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Locating parking space is becoming a serious problem as more and more employees prefer to switch to personal commute due to prevailing pandemic situation across globe in office campus. Particularly, where there is multiple building inside main campus, and it is quite challenging to keep track of car parking inside a given building. Without parking availability information, commuter must shuttle around builds to find parking space during peak hours, and this will be a loss of time as well as frustration to the commuter. There are few options companies follow like physically counting cars at certain interval and update the dashboard, install badge reader at entry and exit and people must punch card at both the places, sensors have been installed in certain parking lots, allowing management to track when cars enter and exit, install Radio Frequency Identification (RFID) and reader at entry and exit in order to track the capacity. All these methods will bring in burden on additional resource and high cost of installation of reader & necessary barricade systems respectively. Also, will add to regular maintenance of overall system. This project details DL implementation to create a vehicle counting system by using video from camera installed at the entry and exit path for vehicle. Pretrained model of YOLOv4 is used for object detection, vehicle tracking with Euclidean distance and counting. Since vehicle movement is considerably slow in pathway, performance of the model tested is 96.8% to 100% for varied frame rate of the video in parking capacity tracking

Rainbow-Vertex Connection Number [rvcn] is computed for some graphs by the researchers. Here we have considered the subdivision graphs of certain graph classes. The rainbow edge connection number of subdivision of Triangular snake graph was already found[1]. Using the definition of rainbow-vertex connection number [5], which is the smallest positive integer k such that the graph is rainbow-vertex connected, we find the rainbow vertex number of subdivision graph of Friendship graph ,, Triangular snake graph and Comb graph .

Background/Purpose: These days, the involvement of computer science in agriculture and food science is expanding. Classification and fault identification of diverse products employ a variety of Artificial Intelligence (AI), soft computing approaches, and methodologies, which contribute to higher-quality products for consumers. The position of Arecanuts in the international and Indian markets, as well as the application of computer vision and image processing to a system for categorizing and grading Arecanuts, are the main topics of this article. Objective: The development of a system for the automated categorization of Arecanut using images is limited by difficulties. To assess the value of computer vision application for Arecanut, it is critical to taken as account the traditional and economic significance of Arecanut. Design/Methodology/Approach: Several types of Arecanut are prone to great variation in color, texture, and form depending on the category and the area in which they are cultivated. Arecanuts are processed utilizing a variety of techniques, with an emphasis on the finished product's exterior. Here, the color, size, and texture of Arecanut are used to construct a classification or grading system. Findings/Result: With reference to the cited significant work that has been done on other fruits as well as Arecanuts from the standpoint of computer vision. This article provided a thorough introduction to Arecanuts, computer vision, and the uses and benefits of vision-aided technologies in the grading of Arecanuts and categorization. Result Limitations/Implications: This review is based on the detection and classification of the Arecanuts done using computer vision and AI techniques. Originality Value: Several inline resources including review papers on Arecanut, research articles, technical books, and website resources. Paper Type: Literature Review paper on smart auto Arecanut Sorting and Grading of Arecanut using Computer Vision and Image Processing