12 research outputs found
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
Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring
Understanding human behavior and monitoring mental health are essential to
maintaining the community and society's safety. As there has been an increase
in mental health problems during the COVID-19 pandemic due to uncontrolled
mental health, early detection of mental issues is crucial. Nowadays, the usage
of Intelligent Virtual Personal Assistants (IVA) has increased worldwide.
Individuals use their voices to control these devices to fulfill requests and
acquire different services. This paper proposes a novel deep learning model
based on the gated recurrent neural network and convolution neural network to
understand human emotion from speech to improve their IVA services and monitor
their mental health.Comment: 6 pages, 5 figures, 3 tables, accepted in the IEEE WFIoT202
AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities
Accident detection and traffic analysis is a critical component of smart city
and autonomous transportation systems that can reduce accident frequency,
severity and improve overall traffic management. This paper presents a
comprehensive analysis of traffic accidents in different regions across the
United States using data from the National Highway Traffic Safety
Administration (NHTSA) Crash Report Sampling System (CRSS). To address the
challenges of accident detection and traffic analysis, this paper proposes a
framework that uses traffic surveillance cameras and action recognition systems
to detect and respond to traffic accidents spontaneously. Integrating the
proposed framework with emergency services will harness the power of traffic
cameras and machine learning algorithms to create an efficient solution for
responding to traffic accidents and reducing human errors. Advanced
intelligence technologies, such as the proposed accident detection systems in
smart cities, will improve traffic management and traffic accident severity.
Overall, this study provides valuable insights into traffic accidents in the US
and presents a practical solution to enhance the safety and efficiency of
transportation systems.Comment: 8,