The COVID-19 pandemic underscored the critical need for intelligent strategies to combat viral transmission. This research unveils “TriSafeGuard,” an innovative, integrated framework designed to oversee essential preventive measures, amalgamating sophisticated deep learning techniques with contactless temperature sensing. Our first approach involves a unique methodology utilizing Stacked Auto Encoder (SAE), which combines Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). This model is adept at identifying not only the presence of masks on individuals but also assessing their proper positioning. Such a technique achieved an impressive accuracy and F1 score of 94.16

The use of social distance as a tool throughout the battle with COVID-19 has shown promising results. In recent years, artificial intelligence (AI) and deep learning (DL) have emerged as a powerful resource for dealing with a wide range of practical challenges and delivering impressively positive outcomes. This article delves into the usage of Object Recognition, and Deep Learning to monitor personal and professional interactions between people at a distance. This research aims to aid in the pandemic fight by creating a technology that may be used as a social distance monitoring system. As evidence mounts in favour of social separation as a first-line, non-pharmaceutical intervention in the precautionary measure against the fastest spreading disease COVID-19, researchers feel that its rigorous observance should be encouraged. This proposal analyses live or recorded video to identify and evaluate correlation metric distances among individual people to verify to see if social distancing is preserved in crowded places.

The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.

A radial radio labeling ℸ of a connected graph G = (V, E) with radius rad(G) is a mapping from V (G) to N ∪ {0} satisfying |ℸ(u) − ℸ(w)|+ d(u, w) ≥ 1 + rad(G), ∀ u, v ∈ V (G). The span of a radial radio labeling ℸ, denoted by rr(ℸ) is the greatest number in the range of ℸ. The minimum span taken over all radial radio labelings ℸ of G is called the radial radio nmber of G and it is denoted by rr(G). In this article, we have investigated the upper bounds for rr(G) of chess board graphs and king’s graph.

Since its inception, the notion of domination has found vital roles in several real life applications related to facility locations, representatives’ selection, communication networks, electrical networks, etc. The vast application of the notion has paved the way for the development of the notion with several types. The notion of connected domination is a significant domination parameter amongst the several domination varieties emerged in this domain. The problem of determining limited bus stops in a route was effectively addressed by the connected domination parameter. Most of the biological and neural networks effectively use this notion to solve several problems which require the connectedness of the structures. In view of the growing applications of the variant, several researchers and scholars have published numerous research articles on the said parameter. Recently, some researchers attempted on transition of the domination parameter into a connected one. In order to facilitate this transition, another variant viz., connected domination transition number was introduced and its properties and bounds were studied. In this article we explore more properties and bounds of the parameter connected domination transition number for special types of graphs. We also characterize the instances at which the domination and connected domination parameters would be same for few types of graphs. We also attempted to derive few Nordhaus–Gaddum (NG) type results for the same.Â

This research work has resulted out of the real life application that lies in developing a facility location network into a powerful one. For this purpose, we consider a network in which facility location is already established. We want to further improve it so that the existing facility location becomes more stronger and reliable one. Among the varieties of domination available under survey, the connected domination introduced by [&H.B.Walikar] plays a vital role in the context of real life applications. Several researchers and scholars have published numerous research articles on this domination parameter. This article is intended on the task of transforming a dominating set into a connected dominating set and studying the properties involved in such transmission. AMS Subject Classification: 2010 Mathematics Subject Classification. 05C69.

A subset $S$ of $V$ is called a dominating set in $G$ if every vertex in $V-S$ is adjacent to at least one vertex in $S$. A dominating set $S$ is said to be a restrained dominating set if $\langle V-S \rangle$ contains no isolated vertices. The minimum cardinality of a restrained dominating set of $G$ is called the restrained domination number of $G$ and is denoted by $\gamma_{r}(G)$. The connectivity $\kappa(G)$ of a graph $G$ is the minimum number of vertices whose removal results in a disconnected or trivial graph. In this paper we characterized the graphs with sum of restrained domination number and connectivity is equal to $2n-6$.

Let $G = (V, E)$ be a connected graph. A set $S$ of vertices in $G$ is a perfect dominating set if every vertex $v$ in $V-S$ is adjacent to exactly one vertex in $S$. A perfect dominating set $S$ is said to be a neighborhood connected perfect dominating set (ncpd-set) if the induced subgraph $$ is connected. The minimum cardinality of a ncpd-set of $G$ is called the neighborhood connected perfect domination number of $G$ and is denoted by $\gamma_{ncp}(G)$. In this paper we initiate a study of this parameter.

Let G = (V,E) be a connected graph. An edge dominating set X of G is called a neighborhood connected edge dominating set (nced-set) if the edge induced subgraph < N(X) > is connected. The minimum cardinality of a nced-set of G is called the neighborhood connected edge domination number of G and is denoted by. In this paper we initiate a study of this parameter.