Debajyoti Karmaker

Work place: American International University, Bangladesh

E-mail: d.karmaker@aiub.edu

Website:

Research Interests: Deep Learning, Machine Learning, Computer Vision

Biography

Debajyoti Karmaker is working as an Associate professor in the department of computer science at American International University-Bangladesh. He worked as Postdoctoral Research Fellow at University of New South Wales (UNSW), Royal Melbourne Institute of Technology (RMIT), Australian National University (ANU), and Stanford University. Before joining ANU, he completed his Ph.D. from The University of Queensland (UQ). His research interests are in Deep Learning, Computer Vision, & Machine Learning. I am particularly interested in the areas of image classification, object detection, segmentation, bio-inspired collision avoidance strategies, and Robust Decision-making and Learning.  Before starting His Ph.D., he was working as a Lecturer at the American International University-Bangladesh (AIUB) - in the Department of Computer Science also worked as a software engineer at Infra Blue Technology (IBT Games).

Author Articles
Mimicking Nature: Analysis of Dragonfly Pursuit Strategies Using LSTM and Kalman Filter

By Mehedi Hassan Zidan Rayhan Ahmed Khandakar Anim Hassan Adnan Tajkurun Zannat Mumu Md. Mahmudur Rahman Debajyoti Karmaker

DOI: https://doi.org/10.5815/ijitcs.2024.04.06, Pub. Date: 8 Aug. 2024

Pursuing prey by a predator is a natural phenomenon. This is an event when a predator targets and chases prey for consuming. The motive of a predator is to catch its prey whereas the motive of a prey is to escape from the predator. Earth has many predator species with different pursuing strategies. Some of them are sneaky again some of them are bolt. But their chases fail every time. A successful hunt depends on the strategy of pursuing one. Among all the predators, the Dragonflies, also known as natural drones, are considered the best predators because of their higher rate of successful hunting. If their strategy of pursuing a prey can be extracted for analysis and make an algorithm to apply on Unmanned arial vehicles, the success rate will be increased, and it will be more efficient than that of a dragonfly. We examine the pursuing strategy of a dragonfly using LSTM to predict the speed and distance between predator and prey. Also, The Kalman filter has been used to trace the trajectory of both Predator and Prey. We found that dragonflies follow distance maintenance strategy to pursue prey and try to keep its velocity constant to maintain the safe (mean) distance. This study can lead researchers to enhance the new and exciting algorithm which can be applied on Unmanned arial vehicles (UAV).

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From Nature to UAV - A Study on Collision Avoidance in Bee Congregation

By Nahin Hossain Uday Md. Zahid Hasan Rejwan Ahmed Md. Mahmudur Rahman Abhijit Bhowmik Debajyoti Karmaker

DOI: https://doi.org/10.5815/ijisa.2024.03.06, Pub. Date: 8 Jun. 2024

Insects engage in a variety of survival-related activities, including feeding, mating, and communication, which are frequently motivated by innate impulses and environmental signals. Social insects, such as ants and bees, exhibit complex collective behaviors. They carry out well-organized duties, including defense, nursing, and foraging, inside their colonies. For analyzing the behavior of any living entity, we selected honeybees (Apis Mellifera) and worked on a small portion of it. We have captured the video of honeybees flying close to a hive (human-made artificial hive) while the entrance was temporarily sealed which resulted in the” bee cloud”. An exploration of the flight trajectories executed and a 3D view of the” bee cloud” constructed. We analyzed the behaviors of honeybees, especially on their speed and distance. The results showed that the loitering honeybees performed turns that are fully coordinated, and free of sideslips so thus they made no collision between themselves which inspired us to propose a method for avoiding collision in unmanned aerial vehicle. This paper gives the collective behavioral information and analysis report of the small portion of data set (honeybees), that bee maintains a safe distance (35mm) to avoid collision.

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