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Moiz S. - Fullstack Developer, Neural Style Transfer, LLM Prompt Engineering

Being part of Softaims has allowed me to see the full spectrum of what technology can achieve when guided by empathy, discipline, and creativity. Each assignment, regardless of size, represents an opportunity to bring clarity to complexity and to turn ambitious ideas into tangible outcomes. I’ve come to realize that successful development isn’t just about writing code—it’s about listening carefully, understanding deeply, and designing thoughtfully. Every client brings unique challenges, and I make it a priority to align my work with their goals, ensuring that the end result is both effective and lasting. Softaims fosters an environment where collaboration is not optional—it’s essential. The collective expertise within the team pushes me to think beyond conventional boundaries, to question, refine, and innovate. I believe that this process of shared learning and experimentation is what makes our solutions resilient and impactful. My ultimate goal is to build technology that feels effortless to use yet powerful in function. I approach every task with the mindset that small details can make a big difference. Through continuous refinement and dedication, I aim to contribute to the kind of work that not only serves today’s needs but anticipates tomorrow’s possibilities.

Main technologies

  • Fullstack Developer

    3 years

  • PyTorch

    2 Years

  • Machine Learning

    2 Years

  • MATLAB

    1 Year

Additional skills

  • PyTorch
  • Machine Learning
  • MATLAB
  • R
  • Python
  • Computer Vision
  • Deep Learning
  • Data Science
  • NLP Tokenization
  • OpenCV
  • Neural Network
  • MLOps
  • IBM SPSS
  • Neural Style Transfer
  • LLM Prompt Engineering

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Potentially possible

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Experience Highlights

Advanced Image Processing and Enhancement

I developed a comprehensive solution to enhance the quality of digital images. Utilizing both Python and MATLAB, I implemented advanced algorithms for noise reduction, contrast enhancement, and feature extraction to improve image clarity and detail. This project involved challenging tasks such as applying adaptive filters, histogram equalization, and edge detection techniques to various types of images, ranging from medical imagery to natural scenes. The following showcase is implementation on the example of image of minions. The outcome of image processing application is identification of the various features of the image. The project is a sample example of application of machine learning principles to image processing tasks and my ability to adapt and apply different programming tools (Python / MATLAB) to achieve optimal results.

Webscraping GitHub Repository Contributors & Advanced Data Analysis

This advanced project aimed to extract, analyze, and report on a wide range of GitHub repositories related to "white paper" topics, focusing on repository characteristics, contributor details, and content themes. This involved scraping data across multiple pages, cleaning and structuring the data, and applying sophisticated analysis to uncover trends and insights. Process: Data Collection: Utilized Python with requests and BeautifulSoup to scrape over 100 pages of repositories on GitHub, collecting data on repository names, titles, README titles, tags, links, and contributors. Enhanced Contributor Data Extraction: Developed a function to extract detailed contributor information for each repository, highlighting the depth of engagement within each project. Data Structuring: Leveraged pandas for organizing the scraped data into a structured DataFrame. This step was crucial for subsequent analysis and ensured data integrity. Advanced Data Analysis: Applied higher-level analytical techniques, including: Network Analysis to map the relationships between contributors across different repositories, identifying key influencers in the "white paper" domain. Text Mining and Natural Language Processing (NLP) on README titles and tags to identify prevalent themes and trends in white paper topics. Predictive Analytics using machine learning models to forecast the growth of repositories based on contributor engagement and tag popularity. Data Visualization: Crafted insightful visualizations using libraries like matplotlib and seaborn to showcase the analysis results, including network graphs, trend lines, and theme clusters. Findings and Impact: The project uncovered valuable insights, such as dominant themes in white paper repositories, the most influential contributors and their network, and predictors of repository growth and engagement. These insights can help developers and researchers to strategically position their projects for maximum visibility and engagement. Additionally, the predictive models offered actionable forecasts, enabling repository owners to better plan their content and engagement strategies to align with emerging trends. Conclusion: Through this project, I demonstrated not only my proficiency in web scraping and data analysis but also my ability to apply advanced analytical techniques to extract deep insights from complex data sets. The work illustrates the power of combining web scraping with advanced analysis to inform strategic decision-making in software development and research communities.

Classification of Pneumonia Using Chest X-Ray Images using Tensorflow

The objective of this project was to develop a machine-learning model capable of accurately detecting pneumonia in chest X-ray images. Given the growing number of medical images, an automated and dependable identification tool could significantly aid healthcare professionals in diagnosing and treating patients. The challenge lay in creating a robust and precise model that could handle the complexities inherent in medical imaging data, including variations in image quality, imbalances in dataset classes, and the critical need for a high true-positive rate to avoid misdiagnoses' severe consequences. To address this, various image preprocessing techniques and a Convolutional Neural Network (CNN) model were employed. Experimentation with different preprocessing techniques, such as image sharpening, Gaussian blur, average filtering, edge detection, histogram equalization, and adaptive masking, revealed varying degrees of improvement, with edge detection and histogram equalization showing significant enhancements. This iterative process was crucial in identifying the most effective strategies for the task at hand. The final design consisted of a well-optimized CNN model trained on preprocessed X-ray images. The model integrated features like early stopping and adaptive learning rate adjustment to prevent overfitting and enhance performance on unseen data. The results with the model achieving high accuracy, showcasing the potential of machine learning in automating pneumonia diagnosis from chest X-ray images. This tool has the potential to enhance efficiency in the healthcare system by streamlining diagnosis processes and improving accuracy. While the provided template is the basic implementation, there were further changes and improvements that includes alternative preprocessing techniques implementation, more advanced deep learning architectures, or strategies to address class imbalances.

Fashion Apparels Classification Using Deep Learning

The project aimed to develop a sophisticated multilayer perceptron classifier for a Fashion MNIST dataset, fulfilling the client's goal to accurately categorize clothing items. My contribution was multifaceted, involving the importation and preprocessing of data, the iterative training of models with one and two hidden layers, and meticulous model optimization, including hyperparameter tuning. The success of the project was quantified by improved accuracy scores and validated through detailed confusion matrices, effectively meeting the client's objectives by establishing a reliable image classification system.

Education

  • Chhattisgarh Swami Vivekanand Technical University

    Bachelor of Engineering (BEng) in Electronics and telecommunication Engineering

    2005-01-01-2009-01-01

  • Indian Institute of Technology (IIT) Kanpur(India)

    Master of Technology (MTech) in Production and operations management

    2009-01-01-2011-01-01

Languages

  • Arabic
  • English
  • Hindi
  • Urdu

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