|
Showing 1 - 5 of
5 matches in All Departments
This book presents essentially a collection of proceedings that
deliberate on the key challenges and recent trends on robotics,
automation and data analytics which are the pillars of Industry
4.0. Solutions that are employed in the multitude spectra of
innovative robotics & automation and data analytics are
discussed. The readers are expected to gain an insightful view on
the current trends, issues, mitigating factors as well as solutions
from the book. This book consists of selected papers presented at
the 2nd International Conference on Innovative Technology,
Engineering and Sciences 2020 (iCITES) hosted virtually by
Universiti Malaysia Pahang on 22nd December 2020. iCITES is a
biennial conference, aimed at building a platform that allows
relevant stakeholders to share and discuss their latest researches,
ideas and survey reports from theoretical to a practical standpoint
especially in the Innovative Robotics & Automation and Data
Analytics tracks which was published in this book.
This book explores the application of data mining and machine
learning techniques in studying the activity pattern,
decision-making skills, misconducts, and actions resulting in the
intervention of VAR in European soccer leagues referees. The game
of soccer at the elite level is characterised by intense
competitions, a high level of intensity, technical, and tactical
skills coupled with a long duration of play. Referees are required
to officiate the game and deliver correct and indisputable
decisions throughout the duration of play. The increase in the
spatial and temporal task demands of the game necessitates that the
referees must respond and cope with the physiological and
psychological loads inherent in the game. The referees are also
required to deliver an accurate decision and uphold the rules and
regulations of the game during a match. These demands and
attributes make the work of referees highly complex. The increasing
pace and complexity of the game resulted in the introduction of the
Video Assistant Referee (VAR) to assist and improve the
decision-making of on-field referees. Despite the integration of
VAR into the current refereeing system, the performances of the
referees are yet to be error-free. Machine learning coupled with
data mining techniques has shown to be vital in providing insights
from a large dataset which could be used to draw important
inferences that can aid decision-making for diagnostics purposes
and overall performance improvement. A total of 6232 matches from 5
consecutive seasons officiated across the English Premier League,
Spanish LaLiga, Italian Serie A as well as the German Bundesliga
was studied. It is envisioned that the findings in this book could
be useful in recognising the activity pattern of top-class
referees, that is non-trivial for the stakeholders in devising
strategies to further enhance the performances of referees as well
as empower talent identification experts with pertinent information
for mapping out future high-performance referees.
This brief highlights the use of various Machine Learning (ML)
algorithms to evaluate training and competitional strategies in
Volleyball, as well as to identify high-performance players in the
sport. Several psychological elements/strategies coupled with human
performance parameters are discussed in view to ascertain their
impact on performance in elite Volleyball competitions. It presents
key performance indicators as well as human performance parameters
that can be used in future evaluation of team performance and
players. The details outlined in this brief are vital to coaches,
club managers, talent identification experts, performance analysts
as well as other important stakeholders in the evaluation of
performance and to foster improvement in this sport.
Cancer is the leading cause of mortality in most, if not all,
countries around the globe. It is worth noting that the World
Health Organisation (WHO) in 2019 estimated that cancer is the
primary or secondary leading cause of death in 112 of 183 countries
for individuals less than 70 years old, which is alarming. In
addition, cancer affects socioeconomic development as well. The
diagnostics of cancer are often carried out by medical experts
through medical imaging; nevertheless, it is not without
misdiagnosis owing to a myriad of reasons. With the advancement of
technology and computing power, the use of state-of-the-art
computational methods for the accurate diagnosis of cancer is no
longer far-fetched. In this brief, the diagnosis of four types of
common cancers, i.e., breast, lung, oral and skin, are evaluated
with different state-of-the-art feature-based transfer learning
models. It is expected that the findings in this book are
insightful to various stakeholders in the diagnosis of cancer.
This book highlights the fundamental association between
aquaculture and engineering in classifying fish hunger behaviour by
means of machine learning techniques. Understanding the underlying
factors that affect fish growth is essential, since they have
implications for higher productivity in fish farms. Computer vision
and machine learning techniques make it possible to quantify the
subjective perception of hunger behaviour and so allow food to be
provided as necessary. The book analyses the conceptual framework
of motion tracking, feeding schedule and prediction classifiers in
order to classify the hunger state, and proposes a system
comprising an automated feeder system, image-processing module, as
well as machine learning classifiers. Furthermore, the system
substitutes conventional, complex modelling techniques with a
robust, artificial intelligence approach. The findings presented
are of interest to researchers, fish farmers, and aquaculture
technologist wanting to gain insights into the productivity of fish
and fish behaviour.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|