A Complete Crop Guide Using Machine Learning and AI-Based Chat Bot

2020 ◽

Vol 9 (3) ◽

pp. 2892-2895

Keyword(s):

Machine Learning ◽

Language Processing ◽

Mobile Application ◽

Driving Force ◽

User Behavior ◽

Data Entry ◽

Automated System ◽

Visual Medium ◽

Customer Interaction ◽

Chat Bot

An intelligent application is an instrumental driving force in retention and satisfaction of customers. Consertle would be one of the first banking applications in India that enables users to interact with an intelligent application through a chat bot that is specifically designed to understand, interpret and analyze user behavior so as to provide better and more efficient results. While chat bots itself are a new introduction to the Indian financial system, an intelligent chat bot enables customer to instant and more efficient query resolution.Currently, most banking applications are visual medium which requires customer to proceed through various levels of data entry and selection in order to get the desired response. It may be a query related to one’s account, transactions or information about the bank in general; but the process to get a satisfactory result is a relatively long and tedious process. It is notable that automation in the financial sector is largely primitive even after the outbreak of technologies such as AI, CV, ML, etc.Natural Language Processing and Machine Learning enables the creation of an automated system that takes in input in the form of voice and/or text, processes it and gives an intelligent response to the user which would be aimed at satisfying their current requirements along with the possible, predicted, immediate query that is likely to arise. The key component of application Consertle is the portable mobile application that upholds the chatbot, where NLP based speech to text conversion and interpretation takes place, thus production accurate results and also providing suggestions, by analyzing user behavior which is dependent upon many factors.

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Depression Therapy Using Chatbot

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst061260 ◽

2020 ◽

Vol 6 (12) ◽

pp. 323-327

Author(s):

Arkodeep Biswas and Ajay Kaushik

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Operating System ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Web Application ◽

Text Chat ◽

Communication History ◽

Chat Bot

The objective of this paper is to build a Web Application based on Virtual voice and chat Assistant. The current study focuses on development of voice and text/chat bot specifically. It is specially being built for people who feel depressed and insists them to talk open mindedly which in turn pacifies them. As the name of the application suggests, App: An application to pacify people and make them as happy as a cat would be with his or her mother (the reason why a cat purrs). We will be using Dialog flow for the application design and Machine Learning as a part of Artificial Intelligence for Natural Language Processing (NLP), an easiest way to use Machine Learning libraries. At the back-end we will be using a database to store the communication history between the user and the bot. This application will only work on devices with Web operating system version-5.0 and above.

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HOLMeS: eHealth in the Big Data and Deep Learning Era

10.20944/preprints201811.0339.v1 ◽

2018 ◽

Author(s):

Flora Amato ◽

Stefano Marrone ◽

Vincenzo Moscato ◽

Gabriele Piantadosi ◽

Antonio Picariello ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cluster Computing ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Learning Approaches ◽

Human Machine Interaction ◽

Novel Technologies ◽

Area Under Roc Curve ◽

Chat Bot

Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between users and software, exhibiting a human-like behavior. Results demonstrate the effectiveness of the machine learning algorithms showing 74.65% of Area Under ROC Curve (AUC) when first-level features are used to assess the occurrence of different prevention pathways. When disease-specific features are added, HOLMeS shows 86.78% of AUC achieving a more specific prevention pathway evaluation.

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AI Based Student Bot for Academic Information System using Machine Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952171 ◽

2019 ◽

pp. 590-596

Author(s):

Vijayakumar R ◽

Bhuvaneshwari B ◽

Adith S ◽

Deepika M

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Python Language ◽

Time Period ◽

Long Time ◽

Academic Information ◽

Chat Bot

In General all the institutions like colleges sends their notes and information to students individually. Sometimes the student can�t access it quickly and repetition of data also increased. The realm of this work is to create a Chatbot for the college purpose. Our work reduces the human work to send every details and notes to all departments by email or some other medium. In this work, academic information's /details feed it to the database which will be available for the long time period. The academic information consists of information about placements details, exam time tables, semester notes and upcoming events. A Chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. The chat bot stores the data by key words and when the user entered data is matched with the key it reply the assigned data for it. The Chatbot is created by using python language and Natural language processing. This project make use of the MySQL database to store the information. With the help of natural language processing the bot AI understand the message sent by the user and reply with the matched key value. In this Chatbot the user first need to login by their college roll number and Department. When the valid person asks about the particular information by text the information gets retrieved from the updated database that related to their department. Through this chat box the student can easily access whenever they want and the data need not to be update more than once.

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Research Paper: Artificial Intelligence in Medical: JEEV AI

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37443 ◽

2021 ◽

Vol 9 (VIII) ◽

pp. 611-616

Author(s):

Rohit Katyal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Visual Inspection ◽

Learning Algorithm ◽

Skin Lesions ◽

Health Conditions ◽

Machine Learning Algorithm ◽

Medical Field ◽

Machine Learning Model ◽

Chat Bot

Artificial intelligence is one of the most discussed topics of the present time.The burning question of today about artificial intelligence is “will it be beneficial or dangerous for a human being”. This MODEL analyzes the benefits of artificial intelligence in medicine. It examines how artificial intelligence assists the medical field as well as how patient's health is affected using this popular phenomenon in diagnosing diseases, patient's treatment, reducing errors, and virtually being present with the patients.In this model we have the data of previous patients and have created a machine learning model that tells what is the probability of a person suffering from that skin disease and after running the machine learning algorithm we have make an model as Dermatologists examine skin lesions by visual inspection and dermoscopy, similarly we have used our JEEV AI device to do skin lesion examination based on AI algorithms and machine vision. So we have created a SMS based chat bot through which user can send his feedback on his skin health conditions to our best crowd sourced doctors, Three-Stage Healthcare support system in covid time so people can get their skin problems solved at home by getting best treatment in covid time

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HOLMeS: eHealth in the Big Data and Deep Learning Era

Information ◽

10.3390/info10020034 ◽

2019 ◽

Vol 10 (2) ◽

pp. 34 ◽

Cited By ~ 7

Author(s):

Flora Amato ◽

Stefano Marrone ◽

Vincenzo Moscato ◽

Gabriele Piantadosi ◽

Antonio Picariello ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Deep Learning ◽

Cluster Computing ◽

Learning Algorithm ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Human Machine Interaction ◽

Novel Technologies ◽

Chat Bot

Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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