Search Results for author: Junaid Qadir

Found 50 papers, 4 papers with code

MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis

no code implementations23 Nov 2023 Adam Byfield, William Poulett, Ben Wallace, Anusha Jose, Shatakshi Tyagi, Smita Shembekar, Adnan Qayyum, Junaid Qadir, Muhammad Bilal

Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and trustworthiness.

Fairness Tumour Classification

Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning

no code implementations27 Oct 2023 Muhammad Bilal, Dinis Martinho, Reiner Sim, Adnan Qayyum, Hunaid Vohra, Massimo Caputo, Taofeek Akinosho, Sofiat Abioye, Zaheer Khan, Waleed Niaz, Junaid Qadir

This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms.

Coronary Artery Segmentation Ensemble Learning +2

Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

no code implementations5 Oct 2023 Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali, Mohamed Abdallah, Junaid Qadir, Dinh Thai Hoang, Dusit Niyato, Ala Al-Fuqaha

This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.

Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey

no code implementations19 Sep 2023 Mahdi Alkaeed, Adnan Qayyum, Junaid Qadir

In this paper, we explore various privacy challenges that future metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences, and facilitating interactions.

Federated Learning Mixed Reality

Membership Inference Attacks on DNNs using Adversarial Perturbations

1 code implementation11 Jul 2023 Hassan Ali, Adnan Qayyum, Ala Al-Fuqaha, Junaid Qadir

Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR.

Inference Attack Membership Inference Attack

Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques

no code implementations15 Jun 2023 Mohammed Aledhari, Mohamed Rahouti, Junaid Qadir, Basheer Qolomany, Mohsen Guizani, Ala Al-Fuqaha

We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies.

Autonomous Driving

Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?

no code implementations25 Mar 2023 Adnan Qayyum, Muhammad Bilal, Muhammad Hadi, Paweł Capik, Massimo Caputo, Hunaid Vohra, Ala Al-Fuqaha, Junaid Qadir

Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science.

Transformers in Speech Processing: A Survey

no code implementations21 Mar 2023 Siddique Latif, Aun Zaidi, Heriberto Cuayahuitl, Fahad Shamshad, Moazzam Shoukat, Junaid Qadir

The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences.

Automatic Speech Recognition Speech Enhancement +4

Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models

no code implementations5 Mar 2023 Hassan Ali, Muhammad Atif Butt, Fethi Filali, Ala Al-Fuqaha, Junaid Qadir

Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored.

Adversarial Attack Management +1

Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses

no code implementations24 Oct 2022 Adnan Qayyum, Muhammad Atif Butt, Hassan Ali, Muhammad Usman, Osama Halabi, Ala Al-Fuqaha, Qammer H. Abbasi, Muhammad Ali Imran, Junaid Qadir

Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s).

Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques

no code implementations6 Apr 2021 Ghezlane Halhoul Merabet, Mohamed Essaaidi, Mohamed Ben Haddou, Basheer Qolomany, Junaid Qadir, Muhammad Anan, Ala Al-Fuqaha, Mohamed Riduan Abid, Driss Benhaddou

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort.

District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

no code implementations5 Mar 2021 Ahmed Rasheed, Muhammad Shahzad Younis, Farooq Ahmad, Junaid Qadir, Muhammad Kashif

Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors.

Time Series Time Series Analysis

Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset

no code implementations1 Mar 2021 Kashif Ahmad, Firoj Alam, Junaid Qadir, Basheer Qolomany, Imran Khan, Talhat Khan, Muhammad Suleman, Naina Said, Syed Zohaib Hassan, Asma Gul, Ala Al-Fuqaha

In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews.

Sentiment Analysis

Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

no code implementations19 Jan 2021 Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, Junaid Qadir

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency).

COVID-19 Diagnosis Edge-computing +1

Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks

no code implementations3 Jan 2021 Bilal Yousaf, Muhammad Usama, Waqas Sultani, Arif Mahmood, Junaid Qadir

The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.

Vocal Bursts Valence Prediction

Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning

no code implementations22 Dec 2020 Inaam Ilahi, Muhammad Usama, Muhammad Omer Farooq, Muhammad Umar Janjua, Junaid Qadir

The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low.

Management reinforcement-learning +1

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

no code implementations14 Dec 2020 Kashif Ahmad, Majdi Maabreh, Mohamed Ghaly, Khalil Khan, Junaid Qadir, Ala Al-Fuqaha

In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges.


Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

no code implementations16 Nov 2020 Ihab Mohammed, Shadha Tabatabai, Ala Al-Fuqaha, Faissal El Bouanani, Junaid Qadir, Basheer Qolomany, Mohsen Guizani

In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.

Federated Learning

Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services

no code implementations5 Sep 2020 Basheer Qolomany, Kashif Ahmad, Ala Al-Fuqaha, Junaid Qadir

Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices.

Federated Learning Traffic Prediction

Examining Machine Learning for 5G and Beyond through an Adversarial Lens

no code implementations5 Sep 2020 Muhammad Usama, Rupendra Nath Mitra, Inaam Ilahi, Junaid Qadir, Mahesh K. Marina

Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e. g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond.

BIG-bench Machine Learning

Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services

no code implementations11 Aug 2020 Basheer Qolomany, Ihab Mohammed, Ala Al-Fuqaha, Mohsen Guizan, Junaid Qadir

With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important.

BIG-bench Machine Learning Model Selection

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

1 code implementation27 Jan 2020 Inaam Ilahi, Muhammad Usama, Junaid Qadir, Muhammad Umar Janjua, Ala Al-Fuqaha, Dinh Thai Hoang, Dusit Niyato

Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.

Autonomous Vehicles reinforcement-learning +1

Secure and Robust Machine Learning for Healthcare: A Survey

no code implementations21 Jan 2020 Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al-Fuqaha

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images.

BIG-bench Machine Learning Privacy Preserving

Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends

no code implementations2 Jan 2020 Siddique Latif, Rajib Rana, Sara Khalifa, Raja Jurdak, Junaid Qadir, Björn W. Schuller

Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Black-box Adversarial ML Attack on Modulation Classification

no code implementations1 Aug 2019 Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha

We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings.

Adversarial Attack BIG-bench Machine Learning +2

The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?

no code implementations3 Jun 2019 Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha, Mounir Hamdi

We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.

Intrusion Detection Traffic Classification

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

no code implementations29 May 2019 Adnan Qayyum, Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha

Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.

Autonomous Vehicles BIG-bench Machine Learning

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

no code implementations1 Apr 2019 Basheer Qolomany, Ala Al-Fuqaha, Ajay Gupta, Driss Benhaddou, Safaa Alwajidi, Junaid Qadir, Alvis C. Fong

In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics.

BIG-bench Machine Learning Decision Making

Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding

no code implementations20 Feb 2019 Muhammad Usman, Muhammad Umar Farooq, Siddique Latif, Muhammad Asim, Junaid Qadir

The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis.

Generative Adversarial Network Motion Correction In Multishot Mri +1

Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness

no code implementations28 Nov 2018 Siddique Latif, Rajib Rana, Junaid Qadir

Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems.

Adversarial Attack BIG-bench Machine Learning +2

Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks

no code implementations24 Nov 2018 Siddique Latif, Muhammad Asim, Muhammad Usman, Junaid Qadir, Rajib Rana

Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as it accelerates the MRI data acquisition process without compromising the quality of final MR image.

Generative Adversarial Network Image Reconstruction +1

FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning

no code implementations4 Nov 2018 Faiq Khalid, Muhammmad Abdullah Hanif, Semeen Rehman, Junaid Qadir, Muhammad Shafique

Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets.

Adversarial Attack BIG-bench Machine Learning +1

SDN Flow Entry Management Using Reinforcement Learning

no code implementations24 Sep 2018 Ting-Yu Mu, Ala Al-Fuqaha, Khaled Shuaib, Farag M. Sallabi, Junaid Qadir

Modern information technology services largely depend on cloud infrastructures to provide their services.

Management reinforcement-learning +1

Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection

no code implementations25 Jan 2018 Siddique Latif, Muhammad Usman, Rajib Rana, Junaid Qadir

Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise.

Heartbeat Classification

Using Deep Autoencoders for Facial Expression Recognition

no code implementations25 Jan 2018 Muhammad Usman, Siddique Latif, Junaid Qadir

Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature.

Dimensionality Reduction Facial Expression Recognition +2

Soft Computing Techniques for Dependable Cyber-Physical Systems

no code implementations25 Jan 2018 Muhammad Atif, Siddique Latif, Rizwan Ahmad, Adnan Khalid Kiani, Junaid Qadir, Adeel Baig, Hisao Ishibuchi, Waseem Abbas

Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements.

Transfer Learning for Improving Speech Emotion Classification Accuracy

1 code implementation19 Jan 2018 Siddique Latif, Rajib Rana, Shahzad Younis, Junaid Qadir, Julien Epps

The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions.

Classification Cross-corpus +4

Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study

no code implementations23 Dec 2017 Siddique Latif, Rajib Rana, Junaid Qadir, Julien Epps

Inspired by this, we propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions.

Emotion Classification General Classification +1

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

no code implementations19 Sep 2017 Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha

We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.

Anomaly Detection BIG-bench Machine Learning +5

Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks

no code implementations31 Aug 2013 Junaid Qadir

Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design.

Decision Making

Cannot find the paper you are looking for? You can Submit a new open access paper.