Muaaz Saeed

National University of Computer and Emerging Sciences

Bachelor’s Degree, Computer Science

January 1, 2012 – January 1, 2016

Cadet College Kallar Kahar

Pre-Engeenring, Science

January 1, 2010 – January 1, 2012

Air Base Inter College Mushaf, Sargodha

Matriculation, Science Group

January 1, 2005 – January 1, 2010

Studiolab Technologies

Head of Engineering & AI

October 1, 2025 – Present

Dubai, United Arab Emirates · On-site

Value Driven Data

Lead Data & AI Engineer

April 1, 2025 – October 1, 2025

Dubai, United Arab Emirates · On-site

Freelance

Senior AI Consultant | Full Stack & Cloud Engineer (Freelance/Contract)

August 1, 2024 – April 1, 2025

United Arab Emirates · Remote

Edraak Systems AD

Chief Technology Officer

February 1, 2023 – August 1, 2024

Masdar City, Abu Dhabi, UAE · Hybrid

Microsoft for Startups Middle East

Start-up Co-Founder & CTO at 'Edraak Systems'

September 1, 2022 – January 1, 2023

United Arab Emirates

National Incubation Center Karachi (NICK)

Startup Co-founder 'Edraak System'

July 1, 2021 – July 1, 2022

Karachi Division, Sindh, Pakistan · Remote

Edraak Systems

Chief Technology Officer

August 1, 2017 – September 1, 2023

Lahore, Pakistan · On-site

Red Buffer

Machine Learning Engineer

February 1, 2017 – August 1, 2017

Islamabad · On-site

Teradata

Professional Services Consultant

November 1, 2016 – January 1, 2017

Islamabad, Pakistan · On-site

StatusRox.com

Junior Web Developer

June 1, 2015 – August 1, 2015

Islamabad · On-site

Veterinary Radiology Scan and Smart Reports System

November 1, 2023 – Present

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro The Veterinary Radiology Scan and Smart Reports System is a state-of-the-art platform designed to transform veterinary healthcare. It integrates advanced radiology scanning with intelligent reporting and comprehensive hospital management, catering specifically to the dynamic needs of veterinary medicine. Description This innovative system offers a seamless blend of high-definition radiology scanning and AI-driven smart report generation, enhancing diagnostic accuracy and efficiency in veterinary care. It encompasses a robust hospital management ecosystem, providing functionalities for managing doctor profiles, patient records, and appointment scheduling, all within a user-friendly interface. The system also features real-time data analytics for in-depth insights into radiology trends, hospital performance metrics, and patient data analysis, all while ensuring utmost data security and confidentiality. Its cloud integration ensures easy data access, storage, and scalability, accommodating various user roles such as Super Admin, Hospital Admin, and Doctor, each with customized access and functionalities. Developed in collaboration with veterinary experts, this system is poised to elevate the standard of care in veterinary radiology. Tech Stack Frontend: React.js Backend: Node.js Database: MongoDB Cloud Services: Cloud-enabled architecture Security: JWT token management, robust security protocols Server Management: High scalability and reliability Development Tools: Modern tools for continuous integration and deployment Conclusion The Veterinary Radiology Scan and Smart Reports System is a comprehensive solution for veterinary radiology and reporting, revolutionizing veterinary healthcare through precision, efficiency, and enhanced care.

Sales Call Automation AI Robot Tele-Marketing System

January 1, 2023 – Present

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro This project introduces an advanced AI-powered telemarketing system, uniquely designed to revolutionize marketing calls. Leveraging state-of-the-art AI and speech recognition technology, it offers automated, efficient, and intelligent user interactions based on specific keywords and responses, ensuring a more dynamic and effective marketing approach. Description Our development team has crafted a sophisticated auto-dialing system, adept at managing and initiating marketing calls with precision. Employing a robust blend of PHP for backend functionalities and Python for dynamic call handling, the system is engineered to perform real-time speech-to-text keyword detection. This enables instantaneous responses, adhering to the critical 1-second response threshold. The system is further enhanced with an admin interface for comprehensive CRUD operations on contact sheets, including the capability to import contact data from Excel files. To ensure optimal telecommunication efficiency, we've integrated advanced VOIP functionalities using FreePBX and Kamailio, facilitating seamless SIP trunking communication. The core aim is to automate marketing calls seamlessly, with a focus on real-time interaction and response efficiency. Tech Stack Backend Development: PHP, Python Database Management: MySQL Frontend Development: JavaScript, React Server Management: Ubuntu, Apache/Nginx Telecommunication Protocols: SIP, VOIP, Kamailio, FreePBX Speech Recognition Technologies: Google Speech-to-Text API, Microsoft Speech API Version Control and Collaboration: Git, GitHub Conclusion The Sales Call Automation AI Robot Tele-Marketing System represents a significant innovation in the field of telemarketing, leveraging advanced technologies to enhance marketing efficiency and effectiveness.

Edraak Textile Quality Management App

January 1, 2023 – Present

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro The Edraak Textile Quality Management System is a groundbreaking innovation in the textile industry, heralding a new era of digital transformation. This system represents a significant evolution from traditional quality control methods to a sophisticated, sensor-integrated digital approach, designed to enhance operational efficiency and accuracy in fabric quality management. Description The system stands as a pinnacle of technological and business innovation, integrating high-precision sensors and advanced data analytics to revolutionize fabric quality standards. It offers real-time monitoring and meticulous quality assessment, ensuring precision in measurement and efficiency in fault detection. The digital fault entry and automated labeling system streamline defect tracking, significantly reducing manual errors and elevating customer satisfaction. This integration of cutting-edge technology with production machinery and server connectivity enables continuous monitoring of key performance indicators (KPIs), crucial for upholding high-quality standards and securing a competitive advantage in the market. The Edraak Textile Quality Management System is more than a technological breakthrough; it's a comprehensive business tool that enhances productivity, fosters sustainable practices, and drives profitability in the textile industry. Tech Stack Frontend: React Native Backend: Node.js, Express Database: MongoDB Version Control: Git Conclusion The Edraak Textile Quality Management System exemplifies a transformative approach in the textile industry, combining modern technologies and innovative strategies to elevate the standards of fabric quality management.

Scan Smart - Veterinary Radiology Scan and Smart Reports System

January 1, 2023 – Present

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro The Veterinary Radiology Scan and Smart Reports System represents a groundbreaking leap in veterinary healthcare technology. Description This system combines high-resolution radiology scanning with intelligent, AI-enhanced report generation. It includes a hospital management suite for managing doctor profiles, patient records, and appointment scheduling. The system boasts real-time data analytics and cloud integration, catering to various user roles such as Super Admin, Hospital Admin, and Doctor. Tech Stack Frontend: React Native Backend: Node.js Database: MongoDB Cloud Services: Cloud-enabled architecture Security: JWT token management, robust security protocols Server Management: High scalability and reliability Development Tools: Advanced tools for continuous integration and deployment Conclusion The Veterinary Radiology Scan and Smart Reports System revolutionizes veterinary healthcare through precision, efficiency, and enhanced care, setting new benchmarks in veterinary diagnostics and treatment.

Infodart Anomaly Detection

January 1, 2022 – Present

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro Infodart represents a cutting-edge anomaly detection system specifically designed for textile quality management. This comprehensive solution integrates machine learning algorithms with Azure cloud architecture to monitor, analyze, and predict quality deviations in textile manufacturing, ensuring optimal product standards. Description The Infodart Anomaly Detection system is an amalgamation of sophisticated technology and innovative data analysis techniques. It utilizes Azure Virtual Machines to power the Infodart Web Portal for both frontend and backend functionalities. The system is designed to fetch data from various client databases, process it, and identify any anomalies in fabric quality using machine learning algorithms. Data synchronization is efficiently managed by Azure Data Factory, which orchestrates data flow from client databases to the cloud. The core of anomaly detection relies on Azure Web Apps and Virtual Machines running Linux, where complex machine learning models analyze textile data for quality inconsistencies. Azure Functions facilitate serverless computing for real-time data processing and anomaly detection, ensuring scalability and efficient resource utilization. The Elastic Pool Database in Azure accommodates varying data storage needs, supporting multiple SQL. Tech Stack Cloud Infrastructure: Azure Virtual Machines, Azure Data Factory, Elastic Pool Database Servers: Azure Web Apps and Virtual Machines Serverless Computing: Azure Functions Database Management: MS SQL, MySQL, PGSQL in Azure Elastic Pool Database. API and Integration: Azure Logic Apps Data Analysis Tools: Talend Open Studio, Python, Pandas, Numpy Visualization and Reporting: Power BI

Google Places Web Scraper

January 1, 2022 – January 1, 2022

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro Google Places Web Scraper is a specialized tool designed to extract data from various locations based on specific business criteria. It utilizes Google APIs to gather comprehensive information about places in designated cities, tailored to particular business types. Description Our development focused on efficiently parsing CSV files into JSON format. This conversion facilitated the creation of unique combinations of city and business type, which served as parameters for querying the Google Places API. The tool adeptly navigates through the paginated responses from the API, extracting vital place information. The final step involves converting this rich dataset back into a user-friendly CSV format, ready for analysis or integration into other systems. The Google Places Web Scraper stands out for its precision in data collection and transformation, making it an invaluable resource for market research and business analytics. Tech Stack Backend Development: Node.js API Integration: Google Places API Conclusion This web scraper exemplifies the innovative use of technology to access and organize vast amounts of location-specific business information, making it an essential tool for data-driven decision-making in various industries.

REETS Predictive Analysis with Vibration Analysis

January 1, 2022 – Present

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro REETS stands at the forefront of predictive analysis, focusing on vibration analysis to revolutionize operational efficiency in machinery and equipment maintenance. Description At the core of REETS is an intricate vibration analysis system, incorporating Azure Function, meticulously coded in Python, for direct connectivity to SQL Server. This setup not only retrieves data but also compresses it, proving invaluable in IoT data processing scenarios. Complementing this is a multi-threaded Python script that expertly ingests real-time data from RabbitMQ into SQL Server. The user interface of REETS is rich with functionalities, displaying detailed graphs for displacement, temperature, and time, updated at regular intervals. It employs advanced techniques like windowing (Hanning, Hamming, Flatop) and FFT graphs for deep vibration analysis, alongside waterfall graphs for a complete data overview. REETS goes beyond just data visualization; it delves into data engineering with API development for data retrieval and processing, efficient parallel data fetching, and cutting-edge data compression methods. Tech Stack Frontend: React.js Backend: Node.js, Express Database: MongoDB Data Processing: Azure Functions, Python Message Queuing: RabbitMQ Server Database: SQL Server Data Visualization: FFT, Waterfall Graphs Data Compression: Gzip Sensor Integration: RS-WZ3 Communication Protocol: RS-485 Conclusion REETS exemplifies an advanced integration of vibration analysis and sophisticated data processing, emerging as a key solution for enhancing predictive maintenance and operational efficiency in various industries.

Fabric Labeler Dispenser

January 1, 2021 – Present

My Responsibility Growth Stage: Transitioned to team building and task delegation, maintaining active client engagement. Intro The Fabric Labeler Dispenser is an innovative automated labeling system designed for the packaging and manufacturing industries. It combines PLC programming, advanced sensor technology, and a pneumatic system to apply labels precisely and efficiently on various products. Description This Label Applicator System is built around a PLC, integrating gap sensors like the SR21-RG and IFM IM5132 for accurate sticker detection and confirmation. A key component is the Puyon KS-C2 color sensor, which helps in distinguishing labels based on color markers. The system operates via Modbus communication, activated by engaging the M6 coil in the software. The labeler functions in two modes: as a standalone labeler and a pneumatic-assisted labeler, with the latter involving a pneumatic system for controlled label application. Tech Stack PLC Programming Environment: For developing and implementing control logic Sensors: SR21-RG Gap Sensor, IFM IM5132 Inductive Sensor, Puyon KS-C2 Color Sensor Communication Protocol: Modbus Pneumatic System: Includes a cylinder, polyurethane braided tubes, and NPT connections for label application Additional Hardware: Power supply, motor, USB to RS485 converter Tools: Programming cable, soldering kit, multimeter for setup and troubleshooting Conclusion The Fabric Labeler Dispenser is a critical asset for industries requiring accurate and efficient labeling, showcasing advanced automation by integrating PLC, sensor technology, and pneumatic control.

RSL Shipping Line BI Transformation

January 1, 2021 – January 1, 2021

My Responsibility Strategic Leadership Stage: Provided executive direction and strategy, emphasizing high-level client and team leadership. Intro RSL BI marks a pivotal shift in Ravian Shipping Line's approach to data management and reporting. This transformative project heralds the migration from traditional manual reporting systems to an advanced Business Intelligence (BI) framework using Microsoft Power BI. It's an integration of high-powered analytics and sophisticated data handling to streamline and enhance the company's reporting mechanisms. Description The RSL BI project was an intricate endeavor that involved revamping Ravian Shipping Line's reporting structure. The primary objective was to pinpoint inefficiencies in their existing manual reporting processes and substitute them with a more dynamic, automated BI tool. The project necessitated an in-depth analysis of two critical databases – the inventory and transaction databases, the latter comprising over 500 tables. This comprehensive understanding of the databases facilitated the development of a tailored Power BI solution, effectively transforming the company's data visualization and reporting strategies. Tech Stack Database Management: Microsoft SQL Server Business Intelligence Tool: Microsoft Power BI Data Analysis: Comprehensive data analysis to understand and utilize 500+ tables from the transaction database. Automation: Automation of reporting processes to replace manual inefficiencies. Integration: Seamless integration of inventory and transaction databases for holistic business insights. Conclusion RSL BI stands as a beacon of modernization in the shipping industry, showcasing the immense potential of blending advanced BI tools with traditional business operations for enhanced efficiency, accuracy, and strategic growth.

Industrial Production Planning and Tracking System

January 1, 2021 – Present

Intro: The Production Planning and Tracking System marks a revolutionary stride in textile manufacturing at the Production Planning division. Designed as a cutting-edge ERP solution, it aims to optimize operational efficiency, enhance production workflows, and facilitate digital innovation in textile production. Description: The PPT System integrates advanced analytics for fault detection in various fabric types, ensuring quality control at every production stage. Its Agile Production Planning Algorithm (APPA) streamlines planning by integrating marketing insights and real-time machine status. The Supervisor-Manager Interactive Dashboard (SMID) enhances decision-making and operational agility through real-time updates and planning adaptability. The Machine Efficiency & Maintenance Hub (MEMH) ensures optimal machine health and reduces downtime. Interdepartmental Coordination System (ICS) guarantees efficient communication and process flow between departments. Lastly, the Comprehensive Fault Management Interface (CFMI) automates fault detection and classification, leveraging sophisticated algorithms for accuracy and efficiency.

Plink - Reporting

January 1, 2020 – January 1, 2021

My Responsibility Growth Stage: Transitioned to team building and task delegation, maintaining active client engagement. Intro Plink's project encapsulates the essence of modern data-driven decision-making, targeting a spectrum of managerial levels from junior executives to C-suite leadership. The initiative's cornerstone was the fusion of cutting-edge data analytics with intuitive visualization, crafted to deliver actionable insights through a state-of-the-art dashboard. Description Embarking on a journey through the intricate landscape of data science, the project encompassed several pivotal phases: Robust Data Aggregation, Rigorous Data Integrity Assurance, Automated ETL Orchestration, Strategic Data Engineering, In-depth Data Analytics, Innovative Dashboard Creation, Iterative Development & QA, and Seamless Deployment. This resulted in a groundbreaking analytics platform, a beacon for businesses seeking to navigate the complex seas of big data, transforming raw data into a strategic asset. Tech Stack Programming Languages: Python Data Reservoirs: MySQL, Google Sheets, AWS Redshift, Microsoft Excel ETL/Data Engineering Tools: Talend, Informatica Visualization & BI Platforms: PowerBI, Grow Data Science Toolkit: Scikit-learn, NumPy, Pandas Cloud Infrastructure: AWS and GCP Integrations: Hubspot API Conclusion Plink's project stands as a testament to the transformative power of analytics and BI in reshaping business landscapes, driving enhanced, data-informed decision-making across organizational hierarchies.

FootFall - AI Retail Analytics

January 1, 2020 – January 1, 2020

My Responsibility Initial Stage: Solely responsible for hands-on development, leading R&D, and managing client relationships. Intro FootFall Retail Analytics is an innovative solution that utilizes cutting-edge computer vision and machine learning technologies for in-depth demographic analysis in retail environments. Description This state-of-the-art system deploys CCTV cameras integrated with advanced computer vision algorithms to track and analyze customer movements within a retail store. It utilizes a suite of sophisticated machine learning models, including YOLO, TensorFlow, Thano, and Facebook Facter RCNN, to offer comprehensive analytics on various aspects of customer behavior. Tech Stack Computer Vision and Machine Learning Libraries: YOLO, TensorFlow, Thano, Faster RCNN, PyTorch, Keras, OpenCV, Matplotlib, SciPy, YOLO, Detectron2, Imutils, FFmpeg, AV Data Visualization Tools: PowerBI, Tableau Language: Python Conclusion FootFall Retail Analytics embodies the fusion of AI, machine learning, and computer vision, providing retailers with a powerful tool to understand and enhance the customer shopping experience.

Irrigation IoT BI

January 1, 2020 – January 1, 2021

My Responsibility Growth Stage: Transitioned to team building and task delegation, maintaining active client engagement. Intro Irrigation IoT BI represents a significant advancement in agricultural technology, focusing on harnessing IoT data for insightful analytics. Description The core of Irrigation IoT BI lies in its ability to process and analyze massive datasets generated by IoT devices and sensors in real time. The system offers a range of interactive dashboards and reports, enabling users to track key metrics, identify trends, and make data-driven decisions to optimize irrigation practices and enhance crop yield. Tech Stack Data Visualization and Analytics: Microsoft Power BI IoT Data Management: Integration with IoT platforms to gather real-time data from various agricultural sensors. Data Processing: Handling large datasets generated by IoT devices for real-time analysis. Custom Template Design: Tailored Power BI templates to meet specific requirements of IoT data analysts in agriculture. Automated Reporting: Automation of data collection and reporting processes for enhanced efficiency and accuracy. Conclusion Irrigation IoT BI is an innovative solution combining IoT with advanced BI tools, transforming the way agricultural data is analyzed and utilized. It stands as a testament to the power of technology in revolutionizing farming practices, and making agriculture more data-driven, sustainable, and productive.

Irrigation Automation

January 1, 2020 – January 1, 2021

My Responsibility Growth Stage: Transitioned to team building and task delegation, maintaining active client engagement. Intro Introducing a revolutionary Irrigation Automation and Control System, expertly designed to transform the agricultural and gardening sectors. Description At the heart of this system lies the innovative use of solar energy, captured by a 10.6cm solar panel to power a robust battery. The system's dual-communication is facilitated by MQTT Broker, ensuring effective management of both server communications and user interactions. A rechargeable Li-ion battery ensures consistent operation, making it suitable for remote or off-grid locations. Tech Stack Microcontroller and IoT: ESP8266 ESP-12E, MQTT Broker for IoT integration. Sensors: Soil Moisture Sensor, DHT11 Temperature and Humidity Sensor for environmental analysis. Power Source: Solar Panel, TP4056 Li-ion Battery Charging Module, 3.2V 5000mAh LiFePO4 Battery for renewable energy utilization. Server and Backend Management: RabbitMQ, ESPHome, Arduino IDE, and LittleFS for server-side operations. Data Analytics and Visualization: ELK Stack (Elasticsearch, Logstash, Kibana) for data aggregation, analysis, and visualization. Conclusion This Irrigation Automation and Control System is an exemplary model of smart agriculture and efficient water management, merging sustainability, advanced technology, and innovative data analytics for transformative impact in irrigation practices.

Electric Bike

January 1, 2020 – January 1, 2020

My Responsibility Growth Stage: Transitioned to team building and task delegation, maintaining active client engagement. Intro This project aims to upgrade an electric bike with a focus on enhancing performance, safety, and user experience. We're incorporating advanced components like a sophisticated Battery Management System (BMS), a dynamic throttle, and a powerful controller. Description The electric bike is engineered with a 60V to 80V controller, offering a low voltage of 41V/52V/64V±0.5V and a limit current of 40A. The BMS is a 20S 60V LiFePO4 system, ensuring efficient battery management. The throttle is compatible with 12-72V, providing versatile speed control. The motor is rated at 1500W, 25A, 60V, capable of delivering robust performance. The bike's total capacity is 72V, 12A, translating to 864Wh. It was tested for a range of 23 minutes at a maximum speed of 30km/h on a 56kg load with about 68V battery charge. Tech Stack Controller: 60V-80V, Low Voltage: 41V/52V/64V±0.5V, Limit Current: 40A BMS: 20S 60V LiFePO4 Throttle: 12-72V compatibility Motor: 1500W, 25A, 60V Battery: 72V, 12A capacity (864Wh) Additional Components: Footrest, waterproof battery box, proper wiring, and electrical connections for bike accessories Conclusion The electric bike project is a blend of mechanical ingenuity and electrical expertise, aiming to deliver a high-performance, sustainable, and user-friendly mode of transportation. It represents a significant step forward in electric bike technology, with potential applications in both personal transport and commercial delivery systems.

Netra (Advanced Video Analytics Platform)

January 1, 2019 – January 1, 2021

Netra is a pioneering force in video intelligence, leveraging AI and computer vision to revolutionize how visual content is understood and utilized. Description Netra's video analytics capabilities are enabled through the integration of pre-trained or custom-developed machine-learning models into the API server. The platform’s React.js-based user interface and backend operations powered by Node.js and Flask ensure a seamless and interactive user experience. The deployment process involves Git for version control, Docker for containerization, and deployment across AWS and Linux servers. Netra: Advanced Machine Learning Application: Focusing on AWS server deployment, Netra used ML for video content analysis, requiring high-speed, accurate real-time processing. Technical Intricacies: Leveraging NVIDIA graphics, Docker, and Linux, we optimized server performance and developed a Flask-based API system. Tech Stack Backend: Node.js, Flask (Python) AI & ML: Python, TensorFlow, PyTorch, Keras Database: PostgreSQL Cloud & DevOps: AWS, Docker, Kubernetes API Integration: Rabbitmq, Celery Version Control: Git

Fault Registration System (FRS): A Paradigm Shift in Textile Quality Assurance

August 1, 2018 – Present

Innovation and Transition: FRS began as a web app but evolved into a desktop application for seamless integration with essential hardware like wheel sensors. UI and Workforce Adaptation: Designing an intuitive UI and overcoming workforce resistance, including deliberate equipment damage, were significant milestones. System Evolution: FRS transformed into a holistic solution, integrating with ERP systems for barcode-based tracking and fault tagging. Data Warehousing: We created a mini-ERP for top-level reporting, leveraging data warehousing for trend analysis and predictive insights. AI Integration and IoT Synergy: Incorporating AI for optimal cutting patterns and IoT for precision, FRS outpaced competitors and addressed complex challenges like EMF fluctuations.

Cutting Plan Optimization - AI in Industrial Process

January 1, 2018 – Present

Mills faced significant challenges with traditional fabric cutting operations. The AI-driven process reduced the number of machines required and slashed operational expenses, resulting in substantial annual savings. The Cut-Plan Optimization System adheres to the standard four-point rules, offering optimal cut plans, with flexibility for manual adjustments. Solutions have saved over 23 million liters of water and 384 trees in 2022 alone.

Eagle Eye - Computer Vision for Garment Measurement

August 1, 2017 – May 1, 2018

Inception and Objective: Teaming up with a university colleague, we embarked on "Eagle Eye," targeting garment dimension accuracy in factories via computer vision. Technical Integration: The solution encompassed AI, software, a specialized mechanical and embedded paddle, and camera systems, including Raspberry Pi, DSLR, and industrial cameras. R&D and Precision: A year of R&D culminated in a groundbreaking 3mm accuracy. Key Challenges: Overcoming lens distortions (fish-eye and perspective), user-friendly software development, worker training, and intricate integration with camera libraries posed significant hurdles.

Skooly - School Social Media with Management

January 1, 2017 – January 1, 2018

Skooly streamlines communication and management in educational institutions, enabling efficient management of timetables, events, and student-teacher interaction. The system is web-based and covers all of a school's needs, including customization options for specific institutional requirements.

AES and DES (Network Security)

April 1, 2016 – May 1, 2016

AES and DES full encryption/decryption algorithms using java

Distributed Password Cracker (Concurrent & Distributing System)

April 1, 2016 – June 1, 2016

Implemented Password Cracker Client-Server-Slave Distributed System, Multiple Clients can request for Cipher-text and get back plaintext from server, Server on the other hand distributes task to Slave systems connected to the server. Fault Tolerant (Slaves go down) is handled efficiently

Pinspector - Deep Learning-Powered Plant Detection System (Final Year Project)

March 1, 2016 – June 1, 2016

Deep Learning-Powered Plant Detection System | Reveal AI Lab | 2016 (Final Year Project) Developed and implemented a novel Deep Neural Network architecture for accurate plant detection in real-time. Achieved state-of-the-art performance with a 98% accuracy rate on benchmark datasets. Utilized cutting-edge Python libraries like TensorFlow to train and optimize the model. Gained valuable experience in research methodologies, data analysis, and algorithm development.

Convolutional Neural Networks for Visual Recognition (Deep Learning)

December 1, 2015 – December 1, 2015

Implemented a 2 Layer Convolutional Neural Network for identifying Cifar-10(Objects) and Mnist(Numbers) images classification problem using dropout for optimization and 2X2 max-pooling for minimizing computations.

Torch Racing Game Compitetion (Artificial Intelligence)

December 1, 2015 – December 1, 2015

This Project was to design are racing car controller using Artificial Intelligence. We used Artificial Neural Network to train the controller from the last year winners training set.

Monopoly Game (Advance Programming)

January 1, 2014 – January 1, 2014

4 players online monopoly game connected to a central server. Piccolo Tool (Java API for 2D animation) was used for game animation and GUI. Java was the Backend language used to implement all the functionality of basic Monopoly Board Game.

Git Essential Training

LinkedIn

June 24, 2026 – Present

Git from Scratch

LinkedIn

June 24, 2026 – Present