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Outputs (92)

UAV’s applications, architecture, security issues and attack scenarios: A survey (2020)
Book Chapter
Khan, N. A., Brohi, S., & Jhanjhi, N. (2020). UAV’s applications, architecture, security issues and attack scenarios: A survey. In S. Peng, L. H. Son, G. Suseendran, & D. Balaganesh (Eds.), Book cover Book cover Intelligent Computing and Innovation on Data Science (753-760). Online and in print.: Springer. https://doi.org/10.1007/978-981-15-3284-9_81

Unmanned aerial vehicles (UAVs)/drones have become very popular in recent years as they are widely used in several domains. They are widely used in both military and civilian applications such as aerial photography, entertainment, search and rescue m... Read More about UAV’s applications, architecture, security issues and attack scenarios: A survey.

The natural connectivity of autonomous systems (2020)
Journal Article
Battle, S. (2020). The natural connectivity of autonomous systems. Rivista Italiana di Filosofia del Linguaggio, 14(2), 1-16. https://doi.org/10.4396/AISB201901

The principle of biological autonomy, introduced by Francisco J. Varela, addresses the dilemma of Cartesian mind-body dualism by re-casting mind and body, or subject and object, observer and observed, not as irreconcilable categories, but as compleme... Read More about The natural connectivity of autonomous systems.

Adaptive binary artificial bee colony algorithm (2020)
Journal Article
Durgut, R., & Aydin, M. E. (2021). Adaptive binary artificial bee colony algorithm. Applied Soft Computing, 101, Article 107054. https://doi.org/10.1016/j.asoc.2020.107054

Metaheuristics and swarm intelligence algorithms are bio-inspired algorithms, which have long standing track record of success in problem solving. Due to the nature and the complexity of the problems, problem solving approaches may not achieve the sa... Read More about Adaptive binary artificial bee colony algorithm.

Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text (2020)
Conference Proceeding
Arnaud, E., Elbattah, M., Gignon, M., & Dequen, G. (2020). Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text. In 2020 IEEE International Conference on Big Data (Big Data) (4836-4841). https://doi.org/10.1109/bigdata50022.2020.9378073

Overcrowding in Emergency Departments (ED) is considered as an international issue, which could have adverse impacts on multiple care outcomes such as the length of stay for example. Part of the solution could lie in the early prediction of the patie... Read More about Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text.

Performance of deep learning vs machine learning in plant leaf disease detection (2020)
Journal Article
Sujatha, R., Chatterjee, J. M., Jhanjhi, N. Z., & Brohi, S. (2020). Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 80, 103615. https://doi.org/10.1016/j.micpro.2020.103615

Plants are recognized as essential as they are the primary source of humanity's energy production since they are having nutritious, medicinal, etc. values. At any time between crop farming, plant diseases can affect the leaf, resulting in enormous cr... Read More about Performance of deep learning vs machine learning in plant leaf disease detection.

Using active learning to understand the videoconference experience: A case study (2020)
Conference Proceeding
Llewellyn, S., Simons, C., & Smith, J. (2020). Using active learning to understand the videoconference experience: A case study. https://doi.org/10.1007/978-3-030-63799-6_30

Videoconferencing is becoming ubiquitous, especially so during the COVID-19 pandemic. However, user experience of a videoconference call can be variable. To better understand and classify the performance of videoconference call systems, this paper re... Read More about Using active learning to understand the videoconference experience: A case study.

A mixture-of-experts model for learning multi-facet entity embeddings (2020)
Conference Proceeding
Alshaikh, R., Bouraoui, Z., Jeawak, S., & Schockaert, S. (2020). A mixture-of-experts model for learning multi-facet entity embeddings. In Proceedings of the 28th International Conference on Computational Linguistics (5124-5135)

Various methods have already been proposed for learning entity embeddings from text descriptions. Such embeddings are commonly used for inferring properties of entities, for recommendation and entity-oriented search, and for injecting background know... Read More about A mixture-of-experts model for learning multi-facet entity embeddings.

NLP-based approach to detect autism spectrum disorder in saccadic eye movement (2020)
Conference Proceeding
Elbattah, M., Guerin, J. L., Carette, R., Cilia, F., & Dequen, G. (2020). NLP-based approach to detect autism spectrum disorder in saccadic eye movement. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (1581-1587). https://doi.org/10.1109/ssci47803.2020.9308238

Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could be complicated by several factors. Standard tests typically require i... Read More about NLP-based approach to detect autism spectrum disorder in saccadic eye movement.