20 research outputs found
A Novel Zero-Trust Machine Learning Green Architecture for Healthcare IoT Cybersecurity: Review, Analysis, and Implementation
The integration of Internet of Things (IoT) devices in healthcare
applications has revolutionized patient care, monitoring, and data management.
The Global IoT in Healthcare Market value is $252.2 Billion in 2023. However,
the rapid involvement of these devices brings information security concerns
that pose critical threats to patient privacy and the integrity of healthcare
data. This paper introduces a novel machine learning (ML) based architecture
explicitly designed to address and mitigate security vulnerabilities in IoT
devices within healthcare applications. By leveraging advanced convolution ML
architecture, the proposed architecture aims to proactively monitor and detect
potential threats, ensuring the confidentiality and integrity of sensitive
healthcare information while minimizing the cost and increasing the portability
specialized for healthcare and emergency environments. The experimental results
underscore the accuracy of up to 93.6% for predicting various attacks based on
the results demonstrate a zero-day detection accuracy simulated using the
CICIoT2023 dataset and reduces the cost by a factor of x10. The significance of
our approach is in fortifying the security posture of IoT devices and
maintaining a robust implementation of trustful healthcare systems.Comment: 7 pages, 7 figures, 4 tables, under revie
CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation
Suicide is recognized as one of the most serious concerns in the modern
society. Suicide causes tragedy that affects countries, communities, and
families. There are many factors that lead to suicidal ideations. Early
detection of suicidal ideations can help to prevent suicide occurrence by
providing the victim with the required professional support, especially when
the victim does not recognize the danger of having suicidal ideations. As
technology usage has increased, people share and express their ideations
digitally via social media, chatbots, and other digital platforms. In this
paper, we proposed a novel, simple deep learning-based model to detect suicidal
ideations in digital content, mainly focusing on chatbots as the primary data
source. In addition, we provide a framework that employs the proposed suicide
detection integration with a chatbot-based support system.Comment: 5 pages, 6 figures, 4 tables, Under review in IEEE conferenc
IoT Botnet Detection Using an Economic Deep Learning Model
The rapid progress in technology innovation usage and distribution has
increased in the last decade. The rapid growth of the Internet of Things (IoT)
systems worldwide has increased network security challenges created by
malicious third parties. Thus, reliable intrusion detection and network
forensics systems that consider security concerns and IoT systems limitations
are essential to protect such systems. IoT botnet attacks are one of the
significant threats to enterprises and individuals. Thus, this paper proposed
an economic deep learning-based model for detecting IoT botnet attacks along
with different types of attacks. The proposed model achieved higher accuracy
than the state-of-the-art detection models using a smaller implementation
budget and accelerating the training and detecting processes.Comment: The paper under reviewing proces
Vision-Based American Sign Language Classification Approach via Deep Learning
Hearing-impaired is the disability of partial or total hearing loss that
causes a significant problem for communication with other people in society.
American Sign Language (ASL) is one of the sign languages that most commonly
used language used by Hearing impaired communities to communicate with each
other. In this paper, we proposed a simple deep learning model that aims to
classify the American Sign Language letters as a step in a path for removing
communication barriers that are related to disabilities.Comment: 4 pages, Accepted in the The Florida AI Research Society (FLAIRS-35)
202
Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis
A seizure tracking system is crucial for monitoring and evaluating epilepsy
treatments. Caretaker seizure diaries are used in epilepsy care today, but
clinical seizure monitoring may miss seizures. Monitoring devices that can be
worn may be better tolerated and more suitable for long-term ambulatory use.
Many techniques and methods are proposed for seizure detection; However,
simplicity and affordability are key concepts for daily use while preserving
the accuracy of the detection. In this study, we propose a versal, affordable
noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine
learning that can be customized and adapted to individual users in less than
four seconds of training time; the system was verified and validated using 500
subjects, with seizure detection data sampled at 178 Hz, the operated with a
mean accuracy of (94.5%).Comment: Under review, 5 pages, 7 figures, 3 table
Deep Learning Approach for Early Stage Lung Cancer Detection
Lung cancer is the leading cause of death among different types of cancers.
Every year, the lives lost due to lung cancer exceed those lost to pancreatic,
breast, and prostate cancer combined. The survival rate for lung cancer
patients is very low compared to other cancer patients due to late diagnostics.
Thus, early lung cancer diagnostics is crucial for patients to receive early
treatments, increasing the survival rate or even becoming cancer-free. This
paper proposed a deep-learning model for early lung cancer prediction and
diagnosis from Computed Tomography (CT) scans. The proposed mode achieves high
accuracy. In addition, it can be a beneficial tool to support radiologists'
decisions in predicting and detecting lung cancer and its stage.Comment: Under review in FLAIRS 202
A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images
Alzheimer's disease is a degenerative brain disease. Being the primary cause
of Dementia in adults and progressively destroys brain memory. Though
Alzheimer's disease does not have a cure currently, diagnosing it at an earlier
stage will help reduce the severity of the disease. Thus, early diagnosis of
Alzheimer's could help to reduce or stop the disease from progressing. In this
paper, we proposed a deep convolutional neural network-based model for learning
model using to determine the stage of Dementia in adults based on the Magnetic
Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.Comment: Short paper, Under Review in FLAIRS-3
Cyberbullying in Text Content Detection: An Analytical Review
Technological advancements have resulted in an exponential increase in the
use of online social networks (OSNs) worldwide. While online social networks
provide a great communication medium, they also increase the user's exposure to
life-threatening situations such as suicide, eating disorder, cybercrime,
compulsive behavior, anxiety, and depression. To tackle the issue of
cyberbullying, most existing literature focuses on developing approaches to
identifying factors and understanding the textual factors associated with
cyberbullying. While most of these approaches have brought great success in
cyberbullying research, data availability needed to develop model detection
remains a challenge in the research space. This paper conducts a comprehensive
literature review to provide an understanding of cyberbullying detection.Comment: 8 pages. Under revie
TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification
Ensemble modeling has been widely used to solve complex problems as it helps
to improve overall performance and generalization. In this paper, we propose a
novel TemporalAugmenter approach based on ensemble modeling for augmenting the
temporal information capturing for long-term and short-term dependencies in
data integration of two variations of recurrent neural networks in two learning
streams to obtain the maximum possible temporal extraction. Thus, the proposed
model augments the extraction of temporal dependencies. In addition, the
proposed approach reduces the preprocessing and prior stages of feature
extraction, which reduces the required energy to process the models built upon
the proposed TemporalAugmenter approach, contributing towards green AI.
Moreover, the proposed model can be simply integrated into various domains
including industrial, medical, and human-computer interaction applications. Our
proposed approach empirically evaluated the speech emotion recognition,
electrocardiogram signal, and signal quality examination tasks as three
different signals with varying complexity and different temporal dependency
features.Comment: 9 pages, 5 figures, 9 tables, under review proces