Fisheries industry is a vital sector of Sri Lanka’s economy and each departing and arriving fishing vessel should have gone through ample security check by the harbor authorities. But with the COVID 19 pandemic and social distancing procedure, harbor authorities are facing difficulties detecting and recognizing fishing vessels by getting on the boats as usual. Also, currently harbors are using a paper-based system for recording the information on boat departures and arrivals. This leads to the inefficiency of harbor management process, delays in rescue missions and failures of security missions. To solve these problems, this paper introduces a Boat Recognition and Automated Harbor Management System (BRAHMS) which is based on YOLO v5 algorithm. In this research, a novel de-skewing method is discovered for the slanted license plate recognition process. The de-skewing process aims for three main approaches: auto de-skewing, manual de-skewing and a hybrid de-skewing which uses both auto and manual processes together.
With the popularity of Online Social Networks (OSN), the number of different types of digital attacks has been increased causing lots of damages to their users. Identity Clone Attack (ICA) is one of the leading among them which illegally uses the information of a genuine user by duplicating it in another fake profile. These attacks severely affect a true and innocent identity since it can be misused by another malicious profile. Hence these clone profiles need to be identified and removed in order to increase the protection of users. This study introduces a model to detect clone profiles on Facebook by using a dataset that consists of profiles with attributes and network connections. Though the initial dataset was real, it was modified to make some artificial clones. The process of detection included three main stages, namely, filter by name, cluster using weighted categorical attributes and measure the strength of friend relationships among profiles, which follow one after another respectively. Finally, the list of possible clones with their percentages representing the amount of duplicability to a given victim profile was presented as the output of the model. Instead suggesting the exact clones, the representation of this duplicability percentages makes this approach more practical since there are many similar profiles but not clones. With the use of Agglomerative hierarchical clustering algorithm and Jaccard similarity measurement, a low average within cluster distance in cluster density performance and a precision of 88.75% has shown in the results. The present study highly focuses on the distribution of the dataset, where the calculation of weights for the attributes, similarity threshold and even the selection of the clustering algorithm is done based on it and this increases the adjustability of the proposed model to any other dataset. As the future improvements, this newly proposed approach can be extended to find clones of a victim on different platforms and more attributes can be considered for clustering.
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