IntroductionUrinary tract infection (UTI) is a frequently diagnosed infection in women and children. Treatments are often initiated with broad-spectrum antibiotics without performing any culture and sensitivity test. Inappropriate and empirical antimicrobial regimens and poor adherence to the drugs lead to the recurrence of the disease. Moreover, resistance against antibiotics in the urinary tract bacteria due to inadequate therapies is a more significant cause of concern. This systematic review will explore the different antimicrobial options for treating UTIs in children and compare their effectiveness.Methods and analysisFour electronic databases MEDLINE, Cochrane Central Register of Controlled Trials, Scopus and Web of Science will be searched in February 2022 to find relevant studies. After the initial screening by two independent review authors, the selected articles will go through the full-text evaluation to filter the inclusion criteria. Using an appropriate tool, the risk of bias will also be assessed by two independent review authors. The review results showing the treatment effects of different antimicrobials will be presented as a narrative synthesis, and a meta-analysis will be conducted if applicable. Assessment of heterogeneity between studies, assessment of publication bias, and sensitivity analysis will also be performed.Ethics and disseminationThe study protocol of this systematic review has been approved by the institutional review board of North South University. The dissemination of the results will be conducted in the form of scientific publication in a peer-reviewed journal and presentations in different regional and international conferences.PROSPERO registration numberCRD42021260415.

This paper presents an innovative design for Optical Character Recognition (OCR) from text images by using the Template Matching method.OCR is an important research area and one of the most successful applications of technology in the field of pattern recognition and artificial intelligence.OCR provides full alphanumeric visualization of printed and handwritten characters by scanning text images and converts it into a corresponding editable text document. The main objective of this system prototype is to develop a prototype for the OCR system and to implement The Template Matching algorithm for provoking the system prototype. In this paper, we took alphabet (A-Z and a-z), and numbers (0-1), grayscale images, bitmap image format were used and recognized the alphabet and numbers by comparing between two images. Besides, we checked accuracy for different fonts of alphabet and numbers. Here we used Matlab R 2018 a software for the proper implementation of the system.

Biometric authentication is a common way of granting access to a system or device. The ear, like fingerprints, retina, iris, face, voice, and so on, is a biometric modality. Compared to other biometric organs, the anatomy of a human's ear remains stable from birth to old life. As a visible organ with an easily acquired image, it may also be a source of a biometric signature that may be used to identify individuals. This research demonstrates two approaches to recognizing a person from 2D ear images: non-deep ML models and deep learning-based ML models. The first, or classic, model investigates computer vision preprocessing techniques such as converting an RGB image to monochrome, then rescaling and locating the entropy. The key weighted characteristics from the ear images were extracted using Independent Component Analysis (ICA) and Principal Component Analysis (PCA). A Gaussian Process Classifier (GPC) is then utilized for classification and several kernels such as RBF, Rational Quadratic, and Matern. In the second technique, a deep learning-based ML model called You Only Look Once (YOLO) is utilized to categorize the ear images and identify the source individual without preprocessing. We gathered a standard ear dataset (EarVN1.0 Dataset) from 164 people, totaling 27,592 training images. For testing reasons, 820 images were chosen randomly, five images from each of 164 people. The models were built on the Google Colaboratory server using the Python language framework and GPU-based implementation on the Jupyter Notebook.

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