scholarly journals Emotionally Intelligent Chatbot

2021 ◽  
Vol 23 (06) ◽  
pp. 69-76
Author(s):  
Shubham Kokane ◽  
◽  
Shreeyash Khalate ◽  
Shreya Newale ◽  
Sakshi Dubewar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Data Cleaning ◽  
Emotion Classification ◽  
Web Based ◽  
Embedded Knowledge ◽  
Nearest Neighbours ◽  
Emotionally Intelligent ◽  
System Feature

An emotionally intelligent chatbot system aims to make an effective conversation between humans and machines in as natural and interactive a manner as possible. The chatbot agent has the pre-embedded knowledge base to identify the sentences, intents, entities, and context of the input query to be precise for making a valid, predictable decision itself as a self-generated response to answer the query. The present technical project consists of developing an intelligent system for college enquiry purposes using a web-based chatbot agent, through machine learning, query processing, and sentiment and emotion classification system to analyze the sentiment of the visitor towards the college. Emotionally Intelligent College Enquiry Chatbot System is nothing but a chatbot to understand the user queries and respond to them during a conversation. A chatbot can actively help humans to involve in a digital automated conversation with a machine or a system effective. In the following proposed system, feature extraction and data cleaning techniques are applied to the dataset, and classifiers such as multinomial naive Bayes, logistic regression and k nearest neighbours are used to train the model. The classifier with the highest accuracy is further used for the emotion classification of users.

Multi-label Emotion Classification of Tweets Using Machine Learning

2021 ◽  
pp. 705-722
Author(s):  
Simon Islam ◽  
Animesh Chandra Roy ◽  
Mohammad Shamsul Arefin ◽  
Sonia Afroz
Keyword(s):  
Machine Learning ◽  
Emotion Classification

scholarly journals Machine Classification of Transient Images

2014 ◽  
Vol 10 (S306) ◽  
pp. 288-291
Author(s):  
Lise du Buisson ◽  
Navin Sivanandam ◽  
Bruce A. Bassett ◽  
Mathew Smith
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
Transient Detection ◽  
Minimum Error ◽  
Learning Techniques ◽  
Nearest Neighbours ◽  
Transient Imaging

AbstractUsing transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

scholarly journals Machine learning applied to multifrequency data in astrophysics: blazar classification

2020 ◽  
Vol 498 (2) ◽  
pp. 1750-1764
Author(s):  
B Arsioli ◽  
P Dedin
Keyword(s):  
Machine Learning ◽  
Permutation Test ◽  
Spectral Characteristics ◽  
Peak Frequency ◽  
Support Vector ◽  
Spectral Parameters ◽  
Nearest Neighbours ◽  
Svm Algorithm

ABSTRACT The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML to classify blazars according to its synchrotron peak frequency, either lower or higher than 1015 Hz. We select a sample with 4178 blazars labelled as 1279 high synchrotron peak (HSP: $\rm \nu$-peak > 1015 Hz) and 2899 low synchrotron peak (LSP: $\rm \nu$-peak < 1015 Hz). A set of multifrequency features were defined to represent each source that includes spectral slopes ($\alpha _{\nu _1, \nu _2}$) between the radio, infra-red, optical, and X-ray bands, also considering IR colours. We describe the optimization of five ML classification algorithms that classify blazars into LSP or HSP: Random forests (RFs), support vector machine (SVM), K-nearest neighbours (KNN), Gaussian Naive Bayes (GNB), and the Ludwig auto-ML framework. In our particular case, the SVM algorithm had the best performance, reaching 93 per cent of balanced accuracy. A joint-feature permutation test revealed that the spectral slopes alpha-radio-infrared (IR) and alpha-radio-optical are the most relevant for the ML modelling, followed by the IR colours. This work shows that ML algorithms can distinguish multifrequency spectral characteristics and handle the classification of blazars into LSPs and HSPs. It is a hint for the potential use of ML for the autonomous determination of broadband spectral parameters (as the synchrotron ν-peak), or even to search for new blazars in all-sky data bases.

scholarly journals Machine-learning methods in the classification of water bodies

2016 ◽  
Vol 4 (2) ◽  
pp. 34-42 ◽  
Author(s):  
Marek Sołtysiak ◽  
Marcin Blachnik ◽  
Dominika Dąbrowska
Keyword(s):  
Machine Learning ◽  
Water Body ◽  
Urban Areas ◽  
Water Bodies ◽  
Learning Methods ◽  
Amphibian Species ◽  
Data Set ◽  
Machine Learning Methods ◽  
Nearest Neighbours

AbstractAmphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm) have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination), position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities) have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.

scholarly journals Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms

2020 ◽  
Vol 37 (4) ◽  
pp. 611-617
Author(s):  
Ahmet H. Ornek ◽  
Saim Ervural ◽  
Murat Ceylan ◽  
Murat Konak ◽  
Hanifi Soylu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Local Binary Pattern ◽  
Intelligent System ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Harmful Radiation

Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.

scholarly journals Fake Comments Detection with Sentiment Anatomy using Iterative Sequential Minimal Optimization Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 41-46
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Communication Networks ◽  
Main Concern ◽  
Sequential Minimal Optimization ◽  
Classification Methods ◽  
Web Based ◽  
Purchase Rate ◽  
Fundamental Objective

It is significant to create electronicon stream markets,on stream communication networks, peer-to-peer functions, social media providerson stream and convenience customers. In reality, web based amenities are specially designed to overcome the risk of uncertainties & distrust inherent in the main concern of ecommerce applications & to increase the robustness of the system& resistance against fake clients & unbelievers. The aim of the Ecommerce platform is, moreover, to embrace one of the most efficient methods for understanding and evaluating user attempts to expose fraudsters. Or else, the fundamental objective of ecommerce amenities to exploit the profit & purchase rate, will be endangered & deteriorated through fake and ill-intentioned users. Individuals and organizations need to detect fake Comments. With disappointing and hidden features, it is difficult to identify counterfeit Comments simply by looking at a single Comments text. It is also why it is a difficult task to identify falsified Comments.This paper uses the sentiment anatomy (SA) tool for the identification of fake Comments to analyzeon stream film Comments. The texts and the SA system are used for a specific dataset of film Comments. We particularly compared the supervised SVM & SMO machine-learning process with the feeling classification methods of the analyzes in two different cases, without stopping phrases. Measured outcomes display that SMO process compared to the SVM process for both methodes, &it arrives at the maximum precision not only in the classification of text but also for finding duplicate analyses.

scholarly journals Speech-Based Emotion Recognition using Neural Networks and Information Visualization

2020 ◽  
Author(s):  
Jumana Almahmoud ◽  
Kruthika Kikkeri
Keyword(s):  
Machine Learning ◽  
Information Visualization ◽  
Healthcare Professionals ◽  
Health Assessment ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Emotion Classification ◽  
Machine Learning Model ◽  
Speech Data

Emotions recognition is commonly employed for health assessment. However, the typical metric for evaluation in therapy is based on patient-doctor appraisal. This process can fall into the issue of subjectivity, while also requiring healthcare professionals to deal with copious amounts of information. Thus, machine learning algorithms can be a useful tool for the classification of emotions. While several models have been developed in this domain, there is a lack of userfriendly representations of the emotion classification systems for therapy. We propose a tool which enables users to take speech samples and identify a range of emotions (happy, sad, angry, surprised, neutral, clam, disgust, and fear) from audio elements through a machine learning model. The dashboard is designed based on local therapists' needs for intuitive representations of speech data in order to gain insights and informative analyses of their sessions with their patients.

Sign in / Sign up

Export Citation Format

Share Document