http://dspace.aiub.edu:8080/jspui/handle/123456789/78 <P> Repository for Published Conference Articles</P> Wed, 01 Apr 2026 02:03:57 GMT 2026-04-01T02:03:57Z http://dspace.aiub.edu:8080/jspui/handle/123456789/2904 Title: Antimagic Labelling of any Perfect Binary Tree Authors: Mohaimen-Bin-Noor, Md. Manzurul Hasan Abstract: Graph labelling is a very popular and high caliber research topic in graph theory. There are numerous variants of graph labelling. Some are categorized as edge labelling, and some are categorized as vertex labelling. Our paper focuses on proof of one kind of edge labelling known as antimagic labelling of any perfect binary tree. First, we have shown that antimagic labelling is possible by sequential labelling of the edges of any perfect binary trees except for some particular ones. Later we proved that antimagic labelling is also possible for those particular perfect binary trees by swapping the labels of only two egdes. Description: An enhanced approach of edge labeling of graph which is named as the Antimagic labeling for a perfect binary tree. It is proved in the article that it is possible to label all the edges of a perfect tree by the mentioned approach in it. Fri, 10 Jan 2020 00:00:00 GMT http://dspace.aiub.edu:8080/jspui/handle/123456789/2904 2020-01-10T00:00:00Z http://dspace.aiub.edu:8080/jspui/handle/123456789/2616 Title: Banking Queue Waiting Time Prediction based on predicted service time using Support Vector Regression Authors: Gomes, Dipta; Nabil, Rashedul Hasan; Nur, Kamruddin Abstract: Prediction using different machine learning approaches have been applied in last few decades in different areas. Waiting time is an undeniable fact for every queue and it is very important to develop a system that predicts its duration in real life with minimum error. In this paper we applied several machine learning algorithms and among them we chose Support Vector Regression (SVR) in a real life Banking queue dataset that contains real-life queues of multiple Banks where we predicted waiting time for each individual in the queue. Moreover, we have compared the result of prediction using SVR with different classifications and clustering methods such as K-nearest-neighbor and K means Clustering. We have shown the feasibility of applying SVR in prediction of waiting time in banking queues of developing countries for each individual, which is applicable and it performs well in queue analysis. Fri, 10 Jan 2020 00:00:00 GMT http://dspace.aiub.edu:8080/jspui/handle/123456789/2616 2020-01-10T00:00:00Z http://dspace.aiub.edu:8080/jspui/handle/123456789/2615 Title: Robust Underwater Object Detection with Autonomous Underwater Vehicle Authors: Gomes, Dipta; Saif, A.F.M. Saifuddin Abstract: Underwater Object Detection had been one of the most challenging research fields of Computer Vision and Image Processing. Before Computer Vision techniques were used for underwater imaging, all the tasks associated with object detection had to be done manually by marine scientists making the task one of the most tedious and error prone. For this case, Underwater Autonomous Vehicles (UAV) has been developed to capture real time videos for specific object detection. Using different hardware improvements and using many varied forms of algorithms, classification of objects, mainly living objects had been carried with different AUVs and high-resolution cameras. Conventional object detection methods of Computer Vision fail to provide accurate detection results due to some challenges faced underwater. For such reasons, object detection underwater needs to be robust, real time and fast also being accurate, for which deep learning approaches are introduced. In this paper, all the works here all the trending underwater object detection techniques are discussed in details and a comprehensive comparative study is carried out. Fri, 20 Mar 2020 00:00:00 GMT http://dspace.aiub.edu:8080/jspui/handle/123456789/2615 2020-03-20T00:00:00Z http://dspace.aiub.edu:8080/jspui/handle/123456789/2614 Title: Graceful Cascading Labelling Algorithm: Construction of Graceful Labelling of Trees Authors: Gomes, Dipta; Hasan, Md. Manzurul Abstract: The Graceful Labelling of trees is one of the most challenging conjectures in Graph Theory, proudly known as the 'Disease' of Graph Theory which remains a challenge as it remains unsolved. To counter the conjecture, an algorithm is proposed to construct a Graceful Binary Tree and a Graceful Caterpillar Tree. Here, the algorithm puts forward a solution to graceful labelling problem through a very efficient and simple approach. Most importantly, the Binary Tree exhibits the property of gracefulness and the construction of the tree remains one of the major contributions of the paper. The steps regarding the Algorithm are discussed and the other variants of the already known graceful graphs are discussed. Here, a basic initiative to prove the conjecture for complete binary trees as well as a proposed version of Binary Cascading Caterpillar Tree is put forward. The result is known that all graphs are graceful, here a different approach to construct a graceful graph is discussed. There are different works to prove special types of graphs that they have graceful labelling, but here we have tried to give an alternative approach. In this paper, we are proposing a simple method of graceful labelling Binary Cascading Caterpillar Trees and Complete Binary Tree. Thu, 07 Jan 2021 00:00:00 GMT http://dspace.aiub.edu:8080/jspui/handle/123456789/2614 2021-01-07T00:00:00Z