Research

Research Interest:

• • • Machine Learning

    • Network Security

    • Bioinformatics

    • Robotics

Publication:

• • • H. Abid, N. J. Jenny, S. M. Shovan, “Improved Identification Performance of Lysine Glycation PTM using PSI-BLAST”, IEEE Tensymp 2020, Dhaka, Bangladesh, June 2020.     [Full Paper] [Free Version]

Research Projects:

• • • Smart Sailor [2022-Present]                                                   

The goal of the Smart Sailor project is to develop a situational awareness platform and collision avoidance algorithms for Åboat which is an autonomous and remote-controlled research boat platform being developed by the IT department at Åbo Akademi. Currently, we are using the simulated version of Åboat to build our system in AILiveSim. To develop situational awareness, we are combining lidar data with camera images to determine the precise distance of nearby objects. By implementing reinforcement learning on the AiLiveSim data, we are planning to build the collision avoidance algorithm. 

Project Report    Github

• • • Performance Measures of Different Solution Approaches for the Dynamic and Stochastic Vehicle Routing Problem [2016-2017]                                                   

Customers in freight transport desire more flexibility and fast fulfillment of their orders. The advances in communication technology permit to store and analyze huge amounts of data and also help to serve customers in real-time. This motivates a new version of the vehicle routing problem, the so-called Dynamic and Stochastic Vehicle Routing Problem (DSVRP). There are many approaches which solve DSVRP. Among them Multi-Scenario Approach (MSA) and Stochastic Programming with Recourse (SPR) are well known. The goal of this project is to compare between these two approaches on basis of travel cost and computational time. To achieve the goal, generating benchmark data sets for DSVRP and solving the approaches are vital part of this project.

Supervisor:  Prof. Dr. Jürgen Pannek

Project Report

• • • Sentiment Analysis of imdb movie reviews using CNN, LSTM hybrid model  [March 2019 - April 2019] 

Nowadays, Social media has become a great source of getting user's opinion. By using imdb movie reviews as our benchmark data set, we wanted to predict whether a opinion is good or bad. We used Word Embedding for feature extraction. To train the model, first we used CNN and after that LSTM which improved our performance than using only LSTM. We trained our model with different vocabulary size to compare the performance.

Supervisor:  Sajid Ahmed

Project Slide

• • • Prediction of Lysine Glycation PTM Site in Protein Sequence using PSI-Blast Feature Extraction   [July 2019 - November 2019] 

Protein being an important component in human body, undergoes enzymatic modifications referring as Post-transational modification (PTM) which forms a mature product of protein. Glycation is one of the 40 ptms that is discovered so far, a non-anzymatic covalent bonding of sugar and protein or lipid. It is a biomaker for renal failure, diabetes and implicated in other health issues as well, imprinting a significant importance for its site identification in a protein sequence. In our experiment, we intended to improve Glycation prediction using a new feature extraction technique, called PSI-BLAST. We used SVM and Random forests classifier to train the model.

Supervisor:  S.M. Shovan