7 research outputs found
Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
The rampant coronavirus disease 2019 (COVID-19) has brought global crisis
with its deadly spread to more than 180 countries, and about 3,519,901
confirmed cases along with 247,630 deaths globally as on May 4, 2020. The
absence of any active therapeutic agents and the lack of immunity against
COVID-19 increases the vulnerability of the population. Since there are no
vaccines available, social distancing is the only feasible approach to fight
against this pandemic. Motivated by this notion, this article proposes a deep
learning based framework for automating the task of monitoring social
distancing using surveillance video. The proposed framework utilizes the YOLO
v3 object detection model to segregate humans from the background and Deepsort
approach to track the identified people with the help of bounding boxes and
assigned IDs. The results of the YOLO v3 model are further compared with other
popular state-of-the-art models, e.g. faster region-based CNN (convolution
neural network) and single shot detector (SSD) in terms of mean average
precision (mAP), frames per second (FPS) and loss values defined by object
classification and localization. Later, the pairwise vectorized L2 norm is
computed based on the three-dimensional feature space obtained by using the
centroid coordinates and dimensions of the bounding box. The violation index
term is proposed to quantize the non adoption of social distancing protocol.
From the experimental analysis, it is observed that the YOLO v3 with Deepsort
tracking scheme displayed best results with balanced mAP and FPS score to
monitor the social distancing in real-time
Modality specific U-Net variants for biomedical image segmentation: A survey
With the advent of advancements in deep learning approaches, such as deep
convolution neural network, residual neural network, adversarial network; U-Net
architectures are most widely utilized in biomedical image segmentation to
address the automation in identification and detection of the target regions or
sub-regions. In recent studies, U-Net based approaches have illustrated
state-of-the-art performance in different applications for the development of
computer-aided diagnosis systems for early diagnosis and treatment of diseases
such as brain tumor, lung cancer, alzheimer, breast cancer, etc. This article
contributes to present the success of these approaches by describing the U-Net
framework, followed by the comprehensive analysis of the U-Net variants for
different medical imaging or modalities such as magnetic resonance imaging,
X-ray, computerized tomography/computerized axial tomography, ultrasound,
positron emission tomography, etc. Besides, this article also highlights the
contribution of U-Net based frameworks in the on-going pandemic, severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that
resembles pneumonia. The current diagnostic procedure of COVID-19 follows
reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which
however is less sensitive to identify the virus at the initial stage. Hence, a
more robust and alternate diagnosis technique is desirable. Recently, with the
release of publicly available datasets of corona positive patients comprising
of computed tomography (CT) and chest X-ray (CXR) imaging; scientists,
researchers and healthcare experts are contributing for faster and automated
diagnosis of COVID-19 by identifying pulmonary infections using deep learning
approaches to achieve better cure and treatment. These datasets have limited
samples concerned with the positive COVID-19 cases, which raise the challenge
for unbiased learning. Following from this context, this article presents the
random oversampling and weighted class loss function approach for unbiased
fine-tuned learning (transfer learning) in various state-of-the-art deep
learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2,
DenseNet169, and NASNetLarge to perform binary classification (as normal and
COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia,
and normal case) of posteroanterior CXR images. Accuracy, precision, recall,
loss, and area under the curve (AUC) are utilized to evaluate the performance
of the models. Considering the experimental results, the performance of each
model is scenario dependent; however, NASNetLarge displayed better scores in
contrast to other architectures, which is further compared with other recently
proposed approaches. This article also added the visual explanation to
illustrate the basis of model classification and perception of COVID-19 in CXR
images
BERT-Based Sentiment Analysis: A Software Engineering Perspective
Sentiment analysis can provide a suitable lead for the tools used in software
engineering along with the API recommendation systems and relevant libraries to
be used. In this context, the existing tools like SentiCR, SentiStrength-SE,
etc. exhibited low f1-scores that completely defeats the purpose of deployment
of such strategies, thereby there is enough scope for performance improvement.
Recent advancements show that transformer based pre-trained models (e.g., BERT,
RoBERTa, ALBERT, etc.) have displayed better results in the text classification
task. Following this context, the present research explores different
BERT-based models to analyze the sentences in GitHub comments, Jira comments,
and Stack Overflow posts. The paper presents three different strategies to
analyse BERT based model for sentiment analysis, where in the first strategy
the BERT based pre-trained models are fine-tuned; in the second strategy an
ensemble model is developed from BERT variants, and in the third strategy a
compressed model (Distil BERT) is used. The experimental results show that the
BERT based ensemble approach and the compressed BERT model attain improvements
by 6-12% over prevailing tools for the F1 measure on all three datasets
Machine learning equipped web based disease prediction and recommender system
Worldwide, several cases go undiagnosed due to poor healthcare support in
remote areas. In this context, a centralized system is needed for effective
monitoring and analysis of the medical records. A web-based patient diagnostic
system is a central platform to store the medical history and predict the
possible disease based on the current symptoms experienced by a patient to
ensure faster and accurate diagnosis. Early disease prediction can help the
users determine the severity of the disease and take quick action. The proposed
web-based disease prediction system utilizes machine learning based
classification techniques on a data set acquired from the National Centre of
Disease Control (NCDC). -nearest neighbor (K-NN), random forest and naive
bayes classification approaches are utilized and an ensemble voting algorithm
is also proposed where each classifier is assigned weights dynamically based on
the prediction confidence. The proposed system is also equipped with a
recommendation scheme to recommend the type of tests based on the existing
symptoms of the patient, so that necessary precautions can be taken. A
centralized database ensures that the medical data is preserved and there is
transparency in the system. The tampering into the system is prevented by
giving the no "updation" rights once the diagnosis is created
Unleashing the power of disruptive and emerging technologies amid COVID-19: A detailed review
The unprecedented outbreak of the novel coronavirus (COVID-19), during early
December 2019 in Wuhan, China, has quickly evolved into a global pandemic,
became a matter of grave concern, and placed government agencies worldwide in a
precarious position. The scarcity of resources and lack of experiences to
endure the COVID-19 pandemic, combined with the fear of future consequences has
established the need for adoption of emerging and future technologies to
address the upcoming challenges. Since the last five months, the amount of
pandemic impact has reached its pinnacle that is altering everyone's life; and
humans are now bound to adopt safe ways to survive under the risk of being
affected. Technological advances are now accelerating faster than ever before
to stay ahead of the consequences and acquire new capabilities to build a safer
world. Thus, there is a rising need to unfold the power of emerging, future and
disruptive technologies to explore all possible ways to fight against COVID-19.
In this review article, we attempt to study all emerging, future, and
disruptive technologies that can be utilized to mitigate the impact of
COVID-19. Building on background insights, detailed technological specific use
cases to fight against COVID-19 have been discussed in terms of their
strengths, weaknesses, opportunities, and threats (SWOT). As concluding
remarks, we highlight prioritized research areas and upcoming opportunities to
blur the lines between the physical, digital, and biological domain-specific
challenges and also illuminate collaborative research directions for moving
towards a post-COVID-19 world
Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning
With the growing phase of artificial intelligence and autonomous learning,
the self-driving car is one of the promising area of research and emerging as a
center of focus for automobile industries. Behavioral cloning is the process of
replicating human behavior via visuomotor policies by means of machine learning
algorithms. In recent years, several deep learning-based behavioral cloning
approaches have been developed in the context of self-driving cars specifically
based on the concept of transfer learning. Concerning the same, the present
paper proposes a transfer learning approach using VGG16 architecture, which is
fine tuned by retraining the last block while keeping other blocks as
non-trainable. The performance of proposed architecture is further compared
with existing NVIDIA architecture and its pruned variants (pruned by 22.2% and
33.85% using 1x1 filter to decrease the total number of parameters).
Experimental results show that the VGG16 with transfer learning architecture
has outperformed other discussed approaches with faster convergence