User:Amanullahquamer

Amanullah Quamer is an learning candidate in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Robotics. Currently pursuing a Bachelor of Technology (B.Tech) in Computer Science and Engineering with a specialization in Decision Science and Machine Learning at Lovely Professional University, one of the top universities in India. Amanullah is in the third year of the program and is expected to graduate in 2026.

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• Deep Learning and Neural Network Architectures  
• Computer Vision and Image Understanding
• Natural Language Processing and Multimodal AI
• Edge AI and Human–AI Interaction
• Data Science & Predictive Analytics

• AI and ML Algorithms
• Virtualization and Edge Deployment
• Programming Languages: [Python, C, C++, Java, .]
• Data Analysis and Visualization
• Cloud Computing and MLOps
• Microsoft Project

Fusion at the Core: Lightweight CNNs and Temporal Models for Multimodal Emotion Recognition

Developing a multimodal fusion model combining CNNs and temporal networks for emotion recognition across audio, video, and text modalities.

Aiming for a lightweight, real-time system suitable for deployment in emotion-aware AI applications.

A Two-Stage Deep Learning Model for Non-Destructive Leaf Counting in Tomato Crops

Designing a dual-branch CNN–U-Net model for accurate, non-destructive leaf count estimation from greenhouse crop images.

The model incorporates occlusion correction and real-time edge deployment for precision agriculture.

Open Source Machine Learning Framework for Residential Load Prediction: A Temperature-to-kWh model using XGBoost

Building an open-source XGBoost-based model to predict residential energy consumption directly from temperature data.

Focuses on enhancing load forecasting accuracy by capturing spike patterns and seasonal variability.

ReACS 2025

Conference Date: 19-20 December 2025

Paper Title: Leveraging Siamese Neural Network Architectures for the Detection and Classification of Zero-Day Botnet Network Traffic Analysis

Status: To be presented at ReACS 2025

Conference URL: ReACS 2025

A. Quamer and H. Kaur, "Real-Time Botnet Detection Using Temporal Convolutional Networks and Self-Organizing Map," 2025 2nd International Conference On Multidisciplinary Research and Innovations in Engineering (MRIE), Gurugram, India, 2025, pp. 693-698, doi: 10.1109/MRIE66930.2025.11156544

Developed a convolutional neural network (CNN)-based model to detect defects such as cracks, hotspots, and soiling in solar panels using thermal and RGB imagery. This solution enables predictive maintenance for solar farms, improves energy efficiency, and in future it can be possibly deployed using low-cost open-source hardware such as Raspberry Pi or Jetson Nano.

• Tools & Technologies: TensorFlow, Keras, OpenCV, Python, Raspberry Pi
• Features: Automated defect detection, sustainable energy optimization, open-source hardware integration

Developed a fully serverless data processing system using Amazon Web Services. The project enables users to upload .csv files to an S3 bucket, which are then processed via AWS Lambda and stored in a structured format in DynamoDB. This solution supports scalable, low-maintenance data management applicable in open science, citizen data collection, or sustainable system design.

• Tools & Technologies: AWS S3, Lambda, DynamoDB, Python (Boto3)

• Features:Serverless architecture, automated processing, cloud-native design

Created a lightweight natural language processing (NLP)-based chatbot that provides basic health guidance based on user-entered symptoms. Built for offline and low-resource environments, the system leverages pre-trained transformer models and a local medical knowledge base, supporting equitable access to health information in underserved communities.

• Tools & Technologies: Python, HuggingFace Transformers, Streamlit, SQLite
• Features: Offline-friendly AI chatbot, NLP-based advisory system, open-source for rural healthcare

Implemented AI-driven analysis on Superstore sales data to identify performance trends, regional profitability, and seasonal variations. Leveraged machine learning algorithms for classification and regression to generate insights that help optimize sales, manage stock, and reduce product waste.

• Tools & Technologies:Python, Pandas, Scikit-learn, Seaborn, Matplotlib

• Features:Predictive analytics, trend analysis, retail performance insights

Used Google BigQuery to run advanced queries on Superstore data for trend identification and business insight generation. The analysis focused on customer behavior, product category performance, and geographic segmentation. This project demonstrates cloud-based data warehousing and scalable SQL analytics for enterprise reporting.

• Tools & Technologies:Google BigQuery, GCP, SQL

• Features:Cloud-scale analytics, structured querying, business intelligence

• Vehicle Number Plate Recognition System: Built a neural network to detect and read car license plates under varying weather conditions and camera angles [6].
• Data Visualization Projects (Power BI & Tableau):Built interactive dashboards and analytical reports for business insights using Power BI and Tableau [7].
• Big Data Projects (Apache Hive & Hadoop):Worked on large-scale data processing and querying using Hadoop ecosystem tools like HDFS and HiveQL [8].
• Statistical Analysis of Superstore Data: Performed descriptive stats, hypothesis testing, and probability analysis on business data [9] .
• French To English Neural Translator: Developed a bi-directional language translation model using RNN-based architectures for French and English text [10].
• Urban Navigator for the Visually Impaired (UI/UX Research): Proposed and prototyped a navigation aid focused on urban mobility for disabled and vision-impaired individuals.
• Continuous Assessment Approval System (UI/UX): Designed a streamlined interface to facilitate approval workflows between faculty and HODs for student assessments.
• Air Passenger Forecasting Model:Built an LSTM/GRU-based time series model to predict future air passenger counts using historical flight data [11].
• DSA Explorer (Java): Created an educational tool offering insights and practice for DSA topics like sorting, searching, and graphs [12].
• File Manipulation Tool (Java): Built a CLI tool to list, copy, delete, and concatenate files through command-line operations [13].
• IRCTC App Redesign (UI/UX): Revamped the user interface of the Indian Railways ticket booking app to improve usability, accessibility, and aesthetics.  
• Library Management System: Developed a complete library platform with loan management, fine tracking, recommendations, and search features [14].
• Medicine Record Management (C): Designed a system to track, update, and retrieve medication data for patients [15].
• Grocery Website (HTML/CSS/JS): Designed a responsive e-commerce site for browsing, cart management, and checkout of grocery items [16].
• SRS for GAANA (Music Streaming Website): Authored a detailed SRS outlining streaming, playlist, and search functionalities for GAANA [17].

• Organization: Quantiphi (AI-first digital engineering company)
• Achievement: Successfully led a team to develop a high-performing Recommendation System using the Superstore dataset. Our solution, which balanced accuracy, scalability, and business relevance, was honored with 1st place recognition.

• Title: Health Awareness for Excessive Use of Electronic Gadgets
• Recognition: Awarded top honors by the Department of Chemical Engineering and Biosciences
• Description: Led a college-wide initiative addressing the health impacts of prolonged electronic gadget use. Conducted comprehensive online and offline surveys to gather data and raise awareness through workshops and presentations, promoting digital wellness among students and faculty.

• Details: Successfully cleared the All India Joint Entrance Examination (JEE Mains) for eligibility in India’s top-tier engineering institutions (IITs).

• Details: Qualified in the West Bengal Joint Entrance Examination, gaining eligibility for leading technical universities in the state.

• Organizer: FIITJEE Details: Achieved a Top 100 National Rank in the Fortunate-40 exam, a competitive scholarship test for meritorious students aspiring to pursue careers in science and engineering.

• Participated in Build-a-thon 2.0, a national-level hackathon organized by Board Infinity at Lovely Professional University. Demonstrated innovation, collaboration, and technical skills to address real-world challenges using AI and emerging technologies.

• Attended North India's Largest Cyber Security Conference, CyberSec Symposium 2.0, and actively participated in the Cybersecurity & Bug Bounty Workshop, gaining practical exposure to ethical hacking, real-time vulnerability analysis, and responsible disclosure practices.

• Completed specialized hands-on sessions in Penetration Testing and Android Application Pentesting during CyberSec Symposium 2.0, enhancing skills in mobile app security, vulnerability exploitation, and secure development life cycles.

• Institution: Indian Institute of Technology Madras (IIT Madras)
• Recognition: Successfully completed national-level certification with exam conducted in May 2025

Completed an intensive course covering foundational and advanced topics in probability theory and statistics. Gained a strong understanding of statistical reasoning, probabilistic models, and data interpretation - essential for data science and AI-related research.

Participated in a structured, work-integrated training program with Quantiphi, an AI-first digital engineering company, during the 3rd to 5th semesters. The program blended academic learning with real-world industry exposure and hands-on experience in applied AI and data science.

• Collaborated on multiple industry-aligned projects focused on machine learning, data analytics, and cloud-based solutions.
• Attended expert-led workshops and technical sessions on AI model development, data pipelines, cloud deployment, and responsible AI practices.
• Gained practical skills in project execution, client problem-solving, and teamwork in a professional setting.

• Duration: 8th June 2024 – 13th July 2024

• Location: Jalandhar, Punjab, India

• Tech Stack: JavaScript, React.js, Node.js

Successfully completed a 35-day internship focusing on full-stack web development. Worked on both the front-end and back-end using modern JavaScript frameworks and libraries. Demonstrated strong technical skills, teamwork, and adaptability throughout the internship.