88 research outputs found
An Investigation of Hepatitis B Virus Genome using Markov Models
The human genome encodes a family of editing enzymes known as APOBEC3
(apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3). Several
family members, such as APO-BEC3G, APOBEC3F, and APOBEC3H haplotype II, exhibit
activity against viruses such as HIV. These enzymes induce C-to-U mutations in
the negative strand of viral genomes, resulting in multiple G-to-A changes,
commonly referred to as 'hypermutation.' Mutations catalyzed by these enzymes
are sequence context-dependent in the HIV genome; for instance, APOBEC3G
preferen-tially mutates G within GG, TGG, and TGGG contexts, while other
members mutate G within GA, TGA, and TGAA contexts. However, the same sequence
context has not been explored in relation to these enzymes and HBV. In this
study, our objective is to identify the mutational footprint of APOBEC3 enzymes
in the HBV genome. To achieve this, we employ a multivariable data analytics
technique to investigate motif preferences and potential sequence hierarchies
of mutation by APOBEC3 enzymes using full genome HBV sequences from a diverse
range of naturally infected patients. This approach allows us to distinguish
between normal and hypermutated sequences based on the representation of mono-
to tetra-nucleotide motifs. Additionally, we aim to identify motifs associated
with hypermutation induced by different APOBEC3 enzymes in HBV genomes. Our
analyses reveal that either APOBEC3 enzymes are not active against HBV, or the
induction of G-to-A mutations by these enzymes is not sequence
context-dependent in the HBV genome
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
A Comprehensive Review on Autonomous Navigation
The field of autonomous mobile robots has undergone dramatic advancements
over the past decades. Despite achieving important milestones, several
challenges are yet to be addressed. Aggregating the achievements of the robotic
community as survey papers is vital to keep the track of current
state-of-the-art and the challenges that must be tackled in the future. This
paper tries to provide a comprehensive review of autonomous mobile robots
covering topics such as sensor types, mobile robot platforms, simulation tools,
path planning and following, sensor fusion methods, obstacle avoidance, and
SLAM. The urge to present a survey paper is twofold. First, autonomous
navigation field evolves fast so writing survey papers regularly is crucial to
keep the research community well-aware of the current status of this field.
Second, deep learning methods have revolutionized many fields including
autonomous navigation. Therefore, it is necessary to give an appropriate
treatment of the role of deep learning in autonomous navigation as well which
is covered in this paper. Future works and research gaps will also be
discussed
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper
Robust adaptive synchronization of a class of uncertain chaotic systems with unknown time-delay
In this paper, a robust adaptive control strategy is proposed to synchronize a class of uncertain chaotic systems with unknown time delays. Using Lyapunov theory and Lipschitz conditions in chaotic systems, the necessary adaptation rules for estimating uncertain parameters and unknown time delays are determined. Based on the proposed adaptation rules, an adaptive controller is recommended for the robust synchronization of the aforementioned uncertain systems that prove the robust stability of the proposed control mechanism utilizing the Lyapunov theorem. Finally, to evaluate the proposed robust and adaptive control mechanism, the synchronization of two Jerk chaotic systems with finite non-linear uncertainty and external disturbances as well as unknown fixed and variable time delays are simulated. The simulation results confirm the ability of the proposed control mechanism in robust synchronization of the uncertain chaotic systems as well as to estimate uncertain and unknown parameters
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets
The free flow of information has been accelerated by the rapid development of
social media technology. There has been a significant social and psychological
impact on the population due to the outbreak of Coronavirus disease (COVID-19).
The COVID-19 pandemic is one of the current events being discussed on social
media platforms. In order to safeguard societies from this pandemic, studying
people's emotions on social media is crucial. As a result of their particular
characteristics, sentiment analysis of texts like tweets remains challenging.
Sentiment analysis is a powerful text analysis tool. It automatically detects
and analyzes opinions and emotions from unstructured data. Texts from a wide
range of sources are examined by a sentiment analysis tool, which extracts
meaning from them, including emails, surveys, reviews, social media posts, and
web articles. To evaluate sentiments, natural language processing (NLP) and
machine learning techniques are used, which assign weights to entities, topics,
themes, and categories in sentences or phrases. Machine learning tools learn
how to detect sentiment without human intervention by examining examples of
emotions in text. In a pandemic situation, analyzing social media texts to
uncover sentimental trends can be very helpful in gaining a better
understanding of society's needs and predicting future trends. We intend to
study society's perception of the COVID-19 pandemic through social media using
state-of-the-art BERT and Deep CNN models. The superiority of BERT models over
other deep models in sentiment analysis is evident and can be concluded from
the comparison of the various research studies mentioned in this article.Comment: 20 pages, 5 figure
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
The field of drug discovery has experienced a remarkable transformation with
the advent of artificial intelligence (AI) and machine learning (ML)
technologies. However, as these AI and ML models are becoming more complex,
there is a growing need for transparency and interpretability of the models.
Explainable Artificial Intelligence (XAI) is a novel approach that addresses
this issue and provides a more interpretable understanding of the predictions
made by machine learning models. In recent years, there has been an increasing
interest in the application of XAI techniques to drug discovery. This review
article provides a comprehensive overview of the current state-of-the-art in
XAI for drug discovery, including various XAI methods, their application in
drug discovery, and the challenges and limitations of XAI techniques in drug
discovery. The article also covers the application of XAI in drug discovery,
including target identification, compound design, and toxicity prediction.
Furthermore, the article suggests potential future research directions for the
application of XAI in drug discovery. The aim of this review article is to
provide a comprehensive understanding of the current state of XAI in drug
discovery and its potential to transform the field.Comment: 13 pages, 3 figure
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