Designed and developed a robust diagnostics portal that seamlessly integrates medicine-barcode mapping functionality. This innovation significantly enhanced delivery scheduling efficiency and improved inventory material tracking processes, resulting in a 40% optimization in overall logistics

Engineered an advanced audio frequency enhancement tool using Python and signal processing techniques. The solution simulated diverse patient voice variations, enabling more realistic medical training scenarios for healthcare professionals

Created a scalable remote API pipeline for medical data collection from platforms such as Reddit and YouTube. Leveraged Python for data processing and stored the retrieved data in AWS Snowflake, PostgreSQL, and MongoDB, ensuring efficient cloud-based storage and API access. This system successfully handled large-scale, real-time data retrieval tasks with precision

Implemented a BERT transformer model and performed exploratory data analysis (EDA) with attention mechanisms, time series analysis, and statistical modeling. Integrated generative AI (GenAI) and large language models (LLMs) with hyperparameter tuning, leading to a 38% improvement in model evaluation metrics

Designed a secure connected messaging protocol architecture to streamline communication between doctors and nurses. This architecture ensured efficient and encrypted data exchange within healthcare workflows, fostering improved collaboration and patient care

Built an interactive website featuring data analysis dashboards with advanced filtering algorithms. Users could adjust parameters by time ranges, compare historical and latest results, and access comprehensive reports with detailed insights, enhancing decision-making in healthcare processes

Conducted in-depth analysis of toxic behaviors and cultural influences using natural language processing (NLP) and machine learning techniques. This effort yielded predictive trends, sentiment analysis, and actionable insights, enabling better understanding of behavioral patterns and their implications

Designed and developed an automated assembly line sorting algorithm, rectified a critical security flaw in the authentication process, and deployed an equitable new assembly line orders assignment endpoint API. Implemented AI-driven solutions for dynamic data sorting, filtering, and process automation using Python, optimizing overall system efficiency.

Collaborated with a team of 5, working closely with managers and clients to translate requirements into actionable JIRA tasks, execute user stories, manage change requests, ensure adherence to acceptance criteria, and resolve newly reported issues and bugs

Employed a diverse set of technologies, including .NET, .NET Framework, .NET WebAPI, Angular, JavaScript, C#, Microsoft SQL, Postman, Kibana, and Python to enhance software functionality, user experience, and AI-based automation

Orchestrated deployment to the pre-production and production environment using Azure DevOps, involved clients in the testing phase, encouraged feedback, and applied further changes as required to ensure smooth application operation

Enhanced the user experience, streamlined production and deployment, and provided optimized solutions and process enhancements that improved efficiency, productivity, data protection, reduced manual workload, and earned client appreciation for the security enhancement

Implemented a pickle file-based caching algorithm that optimized the system’s speed by 60%, avoiding redundant model training and improving overall system performance

Developed a specialized website using Django and SQL Server tailored for visually impaired users in healthcare. The platform enabled users to learn from audio read-back of scanned text, improving accessibility and inclusivity in healthcare learning environments

Deployed a comprehensive text-to-speech system leveraging Pytesseract, pyttsx3 libraries, and advanced techniques such as text clustering, topic modeling, and text-to-audio alignment mapping. Designed a dynamic data upload section and a structured backend engine, achieving a remarkable 92% system accuracy and providing an intuitive user experience

Collaborated with a team of four to develop a cyberbullying detection and dummy account identification system. Utilized Scikit-Learn, Python, NLP, random forest, and decision tree classification models, along with advanced text classification and anomaly detection techniques. This project resulted in a 78% improvement in system performance, enhancing cybersecurity and user safety

Designed and implemented a UI/UX web page system for streamlined incident notifications to an admin page. Leveraged DevOps practices, Object-Oriented Programming principles, and JavaScript for dynamic functionality. Analyzed crucial features and utilized GitHub for distributed deployment, ensuring efficient and direct issue reporting to the administrative panel

Developed an "Attendance Monitoring System" in a group of 3 using Django and Machine Learning, for precise and efficient student attendance tracking.

Performed NLP techniques that enhanced the analysis process and deeply analyzed user-generated content from online channels, identifying toxic behaviors and assessing regional cultural influences on technology, education, and advancements.

Implemented a pickle file-based caching algorithm which optimized the system’s speed by 60%, avoiding redundant model training.