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The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19. [ABSTRACT FROM AUTHOR]

A new method for computing the optimal control, i.e., minimizing a given performance index, thereby steering the given initial state of a system to some point in the state space which may or may not be specified, is proposed here. The classical Calculus of Variations approach and the modern approach of variation in control that leads to Pontriagin's principle have been first compared. Moreover, based on the ideas in the derivation of the Maximum Principle of Pontryagin, we have derived the maximum principle of Pontryagin using the approach of Classical Calculus of Variations modified along the line of brief perturbation and derived an expression for the change in the value of performance index over each discretized interval. First, we derived an expression for the difference in the performance index due to perturbation in control u that differs slightly from u ∗ . Then, using the calculus of variations once again, we derived the formula for calculating the difference in the performance index using the brief perturbation suggested by Agashe. Also, we have given three examples of linear and non-linear systems for establishing the expediency of our derivations. Two examples are on the calculus of variations using the perturbation approach and one based on the derivation of Pontryagin's principle. [ABSTRACT FROM AUTHOR]

In today's world, video captioning is extensively used in various applications for specially-abled and, more specifically, visually abled persons. With advancements in technology for object detection and natural processing, there has been an instant surge infusing the above mainstream tasks. One such example of this fusion resulted in the generation of Image captions when an input image is fed to the system, and it gives a short description of what is present in the image. This fusion pertained to images and was further moved to be implemented on the Videos, with some tweaking in the current methods. This paper presents the survey of the state of art techniques of various video captioning methods. There have been many inputs provided by people worldwide in this domain; thus, there was a need to compile, study and analyze all the results and present that in a comprehensive study, which we have done in this paper. The comparison of various video captioning methods on the distinct dataset was evaluated on different parameters, which were most common and mainly used for image and video analysis. This review was done for methods used from the year 2015–2019 (year by year). The most commonly used dataset and evaluation method are also pictorially represented in a bar graph and scatter plot for each year for the respective evaluation parameter. Though a lot of analysis and research has been done on video captioning, our survey shows many problems. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Video captioning: a review of theory, techniques and practices" published in the journal Multimedia Tools & Applications. The publisher has retracted the article due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant references, and the article not being in scope of the journal or guest-edited issue. The publisher, in consultation with the Editor-in-Chief, no longer has confidence in the results and conclusions of the article. The publisher has been unable to contact one of the authors, Devang Nayar, and the other authors have not responded to correspondence regarding the retraction. [Extracted from the article]

Almost all of the automatic accident detection (AAD) system suffers from the tradeoff between computational overhead and detection accuracy. Recent advances in detection and classification methodologies have shown phenomenal improvements in accuracy but these systems require a huge number of computational resources making them unviable for deployment requiring real-time feedback. This paper proposes a methodology to develop a reliable and computationally inexpensive real-time automatic accident detection system that can be deployed with minimum hardware requirements. Specifically, we split our AAD system into three major stages (Detection, Tracking and Classification) and propose algorithms for each stage with reduced computational need. For the detection stage, we propose Mini-YOLO, a deep learning model architecture trained using knowledge distillation that has comparable accuracy with its counterpart YOLO(You-Only-Look-Once) with reduced model size and computational overhead. Mini-YOLO achieves an average precision (AP) score of 34.2 on the MS-COCO dataset while outperforming all other detection algorithms in runtime complexity, achieving a staggering 28 frames per second on a low-end machine. For the tracking stage, we adopt SORT (Simple Online Real-time Tracking) and for classification stage, we compare multiple machine learning algorithms and show that a support vector machine with radial basis kernel performs the best with an area under the curve (AUC) score of 0.98, model size of 448 KB (kilobytes) and 12.73 ms (milliseconds) latency. [ABSTRACT FROM AUTHOR]

In this paper, a noise-robust palmprint recognition system is discussed with a novel feature extraction technique: two-dimensional Cochlear transform (2D-CT) based on the textural analysis of image sample. Orthogonality of 2D-CT is proved which shows the high robustness of the proposed 2D-CT to noise. To validate the proposed feature extraction technique, palmprint recognition has been tested on both left and right palm of IITD database of 230 persons, CASIA palmprint database of 312 persons, polyU palmprint database of 386 persons and achieved high accuracy. The proposed 2D-CT method is compared with discriminative and robust competitive code, double orientation code, competitive coding, ordinal coding, Gabor transform, Gaussian membership-based features, absolute average deviation and mean features. Further, K-nearest neighbor is used to validate the matching stage. The results show superiority of the proposed method over other feature extraction methods. [ABSTRACT FROM AUTHOR]