no code implementations • 23 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.
no code implementations • 27 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.
Ranked #3 on Coronary Artery Segmentation on ARCADE
no code implementations • 5 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.
no code implementations • 19 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.
no code implementations • 11 Aug 2023 • Muhammad Atif Butt, Hassan Ali, Adnan Qayyum, Waqas Sultani, Ala Al-Fuqaha, Junaid Qadir
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
1 code implementation • 11 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.
no code implementations • 3 Jul 2023 • Adnan Qayyum, Hassan Ali, Massimo Caputo, Hunaid Vohra, Taofeek Akinosho, Sofiat Abioye, Ilhem Berrou, Paweł Capik, Junaid Qadir, Muhammad Bilal
In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data.
no code implementations • 23 Jun 2023 • Syed Aun Muhammad Zaidi, Siddique Latif, Junaid Qadir
In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER.
no code implementations • 15 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.
no code implementations • 25 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.
no code implementations • 21 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.
no code implementations • 5 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.
no code implementations • 2 Nov 2022 • Shawqi Al-Maliki, Faissal El Bouanani, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha
Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data.
no code implementations • 24 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).
no code implementations • 6 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.
no code implementations • 5 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.
no code implementations • 5 Mar 2021 • Ahmed Rasheed, Muhammad Shahzad Younis, Junaid Qadir, Muhammad Bilal
Breast cancer is one of the most common cause of deaths among women.
no code implementations • 1 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.
no code implementations • 19 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).
no code implementations • 3 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.
no code implementations • 24 Dec 2020 • Muhammad Ahtazaz Ahsan, Adnan Qayyum, Junaid Qadir, Adeel Razi
In recent years, deep learning (DL) techniques have provided state-of-the-art performance on different medical imaging tasks.
no code implementations • 22 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.
no code implementations • 14 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.
no code implementations • 1 Dec 2020 • Khansa Rasheed, Junaid Qadir, Terence J. O'Brien, Levin Kuhlmann, Adeel Razi
One of the roadblocks for accurate seizure prediction is scarcity of epileptic seizure data.
no code implementations • 16 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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 11 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.
no code implementations • 22 Jun 2020 • Ghezlane Halhoul Merabet, Mohamed Essaaidi, Mohamed Ben-Haddou, Basheer Qolomany, Junaid Qadir, Muhammad Anan, Ala Al-Fuqaha, Riduan Mohamed Abid, Driss Benhaddou
Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior.
no code implementations • 4 Feb 2020 • Khansa Rasheed, Adnan Qayyum, Junaid Qadir, Shobi Sivathamboo, Patrick Kwan, Levin Kuhlmann, Terence O'Brien, Adeel Razi
Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals.
1 code implementation • 27 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.
no code implementations • 21 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.
no code implementations • 2 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
no code implementations • 1 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.
no code implementations • 3 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.
no code implementations • 29 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.
no code implementations • 1 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.
no code implementations • 20 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
1 code implementation • 15 Dec 2018 • Siddique Latif, Adnan Qayyum, Muhammad Usman, Junaid Qadir
Cross-lingual speech emotion recognition is an important task for practical applications.
no code implementations • 28 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.
no code implementations • 24 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.
no code implementations • 4 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.
no code implementations • 24 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.
no code implementations • 25 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.
no code implementations • 25 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.
no code implementations • 25 Jan 2018 • Muhammad Usman, Siddique Latif, Junaid Qadir
Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature.
1 code implementation • 19 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.
no code implementations • 23 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.
no code implementations • 19 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.
no code implementations • 31 Aug 2013 • Junaid Qadir
Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design.