scholarly journals Virtualization-Based Digitization of a Retail Store: An Enhanced Implementation of Digital Transformation

2021 ◽  
Vol 10 (11) ◽  
pp. 133-136
Author(s):  
B.K Praveen Kumar ◽  
◽  
Dr. K. Santhi Sree ◽  
Keyword(s):  
Machine Learning ◽  
Retail Store ◽  
Digital Transformation ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Seamless Integration ◽  
Digital Revolution ◽  
Host Machine ◽  
New Business ◽  
Almost All

The rise of neoteric technologies like Machine Learning, the Internet of Things, Cloud Services, etc has affected the life of a common man at various levels. Irrespective of the size or domain, almost all companies are now incorporating digitization to various degrees and thus progressing towards a new business model with little or no significance to geographical and physical barriers. This shift from traditional store models to automated entities is referred to as Digital Transformation. It is the simplified way of outlining how digital technologies are transforming and automating business operations across all organizations irrespective of their domain. This digital revolution relies on a whole range of machinery, networks, services, and operations to expand their power of communication, thus ensuring seamless integration with the technologies. At this juncture, there would be many challenges in both technical and non-technical aspects. To facilitate successful automation by resolving those issues, using the concept of virtualization can be very helpful. Virtualization is the process of creating a virtual instance of hardware resources like virtual applications, servers, or storage by logically separating them from the hardware. It enables multiple applications or operations to gain access to the hardware resources/ software resources of the host machine. In a sense, virtualization is always at the center of all this revolution providing a rock-solid foundation. For example, when digitizing an organization, machine learning algorithms are applied to the IoT data in addition to the organizational data. Given the huge size of data, companies adapting to this automation rely on cloud services for data management because of the reliability it provides. This issue is solved using the Edge Computing concept which is an advanced implementation of Virtualization. In this paper, we try to discuss such challenges and try to understand how virtualization can be useful in solving them. This can be exemplified using a hypothetical digitalization in a retail store scenario.

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

scholarly journals A Review on Linear Regression Comprehensive in Machine Learning

2020 ◽  
Vol 1 (4) ◽  
pp. 140-147
Author(s):  
Dastan Maulud ◽  
Adnan M. Abdulazeez
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Explanatory Variables ◽  
Almost All ◽  
Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

scholarly journals Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties

2021 ◽  
Author(s):  
Dhairya Vyas
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Numerical Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Almost All

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.

scholarly journals Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 245-250
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Differential Diagnosis ◽  
Medical Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Care Practice ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Classification Prediction

Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.

scholarly journals Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

2019 ◽  
Vol 33 ◽  
pp. 2727-2735 ◽  
Author(s):  
Andreas Bunte ◽  
Benno Stein ◽  
Oliver Niggemann
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Quantitative Model ◽  
Machine Learning Algorithms ◽  
Seamless Integration ◽  
Model Based ◽  
Novel Approach ◽  
Qualitative Models ◽  
Easy Integration ◽  
Cyber Physical Production Systems

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

Machine Learning Algorithms for Solving Linear Systems of Equations

2021 ◽  
pp. 155-184
Author(s):  
Hilal Arslan ◽  
Fatma Bozyigit
Keyword(s):  
Machine Learning ◽  
Linear Systems ◽  
Industrial Revolution ◽  
Learning Algorithms ◽  
Digital Transformation ◽  
Machine Learning Algorithms ◽  
Suitable Method ◽  
Systems Of Equations ◽  
Fourth Industrial Revolution ◽  
Linear Systems Of Equations

Moving into the fourth industrial revolution and the rapid digital transformation, there is a huge volume of data to be managed in each industry. Industrial simulations commonly produce data including the inputs and outputs of linear systems with several million unknowns. Solving linear systems is one of the fundamental problems in scientific computing, and it requires significant system resources. Determining a suitable method to solve linear systems can be a challenging task, since there is not a certain knowledge about which method is the most suitable for different numerical problems. In this study, the authors demonstrate how machine learning (ML) approach can be used in selecting solvers for linear systems. The chapter includes frequently used ML methods from literature and explain the usage of them to select optimal solvers and preconditioners.

URLCam: Toolkit for malicious URL analysis and modeling

2021 ◽  
pp. 1-15
Author(s):  
Mohammed Ayub ◽  
El-Sayed M. El-Alfy
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Imbalanced Learning ◽  
Almost All

Web technology has become an indispensable part in human’s life for almost all activities. On the other hand, the trend of cyberattacks is on the rise in today’s modern Web-driven world. Therefore, effective countermeasures for the analysis and detection of malicious websites is crucial to combat the rising threats to the cyber world security. In this paper, we systematically reviewed the state-of-the-art techniques and identified a total of about 230 features of malicious websites, which are classified as internal and external features. Moreover, we developed a toolkit for the analysis and modeling of malicious websites. The toolkit has implemented several types of feature extraction methods and machine learning algorithms, which can be used to analyze and compare different approaches to detect malicious URLs. Moreover, the toolkit incorporates several other options such as feature selection and imbalanced learning with flexibility to be extended to include more functionality and generalization capabilities. Moreover, some use cases are demonstrated for different datasets.

scholarly journals Classification of Retail Products: From Probabilistic Ranking to Neural Networks

2021 ◽  
Vol 11 (9) ◽  
pp. 4117
Author(s):  
Manar Mohamed Hafez ◽  
Ana Fernández Vilas ◽  
Rebeca P. Díaz Redondo ◽  
Héctor Olivera Pazó
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Text Processing ◽  
Learning Algorithms ◽  
Digital Transformation ◽  
Machine Learning Algorithms ◽  
Quality Data ◽  
High Dispersion ◽  
Food Retailing ◽  
Quality Of Data

Food retailing is now on an accelerated path to a success penetration into the digital market by new ways of value creation at all stages of the consumer decision process. One of the most important imperatives in this path is the availability of quality data to feed all the process in digital transformation. However, the quality of data are not so obvious if we consider the variety of products and suppliers in the grocery market. Within this context of digital transformation of grocery industry, Midiadia is a Spanish data provider company that works on converting data from the retailers’ products into knowledge with attributes and insights from the product labels that is maintaining quality data in a dynamic market with a high dispersion of products. Currently, they manually categorize products (groceries) according to the information extracted directly (text processing) from the product labelling and packaging. This paper introduces a solution to automatically categorize the constantly changing product catalogue into a 3-level food taxonomy. Our proposal studies three different approaches: a score-based ranking method, traditional machine learning algorithms, and deep neural networks. Thus, we provide four different classifiers that support a more efficient and less error-prone maintenance of groceries catalogues, the main asset of the company. Finally, we have compared the performance of these three alternatives, concluding that traditional machine learning algorithms perform better, but closely followed by the score-based approach.

scholarly journals Comparison of classical machine learning algorithms in the task of handwritten digits classification

2021 ◽  
Vol 21 ◽  
pp. 279-286
Author(s):  
Oleksandr Voloshchenko ◽  
Małgorzata Plechawska-Wójcik
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Classification Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Speed ◽  
Host Machine ◽  
Speed Prediction ◽  
Handwritten Digit

The purpose of this paper is to compare classical machine learning algorithms for handwritten number classification. The following algorithms were chosen for comparison: Logistic Regression, SVM, Decision Tree, Random Forest and k-NN. MNIST handwritten digit database is used in the task of training and testing the above algorithms. The dataset consists of 70,000 images of numbers from 0 to 9. The algorithms are compared considering such criteria as the learning speed, prediction construction speed, host machine load, and classification accuracy. Each algorithm went through the training and testing phases 100 times, with the desired KPIs retained at each iteration. The results were averaged to reach reliable outcomes.

scholarly journals Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties

2021 ◽  
Author(s):  
Dhairya Vyas
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Numerical Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Almost All

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.

Sign in / Sign up

Export Citation Format

Share Document