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[46] viXra:2109.0220 [pdf] submitted on 2021-09-30 01:04:38
Authors: Prudhvi Parne
Comments: 6 Pages.
Financial services are the economical backbone of any nation in the world. There are billions of financial transactions which are taking place and all this data is stored and can be considered as a gold mine of data for many different organizations. No human intelligence can dig in this amount of data to come up with something valuable. This is the reason financial organizations are employing artificial intelligence to come up with new algorithms which can change the way financial transactions are being carried out. Artificial Intelligence can complete the task in a very short period. Artificial intelligence can be used to detect frauds, identify possible attacks, and any other kind of anomalies that may be detrimental for the institution. This paper discusses the role of artificial intelligence and machine learning in the finance sector.
Category: Artificial Intelligence
[45] viXra:2109.0203 [pdf] submitted on 2021-09-28 19:31:25
Authors: Matthew Groom
Comments: 5 Pages. [Corrections made by viXra Admin to conform with scholarly norm]
This is going to be one strange and yet rewarding paper for everyone. It consists of two parts.
1.The Rapture is here [.] 2.I also provide a proof of our inner-self duality and answer the other question everyone wants to know, self - what makes you, you. This is what every AI researcher has requested.
Category: Artificial Intelligence
[44] viXra:2109.0200 [pdf] submitted on 2021-09-28 19:13:38
Authors: Murat Koklu, Ilkay Cinar, Yavuz Selim Taspinar
Comments: 8 Pages.
Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.
Category: Artificial Intelligence
[43] viXra:2109.0124 [pdf] submitted on 2021-09-13 10:29:37
Authors: J Gerard Wolff
Comments: 15 Pages.
Three problems in learning knowledge for self-driving vehicles are: how
a finite sample of information about driving, N, can yield an ability to deal
with the infinity of possible driving situations; the problem of generalising
from N without over- or under-generalisation; and how to weed out errors in
N. A theory developed with computer models to explain a child’s learning
of his or her first language, now incorporated in the SP System, suggests:
compress N as much as possible by a process that creates a grammar, G, and
an encoding of N in terms of G called E. Then discard E which contains all
or most of the errors in N, and retain G which solves the first two problems.
Category: Artificial Intelligence
[42] viXra:2109.0110 [pdf] submitted on 2021-09-09 22:16:02
Authors: Yew Kee Wong
Comments: 7 Pages. AIAA CONFERENCE 2021 (NOV 2021), DUBAI, UAE
Online learning is the emerging technique in education and learning during the COVID-19 pandemic
period. Traditional learning is a complex process as learning patterns, approach, skills and performance
varies from person to person. Adaptive online learning focuses on understanding the learner’s
performance, skills and adapts to it. The use of advanced technology also provides a means to analyse
the behavioural learning pattern. As it provides the detailed skill mapping and performance which
enables the learner to understand the areas needs to be improved. The information can also be used by
assessors to improve the teaching approach. Advanced online learning system using artificial
intelligence is an emerging concept in the coming years. In this new concept, the classes are not taken
face-to-face in a classroom but through an electronic medium as a substitute. These virtual learning
approach are gaining importance every day and very soon they are going to be an integral part of our
world. Taking up these virtual learning through an electronic medium is termed as online learning. We
proposed two new models which are powered by artificial intelligence (AI) tools. A number of examples
of using these new models are presented.
Category: Artificial Intelligence
[41] viXra:2109.0109 [pdf] submitted on 2021-09-09 22:17:57
Authors: Yew Kee Wong
Comments: 7 Pages. ACITY CONFERENCE 2021 (NOV 2021), DUBAI, UAE
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[40] viXra:2109.0108 [pdf] submitted on 2021-09-09 22:19:40
Authors: Yew Kee Wong
Comments: 7 Pages. SCAI CONFERENCE 2021 (NOV 2021), ZURICH, SWITZERLAND
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective
supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some
of the different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Category: Artificial Intelligence
[39] viXra:2109.0107 [pdf] submitted on 2021-09-09 22:21:06
Authors: Yew Kee Wong
Comments: 6 Pages. BIOM CONFERENCE 2021 (OCT 2021), VIENNA, AUSTRIA
The assessment outcome for many online learning methods are based on the number of correct answers
and than convert it into one final mark or grade. We discovered that when using online learning, we can
extract more detail information from the learning process and these information are useful for the
assessor to plan an effective and efficient learning model for the learner. Statistical analysis is an
important part of an assessment when performing the online learning outcome. The assessment
indicators include the difficulty level of the question, time spend in answering and the variation in
choosing answer. In this paper we will present the findings of these assessment indicators and how it can
improve the way the learner being assessed when using online learning system. We developed a
statistical analysis algorithm which can assess the online learning outcomes more effectively using
quantifiable measurements. A number of examples of using this statistical analysis algorithm are
presented.
Category: Artificial Intelligence
[38] viXra:2109.0106 [pdf] submitted on 2021-09-09 22:24:11
Authors: Yew Kee Wong
Comments: 7 Pages. MLNLP CONFERENCE 2021 (SEP 2021), COPENHAGEN, DENMARK
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective
supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some
of the different methods and scenario which can be applied to AI and big data, as well as the
opportunities provided by the application in various sensitive operations and disaster management.
Category: Artificial Intelligence
[37] viXra:2109.0104 [pdf] submitted on 2021-09-09 22:28:20
Authors: Yew Kee Wong
Comments: 7 Pages. IJAIA JOURNAL (2021) VOL. 12, NO. 5
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[36] viXra:2109.0103 [pdf] submitted on 2021-09-09 22:30:00
Authors: Yew Kee Wong
Comments: 8 Pages. EEIJ JOURNAL (2021), VOL. 7, ISSUE. 3
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With
the rise of such advanced technology, there will be always a question regarding its impact on our social
life, environment and economy thus impacting all efforts exerted towards continuous development. From
the definition, the welfare of human beings is the core of continuous development. Continuous
development is useful only when ordinary people’s lives are improved whether in health, education,
employment, environment, equality or justice. Securing decent jobs is a key enabler to promote the
components of continuous development, economic growth, social welfare and environmental
sustainability. The human resources are the precious resource for all nations. The high unemployment
and underemployment rates especially in youth is a great threat affecting the continuous economic
development of many countries and is influenced by investment in education, and quality of living.
Category: Artificial Intelligence
[35] viXra:2109.0102 [pdf] submitted on 2021-09-09 22:34:12
Authors: Yew Kee Wong
Comments: 8 Pages. ARIA CONFERENCE 2021 (DEC 2021), SYDNEY, AUSTRALIA
In the information era, enormous amounts of data have become available on hand to decision makers. Big
data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets. The Internet of
Things, or "IoT" for short, is about extending the power of the internet beyond computers and smartphones to
a whole range of other things, processes and environments. IoT is at the epicentre of the Digital
Transformation Revolution that is changing the shape of business, enterprise and people’s lives. This
transformation influences everything from how we manage and operate our homes to automating processes
across nearly all industries. This paper aims to analyse the relationships of AI, big data and IoT, as well as
the opportunities provided by the applications in various operational domains.
Category: Artificial Intelligence
[34] viXra:2109.0101 [pdf] submitted on 2021-09-09 22:35:42
Authors: Yew Kee Wong
Comments: 8 Pages. NeTIOT CONFERENCE 2021 (DEC 2021), SYDNEY, AUSTRALIA
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
The Internet of Things, or "IoT" for short, is about extending the power of the internet beyond computers
and smartphones to a whole range of other things, processes and environments. IoT is at the epicentre of
the Digital Transformation Revolution that is changing the shape of business, enterprise and people’s
lives. This transformation influences everything from how we manage and operate our homes to
automating processes across nearly all industries. This paper aims to analyse the relationships of AI, big
data and IoT, as well as the opportunities provided by the applications in various operational domains.
Category: Artificial Intelligence
[33] viXra:2109.0100 [pdf] submitted on 2021-09-09 22:37:10
Authors: Yew Kee Wong
Comments: 7 Pages. SIPR CONFERENCE 2021 (OCT 2021), SYDNEY, AUSTRALIA
In the information era, enormous amounts of data have become available on hand to decision makers. Big
data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[32] viXra:2109.0099 [pdf] submitted on 2021-09-09 22:39:20
Authors: Yew Kee Wong
Comments: 8 Pages. IJCST JOURNAL 2021 OCT, VOL. 9, ISSUE. 6
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in volume, velocity, variety and veracity
(the four V’s of big data), which makes them difficult to handle using traditional tools and techniques.
Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and
extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain
valuable insights from such varied and rapidly changing data, ranging from daily transactions to
customer interactions and social network data. Such value can be provided using big data analytics,
which is the application of advanced analytics techniques on big data. This paper aims to analyse some
of the use of big data for the artificial intelligence development and its applications in various decision
making domains.
Category: Artificial Intelligence
[31] viXra:2109.0098 [pdf] submitted on 2021-09-09 22:40:43
Authors: Yew Kee Wong
Comments: 7 Pages. IJCST JOURNAL 2021 OCT, VOL. 9, ISSUE. 6
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With
the rise of such advanced technology, there will be always a question regarding its impact on our social
life, environment and economy thus impacting all efforts exerted towards continuous development. From
the definition, the welfare of human beings is the core of continuous development. Continuous
development is useful only when ordinary people’s lives are improved whether in health, education,
employment, environment, equality or justice. Securing decent jobs is a key enabler to promote the
components of continuous development, economic growth, social welfare and environmental
sustainability. The human resources are the precious resource for all nations. The high unemployment
and underemployment rates especially in youth is a great threat affecting the continuous economic
development of many countries and is influenced by investment in education, and quality of living.
Category: Artificial Intelligence
[30] viXra:2109.0097 [pdf] submitted on 2021-09-09 22:42:18
Authors: Yew Kee Wong
Comments: 6 Pages. IJCST JOURNAL 2022 FEB, VOL. 10, ISSUE. 1
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective
supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some
of the different methods and scenario which can be applied to AI and big data, as well as the
opportunities provided by the application in various business operations and crisis management domains.
Category: Artificial Intelligence
[29] viXra:2109.0096 [pdf] submitted on 2021-09-09 22:43:47
Authors: Yew Kee Wong
Comments: 10 Pages. IJCST JOURNAL 2022 FEB, VOL. 10, ISSUE. 1
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using
machine learning, which is the application of advanced deep learning techniques on big data. This paper
aims to analyse some of the different machine learning and deep learning algorithms and methods, as
well as the opportunities provided by the AI applications in various decision making domains.
Category: Artificial Intelligence
[28] viXra:2109.0095 [pdf] submitted on 2021-09-09 22:45:19
Authors: Yew Kee Wong
Comments: 7 Pages. IJIT JOURNAL 2021 DEC, VOL. 7, ISSUE. 6
The assessment outcome for many online learning methods are based on the number of correct answers
and than convert it into one final mark or grade. We discovered that when using online learning, we can
extract more detail information from the learning process and these information are useful for the
assessor to plan an effective and efficient learning model for the learner. Statistical analysis is an
important part of an assessment when performing the online learning outcome. The assessment
indicators include the difficulty level of the question, time spend in answering and the variation in
choosing answer. In this paper we will present the findings of these assessment indicators and how it can
improve the way the learner being assessed when using online learning system. We developed a
statistical analysis algorithm which can assess the online learning outcomes more effectively using
quantifiable measurements. A number of examples of using this statistical analysis algorithm are
presented.
Category: Artificial Intelligence
[27] viXra:2109.0094 [pdf] submitted on 2021-09-09 22:46:44
Authors: Yew Kee Wong
Comments: 9 Pages. IJIT JOURNAL 2021 DEC, VOL. 7, ISSUE. 6
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[26] viXra:2109.0093 [pdf] submitted on 2021-09-09 22:50:32
Authors: Yew Kee Wong
Comments: 7 Pages. IJIT JOURNAL 2022 FEB, VOL. 8, ISSUE. 1
The assessment outcome for many online learning methods are based on the number of correct answers
and than convert it into one final mark or grade. We discovered that when using online learning, we can
extract more detail information from the learning process and these information are useful for the
assessor to plan an effective and efficient learning model for the learner. Statistical analysis is an
important part of an assessment when performing the online learning outcome. The assessment
indicators include the difficulty level of the question, time spend in answering and the variation in
choosing answer. In this paper we will present the findings of these assessment indicators and how it can
improve the way the learner being assessed when using online learning system. We developed a
statistical analysis algorithm which can assess the online learning outcomes more effectively using
quantifiable measurements. A number of examples of using this statistical analysis algorithm are
presented.
Category: Artificial Intelligence
[25] viXra:2109.0092 [pdf] submitted on 2021-09-09 22:51:52
Authors: Yew Kee Wong
Comments: 7 Pages. IJIT JOURNAL 2022 FEB, VOL. 8, ISSUE. 1
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[24] viXra:2109.0091 [pdf] submitted on 2021-09-09 22:53:38
Authors: Yew Kee Wong
Comments: 7 Pages. IJETA JOURNAL 2021 DEC, VOL. 8, ISSUE. 6
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[23] viXra:2109.0090 [pdf] submitted on 2021-09-09 22:55:07
Authors: Yew Kee Wong
Comments: 8 Pages. IJETA JOURNAL 2021 DEC, VOL. 8, ISSUE. 6
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
The Internet of Things, or "IoT" for short, is about extending the power of the internet beyond computers
and smartphones to a whole range of other things, processes and environments. IoT is at the epicentre of
the Digital Transformation Revolution that is changing the shape of business, enterprise and people’s
lives. This transformation influences everything from how we manage and operate our homes to
automating processes across nearly all industries. This paper aims to analyse the relationships of AI, big
data and IoT, as well as the opportunities provided by the applications in various operational domains.
Category: Artificial Intelligence
[22] viXra:2109.0088 [pdf] submitted on 2021-09-09 22:58:19
Authors: Yew Kee Wong
Comments: 8 Pages. IJETA JOURNAL 2022 FEB, VOL. 9, ISSUE. 1
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in volume, velocity, variety and veracity
(the four V’s of big data), which makes them difficult to handle using traditional tools and techniques.
Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and
extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain
valuable insights from such varied and rapidly changing data, ranging from daily transactions to
customer interactions and social network data. Such value can be provided using big data analytics,
which is the application of advanced analytics techniques on big data. This paper aims to analyse some
of the use of big data for the artificial intelligence development and its applications in various decision
making domains.
Category: Artificial Intelligence
[21] viXra:2109.0087 [pdf] submitted on 2021-09-09 23:01:19
Authors: Yew Kee Wong
Comments: 7 Pages. BIBC CONFERENCE 2021 (OCT 2021), SYDNEY, AUSTRALIA
In the information era, enormous amounts of data have become available on hand to decision makers. Big
data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
The Internet of Things, or "IoT" for short, is about extending the power of the internet beyond computers
and smartphones to a whole range of other things, processes and environments. IoT is at the epicentre of
the Digital Transformation Revolution that is changing the shape of business, enterprise and people’s
lives. This transformation influences everything from how we manage and operate our homes to
automating processes across nearly all industries. This paper aims to analyse the relationships of AI, big
data and IoT, as well as the opportunities provided by the applications in various operational domains.
Category: Artificial Intelligence
[20] viXra:2109.0086 [pdf] submitted on 2021-09-09 23:03:06
Authors: Yew Kee Wong
Comments: 8 Pages. JOURNAL OF SOFTWARE, ICCSIT 2021, PARIS, FRANCE
Online learning is the emerging technique in education and learning during the COVID-19
pandemic period. Traditional learning is a complex process as learning patterns, approach, skills and
performance varies from person to person. Adaptive online learning focuses on understanding the
learner’s performance, skills and adapts to it. The use of advanced technology also provides a means to
analyze the behavioral learning pattern. As it provides the detailed skill mapping and performance which
enables the learner to understand the areas needs to be improved. The information can also be used by
assessors to improve the teaching approach. Advanced online learning system using arti=icial intelligence is
an emerging concept in the coming years. In this new concept, the classes are not taken face-to-face in a
classroom but through an electronic medium as a substitute. These virtual learning approach are gaining
importance every day and very soon they are going to be an integral part of our world. Taking up these
virtual learning through an electronic medium is termed as online learning. We proposed two new models
which are powered by arti=icial intelligence (AI) tools. A number of examples of using these new models are
presented.
Category: Artificial Intelligence
[19] viXra:2109.0085 [pdf] submitted on 2021-09-09 23:04:52
Authors: Yew Kee Wong
Comments: 8 Pages. CIoT CONFERENCE 2021 (SEP 2021), TORONTO, CANADA
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
The Internet of Things, or "IoT" for short, is about extending the power of the internet beyond computers
and smartphones to a whole range of other things, processes and environments. IoT is at the epicentre of
the Digital Transformation Revolution that is changing the shape of business, enterprise and people’s
lives. This transformation influences everything from how we manage and operate our homes to
automating processes across nearly all industries. This paper aims to analyse the relationships of AI, big
data and IoT, as well as the opportunities provided by the applications in various operational domains.
Category: Artificial Intelligence
[18] viXra:2109.0083 [pdf] submitted on 2021-09-09 23:07:37
Authors: Yew Kee Wong
Comments: 10 Pages. BMLI CONFERENCE 2021 (DEC 2021), CHENNAI, INDIA
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using
machine learning, which is the application of advanced deep learning techniques on big data. This paper
aims to analyse some of the different machine learning and deep learning algorithms and methods, as
well as the opportunities provided by the AI applications in various decision making domains.
Category: Artificial Intelligence
[17] viXra:2109.0068 [pdf] submitted on 2021-09-09 22:13:52
Authors: Yew Kee Wong
Comments: 7 Pages.
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With
the rise of such advanced technology, there will be always a question regarding its impact on our social
life, environment and economy thus impacting all efforts exerted towards continuous development. From
the definition, the welfare of human beings is the core of continuous development. Continuous
development is useful only when ordinary people’s lives are improved whether in health, education,
employment, environment, equality or justice. Securing decent jobs is a key enabler to promote the
components of continuous development, economic growth, social welfare and environmental sustainability.
The human resources are the precious resource for all nations. The high unemployment and
underemployment rates especially in youth is a great threat affecting the continuous economic development
of many countries and is influenced by investment in education, and quality of living.
Category: Artificial Intelligence
[16] viXra:2109.0067 [pdf] submitted on 2021-09-09 22:14:14
Authors: Yew Kee Wong
Comments: 7 Pages.
Online learning is the emerging technique in education and learning during the COVID-19 pandemic
period. Traditional learning is a complex process as learning patterns, approach, skills and performance
varies from person to person. Adaptive online learning focuses on understanding the learner’s
performance, skills and adapts to it. The use of advanced technology also provides a means to analyse
the behavioural learning pattern. As it provides the detailed skill mapping and performance which
enables the learner to understand the areas needs to be improved. The information can also be used by
assessors to improve the teaching approach. Advanced online learning system using artificial
intelligence is an emerging concept in the coming years. In this new concept, the classes are not taken
face-to-face in a classroom but through an electronic medium as a substitute. These virtual learning
approach are gaining importance every day and very soon they are going to be an integral part of our
world. Taking up these virtual learning through an electronic medium is termed as online learning. We
proposed two new models which are powered by artificial intelligence (AI) tools. A number of examples
of using these new models are presented.
Category: Artificial Intelligence
[15] viXra:2109.0066 [pdf] submitted on 2021-09-09 21:48:06
Authors: Yew Kee Wong
Comments: 7 Pages. IJETA JOURNAL 2021 OCT, VOL. 8, ISSUE. 5
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[14] viXra:2109.0065 [pdf] submitted on 2021-09-09 21:49:38
Authors: Yew Kee Wong
Comments: 9 Pages. IJETA JOURNAL 2021 OCT, VOL. 8, ISSUE. 5
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective
supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some
of the different methods and scenario which can be applied to AI and big data, as well as the
opportunities provided by the application in various business operations and disaster management
domains.
Category: Artificial Intelligence
[13] viXra:2109.0064 [pdf] submitted on 2021-09-09 21:51:33
Authors: Yew Kee Wong
Comments: 8 Pages. IJIT JOURNAL 2021 AUG, VOL. 7, ISSUE. 4
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in volume, velocity, variety and veracity
(the four V’s of big data), which makes them difficult to handle using traditional tools and techniques.
Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and
extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain
valuable insights from such varied and rapidly changing data, ranging from daily transactions to
customer interactions and social network data. Such value can be provided using big data analytics,
which is the application of advanced analytics techniques on big data. This paper aims to analyse some
of the use of big data for the artificial intelligence development and its applications in various decision
making domains.
Category: Artificial Intelligence
[12] viXra:2109.0063 [pdf] submitted on 2021-09-09 21:53:14
Authors: Yew Kee Wong
Comments: 6 Pages. IJIT JOURNAL 2021 AUG, VOL. 7, ISSUE. 4
Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as
recognizing speech, identifying images or making predictions. Instead of organizing data to run through
predefined equations, deep learning sets up basic parameters about the data and trains the computer to
learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate
some of the different deep learning algorithms and methods which can be applied to artificial intelligence
analysis, as well as the opportunities provided by the application in various decision making domains.
Category: Artificial Intelligence
[11] viXra:2109.0062 [pdf] submitted on 2021-09-09 21:54:49
Authors: Yew Kee Wong
Comments: 6 Pages. IJIT JOURNAL 2021 OCT, VOL. 7, ISSUE. 5
Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as
recognizing speech, identifying images or making predictions. Instead of organizing data to run through
predefined equations, deep learning sets up basic parameters about the data and trains the computer to
learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate
some of the different deep learning algorithms and methods which can be applied to artificial intelligence
analysis, as well as the opportunities provided by the application in various decision making domains.
Category: Artificial Intelligence
[10] viXra:2109.0061 [pdf] submitted on 2021-09-09 21:56:12
Authors: Yew Kee Wong
Comments: 9 Pages. IJIT JOURNAL 2021 OCT, VOL. 7, ISSUE. 5
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[9] viXra:2109.0060 [pdf] submitted on 2021-09-09 21:58:28
Authors: Yew Kee Wong
Comments: 7 Pages. IJCST JOURNAL 2021 OCT, VOL. 9, ISSUE. 5
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[8] viXra:2109.0059 [pdf] submitted on 2021-09-09 22:00:33
Authors: Yew Kee Wong
Comments: 6 Pages. IJCST JOURNAL 2021 OCT, VOL. 9, ISSUE. 5
Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as
recognizing speech, identifying images or making predictions. Instead of organizing data to run through
predefined equations, deep learning sets up basic parameters about the data and trains the computer to
learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate
some of the different deep learning algorithms and methods which can be applied to artificial intelligence
analysis, as well as the opportunities provided by the application in various decision making domains.
Category: Artificial Intelligence
[7] viXra:2109.0058 [pdf] submitted on 2021-09-09 22:13:33
Authors: Yew Kee Wong
Comments: 6 Pages. IJCST JOURNAL 2021 AUG, VOL. 9, ISSUE. 4
Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as
recognizing speech, identifying images or making predictions. Instead of organizing data to run through
predefined equations, deep learning sets up basic parameters about the data and trains the computer to
learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate
some of the different deep learning algorithms and methods which can be applied to artificial intelligence
analysis, as well as the opportunities provided by the application in various decision making domains.
Category: Artificial Intelligence
[6] viXra:2109.0057 [pdf] submitted on 2021-09-09 22:13:11
Authors: Yew Kee Wong
Comments: 7 Pages. IJCST JOURNAL 2021 AUG, VOL. 9, ISSUE. 4
In the information era, enormous amounts of data have become available on hand to decision makers.
Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them
difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions
need to be studied and provided in order to handle and extract value and knowledge from these datasets.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of
artificial intelligence based on the idea that systems can learn from data, identify patterns and make
decisions with minimal human intervention. Such minimal human intervention can be provided using big
data analytics, which is the application of advanced analytics techniques on big data. This paper aims to
analyse some of the different machine learning algorithms and methods which can be applied to big data
analysis, as well as the opportunities provided by the application of big data analytics in various decision
making domains.
Category: Artificial Intelligence
[5] viXra:2109.0056 [pdf] submitted on 2021-09-09 22:12:50
Authors: Yew Kee Wong
Comments: 8 Pages. NATL CONFERENCE 2021 (NOV 2021), LONDON, UK
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With
the rise of such advanced technology, there will be always a question regarding its impact on our social
life, environment and economy thus impacting all efforts exerted towards continuous development. From
the definition, the welfare of human beings is the core of continuous development. Continuous
development is useful only when ordinary people’s lives are improved whether in health, education,
employment, environment, equality or justice. Securing decent jobs is a key enabler to promote the
components of continuous development, economic growth, social welfare and environmental
sustainability. The human resources are the precious resource for nations. The high unemployment and
underemployment rates especially in youth is a great threat affecting the continuous economic
development of many countries and is influenced by investment in education, and quality of living.
Category: Artificial Intelligence
[4] viXra:2109.0055 [pdf] submitted on 2021-09-09 22:12:06
Authors: Yew Kee Wong
Comments: 6 Pages. CRBL CONFERENCE 2021 (OCT 2021), VIENNA, AUSTRIA
Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as
recognizing speech, identifying images or making predictions. Instead of organizing data to run through
predefined equations, deep learning sets up basic parameters about the data and trains the computer to
learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate
some of the different deep learning algorithms and methods which can be applied to artificial intelligence
analysis, as well as the opportunities provided by the application in various decision making domains.
Category: Artificial Intelligence
[3] viXra:2109.0054 [pdf] submitted on 2021-09-09 22:13:21
Authors: Yew Kee Wong
Comments: 7 Pages. ITCCMA CONFERENCE 2021 (SEP 2021) COPENHAGEN, DENMARK
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective
supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some
of the different methods and scenario which can be applied to AI and big data, as well as the
opportunities provided by the application in various business operations and crisis management domains.
Category: Artificial Intelligence
[2] viXra:2109.0047 [pdf] submitted on 2021-09-07 04:43:30
Authors: Amey Thakur, Karan Dhiman, Mayuresh Phansikar
Comments: 7 pages, 7 figures, Volume 9, Issue IX, International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2021. DOI: https://doi.org/10.22214/ijraset.2021.37930
Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights).
Category: Artificial Intelligence
[1] viXra:2109.0028 [pdf] replaced on 2022-03-30 15:11:58
Authors: Jeongik Cho
Comments: 22 Pages.
Generator of generative adversarial networks (GAN) maps latent random variable into data random variable. GAN inversion is mapping data random variable to latent random variable by inverting the generator of GAN.
When training the encoder for generator inversion, using the mean squared error causes the encoder to not converge because there is information loss on the latent random variable in the generator. In other words, it is impossible to train an encoder that inverts the generator as it, because the generator may ignore some information of the latent random variable.
This paper introduces a dynamic latent scale GAN, a method for training a generator that does not lose information from the latent random variable, and an encoder that inverts the generator. When the latent random variable is an i.i.d. (independent and identically distributed) random variable, dynamic latent scale GAN dynamically scales each element of the latent random variable during GAN training to adjust the entropy of the latent random variable. As training progresses, the entropy of the latent random variable decreases until there is no information loss on the latent random variable in the generator. If there is no information loss on the latent random variable in the generator, the encoder can converge to invert the generator.
The scale of the latent random variable depends on the amount of information that the encoder can recover. It can be calculated from the element-wise variance of the predicted latent random variable from the encoder.
Since the scale of latent random variable changes dynamically in dynamic latent scale GAN, the encoder should be trained with a generator during GAN training. The encoder can be integrated with the discriminator, and the loss for the encoder is added to the generator loss for fast training. Also, dynamic latent scale GAN can be used for continuous attribute editing with InterFaceGAN.
Category: Artificial Intelligence
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