Recent advances in the field of artificial intelligence (AI) have been pivotal in enhancing the effectiveness and efficiency of many systems and in all fields of knowledge, including medical diagnosis [...]
Niraj Thapa, Zhipeng Liu, Addison Shaver, Albert Esterline, Balakrishna Gokaraju, Kaushik Roy
Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack…
Tanjim Mahmud, Michal Ptaszynski, Fumito Masui
Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian langu…
Geeta Joshi, Aditi Jain, Shalini Reddy Araveeti, Sabina Adhikari, Harshit Garg, Mukund Bhandari
As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the…
George Vardakis, George Tsamis, Eleftheria Koutsaki, Kondylakis Haridimos, Nikos Papadakis
Despite the general improvement of living conditions and the ways of building buildings, the sense of security in or around them is often not satisfactory for their users, resulting in the search and implementation of increasingly effective protection measures. The insecurity tha…
Andra Sandu, Liviu-Adrian Cotfas, Camelia Delcea, Corina Ioanăș, Margareta-Stela Florescu, Mihai Orzan
Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on the population’s decision-making and way of thinking. Disinformation can be classified as a subdivision of fake news, the main purpose of which is to manipulate and generate…
Chenyu Ling, Junyi Luo, Kaibo Shi, Lusheng Liu
This paper proposes a structure-constrained deep reconstruction framework for compressed sensing in shift-invariant spaces (SISs). The reconstruction is formulated as an inverse operator estimation problem derived from the matrix factorization H(ω)=W(ω)A and approximated using a…