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Handwritten Character recognition Using machine learning 3

Handwritten Character Recognition Using Machine Learning

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Kumar, Abhishek Srivastava, Kiran Saraswat, Aman Mishra, Ayush Masand, Ishika Fhadil, Fay GCET Dept of EEE UP Gr Noida India Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

Within the domain of pattern recognition, the automated identification of handwritten characters or symbols presents a complex handwriting recognition challenges. In this paper, a novel methodology is presented, which employs machine learning techniques to achieve accurate and efficient handwritten character recognition. The proposed method utilizes artificial neural networks (ANN), specifically convolutional neural networks (CNNs), to train a model capable of accurately recognizing and classifying handwritten characters. Experimental results demonstrate that the proposed approach achieves an impressive accuracy of 98.6% on a standard dataset. The high accuracy achieved demonstrates the effectiveness and feasibility of machine learning algorithms for handwriting recognition tasks and opens up possibilities for various applications in areas such as document analysis, optical character recognition and handwriting-based interfaces. increase. © 2023 IEEE.

关键词： Optical character recognition

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Artificial Intelligence Platform for Low-Cost Robotics 3

Artificial Intelligence Platform for Low-Cost Robotics

引用

3rd international conference on Signal Processing and Information Security, ICSPIS 2020

作者： Afsari, Kiyan Saadeh, Maha Middlesex University Dubai United Arab Emirates

ISBN: (纸本)9781728189987

The rise of Artificial Intelligence (AI) and robotics in the past decade has created various career opportunities in many industries such as robotics, manufacturing and healthcare. Thus, skills such as AI and robotics will play a critical role in the coming years. This project focuses on introduction of an AI platform for Low-cost robotics using MATLAB. The graphical based interface simplifies the task of design and implantation of machine learning functionalities such as object/pattern recognition and classification. The application offers three types of learning methods such as machine learning, Deep learning and Transfer learning. Currently, the end devices are Arduino based and raspberry pi processors. The communication can be wired or wireless. The MATLAB app is tested using an Arduino based Robot (DFRobot Turtle 2WD) and a Raspberry pi-based robot to achieve object recognition and voice recognition. © 2020 IEEE.

关键词： Deep learning

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Facial Expression recognition using machine learning models in FER2013 3

Facial Expression Recognition using Machine Learning models ...

引用

3rd IEEE international conference on Frontiers Technology of Information and Computer, ICFTIC 2021

作者： Luo, Jinyu Xie, Zhuocheng Zhu, Feiyao Zhu, Xiaohu University of Toronto Department of Statistics TorontoON Canada Mathematics and Shanghai University Shanghai China Hefei University of Technology School of Mathematics of HFUT Hefei China Arizona State University Ira A. Fulton Schools of Engineering Tempe United States

ISBN: (纸本)9781665406055

Facial expression recognition aims to classify human expression using the designed algorithm. Recently, the popular FER2013 dataset suffered several bottlenecks for learning a good model. In this paper, we aim to design a data augmentation method to solve the issues in FER2013. Furthermore, we compare four different machine learning methods, including SVM, AlexNet, VGG, and ResNet. The experiments on FER2013 show VGG16 model is the best model for facial expression recognition, and our data augmentation achieves a significant performance promotion in FER2013. Our method provides an in-depth research on image classification, which can be widely applied in most of real-world applications. © 2021 IEEE.

关键词： Face recognition

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Predictions with Confidence in Applications

引用

6th international conference on machine learning and data mining in pattern recognition

作者： Dashevskiy, Mikhail Luo, Zhiyuan Univ London Comp Learning Res Ctr Egham TW20 0EX Surrey England

ISBN: (纸本)9783642030697

Many applications require predictions with confidence. We are interested in Confidence machines which are algorithms that call provide some measure oil how confident they are that their Output is correct. Confidence machines are quite general and there are many algorithms solving the problem of prediction with confidence. As predictors we consider Venn Probability machines and Conformal Predictors. Both of these algorithms rely oil all underlying algorithm for prediction and in this paper We use two simple algorithms, namely the Nearest Neighbours and Nearest Centroid algorithms. Our aim is to provide some guidelines on how to choose the Most Suitable algorithm for a practical application where confidence is needed.

关键词： Prediction with Confidence machine learning Venn Probability machine Conformal Predictors Probabilistic Prediction Confidence machines

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Defensibility-based Classification for Argument mining 14

Defensibility-based Classification for Argument Mining

引用

14th IEEE international conference on data mining (IEEE ICDM)

作者： Kido, Hiroyuki Ohsawa, Yukio Univ Tokyo Sch Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9781479942749

This paper shows a preliminary report regarding classification techniques based on argumentation theory in artificial intelligence. A classification problem is defined on a directed graph, i.e., an argumentation framework, where each node represents an argument and each edge an attack relation between connected arguments. A hypothesis space is defined by all possible argumentation consequences, i.e., extensions. A target argument is classified as justified or overruled, according to the best extensions minimizing errors with respect to training examples, i.e.,tuples of arguments and their correct classes. We give ideal downward and upward refinement operators for calculating hypotheses step by step. Algorithm analysis and performance evaluation are future work.

关键词： data mining directed graphs heuristic programming pattern classification argument mining argumentation consequences argumentation theory artificial intelligence defensibility-based classification directed graph downward refinement operators hypothesis space upward refinement operators conferences Cost function data mining Lattices machine learning algorithms Semantics Training directed graphs data mining Heuristic programming machine learning algorithms Artificial Intelligence Cost functions pattern recognition crystal lattices Semantics Disputes Functional training

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Actitracker: A Smartphone-based Activity recognition System for Improving Health and Well-Being 3

Actitracker: A Smartphone-based Activity Recognition System ...

引用

3rd IEEE/ACM international conference on data Science and Advanced Analytics (DSAA)

作者： Weiss, Gary M. Lockhart, Jeffrey W. Pulickal, Tony T. McHugh, Paul T. Ronan, Isaac H. Timko, Jessica L. Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA

ISBN: (纸本)9781509052066

Actitracker is a smartphone-based activity-monitoring service to help people ensure they receive sufficient activity to maintain proper health. This free service allowed people to set personal activity goals and monitor their progress toward these goals. Actitracker uses machine learning methods to recognize a user's activities. It initially employs a "universal" model generated from labeled activity data from a panel of users, but will automatically shift to a much more accurate personalized model once a user completes a simple training phase. Detailed activity reports and statistics are maintained and provided to the user. Actitracker is a research-based system that began in 2011, before fitness trackers like Fitbit were popular, and was deployed for public use from 2012 until 2015, during which period it had 1,000 registered users. This paper describes the Actitracker system, its use of machine learning, and user experiences. While activity recognition has now entered the mainstream, this paper provides insights into applied activity recognition, something that commercial companies rarely share.

关键词： Activity recognition sensors smartphones data mining health mobile health fitness

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Prediction of Coronary Heart Disease using machine learning: An Experimental Analysis 19

Prediction of Coronary Heart Disease using Machine Learning:...

引用

3rd international conference on Deep learning Technologies (ICDLT)

作者： Gonsalves, Amanda H. Thabtah, Fadi Mohammad, Rami Mustafa A. Singh, Gurpreet Manukau Inst Technol Digital Technol Manukau Stn Rd Auckland New Zealand Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Informat Syst POB 1982 Dammam Saudi Arabia

ISBN: (纸本)9781450371605

The field of medical analysis is often referred to be a valuable source of rich information. Coronary Heart Disease (CHD) is one of the major causes of death all around the world therefore early detection of CHD can help reduce these rates. The challenge lies in the complexity of the data and correlations when it comes to prediction using conventional techniques. The aim of this research is to use the historical medical data to predict CHD using machine learning (ML) technology. The scope of this research is limited to using three supervised learning techniques namely Naive Bayes (NB), Support Vector machine (SVM) and Decision Tree (DT), to discover correlations in CHD data that might help improving the prediction rate. Using the South African Heart Disease dataset of 462 instances, intelligent models are derived by the considered ML techniques using 10-fold cross validation. Empirical results using different performance evaluation measures report that probabilistic models derived by NB are promising in detecting CHD.

关键词： Coronary Heart Disease data mining machine learning Medical Informatics Supervised learning

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Human Action recognition Using Autoencoder 3

Human Action Recognition Using Autoencoder

引用

3rd IEEE international conference on Computer and Communications (ICCC)

作者： Xiao, Qinkun Si, Yang Xian Technol Univ Sch Elect Informat Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781509063529

In this research, we developed a new deep neural network model to identify human action that was composed of an autoencoder and a pattern recognition neural network (PRNN). Our approach was divided into two parts: a system learning stage and an action recognition stage. In the system learning stage, first we secured human body outlines for each image frame, and combined the outlines to build an overlay of binary images to use as training data. Based on deep neural network learning, an autoencoder was trained to extract action features. Next, we used supervised learning to train a PRNN on the obtained features. Last, we combined the autoencoder with the PRNN to build a new deep neural network called the APRNN. Using fine tuning, the APRNN achieved optimal performance. In the action recognition stage of our approach, human action sequences were translated into binary overlay images, and the ARPNN was used to identify the actions. Test results showed our method had better performance than existing approaches.

关键词： action recognition autoencoder binary overlay image deep neural network neural network pattern recognition

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Study of models clustering and its application to ensemble learning

Study of models clustering and its application to ensemble l...

引用

3rd international conference on Natural Computation (ICNC 2007)

作者： Li, Kai Cui, Lijuan HeBei Univ Sch Math & Comp Baoding 071002 Peoples R China Hebei Univ Library Baoding 071002 Peoples R China

ISBN: (纸本)0769528759

Clustering technique is an important tool for data analysis and has a promising prospect in data mining, pattern recognition, etc. Usually, objects in clustering analysis are of vectors, which consist of some features.. They may be represented as points in Euclidean space. However, in some tasks, objects in clustering analysis may be some abstract models other than data points, for example neural networks, decision trees, support vector machines, etc. By defining the extended distance (in real tasks, there are some different definition forms about distance), clustering method is studied for the abstract data objects. Framework of clustering algorithm for objects of models is presented As its application, a method for improving diversity of ensemble learning with neural networks is investigated. The relations between the number of clusters in clustering analysis, the size of ensemble learning, and performance of ensemble learning are studied by experiments.

关键词： Clustering algorithms

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AdaBoost learning in Compressed Space for Visual Object recognition 3

AdaBoost Learning in Compressed Space for Visual Object Reco...

引用

3rd international conference on Information Science and Control Engineering (ICISCE)

作者： Pei, Zhijun Han, Lei Tianjin Univ Technol & Educ Dept Elect Engn Tianjin Peoples R China

ISBN: (纸本)9781509025350

The CS framework is useful for a much wide range of pattern recognition tasks such as visual object classification. It is possible to directly extract features from a small number of random projections without ever reconstructing the signal, which results in compressed learning. As for compressed learning, it is to learn with randomly projected data, compressed data, instead of original data. learning with compressed data saves considerable running time and storage since random projection can effectively reduce the dimension of data. In this paper, works dealing with compressed data concentrate on the adaboost classification case. It has been verified by the experiments that the possibility of AdaBoost algorithm learning in compressed space, and a better test error result has been acquired although only the simple stump is adopted as the weak classifiers.

关键词： adaboost random projection compressed learning compressed sampling classification

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