mehediazim / keras Goto Github PK

I am a Computer Science graduate, currently working as a Machine Learning Engineer at Apurba Technologies. I am experienced in creating advanced analytics strategies using data & intelligent machine learning algorithms with creative interfaces.

Throughout my student life, I have worked on various projects and research work. I prefer to solve real-life problems in our daily life. Regardless of the way that Bioinformatics intrigues me, my research interest lies in various fields which are Image processing, Algorithm design, and Human-centered computing.

In my leisure time, I write poetry and short stories for encircling the time. My favorite kinds of music usually revolve around rocks and melodies. I watch a handful of movies, biographies attract me the most.

📫 Reach me out!

• Computational biology
• Machine Learning
• Deep Learning
• Image Processing
• Human centered computing
• Algorithms

• Sayed Mehedi Azim, Noor Hossain Nuri Sabab, Iman Noshadi, Hamid Alinejad-Rokny, Alok Sharma, Swakkhar Shatabda, Abdollah Iman Dehzangi. Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers Informatics in Medicine Unlocked (2023)
• Sayed Mehedi Azim, Alok Sharma, Iman Noshadi, Swakkhar Shatabda, Abdollah Iman Dehzangi. DeepAmp: A Convolutional Neural Network based tool for predicting protein AMPylation sites from binary profile representation Scientific Reports (2022)
• Sayed Mehedi Azim, Md. Rakibul Haque, and Swakkhar Shatabda. OriC-ENS: A Sequence-Based Ensemble Classifier for Predicting Origin of Replication in S. cerevisiae. Computational Biology and Chemistry (2021)

• OriC-ENS: A Sequence-Based Ensemble Classifier for Predicting Origin of Replication in S. cerevisiae. 19th International Conference On Bioinformatics (InCoB), 25 - 29 Nov 2020
• Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms. 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)

• Sayed Mehedi Azim, Sajid Ahmed, Swakkhar Shatabda, Abdollah Iman Dehzangi. Antimicrobial Peptides Prediction Using Multi-head Convolutional Neural Network. Developed a machine learning tool to accurately identify bacteriocins. Built multi-head CNN using TensorFlow.

• Sayed Mehedi Azim, Swakkhar Shatabda. PIR-Deep: A Tool for Proinflammatory Peptides Prediction from Image Representation of features using Hybrid Deep Learning Model. In this research, a hybrid model is introduced, which uses CNN and LSTM for predicting proinflammatory peptides from image representation of peptide sequences. Images were created from Binary profile features using SuperTML.

• Sayed Mehedi Azim, Mazharul Islam Leon, Noor Hossain Sabab, Swakkhar Shatabda. White Blood Cell Sub-type Classification Using Deep Ensemble Model. In this research, a deep ensemble learning method is introduced, which uses 5 different neural network models: ResNet-18, ResNet-34, ResNet-50, Densenet121, and Alexnet for identification of four types of WBC (neutrophil,eosinophil,lymphocyte and monocyte)