Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification

An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems

Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at s√=13TeV

The rate for Higgs (H) bosons production in association with either one (tH) or two (tt¯H) top quarks is measured in final states containing multiple electrons, muons, or tau leptons decaying to hadrons and a neutrino, using proton–proton collisions recorded at a center-of-mass energy of 13TeV by the CMS experiment. The analyzed data correspond to an integrated luminosity of 137fb−1. The analysis is aimed at events that contain H→WW, H→ττ, or H→ZZ decays and each of the top quark(s) decays either to lepton+jets or all-jet channels. Sensitivity to signal is maximized by including ten signatures in the analysis, depending on the lepton multiplicity. The separation among tH, tt¯H, and the backgrounds is enhanced through machine-learning techniques and matrix-element methods. The measured production rates for the tt¯H and tH signals correspond to 0.92±0.19(stat)+0.17−0.13(syst) and 5.7±2.7(stat)±3.0(syst) of their respective standard model (SM) expectations. The corresponding observed (expected) significance amounts to 4.7 (5.2) standard deviations for tt¯H, and to 1.4 (0.3) for tH production. Assuming that the Higgs boson coupling to the tau lepton is equal in strength to its expectation in the SM, the coupling yt of the Higgs boson to the top quark divided by its SM expectation, κt=yt/ySMt, is constrained to be within −0.9<κt<−0.7 or 0.7<κt<1.1, at 95% confidence level. This result is the most sensitive measurement of the tt¯H production rate to date.SCOAP

Book of Abstracts of the 2nd International Conference on Applied Mathematics and Computational Sciences (ICAMCS-2022)

It is a great privilege for us to present the abstract book of ICAMCS-2022 to the authors and the delegates of the event. We hope that you will find it useful, valuable, aspiring, and inspiring. This book is a record of abstracts of the keynote talks, invited talks, and papers presented by the participants, which indicates the progress and state of development in research at the time of writing the research article. It is an invaluable asset to all researchers. The book provides a permanent record of this asset. Conference Title: 2nd International Conference on Applied Mathematics and Computational SciencesConference Acronym: ICAMCS-2022Conference Date: 12-14 October 2022Conference Organizers: DIT University, Dehradun, IndiaConference Mode: Online (Virtual

Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at s=\sqrt{s} = 13 TeV

The rate for Higgs (H) bosons production in association with either one (tH) or two ($\mathrm{t\bar{t}}$H) top quarks is measured in final states containing multiple electrons, muons, or tau leptons decaying to hadrons and a neutrino, using proton-proton collisions recorded at a center-of-mass energy of 13 TeV by the CMS experiment. The analyzed data correspond to an integrated luminosity of 137 fb$^{-1}$. The analysis is aimed at events that contain H $\to$ WW, H $\to \tau\tau$, or H $\to$ ZZ decays and each of the top quark(s) decays either to lepton+jets or all-jet channels. Sensitivity to signal is maximized by including ten signatures in the analysis, depending on the lepton multiplicity. The separation among the tH, the $\mathrm{t\bar{t}}$H, and the backgrounds is enhanced through machine-learning techniques and matrix-element methods. The measured production rates for the $\mathrm{t\bar{t}}$H and tH signals correspond to 0.92 $\pm$ 0.19 (stat) $^{+0.17}_{-0.13}$ (syst) and 5.7 $\pm$ 2.7 (stat) $\pm$ 3.0 (syst) of their respective standard model (SM) expectations. The corresponding observed (expected) significance amounts to 4.7 (5.2) standard deviations for $\mathrm{t\bar{t}}$H, and to 1.4 (0.3) for tH production. Assuming that the Higgs boson coupling to the tau lepton is equal in strength to its expectation in the SM, the coupling $y_{\mathrm{t}}$ of the Higgs boson to the top quark divided by its SM expectation, $\kappa_\mathrm{t}=y_\mathrm{t}/y_\mathrm{t}^\mathrm{SM}$, is constrained to be within -0.9 $\lt \kappa_\mathrm{t} \lt$ $-$0.7 or 0.7 $\lt \kappa_\mathrm{t} \lt$ 1.1, at 95% confidence level. This result is the most sensitive measurement of the $\mathrm{t\bar{t}}$H production rate to date