Publications tagged with Artificial intelligence
Published:
Publications tagged with "Artificial intelligence"
- DetailsMarulli, F., Campanile, L., Ragucci, G., Carbone, S., & Bifulco, M. (2025). Data Generation and Cybersecurity: A Major Opportunity or the Next Nightmare? [Conference paper]. Proceedings of the 2025 IEEE International Conference on Cyber Security and Resilience, CSR 2025, 969–974. https://doi.org/10.1109/CSR64739.2025.11130069
Abstract
In recent years, the proliferation of synthetic data generation techniques-driven by advances in artificial intelli-gence-has opened new possibilities across a wide range of fields, from healthcare to autonomous systems, by addressing critical data scarcity issues. However, this technological progress also brings with it a growing concern: the dual-use nature of synthetic data. While it offers powerful tools for innovation, it simultaneously introduces significant risks related to information disorder and cybersecurity. As AI systems become increasingly capable of producing highly realistic yet entirely fabricated content, the boundaries between authentic and artificial information blur, making it more difficult to detect manipulation, protect digital infrastructures, and maintain public trust. This work undertakes a preliminary exploration of the evolving nexus between Generative AI, Information Disorder, and Cybersecurity: it aims to investigate the complex interplay among these three and to map their dynamic interactions and reciprocal influences, highlighting both the potential benefits and the looming challenges posed by this evolving landscape. Moreover, it seeks to propose a conceptual framework for assessing these interdependencies through a set of indicative metrics, offering a foundation for future empirical evaluation and strategic response. © 2025 IEEE. - DetailsCampanile, L., De Fazio, R., Di Giovanni, M., & Marulli, F. (2024). Beyond the Hype: Toward a Concrete Adoption of the Fair and Responsible Use of AI [Conference paper]. CEUR Workshop Proceedings, 3762, 60–65. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205601768&partnerID=40&md5=99140624de79e37b370ed4cf816c24e7
Abstract
Artificial Intelligence (AI) is a fast-changing technology that is having a profound impact on our society, from education to industry. Its applications cover a wide range of areas, such as medicine, military, engineering and research. The emergence of AI and Generative AI have significant potential to transform society, but they also raise concerns about transparency, privacy, ownership, fair use, reliability, and ethical considerations. The Generative AI adds complexity to the existing problems of AI due to its ability to create machine-generated data that is barely distinguishable from human-generated data. Bringing to the forefront the issue of responsible and fair use of AI. The security, safety and privacy implications are enormous, and the risks associated with inappropriate use of these technologies are real. Although some governments, such as the European Union and the United States, have begun to address the problem with recommendations and proposed regulations, it is probably not enough. Regulatory compliance should be seen as a starting point in a continuous process of improving the ethical procedures and privacy risk assessment of AI systems. The need to have a baseline to manage the process of creating an AI system even from an ethics and privacy perspective becomes progressively more important In this study, we discuss the ethical implications of these advances and propose a conceptual framework for the responsible, fair, and safe use of AI. © 2024 Copyright for this paper by its authors. - DetailsCampanile, L., Di Bonito, L. P., Gribaudo, M., & Iacono, M. (2023). A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering [Conference paper]. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 482 LNICST, 133–146. https://doi.org/10.1007/978-3-031-31234-2_8
Abstract
Processes in chemical engineering are frequently enacted by one-of-a-kind devices that implement dynamic processes with feedback regulations designed according to experimental studies and empirical tuning of new devices after the experience obtained on similar setups. While application of artificial intelligence based solutions is largely advocated by researchers in several fields of chemical engineering to face the problems deriving from these practices, few actual cases exist in literature and in industrial plants that leverage currently available tools as much as other application fields suggest. One of the factors that is limiting the spread of AI-based solutions in the field is the lack of tools that support the evaluation of the needs of plants, be those existing or to-be settlements. In this paper we provide a Domain Specific Language based approach for the evaluation of the basic performance requirements for cloud-based setups capable of supporting chemical engineering plants, with a metaphor that attempts to bridge the two worlds. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. - DetailsMainenti, G., Campanile, L., Marulli, F., Ricciardi, C., & Valente, A. S. (2020). Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 533–540. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089519717&partnerID=40&md5=bf7cc36e86c1988dd85e04c2fce06de1
Abstract
In recent years the application of Machine Learning (ML) and Artificial Intelligence (AI) techniques in healthcare helped clinicians to improve the management of chronic patients. Diabetes is among the most common chronic illness in the world for which often is still challenging do an early detection and a correct classification of type of diabetes to an individual. In fact it often depends on the circumstances present at the time of diagnosis, and many diabetic individuals do not easily fit into a single class. The aim is this paper is the application of ML techniques in order to classify the occurrence of different mellitus diabetes on the base of clinical data obtained from diabetic patients during the daily hospitals activities. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.