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

Gustavo A. Mesías-Ruiz, Gustavo A. Mesías-Ruiz, María Pérez-Ortiz, José Dorado et al. • Frontiers in Plant Science (2023)

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...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpls.2023.1143326

Quantum Computing in Cybersecurity

• Wiley eBooks (2023)

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...

OPENALEX DOI: https://doi.org/10.1002/9781394167401

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

Amna Ikram, Azhar Imran • Informatics in Medicine Unlocked (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.imu.2026.101744

Multimodal deep learning for automated atomic qubits fabrication in silicon

Yuwei Cui, Kunrong Wu, Ping Wu, Luyan Yang et al. • Machine Learning: Science and Technology (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.1088/2632-2153/ae8e30

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

Shtwai Alsubai, Mohamed Ayari, Natalia Kryvinska, Ahmad Almadhor et al. • Scientific Reports (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-40634-z

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

Sonwane Suchitra Shivaji Rao, K. Gangadhara Rao • Journal of Applied Science and Engineering (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.6180/jase.202701_36.052

Recurrent quantum embedding neural network and its application in vulnerability detection

Zhihui Song, Xin Zhou, Jinchen Xu, Xiaodong Ding et al. • Scientific Reports (2024)

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...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-63021-y

Forthcoming applications of quantum computing: peeking into the future

Vikas Hassija, Vinay Chamola, Adit Goyal, Salil S. Kanhere et al. • IET Quantum Communication (2020)

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 ...

OPENALEX cc-by-nc DOI: https://doi.org/10.1049/iet-qtc.2020.0026

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Sherali Zeadally, Erwin Adi, Zubair Ahmed Baig, Imran Ali Khan • IEEE Access (2020)

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...

OPENALEX cc-by DOI: https://doi.org/10.1109/access.2020.2968045

Machine learning in cybersecurity: a comprehensive survey

Dipankar Dasgupta, Zahid Akhtar, Sajib Sen • The Journal of Defense Modeling and Simulation Applications Methodology Technology (2020)

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...

OPENALEX DOI: https://doi.org/10.1177/1548512920951275

Deep Learning in Cybersecurity

Yadigar İmamverdiyev, Fargana J. Abdullayeva • International Journal of Cyber Warfare and Terrorism (2020)

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...

OPENALEX DOI: https://doi.org/10.4018/ijcwt.2020040105

Explainable artificial intelligence for cybersecurity: a literature survey

Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications (2022)

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...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12243-022-00926-7

THE ROLE OF NATURAL LANGUAGE PROCESSING

V Geetha, C.K. Gomathy, Mr.D.Sri Datta Vallab Yaratha Yagn, Sai Praneesh • INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (2023)

Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-ba...

OPENALEX Open Access DOI: https://doi.org/10.55041/ijsrem27094

Quantifying Uncertainties in Natural Language Processing Tasks

Yijun Xiao, William Yang Wang • Proceedings of the AAAI Conference on Artificial Intelligence (2019)

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

OPENALEX Open Access DOI: https://doi.org/10.1609/aaai.v33i01.33017322