scholarly journals LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 170
Author(s):  
Rafael Lahoz-Beltra ◽  
Claudia Corona López
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fourier Analysis ◽  
Sentiment Analysis ◽  
Emotional State ◽  
Fundamental Issue ◽  
Multivariate Statistical Methods ◽  
Simulation Experiments ◽  
Multivariate Statistical ◽  
Analysis Techniques

Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in medicine, there is growing interest in the study and simulation of human emotions. However, there is a fundamental issue that is not commonly addressed, and it is the design of protocols for quantitatively evaluating an empathic chatbot by utilizing the analysis of the conversation between the bot and an interlocutor. This study is motivated by the aforementioned scenarios and by the lack of methods for assessing the performance of an empathic bot; thus, a chatbot with the ability to recognize the emotions of its interlocutor is needed. The main novelty of this study is the protocol with which it is possible to analyze the conversations between a chatbot and an interlocutor, regardless of whether the latter is a person or another chatbot. For this purpose, we have designed a minimally viable prototype of an empathic chatbot, named LENNA, for evaluating the usefulness of the proposed protocol. The proposed approach uses Shannon entropy to measure the changes in the emotional state experienced by the chatbot during a conversation, applying sentiment analysis techniques to the analysis of the conversation. Once the simulation experiments were performed, the conversations were analyzed by applying multivariate statistical methods and Fourier analysis. We show the usefulness of the proposed methodology for evaluating the emotional state of LENNA during conversations, which could be useful in the evaluation of other empathic chatbots.

scholarly journals Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1548
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Thin Layers ◽  
Well Logs ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Artificial Neural ◽  
Detailed Interpretation ◽  
Group B

The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.

Multivariate statistical methods for detection of spur gear faults

2014 ◽  
Vol 229 (14) ◽  
pp. 2586-2598 ◽  
Author(s):  
Mustafa Yuzukirmizi ◽  
Hakan Arslan ◽  
Oznur Duman
Keyword(s):  
Spur Gear ◽  
Covariance Matrices ◽  
Three Dimensions ◽  
Maximum Likelihood Estimates ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Mahalanobis Distances ◽  
Analysis Techniques ◽  
Vibration Data ◽  
Mean Vectors

In this study, multivariate statistical techniques are experimented for a spur gear system and a methodology is proposed. The approach is based on the analysis of multidimensional gear vibration data without any feature extradiction and data transformation. The scheme is performed using the vibration signals acquired from a lab-scale single stage gearbox in three dimensions of x, y and z directions. As a groundwork, multi-normality assumptions are established using homogeneity, autocorrelation, and univariate normality tests. The bi-dimensional frequency histograms are also plotted to show bi-normality for experimental gear data. Then, mean vectors and covariance matrices of conditions of good, worn, 1-tooth broken from wheel gear and 1-tooth broken from each pinion and wheel gear are estimated. To compare gear conditions statistically, multivariate analysis of variance is proposed and applied. Moreover, the single metric of Mahalanobis distances are calculated to classify unknown test samples, utilizing the maximum likelihood estimates. The numerical results indicate that multivariate statistical analysis techniques can be applied in early detection of spur gear faults, in which univariate tests fail.

scholarly journals A Culling System for A Recipe using Sentiment Analysis Techniques

2019 ◽  
Vol 8 (5S3) ◽  
pp. 123-127
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Visual Recognition ◽  
Text To Speech ◽  
Web Crawler ◽  
Analysis Techniques ◽  
Bayes Algorithm ◽  
Different Sources

This System helps the user for providing the recipes in which they are particularly interested in a mentioned criteria of ingredients on which the user wants to prepare a recipe. The input given by the user like the ingredient on which he/she wants to prepare a recipe can be of text, speech or visual. The process of providing a list of gathered recipes from different trusted and verified sources is performed with the help of Sentiment Analysis(SA), Watson Text-to-Speech and Watson Visual Recognition(WVR). The recipe extraction from different sources which are required for the user is retrieved with the help of a standard Web Crawler. The tools and technologies used for the proposed system are from Artificial Intelligence(AI), Natural Language Processing(NLP). The system proposed assists the user in providing a list of recipes in a prioritized order based on the optimization process performed by the Naïve Bayes Algorithm(NBA) of Sentiment Analysis. In addition, the displayed results of recipes have been reviewed and rated by different users from different sources.

Application of Factor Analysis and Artificial Neural Network in Electron Spectroscopic Depth Profiling

1996 ◽  
Vol 2 (1) ◽  
pp. 35-41
Author(s):  
Henning Bubert ◽  
Tamara Niebuhr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Depth Profiling ◽  
Data Sets ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Actual Problem ◽  
Artificial Neural ◽  
Electron Spectrometry

Depth profiling has been performed by using Auger electron spectrometry and X-ray photoelectron spectrometry in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and partly by applying artificial neural network in order to determine the compositional layering of different thin-films such as TiNx on Ti, Cr2O3/CrN sandwich layer, and copper oxide on Cu. Both multivariate statistical methods applied to the same data sets lead to results that agree well within statistical deviations provided that the structure of the artificial neural network is constructed appropriate to the actual problem.

scholarly journals Corporate rating forecasting using Artificial Intelligence statistical techniques

2019 ◽  
Vol 16 (2) ◽  
pp. 295-312 ◽  
Author(s):  
Daniel Caridad ◽  
Jana Hančlová ◽  
Hosn el Woujoud Bousselmi ◽  
Lorena Caridad y López del Río
Keyword(s):  
Artificial Intelligence ◽  
Credit Rating ◽  
Financial Health ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Rating Agencies ◽  
Large Samples ◽  
Public Data ◽  
High Level

Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.

Long-term variations in individual particle types in the aerosol of Phoenix, Arizona, as determined using automated electron microprobe analysis and multivariate statistical techniques

1993 ◽  
Vol 51 ◽  
pp. 742-743
Author(s):  
Karen A. Katrinak ◽  
James R. Anderson ◽  
Peter R. Buseck
Keyword(s):  
Electron Microprobe ◽  
Fine Fraction ◽  
Coarse Fraction ◽  
Motor Vehicles ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
X Ray ◽  
Mineral Particles ◽  
Anthropogenic Air Pollutants

Aerosol samples were collected in Phoenix, Arizona on eleven dates between July 1989 and April 1990. Elemental compositions were determined for approximately 1000 particles per sample using an electron microprobe with an energy-dispersive x-ray spectrometer. Fine-fraction samples (particle cut size of 1 to 2 μm) were analyzed for each date; coarse-fraction samples were also analyzed for four of the dates.The data were reduced using multivariate statistical methods. Cluster analysis was first used to define 35 particle types. 81% of all fine-fraction particles and 84% of the coarse-fraction particles were assigned to these types, which include mineral, metal-rich, sulfur-rich, and salt categories. "Zero-count" particles, consisting entirely of elements lighter than Na, constitute an additional category and dominate the fine fraction, reflecting the importance of anthropogenic air pollutants such as those emitted by motor vehicles. Si- and Ca-rich mineral particles dominate the coarse fraction and are also numerous in the fine fraction.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Pollen morphology of some Tamarix (Tamaricaceae) species of Iran

2020 ◽  
Vol 62 (1-2) ◽  
pp. 151-161
Author(s):  
T. Shagholi ◽  
M. Keshavarzi ◽  
M. Sheidai
Keyword(s):  
North Africa ◽  
Pollen Grains ◽  
Ecological Niches ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Qualitative And Quantitative ◽  
Petal Length ◽  
Electron Microscopes ◽  
High Level ◽  
Scanning Electron Microscopes

Tamarix L. (Tamaricaceae) is a halophytic shrub in different parts of Asia and North Africa. Taxonomy and species limitation of Tamarix is very complex. This genus has three sections as Tamarix, Oligadenia, and Polyadenia, which are mainly separated by petal length, the number of stamens, the shape of androecial disk and attachment of filament on the androecial disk. As there was no palynological data on pollen features of Tamarix species of Iran, in the present study 12 qualitative and quantitative pollen features were evaluated to find diagnostic ones. Pollen grains of 8 Tamarix species were collected from nature. Pollen grains were studied without any treatment. Measurements were based on at least 50 pollen grains per specimen. Light and scanning electron microscopes were used. Multivariate statistical methods were applied to clarify the species relationships based on pollen data. All species studied showed monad and tricolpate (except some individuals of T. androssowii). Some Tamarix species show a high level of variability, in response to ecological niches and phenotypic plasticity, which make Tamarix species separation much more difficult. Based on the results of the present study, pollen grains features are not in agreement with previous morphological and molecular genetics about the sectional distinction.

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