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Nauman Irshad Lab

Projects

Research projects

Each project has a named supervisor, a student lead and a defined artefact release. Timelines, funding sources and technology choices are documented openly.

8

Projects on record

2

Currently running

6

Research areas covered

35

Tools in active use

Portfolio

Filter by research area, status or technology

Project records link directly to the publications, repositories and datasets they produced.

Research areas

8

projects shown

Status
Research area
Technology
CXG-DT · Confidence- and Explanation-Gated Digital Twin IoT Response project imagery
Under reviewDigital Twin2025 – 2026

CXG-DT · Confidence- and Explanation-Gated Digital Twin IoT Response

CXG-DT addresses the gap between high IDS accuracy and safe operational response. The pipeline detects attacks with lightweight models, estimates calibrated confidence, runs selective SHAP only when a risk gate opens, validates alerts against virtual device twin states, and permits monitor, throttle, isolate or escalate actions only when uncertainty is acceptable. Evaluated on a locked multiclass IoT set (CICIoT-style flows) with CatBoost as the primary detector.

Publications

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Python
  • CatBoost
  • SHAP
  • Digital Twin
  • scikit-learn
  • CICIoT2023

Paper · download & cite

Download PDFGitHub
Repository
CARE-GATE · Cost-Aware Uncertainty Gating for DT Intrusion Response project imagery
Under reviewIntrusion Detection2025 – 2026

CARE-GATE · Cost-Aware Uncertainty Gating for DT Intrusion Response

CARE-GATE focuses on the decision layer after detection: when should an IoT gateway isolate, throttle or only monitor? The framework uses an explicit cost matrix with uncertainty evidence from confidence scores, selective SHAP stability and twin-state residuals. Evaluated on a balanced eight-class CICIoT2023 subset with six detectors; CatBoost provides the best weighted F1 trade-off.

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Python
  • CatBoost
  • SHAP
  • Cost-sensitive learning
  • CICIoT2023

Paper · download & cite

Download PDFGitHub
Repository
PoseDepth-CMP · OpenPose COCO vs MPI for Person Depth Estimation project imagery
Under reviewComputer Vision2025 – 2026

PoseDepth-CMP · OpenPose COCO vs MPI for Person Depth Estimation

PoseDepth-CMP gives practitioners evidence for choosing between OpenPose body-model variants for person-specific monocular depth. On 240 controlled samples, COCO reaches higher keypoint accuracy and lower depth error; MPI is faster and lighter. The project contributes paired statistics, cue ablations, uncertainty analysis and a learned adaptive selector.

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Python
  • OpenPose
  • COCO
  • MPI
  • Contour analysis
  • NumPy

Paper · download & cite

Download PDF
Smart Fitao AI · Cloth Size Prediction, 3D Try-On & Seller Studio (FYP) project imagery
OngoingComputer Vision2024 – 2026

Smart Fitao AI · Cloth Size Prediction, 3D Try-On & Seller Studio (FYP)

Smart Fitao AI asks: what if getting the perfect fit required no guesswork — just AI? The Final Year Project delivers (1) cloth size prediction for tailors from body and garment measurements, (2) 3D virtual try-on for a more personalised shopping experience, (3) a 24/7 AI chatbot with 3D product recommendations, and (4) a 3D studio for sellers to convert 2D products into interactive 3D models (including PIFuHD Metaverse finetuning). Presented at the Lahore Chamber of Commerce and Industry (LCCI). FYP advisor: Asif Farooq. Team: Nauman Irshad Ali Shah, Umer Amir, Ali Ahmad and Abdul Rehman.

Publications
In preparation

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Python
  • PIFuHD
  • PyTorch
  • Flutter
  • Computer Vision
  • Metaverse
  • Chatbot
  • 3D Studio
Crop Disease Detection · ResNet50 + Explainable AI for Agricultural Intelligence project imagery
CompletedDeep Learning2025 – 2026

Crop Disease Detection · ResNet50 + Explainable AI for Agricultural Intelligence

This AgriTech project detects 15 plant diseases across tomato, potato and pepper bell using 20,638 images, transfer learning on ResNet50 (ImageNet) and Explainable AI via Grad-CAM for transparent predictions. Reported metrics: 90.09% accuracy, 88.41% precision, 89.18% recall and 88.73% F1, with sub-200ms FastAPI inference. Presented as an exhibitor at the 19th International Agri Asia & Green Pakistan Exhibition & Conference (09–11 May 2026, Expo Centre Lahore). Team: Nauman Irshad Ali Shah, Ali Ahmad, Abdul Rehman, Danish Ali and Umer Amir.

Publications
In preparation

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Python
  • ResNet50
  • PyTorch
  • Grad-CAM
  • FastAPI
  • Transfer Learning
  • Explainable AI
SAFE · Mining Software Features from App Store Artifacts project imagery
CompletedNatural Language Processing2024 – 2025

SAFE · Mining Software Features from App Store Artifacts

SAFE (published in Spectrum of Engineering and Management Sciences) manually builds POS and sentence patterns frequently used when text refers to app features, then extracts and matches features across developer pages and user reviews without large training corpora. Student–teacher work with Ameera Arif.

Publications

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Python
  • NLP
  • POS patterns
  • App store analytics

Paper · download & cite

Download PDF
CodeVista · 3D Runner Learning Lab project imagery
Ongoingartificial-intelligence2025 – 2026

CodeVista · 3D Runner Learning Lab

CodeVista 3D Runner is an educational coding lab led by teacher Asim Irshad. Students complete weekly programs, earn XP toward skill goals, and practise in a live 3D runner with Pause, Next, Replay and Run controls plus a source panel for the current exercise. Built for classroom and self-paced learning so beginners can see code behaviour while they write it. Live demo: https://codevista-3d-runner.vercel.app/

Publications
In preparation

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Flutter
  • 3D
  • Educational games
  • XP systems
  • Web
Live demo
FIHPS Society · College History Society (FCCU) project imagery
Completeddata-science2021 – 2022

FIHPS Society · College History Society (FCCU)

From May 2021 to May 2022, Nauman Irshad Ali Shah served as Project Manager with the FIHPS Society (College History Society) at Forman Christian College (A Chartered University), Lahore, under Associate Professor Nadia Khushi. The work covered society management and social-services volunteering. A highlight was organising and conducting an interview with Sir Dr. Jonathan S. Addleton, Rector of FCCU, for Iqbal Day — featured on the FCCU Official YouTube Channel as part of FIHPS Society’s Tribute to Allama Iqbal on his 144th birth anniversary, with Nauman’s name credited on the official FCCU YouTube page.

Publications
In preparation

Supervisor & research team

Click a photo to open LinkedIn

Technology stack

  • Project management
  • Student society
  • Event production
  • Social services

Project proposals

Have a dataset, a testbed or a problem you want studied?

We take on a small number of externally proposed projects each year, particularly where a partner can provide operational data or hardware access under a documented agreement.

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