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Hi, my name is

Ehsanur Rahman Rhythm.

I build things for the web.

I’m a software engineer specializing in creating dynamic and user-friendly web applications. Currently, I’m enhancing digital experiences at RhyStart.

About Me

Hello! My name is Ehsanur Rahman Rhythm, and I enjoy creating things that live on the internet. My journey in web development started during my time at Brac University, where I developed a passion for building user-friendly and dynamic web applications.

Fast-forward to today, I have over four years of experience working with various technologies, including PHP, Laravel, and WordPress. I’ve had the privilege of working on a range of projects, from developing a digital database management system for the Marine Fisheries Academy to creating high-traffic educational websites and news portals.

Currently, I'm focused on enhancing digital experiences and delivering innovative solutions at RhyStart Technologies. My main goal is to build accessible, human-centered products that meet and exceed user expectations.

In addition to my work in web development, I have also published research in the fields of natural language processing (NLP) and data science. These publications reflect my commitment to advancing technology and contributing to the academic community.

Here are a few technologies I've been working with recently:

  • React.js
  • Express.js
  • Astro.js
  • Vanilla JavaScript
  • PHP
  • Laravel
  • WordPress
Headshot

Where I’ve Worked

Web Developer @ RhyStart

June 2022 - Present

  • Design and implement interactive web applications with advanced functionality and responsive design principles, leveraging modern frameworks and technologies.
  • Build and maintain custom CMS solutions for various business and consultancy firms, utilizing WordPress, PHP, and Laravel to deliver seamless and efficient user experiences.
  • Conduct code reviews and implement best practices to ensure high-quality, maintainable, and scalable code.
  • Provide ongoing support and troubleshooting for web applications, addressing issues promptly and optimizing systems for peak performance.

Where I’ve Studied

Master of Science in Software Engineering

IIT, University of Dhaka

Feb 28, 2024 - Present

Some Things I've Built

Other Noteworthy Projects

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TitleAuthorsVenueYear
Text-based Q&A: Automated Question Generation and Answering for Enhanced Data Processing

In the age of information overload, where the world is getting increasingly digital, traditional methods of learning are becoming tedious and extensively outdated. Imagine a system with just a few clicks that can quickly generate perplexing questions and enlightening solutions from a given text. Likewise, this paper represents a groundbreaking system that uses stateof-the-art of natural language processing techniques to analyze subject-specific chapters to create questions and corresponding solutions of varying lengths. The system’s versatility as an ideal tool for a wide range of users, including students, researchers, and educators, is a result of its capability to handle a wide range of domains. By offering questions of insight along with appropriate answers, this aforementioned structure demonstrated its extraordinary accuracy and competency in our studies on an array of informational datasets. Altering the learning process and promoting knowledge discovery, this program is flexible in delivering brief or comprehensible solutions to inquiries, having the potential to completely change how individuals interact with written material, whether they are reading for short reference or conducting any depth research. Our suggested framework establishes a dynamic platform for immediate information that enables people to learn substantially more and comprehend any topic in depth. With this leading-edge method, bid goodbye to exhausting manual question generation and get ready to embrace a new era of seamless and fruitful learning with this cutting-edge system.

Ehsanur Rahman Rhythm, Abdul Halim Hosain, Nusrat Zaman Raya, Kazi Al Refat Pranta, Tonusree Talukder Trina, Md Sabbir Hossain, Md Humaion Kabir Mehedi, Annajiat Alim RaselCATS2023
Advancements in Optical Character Recognition for Bangla Scripts

Optical Character Recognition (OCR) systems are very powerful tools that are used to convert handwritten texts or digital data on an image to machine readable texts. The importance of Optical Character Recognition for handwritten documents cannot be overstated due to its widespread use in human transactions. OCR technology allows for the conversion of various types of documents or images into machine understandable data that can be analyzed, edited, and searched. In earlier years, manually crafted feature extraction techniques were used on comparatively small datasets which were not good enough for practical use. With the advent of deep learning, it was possible to perform OCR tasks more efficiently and accurately than ever before. In this paper, several OCR techniques have been reviewed. We mostly reviewed works on Bangla scripts and also gave an overview of the contemporary works and recent progresses in OCR technology (e.g. TrOCR, transformer w/CNN). It was found that for Bangla handwritten texts, CNN models like DenseNet121, ResNet50, MobileNet etc are the commonly adopted techniques because of their state of the art performance in object recognition tasks. Using an RNN layer like LSTM or GRU alongside the base CNN-based architecture, the accuracy can be further improved. TrOCR is a fairly new technique in this field that shows promise. Experimental results show that in synthetic IAM handwriting dataset it showed a Character Error Rate (CER) of 2.89. The goal of this paper is to provide a summary of the research conducted on character recognition of handwritten documents in Bangla Scripts and suggest future research directions.

Md Tanjim Mostafa, Ehsanur Rahman Rhythm, Md Humaion Kabir Mehedi, Annajiat Alim RaselASYU2023
Automatic Subtitle Generation for Bengali Multimedia using Deep Learning

For audio or video material to be more inclusive and accessible, automatic subtitle generation is essential. Nevertheless, implementing this technology into Bengali presents significant challenges due to scarce resources and linguistic difficulty. In this study, a new deep learning based system for creating Subtitles for Bengali multimedia automatically is introduced. The suggested approach makes use of the Wav2vec2 and the Common Voice Bengali Dataset, a large collection of Bengali audio recordings. This study uses the Common Voice Dataset Bengali to train and tune the Wav2vec2 model in order to accurately convert Bengali audio into text. Current automatic speech recognition approaches are combined with Bengali language-specific factors in the created system to give accurate and reliable transcription works. The transcribed text is synced with the matching audio parts throughout the subtitle production process. The produced subtitles are enhanced using post-processing approaches, similar to capitalization and punctuation restoration, to ensure readability and consistency. The findings of this study might greatly improve Bengali language media’s usability and availability across a range of sectors. The created subtitles may enhance the watching experience for Bengali multimedia by easing greater understanding, and expanding availability. The study demonstrates the potential of using deep learning and ASR methods to get over the difficulties of automated subtitle production in the Bengali language, advancing multimedia availability and inclusion.

Ehsanur Rahman Rhythm, Shafakat Sowroar Arnob, Rajvir Ahmed Shuvo, Annajiat Alim Rasel, Sifat E JahanB.Sc, Brac University2023
Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps

In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73%, and 77% respectively.

Ehsanur Rahman Rhythm, Rajvir Ahmed Shuvo, Md Sabbir Hossain, Md. Farhadul Islam, Annajiat Alim RaselESCI2023

What’s Next?

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Although I’m not currently looking for any new opportunities, my inbox is always open. Whether you have a question or just want to say hi, I’ll try my best to get back to you!