Community of Research and Development - CRD

Welcome to the Explanation of Medical Diagnostics IEEE research paper:
“Spectral Analysis of Lung Sounds for Classification of Asthma and Pneumonia Wheezing.”
Paper Published in: IEEE International Conference on Electrical, Communication, and Computer Engineering (ICECCE)

IEEE Research Paper Link: https://ieeexplore.ieee.org/document/9179417
Conference Location: Istanbul, Turkey
Publisher: IEEE Xplore
Github Code link: https://github.com/mishaurooj/spectral-lungsound-classification
Kaggle Code link:
1. https://www.kaggle.com/code/mishauroojkhan/1-spectralpaer-preprocessing/
2. https://www.kaggle.com/code/crdkhan/2-spectralpaper-feature-extraction/
3. https://www.kaggle.com/code/crdkhan/3-spectralpaper-classification/
Code Implementation Video:
In this 17-minute video, I break down the complete research process step by step:
📊 Spectral analysis of lung sounds (centroid, entropy, roll-off, etc.)
🩺 Identifying wheezing patterns in asthma & pneumonia patients
🤖 Applying machine learning (SVM classifier) for disease classification
✅ Achieving up to 96.7% accuracy in lung sound classification
🚑 Real-world applications in AI-powered healthcare & early diagnosis

This IEEE paper shows how AI, signal processing, and biomedical engineering can revolutionize pulmonary disease detection.

🔔 Subscribe for more IEEE Paper Explanations, AI in Healthcare, and Biomedical Engineering research breakdowns.
👍 Like, comment, and share if you believe AI can save lives in healthcare.

https://youtu.be/OxTxGVey2xc

IEEE Research Paper Explained | Medical Data Classification | AI in Healthcare Asthma and Pneumonia Welcome to the Explanation of Medical Diagnostics IEEE research paper:“Spectral Analysis of Lung Sounds for Classification of Asthma and Pneumonia Wheezing.”...

Research paper Title: A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies
Presenter: Engr. Misha Urooj Khan (CERN , Switzerland)
🔗 Research Paper Link: https://ieeexplore.ieee.org/document/9526266
Conference Venue: IEEE MAJICC'21

This presentation explores the advancements in LiDAR-based SLAM (Simultaneous Localization and Mapping) and its crucial role in automation, robotics, autonomous vehicles, and smart cities. The research paper provides:
✅ A comparative analysis of LiDAR, Radar, UWB, and Wi-Fi sensor technologies
✅ An overview of LiDAR sensor classifications
✅ Mathematical & graphical modeling of LiDAR-SLAM
✅ Key SLAM features: Mapping, Localization, and Navigation
✅ A detailed comparison of LiDAR-SLAM vs. other SLAM approaches
✅ Insights into challenges & future research directions

https://youtu.be/KRRcss5vl9k

🔗 Follow us on Facebook: Community of Research and Development (CRD) https://www.facebook.com/Community-of-Research-and-Development-CRD-108405038049656/

If you are passionate about AI, Robotics, SLAM, and Sensor Technology, this research presentation is for you! 🌍✨

Robotics | LiDAR-SLAM vs Other SLAM & Sensor Technologies | Comparative Survey | IEEE Research paper Title: A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor TechnologiesPresenter: Engr. Misha Urooj Khan (CERN , Switzerland)🔗 Research...

New ChatGPT Launch in 2025.

Curious how ChatGPT-5 actually works ? In this video, we break down the entire ChatGPT-5 architecture step-by-step, including the Client UI, Orchestrator, GPT-5 Transformer, Retrieval-Augmented Generation (RAG), Memory/Profile, and Streaming Response system.

Whether you're a developer, researcher, or tech enthusiast, you'll walk away understanding the core components powering ChatGPT-5 and how they interact in real-time.

✅ Learn how ChatGPT-5 uses memory and retrieval to enhance responses
✅ Discover what the "orchestrator" does behind the scenes in ChatGPT
✅ Block diagram workflow explanation of Chat GPT 5 included!

📌 Subscribe for more Chat GPT deep dives and AI architecture breakdowns!
https://youtu.be/rcmCCaByNWk

#2025

What is ChatGPT-5 | ChatGPT-5 Architecture Explained | Everything About ChatGPT-5 Explained (2025) New ChatGPT Launch in 2025. Curious how ChatGPT-5 actually works ? In this video, we break down the entire ChatGPT-5 architecture step-by-step, including t...

📌 Research Paper Title: GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark
📂 Research Paper Code: https://github.com/ZeeshanKaleem/GhostAutoAnchorNet
📖 IEEE Research Paper Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10200720
🎓 Presented at: 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)
👩‍🔬 Presented by: Engr. Misha Urooj Khan

🌍 Collaboration Team:
1. Community of Research and Development (https://www.researchgate.net/lab/Community-of-Research-and-Development-CRD-Misha-Urooj-Khan-2)
2. Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Pakistan
3. Department of Engineering, King’s College London, United Kingdom
4. School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia

🚀 In This Video?
This video explains a cutting-edge deep learning framework (GAANet) designed for autonomous drone detection in low-light/infrared conditions. With custom datasets, optimized anchors for small objects, and ghost convolution modules, GAANet outperforms state-of-the-art UAV detection models, making it highly relevant for security, defense, surveillance, and AI-powered aerospace systems.

https://youtu.be/jstigCgL5sQ

🔔 Subscribe for more insights into AI, Deep Learning, Drone Detection, and Computer Vision research.
👍 Like | 💬 Comment | 🔗 Share to support research dissemination!

GAANet: Ghost Auto Anchor Network forDetecting Varying Size Drones in Dark Presented by: Engr. Misha Urooj KhanPaper Code: https://github.com/ZeeshanKaleem/GhostAutoAnchorNetPaper Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?ar...

🚀 Thrilled to see our recent work on Trigger Rate Anomaly Detection using Conditional Variational Autoencoders (CVAE) presented at an international physics experiment!

From monitoring complex systems to leveraging deep generative models for detecting rare events, this project combines machine learning with real-time data from one of the world’s largest scientific collaborations.

Proud to contribute on the software side of things – scaling algorithms, optimizing inference, and ensuring performance under demanding conditions.

Grateful for the chance to be part of such a cutting-edge, interdisciplinary effort. Collaboration between physics and AI never ceases to amaze me. ✨

🚀 New Research Published – International Collaboration in Quantum AI for Healthcare!
I'm proud to announce the publication of our latest research: "Diabetes Prediction Using an Optimized Variational Quantum Classifier" in the prestigious Q1 journal (IF:3.7) International Journal of Intelligent. Systems.
📄 DOI: https://doi.org/10.1155/int/1351522🔬 International Collaboration Across 3 Countries 🌍
This work reflects a strong global partnership between the following institutions:
🇰🇷 Pukyong National University, Busan, South Korea
🇯🇵 Nagoya University, Japan
🇰🇷 Sejong University, Seoul, South Korea

Diabetes Prediction Using an Optimized Variational Quantum Classifier Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of ...

🚀 New Public Dataset for Affective Computing and Mental Well-being Research

We’re excited to share our latest contribution to the research and development community: the SMSAT dataset, now publicly available on Kaggle!
🔗 https://www.kaggle.com/datasets/crdkhan/qmsat-dataset

🧠 What is SMSAT?
The Spiritual, Music, Silence Acoustic Time Series (SMSAT) dataset is a multimodal collection of 60-second ATS (Acoustic Time Series) signals recorded under three unique auditory conditions:
1️⃣ Natural Silence (NS)
2️⃣ Music (M)
3️⃣ Spiritual Meditation (SM)

🎧 Each recording is captured at 16kHz sampling rate using controlled protocols across a diverse demographic (ages 3–55, gender-balanced).

📊 Why is this important?
Understanding the physiological and emotional effects of auditory environments is critical for:

Affective computing

Mental health technology

Therapeutic audio interventions

Stress and calmness monitoring

🧠 Research Highlights
Our work, under review in IEEE Transactions on Affective Computing and preprinted here 👉 arXiv (https://arxiv.org/abs/2505.00839), introduces:
✅ A contrastive learning audio encoder that achieves 99.99% classification accuracy
✅ The Calmness Analysis Model (CAM): a deep learning framework integrating both handcrafted and learned features
✅ Statistical analysis (ANOVA, t-tests) showing significant physiological variations across auditory stimuli

💻 Coming Soon
The source code for preprocessing, model training, and calmness state classification will be released following publication.

📢 We invite researchers, developers, and health tech innovators to explore the dataset, run experiments, and build on top of this work. Let’s advance the future of emotion-aware AI and mental wellness tech together!

SMSAT Dataset Spiritual, Music, Silence Acoustic Time Series Dataset

A.o.A.
Interested candidates with relevant experience in Artificial Intelligence (AI), Deep Learning (DL), Computer Vision (CV), Large Language Models (LLMs), Quantum Machine Learning (QML), Unmanned Aerial Vehicles (UAVs), Low-Power Wide-Area Networks (LPWANs), High-Altitude Platforms (HAPs), industrial diagnostics, and other emerging technologies are invited to join international collaborative research projects under CRD.
We especially welcome expertise or interest in the following cutting-edge areas:
Generative AI
Multimodal AI
Neural Architecture Search (NAS)
Explainable AI (XAI)
AI for Healthcare
AI in Cybersecurity
Edge AI / TinyML
Human-Centered AI
Autonomous Systems
These collaborations are aimed at producing high-quality research outcomes, with a strong focus on publishing in high-impact factor, Q1-ranked international journals and experience letters .
Self-motivated, research-driven individuals who can dedicate time and contribute meaningfully to global collaborative efforts are highly encouraged to contact.
If you meet the criteria, or know someone who does , please get in touch via the CRD WhatsApp group or contact us directly. Kindly share this message with other serious and relevant candidates as well.
— Chairperson, CRD

The Korean Intellectual Property Office granted our patent.
➤ Registration date: September 11, 2024
➤ Patent Number: 10-2707632
➤ Application Number: 10-2023-0040392
➤ Patent Title: 다중 클래스 다중 타겟 드론 검출 시스템 및 학습모델 (Multi-Class Multi-Target Drone Detection System and Learning Model)
➤ Inventors: Heejung Yu, Waqas Khalid, Misha Urooj Khan, Maham Misbah, Farooq Alam Orakzai, Zeeshan Kaleem hashtag hashtag

https://www.researchgate.net/publication/385750970_Migration_of_CADI_to_Fence_-_NCPAITeC_Pakistan_CERN_Switzerland_Collaboration

(PDF) Migration of CADI to Fence - NCP/AITeC, Pakistan & CERN, Switzerland Collaboration PDF | CMS Analysis Database Interface (CADI) is a management tool for physics publications in the CMS experiment. It acts as a central database for the... | Find, read and cite all the research you need on ResearchGate

Migration of CADI to Fence presented onsite at 42nd International Conference on High Energy Physics (ICHEP 2024) Prague, Czech Republic

cds.cern.ch

Migration of CADI to Fence at European Organization for Nuclear Research - CERN, Switzerland

cds.cern.ch