Artificial Intelligence Based University Chatbot using Machine Learning

2021 ◽  
Vol 4 (2) ◽  
pp. 108-112
Author(s):  
Zimal Mehboob Khan ◽  
Hafeez-ur Rehman ◽  
Maria Maqsood ◽  
Khalid Mehmood
Keyword(s):  
Machine Learning ◽  
Office Visit ◽  
Machine Learning Algorithms ◽  
Long Distance ◽  
Customer Support ◽  
The People ◽  
Customer Care ◽  
Admission Office ◽  
The Cost ◽  
The University

Today the customer support needs 27/7 and the people faced when some issues related to organization they contact the support center and the support centers are people they are sitting to serve the customer care and they need to full fill the requirement of the customer need and demand this should increase the cost of the people setting by day and night for services and organization need to reduce this cost and desire to automate this process. The proposed study will be the replacement for this used in university chatbot for help and support for peoples which are visiting the office this will also decrease the traffic of the admission office some universities are so far away from the city area and people travel a long distance to reach the university for a very ordinary work this will reduce this and allow the office staff to focus on the other productive activities. The University chatbot (UCB) we build the application using the machine learning models that successfully build the model using the machine learning algorithms, DT, RF, SVM, that can response under the domain of the university related help and support and guide the proper way for the use of university website and referee them the proper links and contact to reduce the traffic from physical office visit of the people. The results show that the Random Forest is outperform for the proposed study.

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

scholarly journals A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization

2021 ◽  
Author(s):  
Peter Liptak ◽  
Peter Banovcin ◽  
Robert Rosolanka ◽  
Michal Prokopic ◽  
Ivan Kocan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Random Forest ◽  
Gastrointestinal Symptoms ◽  
Machine Learning Algorithms ◽  
Important Predictor ◽  
University Hospital ◽  
Home Based ◽  
Severity Of The Disease ◽  
The University

Background and aim: COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak several machine learning algorithms have been implemented to assess new diagnostic and therapeutic methods for this disease. Aim of this study is to assess gas-trointestinal and liver related predictive factors for SARS-CoV-2 associated risk of hospitalization. Methods: Data collection was based on questionnaire from the COVID-19 outpatient test center and from the emergency department at the University hospital in combination with data from inter-nal hospital information system and from the mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls to three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). Results: Total of 710 patients were enrolled in the study. Presence of diarrhea and nausea was significantly higher in emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on random forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance although much lower than AST. Conclusion: SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using machine learning random forest algo-rithm, we have identified elevated AST as the most important predictor for COVID-19 related hos-pitalizations.

scholarly journals Unfolding Algorithms

2019 ◽  
Vol 32 (4) ◽  
pp. 119-136
Author(s):  
Bilel Benbouzid
Keyword(s):  
Machine Learning ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Moral Vision ◽  
Predictive Policing ◽  
Method Of Calculation ◽  
Predictive Algorithms ◽  
Cautious Interpretation ◽  
The University ◽  
Two Sides

Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in order to try to understand the predictions of the company’s algorithm. Using the same method of calculation on the same data, the seismologist arrived at a different, more cautious interpretation of the algorithm's capacity to predict crime. How were these predictive analyses formed on the two sides of the Atlantic? How do predictive algorithms come to exist differently in these different contexts? How and why is it that predictive machines can foretell a crime that is yet to be committed in a California laboratory, and yet no longer work in another laboratory in Chambéry?  In answering these questions, I found that machine learning researchers have a moral vision of their own activity that can be understood by analyzing the values and material consequences involved in the evaluation tests that are used to create the predictions.

scholarly journals Predicting Bank Failures with Machine Learning Algorithms: A Comparison of Boosting and Cost-Sensitive Models

2021 ◽  
Vol 3 (2) ◽  
pp. 43-50
Author(s):  
Safa SEN ◽  
Sara Almeida de Figueiredo
Keyword(s):  
Machine Learning ◽  
Early Warning Systems ◽  
Machine Learning Algorithms ◽  
Bank Failures ◽  
Warning Systems ◽  
Economic Prosperity ◽  
Software Defect ◽  
Turbulent Times ◽  
The Cost ◽  
Prediction Capability

Predicting bank failures has been an essential subject in literature due to the significance of the banks for the economic prosperity of a country. Acting as an intermediary player of the economy, banks channel funds between creditors and debtors. In that matter, banks are considered the backbone of the economies; hence, it is important to create early warning systems that identify insolvent banks from solvent ones. Thus, Insolvent banks can apply for assistance and avoid bankruptcy in financially turbulent times. In this paper, we will focus on two different machine learning disciplines: Boosting and Cost-Sensitive methods to predict bank failures. Boosting methods are widely used in the literature due to their better prediction capability. However, Cost-Sensitive Forest is relatively new to the literature and originally invented to solve imbalance problems in software defect detection. Our results show that comparing to the boosting methods, Cost-Sensitive Forest particularly classifies failed banks more accurately. Thus, we suggest using the Cost-Sensitive Forest when predicting bank failures with imbalanced datasets.

scholarly journals Values and Consequences in Predictive Machine Evaluation. A Sociology of Predictive Policing

2018 ◽  
Author(s):  
Bilel Benbouzid
Keyword(s):  
Machine Learning ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Moral Vision ◽  
Predictive Policing ◽  
Method Of Calculation ◽  
Predictive Algorithms ◽  
Cautious Interpretation ◽  
The University ◽  
Two Sides

Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in order to try to understand the predictions of the company’s algorithm. Using the same method of calculation on the same data, the seismologist arrived at a different, more cautious interpretation of the algorithm's capacity to predict crime. How were these predictive analyses formed on the two sides of the Atlantic? How do predictive algorithms come to exist differently in these different contexts? How and why is it that predictive machines can foretell a crime that is yet to be committed in a California laboratory, and yet no longer work in another laboratory in Chambéry? In answering these questions, I found that machine learning researchers have a moral vision of their own activity that can be understood by analyzing the values and material consequences involved in the evaluation tests that are used to create the predictions.

scholarly journals Aspect Based Sentiment Analysis for E-Commerce Websites with Visualization through Machine Learning Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 1020-1024
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Product Reviews ◽  
Customer Expectations ◽  
The People ◽  
Pros And Cons ◽  
Rapid Pace ◽  
Bayes Algorithm

E-commerce is evolving at a rapid pace that new doors have been opened for the people to express their emotions towards the products. The opinions of the customers plays an important role in the e-commerce sites. It is practically a tedious job to analyze the opinions of users and form a pros and cons for respective products. This paper develops a solution through machine learning algorithms by pre-processing the reviews based on features of mobile products. This mainly focus on aspect level of opinions which uses SentiWordNet, Natural Language Processing and aggregate scores for analyzing the text reviews. The experimental results provide the visual representation of products which provide better understanding of product reviews rather than reading through long textual reviews which includes strengths and weakness of the product using Naive Bayes algorithm. This results also helps the e-commerce vendors to overcome the weakness of the products and meet the customer expectations.

scholarly journals Evaluating Human versus Machine Learning Performance in a LegalTech Problem

2021 ◽  
Vol 12 (1) ◽  
pp. 297
Author(s):  
Tamás Orosz ◽  
Renátó Vági ◽  
Gergely Márk Csányi ◽  
Dániel Nagy ◽  
István Üveges ◽  
...  
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Added Value ◽  
Learning Performance ◽  
Production Environment ◽  
Legal Domain ◽  
The Cost

Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process.

scholarly journals Fenix: A Semantic Search Engine Based on an Ontology and a Model Trained with Machine Learning to Support Research

2021 ◽  
Author(s):  
Felipe Cujar-Rosero ◽  
David Santiago Pinchao Ortiz ◽  
Silvio Ricardo Timaran Pereira ◽  
Jimmy Mateo Guerrero Restrepo
Keyword(s):  
Machine Learning ◽  
Virtual Environment ◽  
Search Engine ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Semantic Search ◽  
Research Projects ◽  
Machine Learning Model ◽  
The University ◽  
Semantic Search Engine

This paper presents the final results of the research project that aimed to build a Semantic Search Engine that uses an Ontology and a model trained with Machine Learning to support the semantic search of research projects of the System of Research from the University of Nariño. For the construction of FENIX, as this Engine is called, it was used a methodology that includes the stages: appropriation of knowledge, installation and configuration of tools, libraries and technologies, collection, extraction and preparation of research projects, design and development of the Semantic Search Engine. The main results of the work were three: a) the complete construction of the Ontology with classes, object properties (predicates), data properties (attributes) and individuals (instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the successful training of the model for which Machine Learning algorithms and specifically Natural Language Processing algorithms were used such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also done in Jupyter Notebook with Python within the virtual environment of anaconda and with Elasticsearch; and c) the creation of FENIX managing and unifying the queries for the Ontology and for the Machine Learning model. The tests showed that FENIX was successful in all the searches that were carried out because its results were satisfactory.

scholarly journals Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques

2020 ◽  
Vol 9 (12) ◽  
pp. 19-22
Keyword(s):  
Machine Learning ◽  
Soil Type ◽  
Soil Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
The People ◽  
Learning Techniques ◽  
Crop Type

The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type.

scholarly journals Public Trolling Detection on Twitter Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3097-3100
Author(s):  
Miss. Pooja Dilip Dhotre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
The People ◽  
The World ◽  
Added Benefit ◽  
Current Popularity

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.

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