ODIN IVR-Interactive Solution for Emergency Calls Handling

2024

This paper explores the transformative potential offered by chatbot technology across industries, with a focus on its impact, challenges, and future directions. The study employs qualitative analysis of chatbot use cases, current applications, and advancements in natural language processing. Results indicate that chatbots are effective in automating repetitive tasks, providing personalized interactions, and improving accessibility to services. However, challenges such as limited natural language understanding, multilingual capabilities, and data dependence remain unsolved. Emerging solutions, including explainable artificial intelligence, context-aware designs, and integration with large language models, promise to address inherent limitations of chatbots, and enhance chatbot reliability and scalability to support innovation, revolutionize numerous industries, and finally become indispensable comprehensive tools in the digital economy.

2020

There can be no doubt that the way of human-computer interaction has changed drastically over the last decade. Dialogue systems (or conversational agents) including voice control interfaces, personal digital assistants and chatbots are examples of industrial applications developed to interact with customers in a human-like way using natural language. With continued growth in messaging applications and increasing demand for machine-based communications, conversational chatbot is likely to play a large part in companies’ customer experience strategy. As systems designed for personalized interaction with users, conversational chatbots are becoming increasingly sophisticated in an attempt to mimic human dialogue. However, building an intelligent chatbot is challenging as it requires spoken language understanding, dialogue context awareness and human-like aspects demonstration. In this paper, we present the results of data-driven chatbot implementation in order to better understand the c...

IARJSET, 2018

A dialog-exchanging system generating a meaningful and empathetic conversation between human and a computer is called a chatbot which processes the natural language input that can be either a speech or a textual form and gives the response in the same language and expression as of the human. Various Natural Language Processing (NLP) techniques using the python library, Natural Language Tool Kit (NLTK) these inputs of speech from users can be processed and intelligent responses from such system engine can be gained to build up a human-like interaction. This paper is a survey on chatbots which have made a significant impact in the previous years about their various development techniques from selected papers which are the witness of remarkable improvements of these chatbots. Also some idea about the advanced scale chatbots such as google now and descendants of Microsoft chatbot is also discussed regarding their popularity and aids in different areas of human life. The paper also discusses the usability or performances of different chatbots along with differences and similarities in techniques through Turing test and Loebner prize.

Machine Learning with Applications, 2020

This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.

2020

Abstract: A chatbot is AI computer software that can act as a conversation through textual or auditory methods. The core of chatbots analyses a customer’s data using the artificial intelligence which integrates the response with them. Different tasks can be replaced with AI-powered bots as they are much more powerful—and are capable of performing multiple tasks at once. Machine Learning techniques are basically used in the process of understanding the input that we get from the user and replying to the user. Natural language processing allows a bot to have a conversation as naturally as possible. The ideal interaction between user and chatbot is a balanced mix of Innovative technology and human Intervention.

Chatbots are applications that simulate human chat with artificial intelligence (AI). It is intended to be the ultimate virtual assistant, serving as a source of entertainment while assisting users with duties ranging from answering inquiries to providing driving directions. converting the thermostat into a smart home, playing one's favorite songs, etc. Chatbot is becoming more common in companies because it can minimize customer service costs and accommodate many users simultaneously. Chatbots, on the other hand, must be as efficient as possible when performing certain tasks. In order to resolve this problem, this report provides a chatbot interface that provides an effective and reliable response to any question based on a FAQ dataset using the JavaScript json and a dedicated backend. Template based and general questions such as welcome/greetings and general questions will be answered using json and other service-based questions, Backend is used to offer responses at any moment in order to satisfy customers. Any university may use this chatbot to address questions to interested students in an immersive way.

Proceedings of the Future Technologies Conference (FTC) 2018, 2018

Chatbot (abbreviated Chatting Robot) is a computer system that allows human to interact with computer using Natural Human Language. This paper intents to present a technical review of ve modern chatbot systems namely DeepProbe (

IRJET, 2020

A dialog-exchanging system generating a meaningful and empathetic conversation between human and a computer is called a chatbot which processes the natural language input that can be either a speech or a textual form and gives the response in the same language and expression as of the human. Various Natural Language Processing (NLP) techniques using the python library, Natural Language Tool Kit (NLTK) these inputs of speech from users can be processed and intelligent responses from such system engine can be gained to build up a human-like interaction. The chatbot is built with a limited dictionary but uses a great algorithm to imitate a real person. The chatbot can be used to find you an amusing partner and help you in bad times. If the users, ask the question to the chatbot it will give suggestion based on that question. The answers are appropriate to the user's queries, if the user find his answer to be invalid, it will give default message and will notify to admin. The algorithm used here has a intelligently built in logic and is designed to better chat with users.

IAEME PUBLICATION, 2024

This technical article examines the implementation and evolution of AI/ML-powered chatbots in enterprise telecommunications environments, focusing on deployments at Vodafone and Verizon. Through detailed analysis of system architectures, performance metrics, and business outcomes, we demonstrate how advanced NLP models and machine learning algorithms have transformed customer service operations and incident management. The article reveals significant improvements, including a 42% reduction in customer interaction costs, 78% first-contact resolution rate, and 99.99% system uptime. Vodafone's Tobi virtual assistant, processing 2.5 million monthly interactions, achieved a cost reduction of 67% per interaction (€0.15 versus industry standard €0.45) while maintaining a CSAT score of 4.2 out of 5. Verizon's incident management system demonstrated a 35% reduction in Mean Time to Resolution and 78.7% decrease in false alerts, with classification accuracy improving from 65% to 87%. The article also highlights technological advancements in multi-language support across 12 major languages with 95.8% translation accuracy, predictive maintenance capabilities reducing downtime by 55%, and automated incident resolution. Future developments include GPT-4 integration with enhanced context handling up to 64K tokens, AR/VR technical support integration promising 40% faster issue resolution, and advanced predictive network maintenance systems projected to save €2.8M annually. These implementations establish new benchmarks for enterprise AI systems while providing a framework for future telecommunications service delivery enhancement.

International Journal of Advanced Trends in Computer Science and Engineering , 2021

This study aimed to develop an Interactive Chatbot for Customer Service with Voice Recognition for St. Paul University Philippines students, employees, parents, visitors, and stakeholders. It aimed to provide real time answers to inquiries from the concerned participants. The researcher employed a qualitative approach using descriptive research design and systems development in collecting, analyzing the data and the design and development of the system. The researchers concludes that the chatbot is a real time mobile application for the university that provides frequently asked questions to all Paulinian users anytime and anywhere. Moreover, it will also provide opportunity to have access to the appointed Head of Marketing and Promotions during office hours.