scholarly journals Development and substantiation of the structure of a prototype of a self-learning hardware and software complex for technical vision

2021 ◽  
pp. 106-112
Author(s):  
Р.К. Поляков
Keyword(s):  
Machine Learning ◽  
Continuous Production ◽  
Learning Algorithms ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Software Complex ◽  
Industrial Enterprises ◽  
Technical Vision ◽  
The Subject ◽  
Self Learning

В статье представлены результаты исследования, предметом которых являлась разработка и обоснование структуры прототипа программно-аппаратного комплекса технического зрения с использованием алгоритмов машинного обучения для предприятия пищевой промышленности. Ранее проведённые исследования показали, что современные алгоритмы машинного обучения способны эффективно анализировать и классифицировать изображения, как в статическом, так и в динамическом режиме. Исследования показали, что за последние десятилетия этой проблеме занимались как российские, так и зарубежные учёные. Обзор результатов исследований и функционирования пищевых промышленных предприятий, а также консервных комбинатов с точки зрения гарантированного выявления дефектов в производстве, свидетельствуют о целесообразности научных изысканий в данной области и указывают на актуальность дальнейшего совершенствования устройств и автоматизированных систем контроля герметичности консервов в условиях поточного производства. В статье раскрыты особенности разработки структуры прототипа, его функциональное описание, топологическая модель, морфологическая карта и его иерархическое описание, а также представлена структурно-функциональная схема автоматизации конвейерной линии. The article presents the results of a study, the subject of which was the development and substantiation of the structure of a prototype of a hardware-software complex of technical vision using machine learning algorithms for a food industry enterprise. Previous studies have shown that modern machine learning algorithms are able to efficiently analyze and classify images, both in static and dynamic modes. Studies have shown that over the past decades, both Russian and foreign scientists have dealt with this problem. A review of the results of research and the functioning of food industrial enterprises, as well as canning factories from the point of view of guaranteed detection of defects in production, indicate the expediency of scientific research in this area and indicate the relevance of further improvement of devices and automated systems for monitoring the tightness of canned food in continuous production. The article reveals the features of the development of the structure of the prototype, its functional description, topological model, morphological map and its hierarchical description, and also presents the structural and functional diagram of the automation of the conveyor line.

A Machine Learning Approach for One-Stop Learning

2008 ◽  
pp. 333-357 ◽  
Author(s):  
Marco A. Alvarez ◽  
SeungJin Lim
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Internet Users ◽  
Machine Learning Approach ◽  
Supervised Learning Algorithms ◽  
One Stop ◽  
The Subject

Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The user, searching for good educational materials for a technical subject, often spends extra time to filter irrelevant pages or ends up with commercial advertisements. It would be ideal if, given a technical subject by user who is educationally motivated, suitable materials with respect to the given subject are automatically identified by an affordable machine processing of the recommendation set returned by a search engine for the subject. In this scenario, the user can save a significant amount of time in filtering out less useful Web pages, and subsequently the user’s learning goal on the subject can be achieved more efficiently without clicking through numerous pages. This type of convenient learning is called One-Stop Learning (OSL). In this paper, the contributions made by Lim and Ko in (Lim and Ko, 2006) for OSL are redefined and modeled using machine learning algorithms. Four selected supervised learning algorithms: Support Vector Machine (SVM), AdaBoost, Naive Bayes and Neural Networks are evaluated using the same data used in (Lim and Ko, 2006). The results presented in this paper are promising, where the highest precision (98.9%) and overall accuracy (96.7%) obtained by using SVM is superior to the results presented by Lim and Ko. Furthermore, the machine learning approach presented here, demonstrates that the small set of features used to represent each Web page yields a good solution for the OSL problem.

Self-learning and self-organization as tools for speech research

1998 ◽  
Vol 21 (2) ◽  
pp. 262-263
Author(s):  
R. I. Damper
Keyword(s):  
Machine Learning ◽  
Scientific Inquiry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Self Organization ◽  
Knowledge Based ◽  
Automatic Discovery ◽  
Perceptual Invariance ◽  
Speech Research ◽  
Self Learning

Locus equations offer promise for an understanding of at least some aspects of perceptual invariance in speech, but they were discovered almost fortuitously. With the present availability of powerful machine learning algorithms, ignorance-based automatic discovery procedures are starting to supplant knowledge-based scientific inquiry. Principles of self-learning and self-organization are powerful tools for speech research but remain somewhat under-utilized.

A COVERING ALGORITHM IN MACHINE LEARNING FROM THE POINT OF VIEW OF THE SET THEORY

1996 ◽  
Vol 10 (03) ◽  
pp. 239-250
Author(s):  
SYLVA KOČKOVÁ ◽  
IVAN BRUHA
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Theoretical Approach ◽  
Learning Algorithms ◽  
Decision Rules ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Statistical Tools ◽  
The Core

The empirical inductive algorithms that utilize the covering paradigm (such as the AQ x and CN x families of inductive systems) comprise various heuristics and statistical tools so that the core of the covering paradigm remains often quite hidden. The goal of this paper is thus to disclose theoretical underlying principles of covering learning algorithms. By exploiting the set theory, the paper exhibits how the correctness and generality required for decision rules induced by a covering algorithm may be satisfied. The principle differences between a genuine theoretical approach and actual empirical machine learning algorithms are also discussed.

scholarly journals Automatic Classification of COVID-19 using CT-Scan Images

2021 ◽  
Vol 43 ◽  
pp. e55189
Author(s):  
Hatice Catal Reis
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ct Scan ◽  
Mean Squared Error ◽  
Close Contact ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Engineering Sciences

Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F1- score, root mean squared error (RMSE), and receiver operating characteristic- area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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