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Found 10 papers, 1 papers with code
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis
In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Science based AI model certification for untrained operational environments with application in traffic state estimation
Addressing this issue, this paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in untrained operational environments.
Intelligent Railroad Grade Crossing: Leveraging Semantic Segmentation and Object Detection for Enhanced Safety
Crashes and delays at Railroad Highway Grade Crossings (RHGC), where highways and railroads intersect, pose significant safety concerns for the U. S. Federal Railroad Administration (FRA).
Empowering Healthcare through Privacy-Preserving MRI Analysis
Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors.
An Explainable AI Framework for Artificial Intelligence of Medical Things
The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems.
"When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity
We divide the generation task into two sub tasks: one for generating textual sarcasm and another for collecting emojis associated with those sarcastic sentences.
DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data.
Computational Sarcasm Analysis on Social Media: A Systematic Review
Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone.
Framework for Behavioral Disorder Detection Using Machine Learning and Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic
On the other hand, people are stressed, becoming more anxious during COVID-19 pandemic situation and exhibits symptoms of behavioral disorder.
DBATES: DataBase of Audio features, Text, and visual Expressions in competitive debate Speeches
In this work, we present a database of multimodal communication features extracted from debate speeches in the 2019 North American Universities Debate Championships (NAUDC).
