This document discusses applications of artificial intelligence to real-world problems. It explores using neural networks to develop brain-inspired computing, using machine learning techniques like C4.5 and Naive Bayes algorithms to resolve network traffic issues, and using fuzzy logic to evaluate student performance. The document provides background on the history and fields of AI, and examples of studies applying these AI concepts. It concludes that AI has potential for addressing important issues and its future is promising.
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Bioinformatics and Artificial Intelligence (AI) the interrelation between the...Swapsg
The relation between the Bioinformatics and the Artificial Intelligence (AI) specified in it as both the fields are new.
credits-https://poweredtemplate.com/03075/0/index.html
Artificial Intelligence Future |Impact Of Artificial Intelligence On SocietyYashShah445
the content has artificial intelligence Artificial intelligence is helping farmers, doctors and rescue workers improve their positive impact on society. ... While fear of the negative consequences remain, AI is proving it can bring about enormous societal benefits.
Semantic and Fuzzy Modelling for Human Behaviour Recognition in Smart Spaces. A case study on Ambient Assisted Living.
24.4.2015 Åbo Akademi University, Finland
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
How is Artificial Intelligence shaping the Future of Ecommerce?archana cks
Few industries are as competitive as ecommerce. Not only are online retailers competing with other online stores and brick-and-mortar locations, but also the overall noise that is the Internet.
source <> http://www.ecbilla.com/blogs/how-is-artificial-intelligence-shaping-the-future-of-ecommerce.html
Three Dimensional Database: Artificial Intelligence to eCommerce Web service ...CSCJournals
A main objective of this paper is using artificial intelligence technique to web service agents and increase the efficiency of the agent communications. In recent years, web services have played a major role in computer applications. Web services are essential, as the design model of applications are dedicated to electronic businesses. This model aims to become one of the major formalisms for the design of distributed and cooperative applications in an open environment (the Internet). Current commercial and research-based efforts are reviewed and positioned within these two fields. A web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-process able format (specifically Web Services Description Language WSDL). Other systems interact with the web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards. Particular attention is given to the application of AI techniques to the important issue of WS composition. Within the range of AI technologies considered, we focus on the work of the Semantic Web and Agent-based communities to provide web services with semantic descriptions and intelligent behavior and reasoning capabilities. Re-composition of web services is also considered and a number of adaptive agent approaches are introduced and implemented in publication domain with three dimensional databases and one of the areas of work is eCommerce.
The near future for artificial intelligence and conversation botsPieter Rahier
What will be the near future for artificial intelligence an what are the most important things UX designers have to take in to account while designing a conversation bot.
Beyond Science Fiction, one marketer and a data scientist collaborated to demystify how Artificial Intelligence is improving the way we live, shop, travel and even date! Vote for this talk to see the full presentation at SXSWi 2016 in Austin!
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Bioinformatics and Artificial Intelligence (AI) the interrelation between the...Swapsg
The relation between the Bioinformatics and the Artificial Intelligence (AI) specified in it as both the fields are new.
credits-https://poweredtemplate.com/03075/0/index.html
Artificial Intelligence Future |Impact Of Artificial Intelligence On SocietyYashShah445
the content has artificial intelligence Artificial intelligence is helping farmers, doctors and rescue workers improve their positive impact on society. ... While fear of the negative consequences remain, AI is proving it can bring about enormous societal benefits.
Semantic and Fuzzy Modelling for Human Behaviour Recognition in Smart Spaces. A case study on Ambient Assisted Living.
24.4.2015 Åbo Akademi University, Finland
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
How is Artificial Intelligence shaping the Future of Ecommerce?archana cks
Few industries are as competitive as ecommerce. Not only are online retailers competing with other online stores and brick-and-mortar locations, but also the overall noise that is the Internet.
source <> http://www.ecbilla.com/blogs/how-is-artificial-intelligence-shaping-the-future-of-ecommerce.html
Three Dimensional Database: Artificial Intelligence to eCommerce Web service ...CSCJournals
A main objective of this paper is using artificial intelligence technique to web service agents and increase the efficiency of the agent communications. In recent years, web services have played a major role in computer applications. Web services are essential, as the design model of applications are dedicated to electronic businesses. This model aims to become one of the major formalisms for the design of distributed and cooperative applications in an open environment (the Internet). Current commercial and research-based efforts are reviewed and positioned within these two fields. A web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-process able format (specifically Web Services Description Language WSDL). Other systems interact with the web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards. Particular attention is given to the application of AI techniques to the important issue of WS composition. Within the range of AI technologies considered, we focus on the work of the Semantic Web and Agent-based communities to provide web services with semantic descriptions and intelligent behavior and reasoning capabilities. Re-composition of web services is also considered and a number of adaptive agent approaches are introduced and implemented in publication domain with three dimensional databases and one of the areas of work is eCommerce.
The near future for artificial intelligence and conversation botsPieter Rahier
What will be the near future for artificial intelligence an what are the most important things UX designers have to take in to account while designing a conversation bot.
Beyond Science Fiction, one marketer and a data scientist collaborated to demystify how Artificial Intelligence is improving the way we live, shop, travel and even date! Vote for this talk to see the full presentation at SXSWi 2016 in Austin!
Why Artificial Intelligence is a Game Changer for Retail Data Kantify
In this presentation, Kantify presents why and how Artificial Intelligence has become a source of competitive advantage for retailers, where lies the untapped potential, and what are the limits and challenges of these technologies.
Send us a message if you wish to learn more, we are here to help ! www.kantify.com
A confluence of factors have converged to afford the opportunity to apply data science at large scale to agricultural production. The demand for agricultural outputs is growing and there is a need to meet this demand by utilizing increasingly mechanized precision agriculture and enormous data volumes collected to intelligently optimize agriculture outputs. We will consider the machine learning challenges related to optimizing global food production.
Artificial Intelligence as an Interface - How Conversation Bots Are Changing ...Sage Franch
With the rise of machine learning and artificial intelligence, chat bots have become increasingly used as core elements of our interactions with technology. Ranging from simple FAQ bots to advanced human-like conversational AI, chat bots are changing the way we use, view, and build technology. In this lecture, Microsoft Tech Evangelist Sage Franch will explore today’s ecosystem of intelligent bots, detail the process of building a chat bot from concept to training and deployment, and take a glimpse into the future of conversations as a platform.
This seminar slide will give you the ideas how much the ARTIFICIAL INTELLIGENCE is important gaming and what hard work should do for movements of other persons in a game.
It is technology and a branch of computer science that studies and develops intelligent machines and software. Major AI researchers and textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "The science and engineering of making intelligent machines".
ARTIFICIAL INTELLIGENCE & ROBOTICS – SYNTHETIC BRAIN IN ACTIONijaia
Rapid technological growth has made Artificial Intelligence (AI) and application of robots common among
human lives. The advancements undertaken to make designs with human similarities or adaptations to the
society are elaborated in detail. The increasing manufacturing and use of robots for industrial purposes
have been related to their operating mechanisms. The experiments and laboratory testing of these devices
is analysed in form tables to show the statistical side of the technology. This report explains the
technological aspects and laboratory experiments that have been advanced to increase knowledge on these
digital technologies. This study aims to present an overview of two developing technologies: artificial
intelligence (AI) and robots and their potential applications. The product variety is a primary
characteristic of each of these specialties. In addition, they may be described as disruptive, facilitating,
and transdisciplinary.
1. 1
Applications of Artificial Intelligence to Real World Problems
In recent years, the advancement in logic and theoretical computer science has
encouraged the researchers to understand the importance of Artificial Intelligence (AI). Artificial
Intelligence has been applied to many areas such as military, industry, science, and
manufacturing.
The term ‘Artificial Intelligence’ has become very popular in recent years, but the actual
work on Artificial Intelligence started in the early 1950s. Several researchers have different
opinions on the origin of Artificial Intelligence. According to Munakata (2008), “Artificial
Intelligence is evolved to replace human intelligence with the machine during the industrial
revolution started around 1760” (p.5). This research was emphasized in the early development of
Artificial Intelligence. This finding is in contrast with other research that has been done. Black
and Ertel (2011), states that “Foundation for logic and theoretical computer science laid by
Godel, Church, and Turning in 1930s created interest in Artificial Intelligence” (p.5). He also
states that “the first experimental research conducted by Turning is lead to the invention of
Artificial Intelligence” (p.6). Another researcher Jackson (1985), also supports the experiment
conducted by Turing. He states that “The classic experiment proposed for determining whether a
machine possesses intelligence on a human level is known as Turing’s test” (p.2). These two
findings are crucial because it demonstrated the experiment conducted to identify the human
behavior which in turn lead to invention of Artificial Intelligence. In another study, Swarup
(2012) argues that the “evidence of Artificial Intelligence can be tracked back to ancient Egypt,
but with the development of electronic computer in 1941” (p.2). He also states that “the term
‘Artificial Intelligence’ was first coined by John McCarthy in 1956, at the Dartmouth
2. 2
conference” (p.3). This research gives the evidence of when the term Artificial Intelligence was
used first. These findings provided the origin and the history of Artificial Intelligence.
What is “Artificial Intelligence”? According to Jackson (1985) “Artificial Intelligence is
the ability of the machine to do things that people would say require intelligence” (p.1). This
study provides the basic understanding and definition of the Artificial Intelligence. A few more
authors Russell and Norvig (2003) support this claim, saying “AI definitions vary along two
main dimensions. This study provides two dimensions of Artificial Intelligence. The first one is
mainly concerned with thought processes and reasoning, whereas the second one on the bottom
address behavior” (p.1). This finding helped to measure the human performance and ideal
concept of intelligence. However another study by Munakata (2008) shows that there is no
standard definition for Artificial Intelligence, but he says according to the Webster’s new world
college dictionary “AI is the capability of computers or programs to operate in ways to mimic
human thought processes, such as reasoning and learning” (p.1). This study helps to identify the
dictionary definition of Artificial Intelligence. However, the proper definition of Artificial
Intelligence was provided in the study by Swarup (2012). He states that even though there are
many text book definitions of AI, the actual definition of AI was provided by John McCarthy,
who coined the term in 1956. John McCarthy defines AI as “the science and engineering of
making intelligent machines” (p.1). All these studies and findings are important to understand
the definition of Artificial Intelligence from different researcher’s points of view. These
researchers emphasized understanding the need and importance of the Artificial Intelligence.
Artificial Intelligence has been used for various purposes in different fields. Russell and
Norvig (2003) state that Artificial Intelligence systems should possess the following capabilities:
“Natural language processing to enable it to communicate successfully in English. Knowledge
3. 3
representation to store what it knows or hears; automated reasoning to use the stored information
to answer questions and to draw new conclusions; machine learning to adapt to new
circumstances and to detect and extrapolate patterns. Computer vision to perceive objects, and
robotics manipulate objects and move about” (p. 3). This study helps to differentiate the
characteristics of an Artificial Intelligence system. In addition, Jackson (1985) explains the
problem solving, game playing, theorem proving, semantic information processing, and pattern
recognizing are the main purpose of the Artificial Intelligence (p. 6). These studies provided the
data about the various purposes of Artificial Intelligence in computational and logical reasoning.
These findings lead researchers to emphasize extending Artificial Intelligence to real world
applications.
Artificial Intelligence is classified into different fields based on the application.
According to Munakata (2008), Neural Networks, Fuzzy Systems, Data Mining, and Machine
Learning are the major fields of Artificial Intelligence (p.3). He defines Neural Network as
“Computational models of the brain” and Fuzzy Systems as “A technique of continuation to a
paradigm, especially for discrete disciplines such as sets and logic” (p.3). Another study shows
that neural network applications are crucial for solving practical problems related to cost, speed
of operation, reliability, ease of maintenance, and development (Introduction to Neural
Networks, 1995). This research provided the insight towards major fields of Artificial
Intelligence. In addition to these definitions, Black and Ertel (2011) provided a definition for
Data Mining and Machine Learning. They state that Data Mining is “the task of learning
machine to extract knowledge from training data” while Machine Learning is “the field of study
that gives computers the ability to learn without being explicitly programmed” (p.161, 165). In
4. 4
brief, this research highlighted the prominent fields of Artificial Intelligence, which are used
extensively in computer technology.
The review of the literature shows the history, definition, purpose, and fields of Artificial
Intelligence. Many researchers focused on various aspects of Artificial Intelligence; however,
only a few researchers focused on solving real world issues using Artificial Intelligence. Very
few scientists focused on studying Artificial Intelligence concepts such as neural networks,
machine learning, and fuzzy systems to solve real life problems. This paper explores the
potential application of Artificial Intelligence techniques in resolving real time issues such as
developing brain inspired computing using neural networks, resolving network traffic using
machine learning, and solving student performance issues using fuzzy systems.
The first real time application of AI is developing brain inspired computing using neural
network concepts. Understanding human brains for computing requires study of the neural
networks. Munakata (2008) defines “A neural network (NN) as an abstract computer model of
the human brain” (p.7). A machine can be simulated to mimic the human brain. Beiye et.al
(2015) proposed a model to understand the pattern on how the human brain works. They
demonstrated the model with three main components: training the data, model selection and
training/testing (p.1). An artificially intelligent machine is trained with a specific set of
predefined data to select the required model. The machine is trained to verify the authentication
of the user request. For example, a user sends money and withdraws request from the bank. In
this scenario, an artificially intelligent machine is trained to understand how human requests are
made. The machine will decide a prototype model to interact with humans. Moreover, the
machine is intelligent to differentiate between a human request and a robot request or a fake
request from the hacker. Beiye et al (2015) demonstrated this using test privacy model shown
5. 5
below. This model will help to investigate the problem of understanding the behavior and
communication patterns of the users of social networks.
Figure 1: Test privacy model (Beiye et.al, 2015, p.2)
Pattern recognition is one of the ways to train machines to behave like a human brain.
Beiye et.al(2015) conducted an experiment where machines are provided with some sample
handwritten material to analyze and recognize the pattern (p.4). For example, the figure shown
below is the sample of handwritten numbers. An artificially intelligent system incorporated
machine can recognize the pattern below and can identify different numbers. This kind of pattern
recognition is mainly incorporated to authenticate unique users. Since the handwriting of one
person varies from another person, artificially intelligent systems will easily identify the unique
users.
Figure 2: Training digit pattern samples (Beiye et.al, 2015, p.4)
6. 6
A study by Roy, Sharad, Deliang, and Yogendra (2014) demonstrated the usage of spin
torque devices to low energy non Boolean computing. This research emphasized studying brain
neural network patterns to design non Boolean computing. According to Ishak and Siraj (2002)
“the basic element of the brain is a natural neuron” (p.4). Roy and team (2014) conducted an
experiment where neurons are studied to understand the pattern of image recognition. They
further analyzed how neuron connects and transfers data across them. According to them each
neuron transaction is associated with a voltage. Roy and Colleagues’ (2014) experiment shows
that the communication pattern is studied by varying voltage levels. Association of spin torque
devices demonstrated with high voltage the image quality is distracted, whereas with low voltage
the image is processed completely.
Figure 3: Compressed vector representation of stored image (Roy, Sharad, Deliang, & Yogendra, 2014, p.3)
Brain simulation experiments are also supported by the other research, where the neural network
pattern was analyzed to provide statistical simulation on social networking sites (Aabed &
AlRegib, 2012, p. 111). Pattern recognition and brain simulation research demonstrated the
features of brain computing to secure the confidential data from attackers. In short, neural
network concepts can be incorporated into a machine which can behave like a human brain. This
sophisticated machine can be utilized in developing applications that are useful to humans.
7. 7
The second real time application of AI is resolving network traffic using machine
learning techniques. Machine learning knowledge is required to solve critical problems.
According to Chandra and Hareendran (2014), “Machine Learning is a field of study that gives
computers the ability to learn without being explicitly programmed” (p.161). De Souza, Matwin,
and Fernandes (2014) state that usage of the Internet has increased since 1980s (p.1). In the
Internet all computers are connected to different networks and associated with Network Address
Translations (NAT). This NAT is required to establish communicate between computers.
According to Goksen (2014), NAT is generally associated with one computer connected to
network using Local Area Network (LAN). Multiple users can connect to the LAN using Wi-Fi
where NAT can be provided by Internet Service Providers (ISPs). The ISPs will provide a unique
private Internet Protocol (IP) address to each connection. NAT encapsulates this private address
and displays the different public address to the external users in the network.
Since the number of users of the Internet has increased, resolving NAT has become a
challenge. The servers should be designed with sophisticated technology to interpret and resolve
network addresses. For example, multiple users will be accessing the same website from
different locations at the same time. If the server cannot resolve the IP address, users will not
receive the data. The server design should be robust to resolve network traffic. Goksen and
colleagues (2014) provided a solution for this network traffic problem using machine learning
techniques. They proposed an approach to identify the potential NAT devices on given traffic
traces. They proposed an algorithm called C4.5 to resolve this issue. This algorithm uses
machine learning technique to resolve IP addresses. For example, the table below represents the
data flow from multiple websites. Gokcen’s (2014) C4.5 algorithm will segregate unencrypted
and encrypted data from multiple addresses. After segregation, the algorithm will resolve the IP
8. 8
addresses from different ISPs and process them to identify the traffic type. According to
Foroushani and Zincir-Heywood (2013), a machine incorporated with C4.5 algorithm will
identify 36000 files from different website at any given point of time (p. 75).
Table 1: Network flow from different IP addresses (Foroushani, V.A., & Zincir-Heywood, A.N., 2013, p
.75)
Along with C4.5 algorithm, Gokcen (2014) provided another Machine Learning based
approach to solve network traffic is Naïve Bayes algorithm. In this research two different data
sets were collected from two different network flows such as Nims- NAT and Partner-NAT. The
same data sets had provided to two algorithms C4.5 and Naïve Bayes for analysis. Statistics
conducted by Gokcen (2014) shows that the data was collected from HTTP and SSH networks.
His C4.5 algorithm provides solution based on data analysis, whereas Naive Bayes algorithm a
standard baseline based on statistical learning. The table below represents the results of the data
analysis report from these two algorithms. Gokcen’s C4.5 algorithm evaluates better than Naïve
Bayes for NAT data set. On the other hand, Naïve Bayes algorithm performed well for other data
set compared to C4.5 algorithm.
9. 9
Table 2: Comparison between C4.5 and Naïve Bayes algorithms (Gokcen, 2014, p. 138)
In brief, this reducing network traffic traces experiment provided an insight into network traffic
related problems and demonstrated algorithms to resolve the traffic traces. Overall, this research
helped to resolve network traffic issues using machine learning techniques.
The third real time application of AI is solving student performance issues using fuzzy
logic. Fuzzy systems is one of the mathematical and logical approaches used by researchers to
solve the problems. According to Munakata (2008), “Fuzzy Systems is a technique of
continuation to a paradigm, especially for discrete disciplines such as sets and logic” (p.3).
Yildiz and Baba (2014) state that “The fuzzy logic concept was introduced in 1965 as a
mathematical way to represent linguistic variables” (p. 1023). They further explain that Fuzzy
logic defines problem probability between 0 and 1 like 30% “normal”, 40% “good” and %30
“bad” (p. 1023). Therefore, fuzzy system is the best approach to analyze the performance of any
system. Yildiz and Baba (2014) classified fuzzy logic with four important steps: fuzzification,
knowledge base, decision making scheme and defuzzification (p. 1023).
Fuzzy logic approach is used to solve student performance issues. Yildiz and Baba (2014)
proposed the system interface to assess the student grades using fuzzy logic (p. 1024). They
developed a fuzzy evaluation system shown below. This model collected real time data on
10. 10
student grades periodically to evaluate the performance. This approach first determines the main
criteria for evaluation and associates each criteria with a predefined weight. Yildiz and Baba
(2014) studies demonstrated how students score is calculated using fuzzy interface. Their student
evaluation system will continuously assess the grade after peer, student, and group assessment
using fuzzy interface.
Figure 3: Student Evaluation System (Yildiz & Baba, 2014, p. 1024)
After the assessment each student is associated with weight such as poor, unsatisfactory,
average, good, and excellent based on their performance. In addition, the student evaluation
system by Yildiz and Baba (2014) supports to calculate the curve point average. However,
sometimes the fuzzy system exhibit error, but that is very minimal. Overall, fuzzy logic based
student evaluation system provides more realistic and reliable education assessment.
Application of fuzzy system is also demonstrated by Sardesai, Sambarey, Kharat,
and Deshpande (2014) by providing a case study. They utilized type 1 Fuzzy system interface to
predict patient data (p.2). This case study involves collecting data from 226 gynecology patients
11. 11
and applying fuzzy interface system to apply eight experts perceptions based on patients data.
The case study on fuzzy logic application on gynecology by Sardesai and team (2014) involves
fuzzification of eight perceptions to create production rules followed by aggregation and
defuzzification of patient data to provide diagnosis (p.2). In brief, fuzzy logic based system is
reliable to analyze enormous runtime user data and provide valuable result evaluation of the
collected data.
In conclusion, Artificial Intelligence is the important modeling technique in
computational study. Artificial Intelligence has facilitated the resolution of many real time
issues. The future of Artificial Intelligence is promising. A considerable amount of research is
scheduled in Artificial Intelligent Robots which would replace human in the future. The neural
network based brain simulation can be extended to identify the communication pattern in social
networking sites. The Machine learning based approach to solve network traffic can be further
analyzed with different NAT behavior to explore C4.5 based classifier for automatic signature.
The fuzzy logic based approach can be extensively used in the healthcare system to manage
medical reports. This paper has explored the potential application of Artificial Intelligence
techniques in resolving real time issues such as developing brain inspired computing using
neural networks, resolving network traffic using machine learning, and solving student
performance issues using fuzzy systems.
12. 12
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