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Chatbot-Intelligent Conversational Agents
T.Mounika, Akshita Sangam, Shivani Lakhane, Mr.Rajasekhar Sastry, Dr.B V Ramana Murthy
and Mr.C Kishor Kumar Reddy.
Stanley College of Engineering and Technology for Women,Hyderabad.
[email protected],[email protected],[email protected],
[email protected],[email protected] and [email protected]
Abstract
Chatbots are one class of intelligence, conversational software agents activated by natural
language input . They provide conversational output in response and can also execute tasks if
commanded. Although chatbot technologies have existed since the 1960’s and have influenced user
interface development in games since the early 1980’s, chat bots are now easier to train and implement.
This is due to plentiful open source code, that is widely available for developing platforms, and
implementing options via Software as a Service (SaaS). In addition to enhancing customer experiences and
supporting learning, chatbots can also be used to spread rumours , or attack people for posting their thoughts and
opinions online. This paper presents secondary sources for quality issues and attributes as they relate to the
contemporary issue of chatbot development and implementation. Finally, quality assessment approaches
are reviewed and method based on these attributes and the Analytic Hierarchy Process (AHP) is
proposed and examined.
Keywords:
Chatbot, artificial intelligence, intelligent agents, usability, quality attributes, Analytic Hierarchy
Process (AHP), conversational agents.
1. Introduction
“The most profound technologies are those that disappear. They weave themselves into the fabric of
everyday life until they are indistinguishable from it.” - Weiser (1991) It is not always that the “people”
you interact with online are not all people. Customer service chat and commercial social media
interactions are managed by intelligent agents. Many of them have been developed with human
identities and even personalities. Even though the technology itself is not new, reliable linguistic
functionality and availability through Software as a Service (SaaS),addition of intelligence through
machine learning has increased its popularity. Between 2007 and 2015, chatbots were participating in one
third of all online interactions (Tsvetkova et al., 2016) and the rate at which new chatbots are being
deployed has increased since then.
Social conversational bots can be used to provide benefits to companies, which are used to reduce
time, provide enhanced customer service, increase satisfaction and increase engagement. Sometimes ,
chatbots are specifically designed to be harmful. For example, networks of fake users (called “sybils” on
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Twitter) have been implemented to artificially inflate “follower” counts to increase social status for users
who purchase them and to spread fake news or rumours, and even to intimidate users who express certain
political beliefs.In the 2016 US Presidential elections, up to a fifth of the comments and responses on
Twitter were driven by fully or partially autonomous Twitter accounts.
Due to their flexibility and ease of use , speculations have been made that conversational agents may
be universal user interfaces and may replace apps. In addition ,the chatbots and conversational agents
are anticipated to be important interfaces in Virtual Reality (VR) environments .Hence, it is important to
understand the issues and quality attributes associated with developing , implementing high-quality
chatbots and conversational agents and identifying a mechanism for quality assurance across these
factors.
The rest of the file is arranged in the following manner. First,presentation of the historic context of
chatbots , chatbots background and conversational agents , which are two types of dialogue systems .
Second, outline the methodology for literature rewiew along two topics. a)Quality attributes for chatbots
and conversational agents ,b)Quality assesment approaches . Third and finally , results are produced and
an approach for evaluating the quality of these technologies in terms of key attributes is presented.
2.Background
Chatbot systems that originated with programs like ELIZA were intended to demonstrate the natural
language conversation with a computer. An early stated goal of such systems was to pass the Turing Test ,
in which a human interrogator is considered a computer to pass a test as a human. Primitive systems
like ELIZA used keyword matching and minimal context identification , lacked the ability to keep a
conversation going. Through interactions with program, it was easy to guess that ELIZA was a
computer.Researchers continued to develop the demonstration systems with natural language
capabilities, but none of them were capable of passing the Turing Test. In the 1980’s, ALICE was
created, becoming significant not for its conversational capabilities but because it led the development of
Artificial Intelligence Markup Language . AIML is an eXtensible Markup Language based which
supports most chatbot platforms and services of today’s use.
Chatbots get natural language input which are interpreted through speech recognition
software,sometimes. To get engaged in goal-directed behaviour , they choose to execute one or more
related commands. Chatbots are usually autonomous, reactive, proactive, and social and these factors
make it an intelligent agent. So, in order to adapt to new information or new requests ,most advanced
systems employ machine learning. Ai chatbots or conversational agents are used to automate interaction
between company and server. Chatbots are computer programs that use natural language to communicate.
Now chatbots are shifting towards mobile messenger interface, they are platform independent and use
messenger infrastructure and even download apps. The goal is different from what we think and deal
with. Chatbots and many other kinds of digital bots like Apple-Siri or Amazon-Alexa are aimed to
accomplish on turning on music, checking weather. Bots are popping up every where from facebook and
even to home personal assistants. Advances in natural language processing, machine learning and other
AI technologies. The AI bots are limited to natural language processing and their basic functionality is to
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fix data. Bots have ability to become smarter and more proactive to enable more meaningful
communication.
One category of conversational agents are the chatbots .They are software systems that mimic
interactions with real people. They are not given specific shapes or forms of animals, avatars, humans, or
humanoid robots. Conversational agents are a class of dialog systems that have a subject of research
in communications for decades. Interactive Voice Response (IVR) systems are also dialog systems.
Figure 1: Relationships between classes of software-based dialog systems.
One category of conversational agents are the chatbots .They are software systems that mimic
interactions with real people. They are not given specific shapes or forms of animals, avatars, humans, or
humanoid robots. Conversational agents are a class of dialog systems that have a subject of research in
communications for decades. Interactive Voice Response (IVR) systems are also dialog systems. They
are not usually considered conversational mediators as they implement decision trees. AI chatbots or
conversational agents can be used to automate communication between company and server. Chatbots are
computer programs that communicate with its users using natural language. Chatbots are now shifting to
mobile messenger interface, they are platform independent as they use messenger infrastructure and
downloading apps.
The goal is different from what we think when we deal with chatbots and other kinds of digital
helpers like Apple’s Siri or Amazon’s Alexa,where the aim is to get something accomplished like turning
on some music or checking weather. AI gets smarter the more you interact with it. Bots are popping up
everywhere from Facebook to home personal assistants. Advances in natural language processing
,machine learning and other AI technologies created the substance for bots. The AI part of these bots is
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limited to natural language processing and basic functionality tied to fixed data. Bots need the ability to
become smarter and more proactive to enable more meaningful communication. For example, they learn
user preferences and behaviour to offer the right services at the right time. Without this, two way
communication can’t happen.
3. Chatbot Creation
3.1. Design
The Chatbot design, it is the process that defines the communication between the user and the Chatbot.
The Chatbot designer will classify the chatbot personality, the questions that will be points to the users,
and the overall communication.
It can also be viewed as a division of the conversational design. To regulate the speed up process, user can
use chat design tools which helps for immediate preview, team collaboration and video export. An
important part of the chatbot design is also centered around user testing. User testing can be performed
following the same principles that guide the user testing of graphical interfaces.
3.1.1 Predetermined links and buttons
These saved users from typing. People appreciated having these options and even expected them for common inputs. These were usually displayed in a carousel and could include images.
3.1.2 Text
Allowed users some flexibility in choosing the types of questions they sought to ask and enable them to diverge from the script of the chatbot.
Changing from Choice based to Conversationbased : Here we you remind the user that they can ask many different type of questions, in many different ways. Now-a-days people are used to clicking things on a digital screen, without any thought. They are used or habituated to not interacting to websites. * It’s not supposed to be a menu. It’s a suggestions screen. This change may look drastic, but this changes user behavior at a fundamental level as we have seen.
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Figure 2: home screen designing
3.2 Building The process of building a Chatbot can be divided into two main tasks a) Understanding the user's intent and b) Producing the correct answer.
3.2.1Understanding the user’s intent
The first task involves understanding the user input. In regulate to properly understanding of a user input
in a at no cost text form, a Natural Language processing Engine can be used.
3.2.1.1 Natural language processing (NLP)
It is a subfield of computer science and artificial intelligence worried with the communications between
computers and users (natural) languages, in particular how to program computers to process and analyze
large amounts of natural language data.
Natural language processing systems are based on difficult sets of hand-written rules.Later it was given
by the machine learning systems. It is a static revolution.
The syntaxes are Grammar induction, Lemmatization, Morphological segmentation, Part-of-speech
tagging, parsing, Sentence breaking, Word segmentation, Terminology extraction.
This is even having the speech recognition, given a voice clip of a user or people speaking, resolve the
textual demonstration of the speech. This is the contradictory of text to speech and is one of the
tremendously difficult problems colloquially termed as "AL-Complete". In natural speech there are hardly
many pauses between successive words, and thus speech segmentation is a necessary subtask of speech
recognition. Note that in almost many spoken languages, the sounds representing in a row letters blend
into each other in a process termed co articulation, so the adaptation of the analog signal to discrete
characters would become a very difficult process. Also, given that words in the similar language are
verbal by people with different accent, the speech recognition software must be able to be familiar with
the wide variety of input(user) as being identical to each other in terms of its textual equivalent.
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Natural language processing involves breaking down of sentences and other parts of language, into
components. It processes the semantics of content to identify things like the entities and intents of the
user. This has been a hard problem for computer science to tackle, but in recent years it had advanced in
the field and have made this feasible.
Basic steps ofNatural processing:
Sound
Waves words
Figure 3: basic steps of natural processing
3.2.2 .Producing the correct answer
The second task may involve different approaches depending on the type of the response that the chatbot
will generate.
3.3Analytics
The usage of the chatbot can be monitored in order to spot potential flaws or problems. It can also provide
useful insights that can improve the final user experience.Analysing the data effectively is the key to
finding thevaluable insights into crafting growth stratagies, discoverig the new bussiness opportunities,
determinig effective employee recruiment and retaining stratagies.
Analytics is an extension of Application Insights. Application Insights provides service-level and I
nstrumentation data like traffic, latency, and integrations. Analytics provides interaction-level reporting
on user, message, and channel data.
3.3.1 Specify channel
Choose which channels appear in the graphs below. Note that if a bot is not enabled on a channel, there
will be no data from that channel. 3.3.2 Specify time period
Analysis is available for the past 90 days only. Data collection began when Application Insights was
enabled.
Figure 4: specifying the time period
3.3.3 User
The Users graph tracks how many users accessed the bot using each channel during the specified time frame.
The percentage chart shows what percentage of users used each channel.
The line graph indicates how many users were accessing the bot at a certain time.
syntactic processing
Semitic processing
Pragmatic
processing
phonetics
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The legend for the line graph indicates which color represents which channel and the includes the total number of users during the specified time period.
3.3.4 Activities
The Activities graph tracks how many activities were sent and received using which channel during the
specified time frame.
Figure 5: activity sheet
The percentage chart shows what percentage of activities were communicated over each channel.
The line graph indicates how many activities were sent and received over the specified time frame.
Analytics are not available until Application Insights has been enabled and configured. Application
Insights will begin collecting data as soon as it is enabled. For example, if Application Insights was
enabled a week ago for a six-month-Old bot, it will have collected one week of data.
Analytics requires both an Azure subscription and application insights resource.To access
Application Insights, open the Bot in the Azure Portal.
3.3.5.Self-service rate
This metric helps you identify the number of users who get what they want from the chatbot without any
human input. For example, if your chatbot goal was to sell a particular product, you will measure the
percentage of user interactions that achieved that goal.
3.3.6 Maintenance
To keep the chatbot up to the speed with varying company products and services. Traditional chatbot
improvement platforms require ongoing maintenance. This can either be in the form of an current service
supplier or for larger enterprise in the appearance of an in-house chatbot instruction team. To eliminate these
costs, some startups are experimenting with artificial Intelligence to develop self-learning chatbot, particularly
in customer service applications.
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4. Implementation process
4.1 Fundamental Design techniques and Approaches
4.1.1 creating the dialog bot All the packages which are required for creating the dialog box should be
imported first. And the size of the dialog box and text area withinthe dialog box should be given vertical
scroll bar is used so the screen is scrolled as the discussion goes on.
4.1.2 Creating a database Two dimensional string arrays, these are applied to build a database. Rows in
the array are used to ask for and response. All the even rows hold the request or question and all the odd
rows holdresponse and answer. Columns in the array are functional to save dissimilar types of questions
that could asked by the user and response that a Chatbot can answer. There is one row in the array which
contains default responses which is used when the matching question is not found in the array.
4.1.3. Creating a Chatbot that Uses AI
To find the right answer these systems use machine learning and natural language processing (NLP) to
interpret text like a person and, they also use visual information-such as facial recognition and images etc.
Companies such as Google, IBM, Amazon and Microsoft, offer machine learning AI platforms you can
use to power your own AI-driven chatbot.
4.1.3.1 Machine Learning These systems use a corpus to recover the correct answer. Machine learning
works best with massive repositories. So it will even include open-source repositories, such as the
structured Wikipedia. It uses algorithms to influence the data - particularly, linguistic pattern matching to
find and score the information in the corpus. It continues to improve and its responses based on its
communications with users. In other words, the machine learns and understands data given to it.
4.1.3.2. Feeding the Bots To help the bots to learn machine language, subject experts train it by
uploading pairs of questions and answers. By this it enables the system to check the results of its learning
for accuracy against the real world. This is called as the ground truth. By this it can apply this learning
into any new information added to the corpus or through user feedback. You can even also use
unstructured content to help the system learn, where the bots can contain relevant information. This might
be in blog posts and articles.IBM Watson platform can process a corpus of unstructured content.
However, you do need structured content when you're developing the system - as that of question and
answer key pairs
4.1.4 The Benefit of Structured Content The benefit of structured content ,is chatbot will perform better
if the source content maps well to the chatbot’s information i.e. structured and it is used to process users'
questions . Ideally, a lot of the chatbot's underlying semantic’s that are structured can be mapped to your
existing structured content. For example, if it is structured, it has metadata, and this uses taxonomy, these
will help the chatbot know which piece of information is to be served to the user. This adds to common
metadata such as product, symptom, problem, version, user role and operating system.Many technical
analysis’s have been doing structured content for a good reason. This could mean you're able to reduce
the effort and time significantly. For unstructured content, you need to expect to spend a large proportion
of machine learning project's time on cleaning up of the data. We can consider or require AI-powered
chatbot when we develop information design models for our Help content. This approach makes it more
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likely that your documentation with AI and chatbot will be ready, at the time when it’s needed. You can
build a taxonomy to mark up some or all of your content. You can even check that your content is marked
up correctly when you save a page.
5. Methodology
The purpose of this research was to examine the academic and industry literature by providing a
comprehensive review of quality attributes and conversational agents using chatbots, and to spot the
appropriate quality assurance approaches. For this, publications on quality in chatbots and conversational
agents were identified through systematic searches of Google Scholar, JSTOR, and EBSCO Host from
1990 to 2017. The searching tools that were used were ‘chatbots’, ‘conversational agents’, ‘embodied
conversational agents’, ‘quality’, ‘quality attributes’, ‘quality of chatbots’, ‘quality of conversational
agents’, and ‘quality assurance’ in various combinations. Papers referred were from the domains of
engineering, technology, psychology, communications, and anthropology. The categories selected from
articles were based on the criteria that publications were selected that had quality as a major influential
aspect of the research. Highly technical articles were focused on programming and engineering aspects of
chatbots, including improving the quality of speech recognition, were excluded.
The purpose of this research was to examine the academic and industrial literature to 1) Provide
an overarching review of quality attributes for chatbots and conversational agents, 2) Identify
favourable quality assurance to obtain this, publications and conversational agents were
identified through methodological searches of Google Scholar, JSTOR, and EBSCO Host from
1990 to 2017. Phrases used to search were chatbots, conversational agents, embodied
conversational agents, quality, quality attributes, quality of chatbots, quality of conversational
agents, and quality assurance in various combinations. Papers were obtained from the domains of
engineering, technology, psychology, communications, and anthropology.
The selection of articles was based on , 1) Scholarly references being emphasized which was
supplemented only by industry publications from 2016 and 2017. Publications were selected if they
contained at least one of the search terms in the title and/or abstract to ensure the suitability of data
collected, that had quality as a major or influential aspect of the research. Finally, highly technical
articles focused on programming and engineering aspects of chatbots, including improvement of the
quality of speech recognition, were excluded.
Initial searches generated sample frames of 7,340 articles, which was further refined by adding
search terms for evaluation, assessment, quality metrics and metrics. Most recent articles were
inspected next, followed by articles between 2013 and 2015 and then from 2007 to 2012. The
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asssortment of time interval was made to limit the number of articles for review to less than 300.
Eventually, 36 scholarly articles and conference papers were set to be relevant to the aim of this paper.
They were increased with 10 articles from industry or trade magazines.Futher , only seven articles were
picked for the second portion of the study focusing on quality assurance and all others were used in the
analysis.
6. Quality attributes
We drawn out quality attribute from each of 32 papers and 10 articles, and have grouped them based on
similarity. We noticed that after two or three iterations that ,in general they were aligned with the ISO
9241 concept of usability they are “The effectiveness, efficiency and satisfaction with which it specified
users to achieve specified goals in particular environments.” In particular, effectiveness refers to the
accuracy and completeness which specified users achieve their goals, and efficiency refers to how well
the resources are applied to achieve those goals. Not surprisingly, particuarly in a SaaS environment, the
burden of showing efficiency and effectiveness falls more on the service provider and the need to ensure
that customers are satisfied will remain with the implementer.
Efficiency
There was exceptional consistency across the source material regarding quality attributes; although
several of them were repeated across the sources and only the primary source (or the source where that
attribute was emphasized) appears in Table 1. According to most of the researchers, and the earliest
conversational interfaces, responding and interacting like human should be the top most priority. In fact,
this principle is the guide development for nearly four decades since ELIZA came online.
6.1 Synthesized Approach
Analytic Hierarchy Process (AHP) AHP is a structured approach that is used for navigating complex
decision-making processes and it involves both qualitative and quantitative considerations. First, is to
create a hierarchy of quality attributes then select appropriate metrics to represent each attribute. Second,
construct the pairwise comparisons between the quality attributes for one or more product options. Next,
create comparison matrices and calculate the first principal eigenvector of each one to evaluate relative
Category
Quality Attribute
Reference
Performance ● Graceful degradation
● Robustness to manipulation
● Robustness to unexpected input
● Avoid inappropriate utterances and be
able to perform damage control.
● Cohen & Lane
(2016)
● Thieltges (2016)
● Kluwer (2011)
● Morrissey
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and global priority. Finally, combine all the priorities and compute the inconsistency factor to determine
which product option is best satisfies the hierarchy of quality attributes. For chatbots, conversational
agents the product options might be 1) two or more kinds of the conversational system, or 2) the “as-is”
system and one or more “to-be” systems are under development. Consider an example where you made
improvements to a chatbot system in the past year, and now you want to see if the quality has improved.
First, you examine the quality attributes in Table 1 to see which ones are most important, and select
metrics. Having multiple users participating in sessions with your chatbots, and record the metrics you
have selected.
AHP uses pair wise assessments between categories, and within categories. The first step is to set up the
attribute hierarchy as per the figure. Notice that the top level always displays the goal, the next level
shows the quality attribute categories, and the next level from that includes the nine quality attributes are
selected in the prioritization process. The two alternatives (OLD and NEW) are shown at bottom. Because
the hierarchical structure is central and this approach aligns well with the goal-oriented approach of
Kaleem et al.
Figure 6. Hierarchical model of prioritized quality attributes.
7. Chatbots overview
Chatbots are very simple and user friendly and they are not complicated like any other applications. The
working of the Chatbot is very simple and can be easily understood by any person. The input and output
can be customized according to the user i.e. based on the developer or the user; they can store required
requests and responses in the database. Since any one can create their own database, it allows the user to
understand how the response is generated. Chatbot can be used for the entertainment purpose. Whenever
a person gets bored, he can chat with the bot for some entertainment. It can also modify the program as
needed by the user to provide information .
A Chatbot is a program that can have a interaction with a person using rules and Artificial Intelligence
(AI) in a way that mimics human-like conversations and interactions. Chatbots have become very popular
in the past few years as businesses discover innovative ways to put them to use. Having a Chatbot today
has numerous benefits for businesses – they make life easier and are available 24/7, save time and they
are easy to use.
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Chatbots are consistent, context driven, conversational, compatible and cost effective. These advantages
have led to increased adoption of Chatbots by both businesses and consumers. For businesses and
entrepreneurs, they can develop Chatbots to expand or improve the efficiency of any aspect of their
business operations, improve customer engagement and improve User Experience. The following are
some examples that highlight interesting applications of Chatbots in businesses across various industries –
#1. Content delivery: Media Publishers like CNN and Wall Street Journal’s use chatbots on Facebook
Messenger and even receive the latest news without visiting their websites.#2. Order Food:many fast food
giants like KFC and Pizza hut take orders from customers through conversation in chatbots.#3. Book
Flights:Icelandair’s chatbot gives their customers the ability to search for and book flights in a text-based
conversational manner.#4. Transportation:Uber in partnership with Facebook has enabled users to sign up
for Uber where we can request a ride, without having to leave Messenger or download the Uber app. Ride
status updates and ride receipts are delivered to a private conversation between the customers and Uber on
Messenger, making it easy to track Uber rides and payment history.#5. Health Care:Chatbots have also
made their way into health care by easing the burden on medical professionals by facilitating faster
medical diagnosis, answering health-related questions, booking appointments and lots more.#6. E-
commerce:The e-commerce industry is also humanizing shopping experience with Chatbots. Now
Customers can search and shop more conveniently with the help of chatbots.
8.Future scope
Chatbots are also referred to as practical assistants. It is a basic form of artificial intelligence software that
can mimic human conversation. The Chatbots can be analyzed and enhanced. It can be used in a variety
of fields such as education, business, online chatting etc. It can be used as alearning tool in the field of
education. The information essential for education can be stored in the data base and can be retrieved any
time by querying the bot. In business field, it can be used to provide business solutions in an well-
organized way. When the solutions are efficient, the business can be improved and the development of the
organization will be increased. Chatbot are used in online chatting for entertainment purpose. These bots
can also be used to learn different of language. The language that has to be learnt can be stored in the
database and can be learnt by asking questions to the bot. They are used in the field of medical to solve
health related issues. Chatbots are going to fly into a rage and can really dominate in future. Chatbots can
provide a new and flexible way for users and are giving AI something better to do. Chatbots results in
smart conversation and is advancing at an extraordinary rate with each new development. ChatBots store
contextual data which can be used in the detection of geo location or a state. This could be a telephone
number or other private data, and no one knows the data is encrypted before it gets saved to a database.
Since Chatbot predicts and provides exact response to a posed question and it will be hard to imagine the
future without a Chatbot.
9. Conclusion
The growth of AI-driven chatbots suggests a future for a personalized fluid user experience. It is
one of the most effective ways we'll be able to convey content that is contextually aware. We discussed
that the changes by chatbots towards structure, taxonomy and metadata are also upon us due to other
developments, such as a communal content strategy, a content delivery portal or even a cloud
application.. We can markup the content by using if and then tags to manage content that is specific to
certain conditions. The AI system can even look at that content to determine the information that meets
the user's situation. At this level of precision, we could see and deliver documentation as it is suited for us
in our convenience. The main areas of the conversations comprise feedback-giving, playful chit-chat,
system inquiry, and usual communicative utterances. Through the lens of arithmetical modeling, we
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highlight the rich signals in conversational interactions for inferring user fulfillment, which can be
utilized to develop agents that can become accustomed algorithmic performances and interaction styles.
The results also provide nuanced understanding on the innovative functions of conversational behaviors
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JASC: Journal of Applied Science and Computations
Volume VI, Issue I, January/2019
ISSN NO: 1076-5131
Page No:476