4 research outputs found
Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks
Paramedics often have to make lifesaving decisions within a limited time in
an ambulance. They sometimes ask the doctor for additional medical
instructions, during which valuable time passes for the patient. This study
aims to automatically fuse voice and text data to provide tailored situational
awareness information to paramedics. To train and test speech recognition
models, we built a bidirectional deep recurrent neural network (long short-term
memory (LSTM)). Then we used convolutional neural networks on top of
custom-trained word vectors for sentence-level classification tasks. Each
sentence is automatically categorized into four classes, including patient
status, medical history, treatment plan, and medication reminder. Subsequently,
incident reports were automatically generated to extract keywords and assist
paramedics and physicians in making decisions. The proposed system found that
it could provide timely medication notifications based on unstructured voice
and text data, which was not possible in paramedic emergencies at present. In
addition, the automatic incident report generation provided by the proposed
system improves the routine but error-prone tasks of paramedics and doctors,
helping them focus on patient care
A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges
In recent years, the combination of artificial intelligence (AI) and unmanned
aerial vehicles (UAVs) has brought about advancements in various areas. This
comprehensive analysis explores the changing landscape of AI-powered UAVs and
friendly computing in their applications. It covers emerging trends, futuristic
visions, and the inherent challenges that come with this relationship. The
study examines how AI plays a role in enabling navigation, detecting and
tracking objects, monitoring wildlife, enhancing precision agriculture,
facilitating rescue operations, conducting surveillance activities, and
establishing communication among UAVs using environmentally conscious computing
techniques. By delving into the interaction between AI and UAVs, this analysis
highlights the potential for these technologies to revolutionise industries
such as agriculture, surveillance practices, disaster management strategies,
and more. While envisioning possibilities, it also takes a look at ethical
considerations, safety concerns, regulatory frameworks to be established, and
the responsible deployment of AI-enhanced UAV systems. By consolidating
insights from research endeavours in this field, this review provides an
understanding of the evolving landscape of AI-powered UAVs while setting the
stage for further exploration in this transformative domain