Full Stack Engineer

Shakeef Ahmed Rakin

Full Stack Engineer from Dhaka, Bangladesh, skilled in developing web applications and integrating AI solutions. Experienced in machine learning projects, including natural language processing and computer vision.

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photo

Expertise

Code that solves problems
one at a time.

Full Stack Development

Expertise in building end-to-end web applications

  • Frontend & Backend development
  • SEO & Hosting
  • Scalable and efficient architectures
  • Secure API development

AI & Machine Learning

Experienced in working with different AI technologies

  • Natural Language Processing (NLP)
  • Computer Vision applications
  • LLMs (OpenAI, Gemini, etc.)

What you can expect

Coding for performance and usability with best practices

  • Simple, intuitive, and user-focused designs
  • Efficient code that aligns with best practices
  • Well documented code for future workability

Achievements

Hackathons and Datathons

During my undergraduate years, I actively participated in multiple competitive events, including hackathons and datathons, where I collaborated with teams to develop practical solutions to real-world problems.

New Academia Learning Innovation 2024
GDSC CRCE BitNBuild’24 Hackathon
Kitahack 2024
DevHack 2023
MyRapid Bus x UTM Data Hackathon 2023
InnoJam 2023: Smart Sustainable City

Projects

Featured Side Projects

AgriSmart - Cover Photo
AgriSmart
March 24, 2024
Farm Management Website

Gemini, MongoDB, Express.js, React.js, Node.js, Firebase

Blood Bridge - Cover Photo
Blood Bridge
November 25, 2023
Blood Donation Management Platform

MongoDB, Express.js, React.js, Node.js, Firebase

TopShelf - Cover Photo
Top Shelf
November 9, 2023
Library Management System

MongoDB, Express.js, React.js, Node.js, Firebase

Comp Harbor - Cover Photo
Comp Harbor
October 21, 2023
E-Commerce Platform

MongoDB, Express.js, React.js, Node.js, Firebase

Offensive Tweet Classifier - Cover Photo
Offensive Tweet Classifier
August 30, 2023
Offensive Tweet Detection and Categorization

NLP, Logistic Regression, TF-IDF, Classification, Scikit-learn

Blogs

Latest Articles

Fetching Latest Blogs

Research

Completed & Ongoing

Discover my latest research projects that I've collaborated with esteemed colleagues and institutions

SUBMITTED TO EGU25-12648

HOLOS-IE: A System Model for Assessing Carbon Emissions and Balance in Agricultural Systems

Agriculture significantly contributes to greenhouse gas (GHG) emissions, mainly via enteric and manure methane (CH4) from livestock and fertilizer-induced nitrous oxide (N2O) from soils. Mitigation strategies include dietary changes, feed additives, and fertilisation with circularity approaches. Agroforestry further offsets GHGs through carbon sequestration (soil and biomass) while enhancing soil health and ecosystem services. Achieving carbon-neutral farms by 2050 requires sustainable agricultural transformation. System-based modelling is crucial for understanding agriculture, supporting informed decision-making, and balancing data needs. HOLOS-IE, evolving into HOLOS-EU, simplifies complex modelling for farmers and stakeholders, empowering them to reduce their environmental footprint and achieve sustainable production. The HOLOS-IE v3.0 (www.ucd.ie/holos-ie) utilises large datasets, evidence-based algorithms, GIS, Machine Learning, and C#.NET coding. The ongoing development focuses on refining model components (crops, grasses, livestock, agroforestry and farm infrastructure), and their sub-components. These components are driven by key soil, climate and relevant variables, which are automated or user-defined inputs. As a case study, HOLOS-IE was applied to a 30-hectare Irish dairy farm to explore agroforestry scenarios (silvopastoral systems with Oak and Sycamore hedgerows) by sparing 5% of land without reducing livestock density. The model predicted sectoral GHG emissions, carbon removal, and total/net carbon balance, quantifying soil and biomass carbon sequestration. This analysis highlighted the offsetting potential and provided insights into total and net carbon balances, guiding future land-use planning for climate change mitigation... [TENTATIVE]

• Copernicus Publications• January 2025
PUBLISHED

Malaysian Sign Language Real-Time Tutorial using CNN Algorithm

The communication challenges faced by people with hearing and speech impairments in Malaysia are made worse by effective resources for learning Malaysian Sign Language (MSL). The Malaysian Sign Language Real Time Tutorial (MASRETT) is an instructional website designed to decrease the communication gap between both disabled and non-disabled. MASRETT is developed to assist in learning fundamental MSL virtually besides reducing the communication barriers which leads to a better and stronger social relation in Malaysia. Agile software development is the methodology of choice for creating this system since it facilitates the iterative identification and correction of mistakes. The artificial intelligence (AI) model which is convolutional Neural Network (CNN) algorithm is used to recognize the sign language input. The findings indicate that MASRETT significantly enhances accessibility and effectiveness of MSL learning particularly for individuals with lack access to traditional classes. However, the results showed that only 33% of the signatures are recognized using CNN algorithm for real time signs detection. It is observed that the results are not significant because of the limitation in data and inefficient model usage and can be improved using improved algorithm as in future work.

• SEMARAK ILMU SDN BHD• December 2024
PUBLISHED

Optimizing American Sign Language Recognition with Binarized Neural Networks: A Comparative Study with Traditional Models

Sign language is crucial for communication among individuals with hearing or speech impairments. Automated recognition systems are essential for learning and translating different sign language variants. However, these systems often face high computational demands and large memory footprints, limiting their use in real-time and resource-constrained environments. This research develops an optimized pipeline for American Sign Language (ASL) recognition, comparing Binarized Neural Networks (BNNs) with traditional full-precision neural networks. Using Larq, a library for training binarized models, we leverage BNNs' reduced memory and computational needs, suitable for embedded systems and edge devices. The study uses a dataset of ASL alphabet images, applying data augmentation to address data imbalance and occlusions. Both binarized and traditional models are trained and evaluated on accuracy, precision, recall, F1-score, memory footprints, and inference times. Results show that BNNs offer competitive performance with significantly lower computational requirements, demonstrating their potential for efficient and accessible ASL recognition systems.

• BRAC University• May 2024