k-Nearest Neighbors & Chatbots

Notes:

The k-nearest neighbors (k-NN) algorithm is a non-parametric method for classification and regression that is used in machine learning and data mining. It is a type of instance-based learning, which means that the algorithm does not build a model beforehand and instead makes predictions for new data points based on the similarity of those points to training data.

In the k-NN algorithm, the prediction for a new data point is based on the values of the k nearest neighbors to that point in the training dataset. The value of k is a user-specified parameter that determines the number of neighbors to consider when making a prediction. The prediction is typically based on the average or majority of the values of the nearest neighbors.

The k-NN algorithm is simple to implement and can be used for a variety of tasks, including classification, regression, and density estimation. It is often used in applications such as image recognition, recommendation systems, and anomaly detection. The k-NN algorithm is also known for its simplicity and flexibility, as it does not require a lot of data preparation or assumptions about the underlying data distribution. However, it can be computationally expensive and may not perform well on large datasets.

The k-nearest neighbors (k-NN) algorithm can be used in chatbots as a method for classifying user input and generating appropriate responses. Chatbots are computer programs that are designed to communicate with users through natural language conversations, and they often rely on machine learning algorithms to classify and interpret user input and generate responses.

In a chatbot that uses the k-NN algorithm, the algorithm would be trained on a dataset of example conversations and user inputs. The chatbot would then use the trained k-NN model to classify new user input and identify the most similar conversations in the training dataset. Based on the identified conversations, the chatbot could generate a response using predefined responses or by using natural language generation techniques.

One advantage of using the k-NN algorithm in a chatbot is that it is simple to implement and does not require a lot of data preparation or assumptions about the underlying data distribution. It can also be relatively fast to train and use, which can be important for chatbots that need to respond to user input in real-time. However, the k-NN algorithm may not perform well on large datasets or in situations where there is a lot of noise or variability in the data.

Wikipedia:

k-Nearest Neighbors algorithm

References:

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Page 1. INTELLIGENT TOURIST SYSTEM BY INJAMAM-UL- HAQUE ID: 152-15-445 & MD. ASIF RHAMAN ID: 151-15-341 This Report Presented in Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Computer Science and Engineering …

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Real-World Application of Machine Learning and Deep Learning
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Machine Learning Algorithms
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MSc in Computer Science
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Hands-On Machine Learning with IBM Watson: Leverage IBM Watson to implement machine learning techniques and algorithms using Python
JD Miller – 2019 – books.google.com
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Symptom Based Health Prediction using Data Mining
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How artificial intelligence can improve web development and testing
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Lifelong Machine Learning Methods And Its Application In Multi-Label Classification
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Template-based Auto Social Magazine and Video Creation Service
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Multimodal Analysis and Estimation of Intimate Self-Disclosure
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Bridging the Gap Between Research and Production with CODE
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… The first one is a K-nearest neighbour approach [16], which finds the users who are most similar to the users who clicked on … For example, the business may require that a chatbot to give a meaningful response 95% of the time while the state-of … Peterson, LE: K-nearest neighbor …

Securing Networks Against Anonymous Invaders
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R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
SK Chinnamgari – 2019 – books.google.com
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Assessment of deep learning methodology for self-organizing 5g networks
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RIPPED: Recursive Intent Propagation using Pretrained Embedding Distances
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The roles bots play in Wikipedia
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A Natural Language Processing Approach for Musical Instruments Recommendation System
A Dahale – 2019 – trap.ncirl.ie
… it with a collaborative-filtering tech- nique: matrix factorization and KNN(k nearest neighbor) … This research involves using chatbot that will recommend items considering user’s sentiment … learners and target learners was calculated to form a proximity-based neighborhood …

A Distributed Multi-Agent System (MAS) Application For continuous and Integrated Big Data Processing
A Shashaj, F Mastrorilli, M Morrelli, G Pansini… – … Conference on Ambient …, 2019 – Springer
… processes of Customer/Citizen Operations (CO), where human operators/users need to interact with virtual ‘bots’ (chatbot, conversational AI … MLlib gives us a library to implement Machine Learning methods (in addition to which there’s a K-nearest Neighbours model implemented …

Advanced Methods of Statistical Machine Learning and Applications
A Fanzott – 2019 – netlibrary.aau.at
… used for categorical variables). For more information on k-nearest neighbours consider … using a 15-nearest neighbours approach and the Bayes classifier. The orange and blue … class. The boundaries in the plots show the nearest neighbour decision rule respectively …

Mental Health Monitoring System using Artificial Intelligence: A Review
V Mody, V Mody – 2019 IEEE 5th International Conference for …, 2019 – ieeexplore.ieee.org
… t differ significantly from other classification methods like Quadratic Bayes Normal, K-Nearest Neighbour and Fisher’s … Chatbots are an efficient way of delivering mental health services via a device … data and inferences from precision therapy as an input to the chatbot along with …

A domain-oriented analysis of the impact of machine learning—the case of retailing
F Weber, R Schütte – Big Data and Cognitive Computing, 2019 – mdpi.com
… Lidl offers a chatbot application called “Margot” applying a recommender system in combination with predictive analytics to recommend wine choices to … At the customer contact and service level, chatbots [29] and voice-supported orders (such as Amazon Echo) [50] are relevant …

Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys)
Y Bi, R Bhatia, S Kapoor – 2019 – books.google.com
… Wu, Huan Li, Hengtian Ding, and Lizhuo Zhang A Hierarchical Missing Value Imputation Method by Correlation-Based K-Nearest Neighbors … User Interface Integration in Controlling IoT Devices Applied to Smart Agriculture: Analysis of a Chatbot System Design …

Towards a Performant Multilingual Model Based on Ensemble Learning to Enhance Sentiment Analysis
O Manad, T Menouer, P Darmon – 2019 IEEE/ACS 16th …, 2019 – ieeexplore.ieee.org
… consists of five of most known super- vised classifiers, Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Support … KNN n neighbors (1, 2 ,3, …, 10) 9 C (10?3, 10?2, 10?1, 1, 10) 10 … Also, to preparing an interactive model for chatbots agents …

Human-Robot Team: Effects of Communication in Analyzing Trust
SD Ciocirlan, R Agrigoroaie… – 2019 28th IEEE …, 2019 – ieeexplore.ieee.org
… The chat-bot used was made using the Dialogflow API4 … Considering [18] the following classifiers were chosen: Random Forest Classifier (RFC), K Nearest Neighbors Classifier (KNNC), Linear Support Vector Classification (LSVC), Decision Tree Classifier (DTC) …

Threat is in the Air: Machine Learning for Wireless Network Applications
L Pajola, L Pasa, M Conti – Proceedings of the ACM Workshop on …, 2019 – dl.acm.org
… in order to de- tect possible attacks over the devices [38]; here we find a k-Nearest Neighbors for the … in the real world where malicious data were used; a famous example is Tay, a Microsoft chat bot lunched on … The aim of the chatbot was to answer to questions made by users …

Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems
A Géron – 2019 – books.google.com
Page 1. 2 TenUspodr n Fa d lo te w Edd 2 f o it r io n Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron Page 2. O’REILLY” Honds …

Artificial intelligence for business
R Akerkar – 2019 – Springer
Page 1. SPRINGER BRIEFS IN BUSINESS 123 Rajendra Akerkar Arti cial Intelligence for Business Page 2. SpringerBriefs in Business Page 3. More information about this series at http://www.springer.com/series/8860 Page 4. Rajendra Akerkar Artificial Intelligence for Business …

Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla – arXiv preprint arXiv:1901.03407, 2019 – arxiv.org
Page 1. DEEP LEARNING FOR ANOMALY DETECTION:ASURVEY APREPRINT Raghavendra Chalapathy University of Sydney, Capital Markets Co-operative Research Centre (CMCRC) rcha9612@uni.sydney.edu.au Sanjay …

Intelligent Conversational Agent Assisted Health Coaching System: The CoachAI Approach
A Fadhil – researchgate.net
… Healthcare chatbots currently are either a mix of both user-only (apps that help a … The initial implementation was based on a K-Nearest Neighbors (KNN) algorithm to create user clusters based … case, the user will get and communicate the activities through the chatbot channel by …

Providing appropriate social support to prevention of depression for highly anxious sufferers
F Hao, G Pang, Y Wu, Z Pi, L Xia… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
… features. Islam et al. [14] adopted k-nearest neighbor classification technology to monitor the likelihood of a user’s depression from the opinions, photos, and videos shared by users on the Facebook social platform. Yang et al. [15 …

Big Data, Data Mining, Machine Learning Und Predictive Analytics–Ein Konzeptioneller Überblick (Big Data, Data Mining, Machine Learning and Predictive Analytics …
V Brühl – Machine Learning Und Predictive Analytics–Ein …, 2019 – papers.ssrn.com
… werden über Aktoren in physische (Robotik) oder virtuelle Handlungen (z. B. Chatbots) umgesetzt … eingesetzt werden, sind beispielsweise Entscheidungsbaumverfahren, Support Vector Machines (SVM), Diskriminanzanalyse oder „k-nearest neighbors“-Algorithmen (Abbildung …

Natural Language Processing Fundamentals: Build intelligent applications that can interpret the human language to deliver impactful results
S Ghosh, D Gunning – 2019 – books.google.com
… Classifiers….. 89 K-Nearest Neighbors ….. 90 … In those sections, you’ll gain an understanding of how to apply NLP techniques to answer questions, as can be used for chatbots. By the …

Twitter bot detection & categorization-a comparative study of machine learning methods
J Novotny – 2019 – lup.lub.lu.se
… 30 3.7 k-Nearest-Neighbors … Crawlers and scrapers: bots programmed to index and archive web- sites, eg to make the sites accessible via search engines. • Chatbots: programs engaging in interactive dialogue with humans via natural language …

Letting the computers take over: using AI to solve marketing problems
G Overgoor, M Chica, W Rand… – California …, 2019 – journals.sagepub.com
Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to im…

Fault Prediction in Android Systems through AI
K Bassuday, M Ahmed – 2019 – gupea.ub.gu.se
… Characteristic AUC Area Under the Curve API Application Programming Interface SCM Source Control Management MLP Multi-Layer Perceptron NN Neural Network RBF Radial Basis Function RF Random Forest DT Decision Tree KNN K-Nearest Neighbour SVM Support …

Deep Exemplar Networks for VQA and VQG
BN Patro, VP Namboodiri – arXiv preprint arXiv:1912.09551, 2019 – arxiv.org
… Generating a natural and engaging question is an interesting and challenging task for a smart robot (like chat-bot) … We used the semantic nearest neighbors in a k-nearest neighbor approach using a KD tree data structure to represent the features …

Machine Learning Architecture to Financial Service Organizations
K Palanivel – 2019 – academia.edu
… NLP powers the voice and text-based interface for virtual assistants and chatbots. NLP is increasingly being used to query data sets as well … The ML can be applied to customer support using chatbots, personalized experience and sentiment analysis …

Understanding the role of artificial intelligence in personalized engagement marketing
V Kumar, B Rajan, R Venkatesan… – California …, 2019 – journals.sagepub.com
This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing—an approach to create, communicate, and deliver personalized offerings to customers. It pr…

Semantselt kahekordsete küsimuste kindlakstegemine: Quora juhtumi uurimine
NMH Ansari – 2019 – web-proxy.io
… sorting user- generated contents online. It can be helpful to build automatic chatbots that reply to user queries online and thus reduces the human effort by avoiding to cater to each individual’s queries. User can search for their …

Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis
A Adadi, S Adadi, M Berrada – Advances in bioinformatics, 2019 – hindawi.com
… k-Nearest neighbours (KNN) KNN is supervised algorithm that classifies new data by a … being assigned to the class most common amongst its K nearest neighbors measured by a … AI provides other tools including robotic surgical systems, conversational AI (chatbots), and human …

State-of-the-art and adoption of artificial intelligence in retailing
FD Weber, R Schütte – Digital Policy, Regulation and Governance, 2019 – emerald.com
… This is a chatbot called “Margot,” who helps the customer in the choice of … AI-related applications can certainly be observed, for example, chatbots that interact … SMS (2017), “Opinion detection of public sector financial statements using K-nearest neighbors”, Electrical Engineering …

Identifying and Mitigating Bias in Machine Learning Applications
L Bald – 2019 – scholarsbank.uoregon.edu
… ML systems accidentally; within 24 hours of release, a Twitter chat bot using AI technologies … variance. The k-Nearest Neighbor technique works by taking an original data point, using … dataset. These nearest neighbors would then be predicted to be in the same category as …

Personality assessment from social media data: An ensemble model
S Taghikhani – 2019 – search.proquest.com
… They used Nave Bayes, K-nearest Neighbor, and SVM, and reached 0.54 accuracy by combining all the elements and using K-Nearest Neighbor but the highest accuracy concluded … accuracy level by using SVM, K-Nearest Neighbors, and Nave Bayes. Furthermore …

Next-generation wireless networks meet advanced machine learning applications
R Trestian – 2019 – books.google.com
… Component Analysis-Based Outliers Detection Through Neighborhood Voting in … Chapter 13 CHEERBOT: A Step Ahead of Conventional Chatbot … most popular machine learning techniques are: PCA, k-Nearest Neighbour, Support Vector …

Criminal Identification from Video Scene Using Conditional Generative Adversarial Network
MJ Uddin, M Rahman, M Tayaba – 2019 – dspace.daffodilvarsity.edu.bd
… They have created a predictive model to predict crime efficiently through machine learning using KNN(K-nearest neighborhood) & boosted decision tree algorithm. The authors … output data length is different from one another. Question answering systems, chatbots …

Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques
AY Appiah, X Zhang, BBK Ayawli… – International Journal of …, 2019 – hindawi.com
Page 1. Review Article Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques Albert Yaw Appiah , 1,2 Xinghua Zhang, 1 Ben Beklisi Kwame Ayawli , 1,3 and Frimpong Kyeremeh 1,2 …

Detecting sentences that may be harmful to children with special needs
M Allouch, A Azaria, R Azoulay – 2019 IEEE 31st International …, 2019 – ieeexplore.ieee.org
… Chkroun and Azaria [5], [6] have developed Safebot, a chatbot system that converses with humans … The methods used in the Voting classifier are: Random forest, Extra trees, K nearest neighbors, SVM, Ridge Classifier, Bayesian inference method, and MLP …

How Technologies Will Change the Way Finance Departments Work: A Target Picture and Guidelines for Digital Finance
MA Eßwein – 2019 – duepublico2.uni-due.de
… 80 4.3. K-nearest neighbors ….. 83 4.4 … IS Information systems IT Information technology KNN k-nearest neighbors (algorithm) KPI Key performance indicator LASSO Least absolute shrinkage and selection operator MAR Missing at random …

MLPXSS: An Integrated XSS-Based Attack Detection Scheme in Web Applications Using Multilayer Perceptron Technique
FMM Mokbal, W Dan, A Imran, L Jiuchuan… – IEEE …, 2019 – ieeexplore.ieee.org
… I. INTRODUCTION Web technology has been increased exponentially in daily volume and interactions involving web-based services, such as self-driving finance in web banking, Chatbots /AI assis- tants and recommendation engines in e-commerce, social networking sites …

METAPHOR-A Multiagent Architecture using IoT and Classification Algorithms for Referral Postoperative Patients
A Lima, T Loureiro, M Fernandez… – 2019 IEEE 31st …, 2019 – ieeexplore.ieee.org
… language processing (NLP), as a chatbot, in order to improve the interaction between mobile agent and the user and increase the knowledge base of the system … Decision Tree 76.8 77.7 77.7 0.72 0.03 K Nearest Neighbors (KNN) 87.5 88 88 0.88 0.16 …

Learning to merge-language and vision: A deep evaluation of the encoder, the role of the two modalities, the role of the training task.
R Shekhar – 2019 – eprints-phd.biblio.unitn.it
… 86 vii Page 14. 5.3 Average nearest neighbour overlap between the encoder mul- timodal representations and the ResNet152 and USE embed … tion’s semantic space using the Representation Similarity Analysis and Nearest Neighbour overlap of the object representation …

LEGAL ISSUES AND COMPUTATIONAL MEASURES AT THE CROSS-SECTION OF AI, LAW AND POLICY
S Rathi – 2019 – web2py.iiit.ac.in
… Interactions: With the growing penetration of chat bots and other interactive … Local models only seek to explain a single decision by the neighborhood around the data point it predicted, and can therefore sometimes disregard large parts of the model in their explanation [15] …

Machine learning methods for adaptive test case generation for Android activities
A Cardone – 2019 – indigo.uic.edu
… 4.4.2 K-Nearest Neighbors … 63 VII COMPOSITION OF THE ACTIVITY CLASSIFICATION DATASET 69 VIII RESULTS USING THE K-NEAREST NEIGHBORS ALGORITHM 75 IX RESULTS USING THE DECISION TREE LEARNING ALGORITHM 80 …

Exploring the value of the Bregman Block Average Co-clustering algorithm for missing value imputation in geo-referenced time series
JM Timmermans – 2019 – dspace.library.uu.nl
… 2.2.2 Neighborhood-based algorithms … both discrete and continuous values. The k-Nearest Neighbour algorithm calculates the distance between each … k-number of nearest neighbours in the distance matrix (Hastie et al., 2009, pp. 463- 474). An advantage of the …

NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
A Khandelwal, S Sawant – arXiv preprint arXiv:1911.04211, 2019 – arxiv.org
… from Sentiment Analysis, where the sentiment of a given sentence is dependent on negation, to query response systems like Chatbots, where negation … They used a k- Nearest Neighbors Classifier with features extracted from the sentence and modified to the task at hand …

Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
VK Ayyadevara – 2019 – books.google.com
… Chapter 13: Sequence-to-Sequence Learning Introduction Returning sequences of outputs from a network Building a chatbot Getting ready How to do it… Intent extraction Putting it all together Machine translation Getting ready How to do it …

Query-driven Exploration of Big Graphs
MH Namaki – 2019 – search.proquest.com
… The system thus can provide the second item as an advertisement to the user. Moreover, answering Why-questions in attributed graphs enables chatbots [125] … It has been studied for spatial (range) queries over moving objects [68] and k-nearest neighbors [155] …

Imitation learning from imperfect demonstration
YH Wu, N Charoenphakdee, H Bao… – arXiv preprint arXiv …, 2019 – arxiv.org
… labels. Another related scheme was considered in El-Zahhar and El-Gayar [2010] where they consid- ered soft labels z ? [0,1] as fuzzy inputs and proposed a classification approach based on k-nearest neighbors. This method …

Deep Learning For Dummies
JP Mueller, L Massaron – 2019 – books.google.com
Page 1. Deep Learning by John Paul Mueller and Luca Massaron 0004332690.INDD April 5, 2019 12:10 PM i Trim size: 7.375 in × 9.25 in Page 2. Deep Learning For Dummies® Published by: John Wiley & Sons, Inc., 111 River …

using Python
MPUD Kumar – academia.edu
Page 1. Manaranjan Pradhan U Dinesh Kumar | Manaranjan Pradhan U Dinesh Kumar| Machine Learning using Machine Learning Python using Python Page 2. Machine Learning using Python Page 3. Page 4. Machine Learning using Python Manaranjan Pradhan …

An Educational Programming Environment for Teaching the Principles of Machine Learning using LEGO MINDSTORMS
L Peters – 2019 – researchgate.net
… based on either Python, App Inventor or Scratch. The projects range from games to chatbots to predicting who survived the sinking of the Titanic. Almost all of the … Decision Tree Learning • Support Vector Machine • k-Nearest Neighbours 17 Page 25. 4 Research and Design …

How to overcome modelling and model risk management challenges with artificial intelligence and machine learning
D Mayenberger – Journal of Risk Management in Financial …, 2019 – ingentaconnect.com
… Automated customer servicing: Assign customers to the right service representative by recognising their reason for calling, or chatbots that emulate a … K-nearest neighbour (KNN) is a clustering method that finds groups of similar data points, based on a given distance metric or …

Designing and evaluating recommender systems with the user in the loop
M Jugovac – 2019 – eldorado.tu-dortmund.de
… context, a so-called nearest-neighbor approach [Ama+11] can be used to compare the viewing history of a target user with the viewing histories of other users to find 2.1 Design Philosophies of Recommenders 9 Page 16. a set of users with similar preferences—called neighbors …

Proceedings of the 11th International Workshop” Data analysis methods for software systems”
J Bernatavi?ien? – Vilnius University Proceedings, 2019 – zurnalai.vu.lt
Page 1. 11th International Workshop on DATA ANALYSIS METHODS FOR SOFTWARE SYSTEMS Druskininkai, Lithuania, Hotel “Europa Royale” http://www.mii.lt/DAMSS LITHUANIAN COMPUTER SOCIETY VILNIUS UNIVERSITY …

Predicting User’s Intent from Text using Machine Learning Methods
A Katsalis – 2019 – repository.ihu.edu.gr
… It is a challenge to create a robust model which can deal with problems from other domains. Search engines, spoken language understanding (SLU) systems, chatbots and even robots need models that can predict queries and orders from humans. The understanding …

Artificial Intelligence Basics
T Taulli, M Oni – 2019 – Springer
… in this chapter, we’ll also cover the advanced algo- rithms, such as k-Nearest Neighbor (k-NN … Pitts, on the other hand, grew up in a low-income neighborhood and was even homeless as … type in questions, and ELIZA would provide counsel (this was the first example of a chatbot) …

Reinforcement learning for telescope optimisation
C Blows – 2019 – open.uct.ac.za
… et al. 2014). As an example, we will focus on K-Nearest Neighbours (KNN) … k (Raschka 2015). The k nearest stored data points (data that was used to train the model) is used as a majority classification … like self-driving cars, voice generation, chat-bots and many more. Page 37 …

Practical Machine Learning with Rust: Creating Intelligent Applications in Rust
J Bhattacharjee – 2019 – books.google.com
… 72 2.8.6 Support Vector Machines…..77 2.8.7 KNearest Neighbors…..79 vi Page 7 … 188 5.2 Named Entity Recognition…..201 5.3 Chatbots and Natural Language …

Learn Computer Vision Using OpenCV
S Gollapudi – 2019 – Springer
… Sentiment analysis is usually used to perceive and provide automated help or feedback on how a product such as a book or a movie is doing in the market. • Answering queries or help is used in medicine or retail services, for example in chat bots …

A comparative study of social bot classification techniques
F Örnbratt, J Isaksson, M Willing – 2019 – diva-portal.org
… Many such machine learning algorithms exist; Decision tree, Random forests, Naïve Bayes, K-Nearest Neighbour, Support Vector Machines or k-means … Guilbeault, 2018) have been constructed: ? Web Robots (crawlers) ? Chatbots (natural language based dialog system) …

Challenges in application of machine learning in insurance industry
BR Pavlovi? – Tokovi osiguranja, 2019 – scindeks.ceon.rs
… 3. Allstate22 Osiguravaju?a ku?a „Allstate“ razvila je virtuelnog asistenta, Chat-bot ABIE (skra?eno od engl. Allstate Business Insurance Expert), zasnovanog na metodama mašinskog u?enja, za pomo? svojim agentima u prodaji proizvoda za korporativne klijente …

Group Activity Recognition Using Channel State Information
HA Visserman – 2019 – essay.utwente.nl
… For example, a computer model exists that wrote its own musical [2] and a chatbot has been created that can participate in impro- visational … To achieve this accuracy they made use of the k-nearest neighbors algorithm and a group size of between one and three people [6] …

A deep evolutionary approach to bioinspired classifier optimisation for brain-machine interaction
JJ Bird, DR Faria, LJ Manso, A Ekárt, CD Buckingham – Complexity, 2019 – hindawi.com
… success in classification of epileptic brain activity through generated features as inputs to Naive Bayes, Support Vector Machine, K-Nearest Neighbours, and Linear … affective sentiment based on negative and positive polarities [36] and was used in the application of a chatbot …

Modeling User Behavior on Socio-Technical Systems: Patterns and Anomalies
H Lamba – 2019 – reports-archive.adm.cs.cmu.edu
… 151 9.6.2 Effectiveness on real data . . . . . 152 9.7 Scalability . . . . . 152 9.8 Conclusion . . . . . 153 10 Detecting Chatbots on Livestreaming applications 155 …

Thrilled to Bits: A Review of Emotional Intelligence in AI
T Wiel – 2019 – dspace.library.uu.nl
… voice commands to Siri, receiving a tour from a robotic museum guide or being assisted by online chatbots, people interact … (2017) identified the four most used classifiers in contemporary literature: (1) Quadratic Discriminant Analysis (QDA), (2) k-Nearest Neighbour (KNN), (3 …

Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings
M Vento, G Percannella – 2019 – books.google.com
Page 1. Mario Vento Gennaro Percannella (Eds.) Computer Analysis of Images and Patterns 18th International Conference, CAIP 2019 Salerno, Italy, September 3–5, 2019 Proceedings, Part I Page 2. Lecture Notes in Computer …

Dynamic algorithm selection for machine learning on time series
L Dahlberg – 2019 – diva-portal.org
… Amazon, Facebook and Microsoft. Machine learning has applications such as image/voice recognition, personal phone assistances, chatbots, weather forecasts, game AI such as AlphaGo [2] and future prospects such as human-like robots and self-driving cars. Although these …

Cognition and the Structure of Bias
GM Johnson – 2019 – escholarship.org
Page 1. UCLA UCLA Electronic Theses and Dissertations Title Cognition and the Structure of Bias Permalink https://escholarship.org/uc/item/7hf582vz Author Johnson, Gabbrielle Michelle Publication Date 2019 Peer reviewed|Thesis/dissertation eScholarship.org …

Advances on Broad-Band Wireless Computing, Communication and Applications: Proceedings of the 14th International Conference on Broad-Band Wireless …
L Barolli, P Hellinckx, T Enokido – 2019 – books.google.com
Page 1. Lecture Notes in Networks and Systems 97 Leonard Barolli Peter Hellinckx Tomoya Enokido Editors Advances on Broad-Band Wireless Computing, Communication and Applications Proceedings of the 14th International …

Atomistic modelling of precipitation in Ni-base superalloys
E Schmidt – 2019 – repository.cam.ac.uk
… tions for 10 repeated optimization jobs updating model- and hyperparame- ters displaying the 70 best potentials each. Eemb,Al (?) was obtained fitting the entire Al-Ni database. The blue dots in b) indicate nearest neighbor distancesinequilibriumfccAl …

Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains
TL Ruas – 2019 – deepblue.lib.umich.edu
… LC Lexical Chains LR Logistic Regression IDC International Data Corporation IEEE Institute of Electrical and Electronics Engineers K-NN K-Nearest Neighbors LDA Latent Dirichlet Allocation LSID Latter SynsetID ML Machine Learning MWE Multiword Expressions …

Exploring automatic approaches for sentiment lexicon creation for Norwegian
KK Amundsen – 2019 – duo.uio.no
… creation is to identify a set of seed words, which is used to increase the size of the lexicon, by for example extracting their neighbours from a … Another application of sentiment analysis is in the field of chatbots and other systems used for communication (Turney & Littman, 2003) …

Voice analysis and business applications. A state-of-the-art
F FEROLDI – 2019 – politesi.polimi.it
Page 1. POLITECNICO DI MILANO SCHOOL OF INDUSTRIAL AND INFORMATION ENGINEERING MANAGEMENT ENGINEERING MASTER DEGREE VOICE ANALYSIS AND BUSINESS APPLICATIONS A STATE-OF-THE-ART Fabio Feroldi – 876701 Supervisor:? Prof …

Adversarial Anomaly Detection
R Bhargava – 2019 – hammer.figshare.com
… A data point that is in a low density neighborhood is an outlier whereas a data point that lies in … score based on the ratio of the average densities of a data instance’s ? neighbors and … est neighbor based methods is that they are faster because the test instance is classified …

Investigation of attack strategies on geoprivacy with spatial obfuscation
MLJ Mol – 2019 – dspace.library.uu.nl
Page 1. Master thesis Investigation of attack strategies on geoprivacy with spatial obfuscation Maarten Mol 27-02-2019 Page 2. Abstract This research looks into the current state of spatial obfuscation algo- rithms and investigates attack strategies to circumvent them …

Using Machine Learning and Artificial Intelligence in Production Technology
SR Sapkota – 2019 – theseus.fi
… investers have never seen before. Chatbot, tracking goods, analyzing supply chain agent behaviours, tracking the … learning library that contains boosting, decision tree learning, gradient boosting trees, expectation-maximization algorithm, k-nearest neighbour algorithm, naive …

Keras to Kubernetes
D Rao – 2019 – Wiley Online Library
Page 1. Keras to Kubernetes® The Journey of a Machine Learning Model to Production Dattaraj Jagdish Rao Page 2. Keras to Kubernetes®: The Journey of a Machine Learning Model to Production Published by John Wiley & Sons, Inc …

A QA-pair generation system for the incident tickets of a public ICT Shared Service Center
M Lammers – 2019 – essay.utwente.nl
… A chatbot or virtual agent is a system that can answer questions of users and drill … During a conversation with the user, they explore the neighborhood of the sub-graph in order to … SVM and MaxEntropy appeared most useful over Naïve Bayes and K-nearest neighbors’ algorithm …

CERIAS Tech Report 2019-4 Adversarial Anomaly Detectio
R Bhargava – 2019 – cerias.purdue.edu
… A data point that is in a low density neighborhood is an outlier whereas a data point that lies in … score based on the ratio of the average densities of a data instance’s ? neighbors and … est neighbor based methods is that they are faster because the test instance is classified …

Machine-Assisted Learning in Highly-Interdisciplinary Media Fields: A Multimedia Guide on Modern Art
E Chatzara, R Kotsakis, N Tsipas, L Vrysis… – Education …, 2019 – mdpi.com
Art and technology have always been very tightly intertwined, presenting strong influences on each other. On the other hand, technological evolution led to today’s digital media landscape, elaborating mediated communication tools, thus providing new creative means of expression …

Narrative Text Generation via Latent Embedding from Visual Stories
??? – 2019 – s-space.snu.ac.kr
… based on similar approaches (Shaw and Jebara, 2009). Terada and von Luxburg (Terada and Luxburg, 2014) showed that if a k-nearest neighbor graph is given as local ordinal constraints, we can reconstruct the point set. Also, they showed that statistical consistency is valid …

Knowledge Discovery from Big Text Data
E Park – 2019 – unsworks.unsw.edu.au
… Chawla et al. [CBHK02a] proposed a synthetic over-sampling method, namely SMOTE, which generates additional samples based on their nearest neighbors. There are many variants of SMOTE. Borderline-SMOTE generates new samples only among the …

Cognitive Computing Recipes
A Masood, A Hashmi – Springer
Page 1. Cognitive Computing Recipes Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow — Adnan Masood Adnan Hashmi Foreword by Matt Winkler Page 2. Cognitive Computing Recipes Artificial Intelligence Solutions Using …

Metaphors, Myths and the Stories We Tell: How to Empower a Flourishing AI Enabled Human in the Future of Work by Enabling Whole Brain Thinking
L Shand – 2019 – openresearch.ocadu.ca
… In the greater context, experiments like the Chatbot Tay Tweets demonstrate how algorithms that rely on the current historical vocabulary of human society with its negative undertones … This is also includes how we interact with the digital world with smart speakers and Chatbots …

Unsupervised Learning in Networks, Sequences and Beyond
Y Liu – 2019 – deepblue.lib.umich.edu
… sides. For example, most EM algorithms are generative as they maximize a posterior likelihood while trying to discover the hidden variable; KNN (k-nearest-neighbors) Page 20. 10 is discriminative as it models a conditional probability. Distinction becomes more …

Utility of Artificial Intelligence and Machine Learning in Cybersecurity
FL Loaiza, JD Birdwell, GL Kennedy, D Visser – 2019 – JSTOR
Page 1. INSTITUTE FOR DEFENSE ANALYSES Utility of Artificial Intelligence and Machine Learning in Cybersecurity Francisco L. Loaiza, Project Leader June 2019 Approved for public release; distribution is unlimited. IDA Non-Standard NS D-10694 …

Distributed traffic forecasting using Apache Spark
N Boufidis – 2019 – repository.ihu.edu.gr
… telligence applications like fully or semi-autonomous vehicles, to clever, humanlike chat- bots like Apple’s Siri and Microsoft’s Cortana … Average (ARIMA) or k-Nearest Neighbors model, in the typical and atypical case respec- tively. Sunderrajan et al …

Automatic Detection of Emotions and Distress in Textual Data
E Mohammadi – 2019 – caiac.ca
Page 1. Automatic Detection of Emotions and Distress in Textual Data Elham Mohammadi A Thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements for the Degree of Master of Computer Science at …

BRB based Deep Learning Approach with Application in Sensor Data Streams
S Kabir – 2019 – diva-portal.org
… Evidential Reasoning EU European Union FC Forward Chaining FOPC First Order Predicate Calculus VGG Visual Geometry Group ICT Information and Communication Technology IoT Internet of Things IQA Image Quality Assessment KNN K-Nearest Neighbours LSTM Long …

Surveying the Development of Authorship Identification of Text Messages
A Majma’ah, S Arabia – 2019 – infonomics-society.org
… system performance as a function of text length. They used the k-Nearest Neighbour as classifier; and achieved a 91.5% rate of accuracy in an authorship authentication. However, this shows that the selection of lexical features …

Visual Dialog: Towards Communicative Visual Agents
S Kottur – 2019 – kilthub.cmu.edu
… 8 Page 23. Conversational Modeling and Chatbots. Visual Dialog is the visual analogue of text-based dialog and conversation modeling. While some of the earliest developed chat- bots were rule-based [122], end-to-end learning based approaches are now being actively …

Detecting summary-worthy sentences: the effect of discourse features
M Droog-Hayes, GA Wiggins… – 2019 IEEE 13th …, 2019 – ieeexplore.ieee.org
… 2We also tested ZeroR, Naive Bayes, Sequential Minimal Optimization, K- Nearest Neighbours, the PART rule based algorithm, and the decision tree algorithms REPTree and J48 … 55–60. [21] T. Wolf, “State-of-the-art neural coreference resolution for chatbots,” Jul 2017 …

Reinforcement learning for Dialogue Systems optimization with user adaptation.
N Carrara – 2019 – tel.archives-ouvertes.fr
Page 1. HAL Id: tel-02422691 https://tel.archives-ouvertes.fr/tel-02422691 Submitted on 23 Dec 2019 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …

Blockchain: The Untold Story: From birth of Internet to future of Blockchain
S Mahankali – 2019 – books.google.com
… In the process they were the first to solve the double-spending problem for digital currency. Incidentally, Hal Finney is the neighbour of a person by name ‘SATOSHINAKAMOTO’ who did not have any clue about the technology aspects of Bitcoin, when interrogated …

Towards the Learning, Perception, and Effectiveness of Teachable Conversational Agents
N Chhibber – 2019 – uwspace.uwaterloo.ca
Page 1. Towards the Learning, Perception, and Effectiveness of Teachable Conversational Agents by Nalin Chhibber A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Computer Science …

APPLYING NATURAL LANGUAGE PROCESSING IN TEXT BASED SUPPLIER DISCOVERY
J Heikkilä, J Kanniainen – 2019 – trepo.tuni.fi
… Density-Based Spatial Clustering of Applications with Noise KNN K-Nearest Neighbor MCDM Multi … Preference by Similarity to Ideal Solution t-SNE t-Distributional Stochastic Neighbor … to domains like speech recognition, lexical analysis, text summarization, chatbots, text tagging …

Web Engineering: 19th International Conference, ICWE 2019, Daejeon, South Korea, June 11–14, 2019, Proceedings
M Bakaev, F Frasincar, IY Ko – 2019 – books.google.com
… Keywords: Deep learning 4 Technical debt 1 Introduction Deep learning has shown amazing results in many areas including image classification [10], object detection [12], pose estimation, machine translation [4], chatbots [5], sentiment analysis, recommendation [11] and …

Information extraction from short text messages
A Abbas, J Holmberg – LU-CS-EX 2019-18, 2019 – lup.lub.lu.se
… Subsequent versions have added such things as LTE support, group chat functionality, IP voice calls, geolocation push, chatbots, and more … the techniques achieved the following F1 values: 88.78% for decision tree, 87.62% for naive Bayes and 84.24% for k-nearest neighbors …

An Urdu semantic tagger-lexicons, corpora, methods and tools
J Shafi – 2019 – eprints.lancs.ac.uk
… [65], software engineering [227], empirical language analysis [171], requirements engineering [182], historical semantic analysis via HTST 1.1 [166], to train a Chatbot [218], and several others [23, 214]. Moreover, USAS [180] has been ported previously …

Regulating ai and robotics: Ethical and legal challenges
M Ebers – 2019 – papers.ssrn.com
Page 1. 1 Chapter 2 Regulating AI and Robotics: Ethical and Legal Challenges Martin Ebers* in: Martin Ebers/Susana Navas Navarro (eds.), Algorithms and Law, Cambridge, Cambridge University Press, 2019 (forthcoming). Version: 17 April 2019 …

A Partially Automated Process For the Generation of Believable Human Behaviors
B Parsons – 2019 – scholarcommons.sc.edu
… on the observer. What is believable to one human may not be believable to another. Early chatbots, programs that respond to user input with textual conversation … case-based reasoning. These cases are then weighted automatically using a k-nearest neighbor classifier …