of 1–9 with respect to their importance. Items meeting a predetermined threshold (rated 7–9 by over 70 per cent of stakeholders) were included in the final set and ratified at a consensus meeting. Feedback was provided between rounds to allow refinement of ratings. Results Some 116 clinicians were recruited from 29 countries. A total of 63 descriptors were longlisted from the literature, and 27 were proposed by stakeholders. After three survey rounds and a consensus meeting, 27 descriptors were included in the CDS. These covered demographic variables and co-morbidities, risk factors for PPU, presentation and pathway factors, need for organ support, biochemical parameters, prognostic tools, perforation details, and surgical history. Conclusion This study defines the core descriptive items for PPU research, which will allow more robust synthesis of studies.

Image restoration approaches are introduced to restore the latent clear images from the degraded images. However, the performance of the existing approaches remains an open problem, which may leads to the further development of advanced image restoration techniques. Therefore, an effective image restoration method is developed for restoring the input image from various noises, like impulse noise and random noise. The generation of pixel map, identification of noisy pixel, and the enhancement of pixel are the three major phases involved in the proposed method. Initially, the noisy pixel map generation is performed from the input image, and then the noisy pixels are identified based on deep convolutional neural network, which is trained by the proposed Jaya‐Bat algorithm. The Jaya‐Bat algorithm is developed by combing the Jaya optimization algorithm and Bat algorithm. Once the noisy pixels are identified, the pixel enhancement is done using the neuro fuzzy system. The experiment is carried out using Statlog (landsat satellite) dataset, and the developed method achieves the maximal peak signal to noise ratio of 51.03 dB, maximal structural similarity index of 0.848 for the image with random noise, and the maximal second derivative like measure of enhancement 62.96 dB with impulse noise, respectively.

A solution to the current problem of wind tunnel wall interference could be achieved by ventilating the test section and thereby controlling the flow pattern around the model. The motivation for the slotted wall test section arises from the fact that a fully open jet and a fully closed jet introduce corrections of opposite sign to the wind tunnel data. This current work is limited to axisymmetric wind tunnels and solid blockage corrections. Such a tolerant axisymmetric wind tunnel (TAWT), which does not need any correction to the measured flow quantities and which is also independent of the test model shape and size would find wide application in the field of industrial aerodynamics. A numerical model based on a surface singularity potential flow method showed that at 70% OAR (open area ratio) for models of size up to 25% blockage and for three different shapes the tunnel design would yield results (coefficient of pressure) with less than 2% error while such models might need up to 75% data correction if tested in a solid wall wind tunnel. Experiments indicated good agreement with the numerical investigation and at 60% OAR the TAWT gave results close to free air results for all the models tested (up to 25% blockage).