Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research.
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This document describes a web application created for the Mathematics Club of Priyadarshini College of Engineering to enable various functions like electing club members, conducting tests, providing study materials, and holding virtual meetings. The application was designed to be a one-stop-shop for students preparing for competitive exams requiring math skills. It includes features like a chatbot, voting system, blog, study materials, online quizzes with face and voice recognition, certificate downloads, and a virtual meeting platform. The application aims to make the preparation process more organized and convenient by consolidating these necessary resources and functions into a single online system.
INTELLIGENT CHATBOT FOR COLLEGE ENQUIRY SYSTEMIRJET Journal
The document describes a proposed intelligent chatbot system for answering student inquiries about a college. The chatbot would allow students to get information about admissions, fees, scholarships, course timetables, and required documents from anywhere at any time through natural language conversations. It uses techniques like pattern matching, artificial intelligence, machine learning and natural language processing to understand questions and provide relevant responses from its knowledge database. The goal is to streamline the student information process and reduce the workload on college staff.
The document describes a WhatsApp chatbot developed to provide career guidance and information to users. The chatbot was created using Twilio, Flask, and ngrok. It contains over 50 career roadmaps with details on how to become different professions and attached videos. Users can ask the chatbot questions about career options and it will respond with the appropriate roadmap and information to guide them. The chatbot aims to help users who are unsure about their career path by providing all relevant career information in an easy-to-understand format through WhatsApp messages and replies.
The document describes the design of a chatbot using deep learning. It proposes using a neural network with multiple hidden layers to learn and process data for the chatbot. The chatbot is intended to be trained on any file based on the user's needs, making it generalized. It will also have text-to-speech conversion to make it more user-friendly. The chatbot is evaluated and achieved 98.24% accuracy in responding to questions within its training data.
An Intelligent Career Counselling Bot A System for CounsellingIRJET Journal
This document describes the development of an intelligent career counseling chatbot. The chatbot uses natural language processing and artificial intelligence algorithms to analyze users' career-related questions and respond with relevant answers from its knowledge base. It allows users to ask open-ended career questions without a specific format. The chatbot's responses aim to simulate a conversation with a real career counselor. It helps users choose careers that match their interests and capabilities. The chatbot's processing involves matching user inputs to patterns in its knowledge base to determine an appropriate response. It has the potential to help many users receive career advice without requiring an in-person counselor.
Artificial Intelligence Approach for Covid Period StudyPalIRJET Journal
The document proposes the development of an AI chatbot named StudyPal that can serve as a virtual tutor for students. StudyPal would use techniques like active recall and mind mapping to teach students curriculum content for their grade and subject without needing an in-person teacher. The goal is for StudyPal to provide individualized attention to effectively communicate concepts to each student. StudyPal would be created using a federated learning model to maintain data privacy and address issues with internet connectivity. The document outlines the proposed design and development process for StudyPal.
IRJET- A Survey to Chatbot System with Knowledge Base Database by using Artif...IRJET Journal
The document discusses chatbots and artificial intelligence. It provides background on chatbots, including how they have advanced from early rule-based models to more advanced intelligent systems capable of natural conversations. Chatbots analyze user input to formulate relevant responses and are gaining popularity for automating customer service. The document also discusses using knowledge bases and databases to improve chatbot responses and abilities. It reviews different techniques used in building chatbots and their components like classifiers, responders, and knowledge management systems.
This document summarizes a survey paper on chatbots. It discusses how chatbots can be used to relieve stress in adolescents through continuous dialogue that provides positive information and guidance. The proposed "HappySoul" chatbot system would act as a virtual friend to help stressed adolescents express their negative feelings and release stress. The technology at the core of such chatbots is natural language processing, recurrent neural networks, and a client-server architecture with an Android GUI. Key applications of chatbots discussed include assisting dementia patients, helping insomniacs, allowing marginalized communities to provide feedback, and making medical diagnoses faster.
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This document describes a web application created for the Mathematics Club of Priyadarshini College of Engineering to enable various functions like electing club members, conducting tests, providing study materials, and holding virtual meetings. The application was designed to be a one-stop-shop for students preparing for competitive exams requiring math skills. It includes features like a chatbot, voting system, blog, study materials, online quizzes with face and voice recognition, certificate downloads, and a virtual meeting platform. The application aims to make the preparation process more organized and convenient by consolidating these necessary resources and functions into a single online system.
INTELLIGENT CHATBOT FOR COLLEGE ENQUIRY SYSTEMIRJET Journal
The document describes a proposed intelligent chatbot system for answering student inquiries about a college. The chatbot would allow students to get information about admissions, fees, scholarships, course timetables, and required documents from anywhere at any time through natural language conversations. It uses techniques like pattern matching, artificial intelligence, machine learning and natural language processing to understand questions and provide relevant responses from its knowledge database. The goal is to streamline the student information process and reduce the workload on college staff.
The document describes a WhatsApp chatbot developed to provide career guidance and information to users. The chatbot was created using Twilio, Flask, and ngrok. It contains over 50 career roadmaps with details on how to become different professions and attached videos. Users can ask the chatbot questions about career options and it will respond with the appropriate roadmap and information to guide them. The chatbot aims to help users who are unsure about their career path by providing all relevant career information in an easy-to-understand format through WhatsApp messages and replies.
The document describes the design of a chatbot using deep learning. It proposes using a neural network with multiple hidden layers to learn and process data for the chatbot. The chatbot is intended to be trained on any file based on the user's needs, making it generalized. It will also have text-to-speech conversion to make it more user-friendly. The chatbot is evaluated and achieved 98.24% accuracy in responding to questions within its training data.
An Intelligent Career Counselling Bot A System for CounsellingIRJET Journal
This document describes the development of an intelligent career counseling chatbot. The chatbot uses natural language processing and artificial intelligence algorithms to analyze users' career-related questions and respond with relevant answers from its knowledge base. It allows users to ask open-ended career questions without a specific format. The chatbot's responses aim to simulate a conversation with a real career counselor. It helps users choose careers that match their interests and capabilities. The chatbot's processing involves matching user inputs to patterns in its knowledge base to determine an appropriate response. It has the potential to help many users receive career advice without requiring an in-person counselor.
Artificial Intelligence Approach for Covid Period StudyPalIRJET Journal
The document proposes the development of an AI chatbot named StudyPal that can serve as a virtual tutor for students. StudyPal would use techniques like active recall and mind mapping to teach students curriculum content for their grade and subject without needing an in-person teacher. The goal is for StudyPal to provide individualized attention to effectively communicate concepts to each student. StudyPal would be created using a federated learning model to maintain data privacy and address issues with internet connectivity. The document outlines the proposed design and development process for StudyPal.
IRJET- A Survey to Chatbot System with Knowledge Base Database by using Artif...IRJET Journal
The document discusses chatbots and artificial intelligence. It provides background on chatbots, including how they have advanced from early rule-based models to more advanced intelligent systems capable of natural conversations. Chatbots analyze user input to formulate relevant responses and are gaining popularity for automating customer service. The document also discusses using knowledge bases and databases to improve chatbot responses and abilities. It reviews different techniques used in building chatbots and their components like classifiers, responders, and knowledge management systems.
This document summarizes a survey paper on chatbots. It discusses how chatbots can be used to relieve stress in adolescents through continuous dialogue that provides positive information and guidance. The proposed "HappySoul" chatbot system would act as a virtual friend to help stressed adolescents express their negative feelings and release stress. The technology at the core of such chatbots is natural language processing, recurrent neural networks, and a client-server architecture with an Android GUI. Key applications of chatbots discussed include assisting dementia patients, helping insomniacs, allowing marginalized communities to provide feedback, and making medical diagnoses faster.
This document summarizes a survey paper on chatbots. It discusses how chatbots use natural language processing to understand user queries and generate responses in a conversational manner. The document outlines the methodology of the proposed chatbot, which uses a client-server architecture with an Android application as the front-end and a recurrent neural network on the server to process inputs and generate outputs. The chatbot is intended to help relieve stress and anxiety in adolescents through positive conversations.
IRJET- Virtual Community Using Cloud Technology “Unitalk”IRJET Journal
This document discusses the development of a virtual community system called "Unitalk" using cloud technology. The system would allow a college community to share notices, posts, notes, and ask student queries through a web portal connected to the cloud. It would help the college administrator get information on registered students and faculty. The system also proposes a chatbot for easy access to common questions. The chatbot would allow users to ask questions like they would to a human. The document discusses how cloud computing, community clouds, software as a service, and chatbots could enable this virtual community system.
The document presents a new SHAN algorithm for developing AI chatbots. The SHAN algorithm combines natural language processing (NLP), recurrent neural networks (RNNs), and long short-term memory (LSTM) to interpret user inputs and generate responses. It works by using NLP to understand language, RNNs to analyze sequential data like text, and LSTMs to maintain context over long periods of time. The authors believe this combination will improve chatbot responses compared to existing algorithms that rely on only NLP, RNN, or LSTM individually.
The document discusses AI-based healthcare chatbots and their advantages and limitations. It provides examples of existing chatbots like Youper, Babylon Health, Woebot, and Safedrugbot that provide various healthcare services like monitoring symptoms of depression/anxiety, providing medical consultations, mental health support, and drug safety information. The key advantages of AI healthcare chatbots are that they can provide 24/7 service, personalized care, have low costs, and quick responses. However, limitations include potential inaccuracies in responses, inability to fully diagnose medical conditions, and lack of human empathy in interactions.
Intelligent Library Assistant (ILA) Using Artificial Intelligence and Natural...IRJET Journal
The document describes a proposed intelligent library assistant chatbot called ILA that would use artificial intelligence and natural language processing to help students access university library resources virtually. ILA would allow students to explore library materials like books 24/7 without having to visit the physical library. The chatbot would be built using Python and integrated with Flask so it could be accessed from mobile devices and computers. The goal is to make library resources easily available to students anytime, anywhere through conversational interactions with the intelligent chatbot.
COLLEGE ENQUIRY CHATBOT SYSTEM IN JAVASCRIPTIRJET Journal
This document describes the development of a college inquiry chatbot system using HTML, CSS, and JavaScript. The system provides students with information about admission requirements, courses, tuition fees, and more through a conversational chatbot interface. It was tested with various queries and provided accurate responses. Future work could improve the system with voice recognition and integrating it with the college's database. Overall, the chatbot was well-received by students and chatbots can benefit the education sector by providing timely information to students.
IRJET- College Enquiry Chatbot System(DMCE)IRJET Journal
The document describes a college enquiry chatbot system called DMCE that was developed by students and a professor at Datta Meghe College of Engineering. The chatbot uses artificial intelligence and machine learning to answer students' questions about college-related activities and events. It analyzes user queries through natural language processing and provides responses using an artificial intelligence markup language called AIML. The chatbot aims to reduce the need for students to personally visit the college to get information by providing an automated online service via a mobile application with a graphical user-friendly interface.
IRJET- College Enquiry Chat-Bot using API.AIIRJET Journal
This document describes a college enquiry chatbot developed using API.ai (now known as Dialogflow). The chatbot is an Android application that allows students to get answers to their college-related queries without having to visit the college in person. It analyzes user queries using natural language processing and responds in text and audio format by integrating text-to-speech. The chatbot was built using Dialogflow to match user inputs with predefined intents and return appropriate responses from a database of FAQs. It aims to provide students with a convenient way to stay updated on college activities and information.
This document discusses a study that developed a model for adopting AI-based chatbots at higher education institutions (HEIs) using a hybrid PLS-SEM-neural network approach. The study aimed to identify factors influencing the effectiveness of chatbot adoption in the HEI context by adapting the UTAUT2 model as a reference. A survey of 302 Malaysian HEI postgraduate students found that perceived trust is influenced by interactivity, design, and ethics, while behavioral intention is influenced by perceived trust, performance expectancy, and habit. The findings can help HEIs improve student services and marketing strategies.
The days of simply engaging with a service through a keyboard are over. Users interact with systems more and more by using voice assistants and chatbots. A chatbot is a computer program that can chat with human’s using Artificial Intelligence in messaging platforms. Every time when the chatbot gets input from the user, it saves the input and response, which helps chatbot with little initial knowledge to evolve using gathered responses. With increased responses, precision of the chatbot also gets increase. The ultimate goal of this project is to add a chatbot feature and API. This project will inquire into the advancement of Artificial Intelligence and Machine Learning technology that are being used to improve many services. Most importantly it will look at development of chatbots as a channel for information distribution. The program will select the closest matching response from the matching statement that matches the input utilizing WordNet, it then chooses the response from the known selection of statements for that response. This project aims to implement online chatbot system to assist users who access college website by using tools that expose Artificial Intelligence methods such as Natural Language Processing in allowing users to communicate with college chatbot using natural language input and to train chatbot using appropriate Machine Learning methods in order to be able to generate a response. There are various applications that are incorporating to a human appearance and intends to simulate human dialog, yet in most cases, knowledge of chatbot is stored in a database created by a human expert. Fredrick B Lyngdoh | Raghavendra R. "Chatbot" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49799.pdf Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/49799/chatbot/fredrick-b-lyngdoh
This document describes a college enquiry chatbot that was developed to provide students with a way to get information about their college without having to visit in person. The chatbot uses algorithms to analyze user queries and respond to common questions about things like fees, admission processes, exams, and other college activities. It was created to reduce the time and effort spent by students and parents in obtaining information from the college. The chatbot system includes a database to store question and answer pairs, and an admin interface to update responses for questions not currently in the database.
IRJET- Information Chatbot for an Educational InstituteIRJET Journal
This document describes a proposed chatbot system designed to provide information to students at an educational institute. The chatbot allows students to access information about exams, activities and admissions through natural language conversations. It recognizes queries related to the engineering college and provides answers from its database. The system also helps prospective students predict their chances of admission based on prior year data. The specialized chatbot is intended to make information more easily accessible for students while reducing the workload of the institution.
IRJET- NEEV: An Education Informational ChatbotIRJET Journal
The document describes the development of an educational chatbot named NEEV. NEEV uses machine learning algorithms and natural language processing to provide students with information about trending courses, guidance on course and college selection, and other educational resources. It was created to address the difficulties students face in keeping up with evolving areas of study and making informed decisions. NEEV is implemented as an Android application using the Dialogflow framework and Firebase database. It is trained using intents, entities, and fulfillment to understand user queries and respond appropriately. The goal of NEEV is to make educational information freely available to students anytime through an intelligent conversational interface.
BANKING CHATBOT USING NLP AND MACHINE LEARNING ALGORITHMSIRJET Journal
The document summarizes a research project that aims to develop a banking chatbot using natural language processing (NLP) and machine learning algorithms. The chatbot will be trained on customer query and response data to understand inquiries about account balances, transaction histories, and other banking information. It will use techniques like support vector machine and Naive Bayes classifiers to accurately respond to customer questions in a conversational manner. The researchers conducted a literature review on existing chatbot systems and their limitations in order to inform the methodology for their project, which is designed to provide more personalized responses and handle complex requests through advanced NLP and ML.
Chatbot for chattint getting requirments and analysis all the toolsSongs24
This document outlines a student chatbot project created by three students - Jaganarul, Akil Vamshi, and Jayamalan. The project aims to build an artificial intelligence chatbot using algorithms that can understand student queries and provide answers, simulating a human conversation. The proposed chatbot will use techniques like natural language processing, recurrent neural networks, and Markov models to classify intents and generate responses from a database. The document discusses the architecture, implementation details, and concludes the chatbot could enhance user engagement and automate customer support processes.
IRJET- Review of Chatbot System in Marathi LanguageIRJET Journal
The proposed system aims to develop a question-answering chatbot in Marathi language to interact with users and answer their queries by utilizing pattern matching techniques and data stored in its database, and it will incorporate optical character recognition using the Tesseract library to extract text from documents like PDFs and images. The system architecture includes modules for the chatbot, OCR text recognition, related data storage, and a user interface system.
A New Framework for Information System Development on Instant Messaging for L...TELKOMNIKA JOURNAL
The increasingly inexpensive Internet has spurred the growth of online information system
services in various companies. Almost all services are available in forms on web or mobile applications.
For small companies, this particular system is more difficult to implement as it requires a substantial cost
allocated for hosting, domain and server devices. The solution is to develop a framework for building
information system services through Instant Messaging (IM) such as Telegram, Line or XMPP / Jabber
using the Design Science Research Methodology. This proposed framework has the ability to transform
the existing information system services into chat services with RBAC role, session, validation and natural
interaction using Indonesian-language conversations. The framework that consists of Initiate layers,
business process and communication, memory group and OLTP DBMS will produce low-cost solution for
the development of integrated information systems service.
User stories collection via interactive chatbot to support requirements gathe...TELKOMNIKA JOURNAL
Nowadays, software products have become an essential part of human life. To build software, developers must have a good understanding of the requirements of the software. However, software developers tend to jumpstart system construction without having a clear and detailed understanding of the requirements. The user story concept is one of the practices of the requirements elicitation. This paper aims to present the work conducted to develop an Android chatbot application to support the requirements elicitation activity in software engineering, making the work less time-consuming and structured even for users not accustomed to requirements engineering. The chatbot uses Nazief & Adriani stemming algorithm to pre-process the natural language it receives from the users and artificial mark-up language (AIML) as the knowledge base to process the bot’s responses. A preliminary acceptance test based on the technology acceptance model results in an 83.03% score for users’ behavioral intention to use.
HealthCare ChatBot Using Machine LearningIRJET Journal
This document discusses the development of a healthcare chatbot using machine learning. Key points:
1. The chatbot will use machine learning algorithms to understand patient symptoms from conversations in their native language and provide medical advice and health monitoring.
2. Deep learning models like BERT and SQLOVA will be used to train the chatbot's data collection and natural language processing abilities.
3. The proposed system aims to address issues with current healthcare platforms like long wait times for responses from doctors by providing timely answers to common patient questions through the chatbot.
IRJET- Cloud based Chat Bot using IoT and ArduinoIRJET Journal
This document describes a cloud-based chatbot using the Internet of Things (IoT) and Arduino. The chatbot can communicate with users via Bluetooth using a mobile app. It tells users the current temperature and humidity and their distance from the robot. It also indicates new message notifications using sound and light. Users can have basic conversations with the robot and get information about it. The chatbot uses IoT and Android technologies and works within a wireless local area network (WLAN). It provides a low-cost way for businesses to improve customer service and interactions through an automated chatbot system.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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The document describes a college enquiry chatbot system called DMCE that was developed by students and a professor at Datta Meghe College of Engineering. The chatbot uses artificial intelligence and machine learning to answer students' questions about college-related activities and events. It analyzes user queries through natural language processing and provides responses using an artificial intelligence markup language called AIML. The chatbot aims to reduce the need for students to personally visit the college to get information by providing an automated online service via a mobile application with a graphical user-friendly interface.
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This document describes a college enquiry chatbot developed using API.ai (now known as Dialogflow). The chatbot is an Android application that allows students to get answers to their college-related queries without having to visit the college in person. It analyzes user queries using natural language processing and responds in text and audio format by integrating text-to-speech. The chatbot was built using Dialogflow to match user inputs with predefined intents and return appropriate responses from a database of FAQs. It aims to provide students with a convenient way to stay updated on college activities and information.
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The increasingly inexpensive Internet has spurred the growth of online information system
services in various companies. Almost all services are available in forms on web or mobile applications.
For small companies, this particular system is more difficult to implement as it requires a substantial cost
allocated for hosting, domain and server devices. The solution is to develop a framework for building
information system services through Instant Messaging (IM) such as Telegram, Line or XMPP / Jabber
using the Design Science Research Methodology. This proposed framework has the ability to transform
the existing information system services into chat services with RBAC role, session, validation and natural
interaction using Indonesian-language conversations. The framework that consists of Initiate layers,
business process and communication, memory group and OLTP DBMS will produce low-cost solution for
the development of integrated information systems service.
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2. Deep learning models like BERT and SQLOVA will be used to train the chatbot's data collection and natural language processing abilities.
3. The proposed system aims to address issues with current healthcare platforms like long wait times for responses from doctors by providing timely answers to common patient questions through the chatbot.
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Automated information retrieval and services of graduate school using chatbot system
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 5, October 2022, pp. 5330~5338
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp5330-5338 5330
Journal homepage: http://ijece.iaescore.com
Automated information retrieval and services of graduate
school using chatbot system
Meennapa Rukhiran1
, Paniti Netinant2
1
Department of Computer Science, Faculty of Social Technology, Rajamangala University of Technology Tawan-OK,
Chanthaburi, Thailand
2
Department of Information Technology, College of Digital Innovation Technology, Rangsit University, Pathum Thani, Thailand
Article Info ABSTRACT
Article history:
Received Jul 16, 2021
Revised Mar 22, 2022
Accepted Apr 15, 2022
Automated information retrieval and servicing systems are a priority demand
system in today's businesses to ensure instantaneous customer satisfaction.
The chatbot system is an incredible technological application that enables
communication channels to automatically respond to end-users in real-time
and 24 hours a day. By providing effective services for retrieving
information and electronic documents continuously and automating the
information service system, the coronavirus disease (COVID-19) is
challenging to promote graduate school programs, update news, and retrieve
student information in this era. This article discusses automated information
retrieval and services based on the architecture, components, technology,
and experiment of chatbots. The chatbot system's primary functions are to
deliver the course and contact information, answer frequency questions, and
provide a link menu to apply for our online course platform. We manage the
entire functional process of gathering course information and submitting an
application for a course online. The final results compare end users'
perceptions of chatbot system usage to onsite services to ensure that the
chatbot system can be integrated into the university's information system,
supporting university-related questions and answers. We may expand our
chatbot system's connection to the university's server to provide information
services to students in various informative areas for future research.
Keywords:
Automated system
Chatbot
Electronic document
Information retrieval
System development
This is an open access article under the CC BY-SA license.
Corresponding Author:
Paniti Netinant
Department of Information Technology, College of Digital Innovation Technology, Rangsit University
52/347 Muang-Ake Phaholyothin Road Lak-Hok Muang Pathum Thani 12000, Thailand
Email: paniti.n@rsu.ac.th
1. INTRODUCTION
A chatbot is an automatic informal agent of computer system that can virtually communicate to
users [1]. User input texts begin the basic operations to ask for information (output). Recently, chatbot
systems have been developed that analyze data using natural language processing (NLP) and machine
learning. By analyzing users’ input, a series of intentions supports information pattern matching, and the
chatbot system provides an intent response to the user [2]. The NLP divides into two parts for understanding
input and generation output. The NLP engines provide capabilities that summarize the complex construction
of a chatbot [3] between humans and computers, providing real-time and 24/7 interaction information
services. Kocaleva et al. [4] stated the standard architecture of NLP flow processing after gathering user
inputs: automatic text recognizer, languages and grammar understanding engine, dialog manager, and final
information output. The probability and data-driven models are involved in supporting processing such as
optical character recognition, data retrieval, speech recognition, answering and questioning [5], machine
2. Int J Elec & Comp Eng ISSN: 2088-8708
Automated information retrieval and services of graduate school using … (Meennapa Rukhiran)
5331
translation, and information diagnosing. Ofer et al. [6] studied proteins and languages to build NLP
algorithms and methods. Many human languages should be concerned, such as uniform punctuation and stop
words. The improvement of the better performances of input dialogs, word separation structures such as
words, paragraphs, and sentences are critically concerned.
The advantages of a chatbot drive a command-line interface using user interface (UI) controls to
respond to users quickly [7] and reduce human service costs [8]. The delivery channels of chatbot systems
are developing on web pages, applications, and messaging platforms [1] such as Line, Telegram, Facebook
Messenger, and Google Assistant. Khan [9] has represented an architectural view of a chatbot solution. The
diagram is divided into sequences of layers: the present layer, which communicates with UI components for
end-user performances. The system components are channels, mobile apps, and message platforms. The
above layer is the business layer that can access capabilities of the system, such as user access and dialog
management. The service layer integrates gathering information and matching it with NLP services, data
access services, and external services. The bottom layer is the data layer, which consists of functions and a
data lake.
Examples of chatbot systems are applied in digital platforms, such as frequently answered question
(FAQ) [3], business card management [10], healthcare [11], teaching and learning [12]–[14], environmental
monitoring [15] approaches. Developing a chatbot may require multipart programming skills [14]. Many
complex concerns include unpredictable questions, mapping users' requirements [13], chatbot's repetition,
and reaction [1]. A competency of answer and question systems in real-time is standard of chatbot
applications from different literatures applied in many areas. Dialog conversational agents use to control the
current intents and utilities permitting the conversation [16]. The enhance of human conversational skills can
decrease response latency during interviews. An intelligent chatbot should provide a valuable service for
application or website access and experience [17]. A library chatbot project was developed to support
conversational artificial intelligence [8]. Natural language understanding (NLU) and dialog management are
provided to support data training and validation. Business card management [10] is designed for user
information's dialog. Users can receive an added contact in many messaging platforms, such as Line and
Skype. The mental healthcare system can also use the concept of a chatbot system [11]. The self-care
application of stress diagnosing from any employee's face is challenging to detect user emotions. The
treatment program includes imagery therapy and emotional stabilization (smile faces) to view the light
images to users. The results have found that the chatbot system is helpful for stress reduction. Moreover, the
chatbot system can develop against the psychic consequences of pandemics and human emotion [18]. The
advanced architecture of an adaptive therapeutic chatbot system is implemented using the TensorFlow
framework and Keras API. The limit of data training is the future studies. The virtual assistant for tourist
spots represents via the chatterbot expert system [19]. Inputs from a user can respond to the spot information
and GPS location. The user acceptance test is collected from participants related to their usage experiences.
The online chatbot system can implement supporting the internet of things (IoT) [20]. IoT devices can
develop a line server for environmental information retrieval by connecting IoT sensors [15]. Ease of use for
automated monitoring systems is a primary investigation of this research.
Additionally, chatbot application tools represent an excellent opportunity for helping human
emotion responses [21] and precisely interactive information retrieval [22], [23]. These alternative tools
allow us to use a chatbot as a conversational channel to respond to end-users effectively and promptly.
Numerous tools are available for creating a chatbot system without professional programming skills, such as
DialogFlow, Bot framework, Lex, Azure, and Watson. Google DialogFlow is one of the most popular
frameworks for constructing chatbots. Natural language understanding (NLU) platform leverages machine
learning capabilities to interface with various messaging apps, including Line, Facebook Messenger, and
Telegram. DialogFlow’s features support multi-interactive conversation. Visual flow builder is easy to
develop with time reduction and fewer programming skills. By building a DialogFlow and Line application
chatbot system, DialogFlow API is an integration system to receive intent requests from Line application
called front-end service and detect and send an intent response through DialogFlow services [24]. The
capability of chatbot development allows developers to build a simple system without coding and less
experience based on programming [25].
With more than 40 million active users in Thailand, the statistical analysis of social media
applications has shown that the Line application is the most famous instant messenger application since 2019
[26]. Line application is a free messenger that allows us to communicate with people worldwide, friendlier,
easy to use, and emotional stickers can express users' feelings. This research approach focuses on developing
a Line chatbot system for graduate school services, such as promoting graduate school programs and
electronic services. Due to the widespread distribution of coronavirus disease (COVID-19) [27], the
university must immediately close for social interactions. Many policies are pronounced, such as avoiding
entering the university, restricting the number of staff in the same area, and work from home. Our research
deliberates on the challenge of a closer connection to new students, current students, and alumni [28].
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Students can retrieve information through the Line chatbot system directly. The university can offer new
programs, course details, and promotions for new students and alumni. Moreover, the current students can
get graduate school electronic services such as tracking studying schedules, course enrollment, uploading and
downloading documents (certificate and payment), and using virtual private network (VPN).
To advance the integration of a chatbot system for automated information publication in
universities, this research aims to design automated information retrieval and services of a graduate school
using a chatbot system based on Line and DialogFlow applications and assess the practical usage of the
chatbot system based on technology acceptance model (TAM). This study focusses on improving the
contribution of course information and news to new students, current students, and alumni. The architecture
concerns are founded on significant platform and application development for the Line chatbot system, a
component model for graduate school information public relations. Rapid application development facilitates
the software life cycle development process in this research. The research outcome can conclude and improve
the final development system by evaluating the proposed Line chatbot's performance. The following research
questions are identified to achieve the research purposes:
− RQ1: How can a chatbot system design and develop for a graduate school's automated information
services and retrieval?
− RQ2: How do users perceive the chatbot system's accomplishments compared to the traditional approach?
2. METHOD
2.1. Experimental design
Rapid application development (RAD) is a form of software development in experimental design
that entails information requirement planning, user information design, construction, and cutover [29]. This
research began the software document plan by requirements gathering from university staff. The chatbot's
components and system prototypes are proposed during the user design phase. The implementation phase is
where the chatbot system is built. Forty current and new students were randomly selected for this quantitative
research to evaluate the overall system. Each questionnaire item was scored on a five-point Likert scale
ranging from "strongly agree" to "strongly disagree." Additionally, ten respondents were interviewed to elicit
their perspectives on chatbot usage. For two weeks prior to the system evaluation, selected graduate students
used traditional information and enrollment services while becoming familiar with Line Apps. Both research
methods use questionnaires to elicit user feedback on TAM questions: perceived ease of use and perceived
usefulness [30]–[32]. Numerous perceived metrics are used to evaluate the chatbot system's performance,
including ease of use, text question usage, buttons and links, output accuracy, and rich menu design. The
satisfaction levels are compared for traditional services, such as course enrollment, and chatbot system
services, such as chatbot achievement. A validation expectation was assumed to have a minimum value of
75%, which is the most useful value for the final acceptance benchmark [33]. Moreover, statistical testing of
the chatbot system is examined. Samples were taken independently. The t-test is used to compare the means
of two distinct groups (gender and student status). The analysis of variance (ANOVA) test with two variables
compares the means of three or more independent groups (age and educational level). The outcomes have an
effect on the system's characteristics and usabilities during the cutover phase.
2.2. Information architecture
The information architecture diagram in Figure 1 illustrates the design of automated information
retrieval and service systems based on the DialogFlow application. Numerous software applications are
configured during this research implementation phase, including the Line application, the Line official
account, DialogFlow, a Google Form, and servers. A Line official account must be created to integrate
DialogFlow, the primary provider of natural language processing services, via an access token. Each
response's information can be determined by the user's intent. Utilizing a training phrase, the DialogFlow
matches the user's intent. The training phase section enables the developer of a chatbot to generate similar
user input. The system can match similar expressions and respond to the appropriate data. Google Forms is a
cloud-based database management system used to retrieve information about courses, academic years,
document requirements, and contact information. However, Google Forms requires an authentication token
string called Google API. DialogFlow is configured on the university's web servers to support the online
student management system via a fulfillment API, enabling login and receiving student information. The
Line chatbot system accepts user input and retrieves information via DialogFlow, the university's web
servers, and a Google Form integration service connected to the Line official account. End-users become
chatbot members by scanning a Line ID or QR code. A chatbot system simulates two-way communication
between users. On Line Apps, users can access services by selecting from a rich menu or typing in a
question. The system responds to requests for services made by intent detection. The DialogFlow agent then
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compares a message to parameters associated with training phrases accessible via the Google API. NLP is a
critical component of DialogFlow because it enables the recognition of input messages, entities, and intents.
When the message's parameters and intent match, actionable data is returned to the users via Line Apps.
Additionally, using natural language processing, a set of response dialogs is identified that corresponds to the
user's input. The user's input is a keyword to match the message's intent. A single piece of course information
can serve multiple purposes. Each intent compares the user's input in the text chat box to the actual data. For
instance, a collection of intents for course information includes the terms course, courses, program, enroll,
and so on. A collection of doctoral program intents includes doctoral, doctoral program, doctoral degree, and
so on. The master program's set of objectives includes the terms master, master program, master degree, and
so on. To respond to user requests, the chatbot system uses intent detection. When the user types "course,"
the dialog displays a course selection menu; when the user types "doctoral program," the dialog displays a
doctoral program selection menu.
Figure 1. Information architecture design of a chatbot system for information retrieval and services
2.3. Rich menu prototype component
The system has been designed by a rich menu prototype providing on the welcome Line chatbot
system. The Line Apps contains a rich menu allowing users to access via the menu selection. Figure 2 shows
the Line chatbot components of the rich menu. There are six components:
− Course online component: the component responds to a command by clicking. The service connects to a
graduated online application system. Users can apply for a course online and fill in the information after
they may gather course information from the chatbot system.
− RSU website entry component: the component provides the link to the university website.
− Course information component: the component is connected to the DialogFlow agent using a dialog menu
to display course information. After users click on the menu, the system displays the popup dialog menu
of course information programs (doctoral programs, master degree programs, and short courses). The
system shows course names of available programs in the semester. After the program is selected, the
course details, including the course name, admission period, documentation requirements, an
announcement of candidates, program structure, program fee, and contact number, display.
− FAQ component: the component shows the frequent questions about the graduate school, such as program
fee, English test, location, and map. The intent of user inputs is detected and matched a response message
to the user.
− Contact component: the component displays a sequence of contact information. For instance, a user types
a word (contact) on a text chat box, then the service responses display a popup QR code and contact
numbers of officers.
− Student system component: the component responds to user login via the university's web servers. Many
students' services are available, such as uploading and downloading documents, tracking student's status,
and course enrollment.
End-User
Line Apps Line Official
Account
DialogFlow Google Form University’s web
servers
System
Login
Intents
Response
Bot Setting
Integration
Channel
Access
Token
Google
API
Backend
Services
Line ID
QR Code
Output
Fulfillment
API
Account
Student
…
Fulfillment
API
Input
DialogFlow
Agent
End-user expression
Response
Training
phrase
University
Services
- PHD programs
- Master degree
programs
- Academic year
enrollment
- Contact
- Enrollment
tracking
- News
- VPN
connection
...
Query
Actionable Data
Enrollment
Connection
E-Forms and
Information
NLP
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Figure 2. Chatbot’ components of graduate school services
2.4. User interface responses and programming language
The DialogFlow Agent retrieves data from Google sheets and training phrases via a Google
Webhook. The training phrases are composed of user expressions that correspond to the initial user's input. A
fulfillment service is defined as a data response that is similar to the Line interface's data response for
retrieving information from a user. The fulfillment service is configured to use a Javascript object notation
(JSON)-based programming structure for its user interface. JSON is the preferred data format and interface
format. Figure 3 illustrates two sample JSON programming packages for course dialog and doctoral program
dialog. Users can type a search keyword and select from a chatbot menu using JSON packages programmed
for multiple intents. The JSON package via DialogFlow is limited to programming with strings and numbers.
Thus, HyperText markup language (HTML) code colors can create more friendly, responsive interfaces for
chatbots, such as image background colors.
Figure 3. JSON programming package for DialogFlow
JSON packages of course dialog
JSON packages of doctoral program dialog
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As illustrated in Figure 4, the pilot Line chatbot's interfaces present a rich menu based on the
component design. A user's response messages and a chatbot system represent the course information's
message dialog. A user can begin the first user interaction by selecting the course information menu. When a
user selects the rich menu item "our course master-doctoral degree information," or when a user types a word
such as "courses" or "course," the system responds. The system will then match the user's actual intent and
response to select course majors. Following the user's selection of a major, the system will match the user's
intent and respond to course information. On the other hand, NLP directly associates supporting course
information with user-input texts. For instance, the user can enter text, such as IT, Information Technology,
or Ph.D. The system then matches the similar intent with the training phrase service, and the user will receive
direct access to the course information.
Figure 4. Interfaces of Line chatbot system
3. RESULTS AND DISCUSSION
This research has developed the pilot automation information services using chatbot according to
the design of system components in Figure 2. Our design promises to achieve the first research question for
the information architecture of the automated information services and retrieval using a chatbot system. The
systems use Line API, Dialog Flow, and Google Forms technologies to compose the whole system
components and develop the automated chatbot system.
Through our component design and prototype of the Line chatbot system for graduate school
services, we demonstrated the possibility of users providing feedback on questions via a chat text box and a
button while receiving responses in both English and Thai. The study enrolled forty students and received a
100% response rate. After analyzing participants' demographic data, gender, and (new and continuing)
student status, it was determined that their scores were equal (50%). Ages ranged from 21 to 30 years (50%).
The overwhelming majority of participants held a master's degree (55%). The following section contains the
results of the user study experiment. This study expresses the significant feedback comparisons for the usage
of traditional information services versus the chatbot system application. Numerous respondents confirmed
the findings in Table 1.
Questionnaires were used in this study to determine the level of acceptance for chatbot system
applications. All research questions are based on validated constructs from [33] and have been adapted to the
context of this study. As a result, overall satisfaction with the chatbot system was more significant than that
with traditional information and enrollment services. The average score was 4.17 and 2.95, respectively. The
highest score for the chatbot system was discovered to be that users felt confident in using it (mean=4.40). In
our opinion, the Line application is extremely user-friendly in Thailand [26]. The user may find it simple to
access and utilize the chatbot system as an informal automated tool. Surprisingly, the traditional service
received the highest score (mean=4.35), while the onsite service has a variety of functionalities for graduate
school information. The study result can state categorically that the automated chatbot system cannot take the
place of in-person service. The study could prefer to integrate some critical services, but face-to-face
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consultation should continue. Our study discovered the qualitative research method's system limitation
through participant interview results.
Table 1. The comparison results of users’ feedback
No. Traditional information and enrollment services Chatbot system applications
Questions of users’ feedback Mean SD % Questions of users’ feedback Mean SD %
Q1 How do you feel self-confident
to use the onsite services?
2.30 0.8013 46.00 How do you feel self-confident
to use the chatbot system?
4.40 0.4961 88.00
Q2 How do you feel that onsite
services easy to serve?
3.80 0.6156 76.00 How do you feel that the chatbot
system easy to use?
4.18 0.5943 83.50
Q3 How do you agree to
continuously use onsite services?
2.25 0.6387 45.00 How do you agree to install Line
Apps to use the chatbot system?
4.15 0.6622 83.00
Q4 How does the onsite service
provide various functionalities
for graduate school services?
4.35 0.4894 87.00 How does the chatbot system
provide various functionalities
for graduate school service?
4.08 0.5723 81.50
Q5 How is administrators helpful and
useful to meet your solutions?
3.35 0.7452 67.00 How is the rich menu helpful and
useful to find your solutions?
4.23 0.6597 84.50
Q6 How would you prefer to use
onsite services rather than
automation information services?
2.10 0.7182 42.00 How do you agree buttons more
useful than a text box?
4.10 0.5454 82.00
Q7 How would you voluntarily
recommend people to use onsite
services?
2.50 0.6070 50.00 How would you recommend the
chatbot system to others?
4.08 0.4168 81.50
Total 2.95 0.6593 59.00 Total 4.17 0.5638 83.43
According to the interview findings, users can use the chat text box messages to include Thai polite
words at the end of sentences. The user may add ending words in Thai cultural language to the sentences,
making errors in determining the correct answer. If the chatbot system cannot recognize a user's message, our
solutions propose that the chatbot system responds directly to staff or displays a rich menu of assistance.
Users have stated that the system enables them to conveniently and quickly access courses and information,
retrieve documents, and update information.
The following questions can match technology acceptance model (TAM) core variables by
analyzing user perception. Q4, Q5, and Q6 were used to determine perceived usefulness, and Cronbach alpha
values of 0.791 were calculated. Q1, Q2, and Q3 consist of the perceived ease of use, and Cronbach alpha
values of 0.832 were calculated. All questions had Cronbach alpha values of 0.857. Effectiveness and ease of
use are combined in an attitude toward Q7 to ensure that participants are willing to introduce others who can
benefit from the chatbot system. The overall average scores of the topic rating values for user usability were
4.14 (82.73%) for perceived usefulness and 4.24 for perceived ease of use (84.87%). As a result, all usability
scores (75%) were subjected to expectation analysis, which was consistently related to [30], [34], [35].
The statistical testing results showed ages of years and education level had a significant and positive
influence on their perceived usefulness and ease of use. Both the perceived usefulness and the ease of use
reached the significant level on education only, as shown in all results in Table 2. Master's degree students
are more provisional on the usefulness of the chatbot system than Doctoral's degree students. Our research
results found that the chatbot system should effectively assist graduate school information facilities'
automated information retrieval and services.
Numerous researchers designed and developed chatbot systems that enable automated information
retrieval in various contexts, including health [11], [36] and education [12]–[14]. Our findings indicate that a
chatbot system enables student information centers to function effectively during epidemic situations.
According to our proposed Line chatbot system, NLP is a critical component of machine translation that
enables the system to comprehend user (human) language in accordance with [8], [10], [19]. Additionally,
our user interview research solution suggests minimizing user text input, and we discovered a button-based
response related to [37]. According to the research, using a button-based response can help avoid user
information input errors, particularly in a chatbot system.
Table 2. The characteristics of respondents through chatbot system
Characteristics Perceived Usefulness Perceived Ease of use
F P Cronbanch’s Alpha F P Cronbanch’s Alpha
Gender 0.228 0.410 0.770 -0.100 0.460 0.758
Age in years 0.288 0.000* 0.224 0.000*
Education 0.135 0.000* 0.105 0.000*
Student status -1.652 0.053 -1.337 0.094
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4. CONCLUSION
A chatbot system provides an alternate channel for ensuring that users are satisfied with their
experience with the COVID-19 disease. Automated information retrieval and real-time services to assist
customers in locating the precise information they require. The entire application process for student courses
is critical to the study's success because it enables automated course information retrieval and access to an
online course without using a human workforce. To automate information services and retrieval, our research
design uses natural language processing technology. By implementing a chatbot system based on a
DialogFlow and Line application for graduate school electronic services, this research achieved 83.43 percent
user feedback and 88 percent user confidence. The system outperforms traditional services by 24.43 percent
during the COVID-19 disease. The information architecture of a chatbot system highlights the various
potential service areas, including promotion of graduate school programs, information access, electronic
document retrieval, and information updating. While numerous research chatbot systems have been
proposed, our chatbot system is built on a divide-and-conquer approach to information components in order
to ensure the quality of automated information retrieval and services: i) user response: the chatbot system
enables the delivery of superior information services and documents wherever and whenever they are needed;
ii) flexibility: the extensive menu can easily be modified to accommodate new information requirements
without interfering with system operations; iii) adaptability: changes require fewer system-wide
modifications and more straightforward support for new information technologies; and iv) extensibility: the
chatbot system rapidly adapts to new keywords that correspond to user services. We designed and
implemented the Line chatbot system in this research to facilitate the automated collection of data from
Google Forms. To advance the chatbot service, we will create an online chatbot for the student management
system that will be capable of retrieving all information from our university's servers, including uploading
and downloading electronic documents and notifying graduates. Additionally, the forthcoming study intends
to enhance the interface design to make it more user-friendly via the chatbot system, including the addition of
carousel views, the addition of additional images, and the use of background color.
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BIOGRAPHIES OF AUTHORS
Meennapa Rukhiran received the B.Sc. in Computer Information System from
Burapha University, Thailand, in 2006, the M.S. in Information Technology management from
Rangsit University, Thailand in 2014, and the Ph.D. in Information Technology from Rangsit
University, Thailand in 2020. Currently, she is a head of Department of Computer Science at
the Faculty of Social Technology, Rajamangala University of Technology Tawan-OK,
Thailand. Her research interests include automated software design, system software
framework, technology acceptance models, Internet of Things, information systems with
chatbots, web-based development, mobile development, multidimensional software design and
implementation, data mining, and theory of computation. She can be contacted at email:
meennapa_ru@rmutto.ac.th.
Paniti Netinant earned the B.S. second class honors in Computer Science from
Bangkok University, Thailand, in 1994, and the M.S. and Ph.D. in Computer Science from
Illinois Institute of Technology, the United States of America in 1996, and 2001, respectively.
He is currently an associate dean of the graduate school at Rangsit University in Thailand,
where he is responsible for information technology and international affairs. Automated
software design and development, adaptive software architecture, operating systems, bot
information systems, mobile development, compiler, object-oriented approach, aspect-oriented
approach, python programming, database system, and cloud computing services are among his
research interests. In addition, he is international technical committees and reviewers of ACM
conferences. He can be contacted at email: paniti.n@rsu.ac.th.