Academic Articles & Research Papers

A Deep Learning Harmonization of Multi-Vendor MRI for Robust Intervertebral Disc Segmentation

Magnetic resonance imaging (MRI) provides enhanced soft tissue contrast and high spatial resolution. However, the relationship between intensity values among soft tissues in MRI is inconsistent, even when obtained under the same conditions ...

Is a Cambrian Explosion Coming for Robotics?

About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, one of the most provocative be...

Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression

Abstract In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast‐Tracker which is applicable to various challenging tracking scenarios. However, it...

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or in...

Quantum Computing in Cybersecurity

Machine learning, deep learning, probabilistic neural networks, blockchain, and other new technologies all demand extremely high processing speeds. A quantum computer is an example of such a system. Quantum computers may be accessed over th...

Fundus imaging based quantum computing: Grading of severity of vision threatening diabetic retinopathy

Diabetic Retinopathy (DR) is a major complication of diabetes that can result in irreversible vision loss if not detected early. Manual ophthalmologic screening is time-consuming and subject to subjectivity, underscoring the need for accura...

Advances in materials informatics for tailoring thermal radiation: A perspective review

Materials informatics has emerged as a powerful tool for the discovery, design, and optimization of materials with tailored thermal radiative properties. This perspective review highlights the recent advances in optimization algorithms, inc...

Multimodal deep learning for automated atomic qubits fabrication in silicon

Donor-based spin qubits in silicon are a promising platform for scalable quantum computing due to their long coherence times and high-fidelity gate operations. A viable path for fabricating donor qubit arrays with atomic precision is scanni...

QEKI: A Quantum–Classical Framework for Efficient Bayesian Inversion of PDEs

Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional para...

Quantum transfer learning for cross-domain cybersecurity threat detection and categorization

Abstract Cybersecurity challenges have become increasingly complex and widespread, and the risks associated with these problems are substantial, affecting thousands of individuals and organisations and being crucial to national security. As...

Using an Inspired Quantum-Classical Sequential Neural Network to Segment Liver Tumor Images Accurately

Medical analytics based on multimodal intelligence are essential to contemporary healthcare, especially in the diagnosis and detection of liver cancer. However, low contrast, varied textures, and irregular tumor forms in medical imaging con...

Deep quantum neural networks on a superconducting processor

Abstract Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report a...

Recurrent quantum embedding neural network and its application in vulnerability detection

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and languag...

Forthcoming applications of quantum computing: peeking into the future

We all have been using classical computers for a long time. Quantum computing uses the phenomena of quantum mechanics like superposition and entanglement. Quantum computations can help achieve for the breakthroughs we have been looking for ...

Machine Learning and Deep Learning Methods for Cybersecurity

With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for net...

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the origina...

Machine learning in cybersecurity: a comprehensive survey

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...

Deep Learning in Cybersecurity

In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existi...

Application of deep learning to cybersecurity: A survey

Explainable artificial intelligence for cybersecurity: a literature survey

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...