Introduction to Machine Learning and Its Implementation Techniques

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Learning Techniques ◽

Implementation Techniques ◽

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Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.

What is Machine Learning? A Primer for the Epidemiologist

American Journal of Epidemiology ◽

10.1093/aje/kwz189 ◽

2019 ◽

Cited By ~ 6

Author(s):

Qifang Bi ◽

Katherine E Goodman ◽

Joshua Kaminsky ◽

Justin Lessler

Keyword(s):

Machine Learning ◽

Big Data ◽

Computer Science ◽

Research Methods ◽

Learning Algorithms ◽

Epidemiologic Research ◽

Learning Techniques ◽

Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

REVIEW OF MACHINE LEARNING TECHNIQUES FOR VOLUMINOUS INFORMATION MANAGEMENT

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2019.2.005 ◽

2019 ◽

Vol 2019 (2) ◽

pp. 103-112

Author(s):

Dr. Pasumpon pandian

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Learning Algorithms ◽

Big Data Analytics ◽

Learning Techniques ◽

Technological Growth ◽

Rapid Pace

The recent technological growth at a rapid pace has paved way for the big data that denotes to the exponential growth of the information’s. The big data analytics are the trending concepts that have emerged as the promising technology that offers more enhanced perceptions from the huge set of the data that have been produced from the diverse areas. The review in the paper proceeds with the methods of the big-data-analytics and the machine-learning in handling, the huge set of data flow. The overview of the utilization of the machine-learning algorithms in the analytics of high voluminous data would provide with the deeper and the richer analysis of the huge set of information gathered to extract the valuable and turn it into actionable information’s. The paper is to review the part of machine-learning algorithms in the analytics of high voluminous data

Artificial Intelligence in Business Simulation Analysis

European Journal of Technology ◽

10.47672/ejt.629 ◽

2020 ◽

Vol 4 (1) ◽

pp. 16-30

Author(s):

Mehreen Arshad

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Systematic Reviews ◽

Simulation Analysis ◽

Learning Algorithms ◽

Business Simulation ◽

Simulation Process ◽

Learning Techniques

Purpose: Research on business simulation and machine learning has attracted immense interest in the last few years. The aim of this study was to provide a comprehensive view of machine learning in business simulation. To review the use of artificial intelligence in business simulation analysis. A review of the literature, however, shows little systematic reviews on the application of machine learning techniques to business simulation, yet systematic reviews have gained prominence in the academic jargon. Methodology: Thus, this study does reviews systematically a total of 123 shortlisted articles that focus on the machine learning techniques in the business simulation process. Findings: There are immense algorithms of machine learning which can be used in a business simulation, although this study was able to review ten machine learning algorithms in the business simulation process. As a whole, the machine learning algorithms have been deployed to yield lead-time production in the industry. In inventory and storage, machine learning has been applied to improve efficiency in identifying inventory patterns that would have never been revealed and thus saves on costs. Future direction also discussed.

Advances in Computer and Electrical Engineering - Challenges and Applications for Implementing Machine Learning in Computer Vision ◽

Artificial Intelligence and Machine Learning Algorithms

10.4018/978-1-7998-0182-5.ch008 ◽

2020 ◽

pp. 188-219

Author(s):

Amit Kumar Tyagi ◽

Poonam Chahal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computer Vision ◽

Big Data ◽

Deep Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

Handbook of Research on Applications and Implementations of Machine Learning Techniques - Advances in Computational Intelligence and Robotics ◽

Application of Machine Learning Techniques in Healthcare

10.4018/978-1-5225-9902-9.ch015 ◽

2020 ◽

pp. 289-304 ◽

Cited By ~ 1

Author(s):

Sathya D. ◽

Sudha V. ◽

Jagadeesan D.

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Social Media ◽

Learning Algorithms ◽

Or Efficiency ◽

Game Development ◽

Learning Techniques ◽

Media Services

Machine learning is an approach of artificial intelligence (AI) where the machine can automatically learn and improve its performance on experience. It is not explicitly programmed; the data is fed into the generic algorithm and it builds logic based on the data provided. Traditional algorithms have to define new rules or massive rules when the pattern varies or the number of patterns increases, which reduces the accuracy or efficiency of the algorithms. But the machine learning algorithms learn new input patterns capable of handling complex situations while maintaining accuracy and efficiency. Due to its effectual benefits, machine learning algorithms are used in various domains like healthcare, industries, travel, game development, social media services, robotics, and surveillance and information security. In this chapter, the application of machine learning technique in healthcare is discussed in detail.

Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18157890 ◽

2021 ◽

Vol 18 (15) ◽

pp. 7890

Author(s):

Bruce Mellado ◽

Jianhong Wu ◽

Jude Dzevela Kong ◽

Nicola Luigi Bragazzi ◽

Ali Asgary ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Developed Countries ◽

Multiple Data ◽

Learning Techniques ◽

Unequal Access ◽

Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Heart disease prediction using machine learning techniques : a survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10557 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 684 ◽

Cited By ~ 12

Author(s):

V V. Ramalingam ◽

Ayantan Dandapath ◽

M Karthik Raja

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Support Vector ◽

Complex Data ◽

Learning Techniques ◽

Vector Machines ◽

Supervised Learning Algorithms ◽

Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

Toward Palmprint Recognition Methodology Based Machine Learning Techniques

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.4.225 ◽

2020 ◽

Vol 4 (4) ◽

Author(s):

M. M. Ata ◽

K. M. Elgamily ◽

M. A. Mohamed

Keyword(s):

Machine Learning ◽

Hough Transform ◽

Recognition Accuracy ◽

Learning Algorithms ◽

Supervised Machine Learning ◽

Palmprint Recognition ◽

Feature Vectors ◽

Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Integrating the Internet of Things Into Software Engineering Practices ◽

Machine Learning Techniques for Internet of Things

10.4018/978-1-5225-7790-4.ch008 ◽

2019 ◽

pp. 160-180

Author(s):

P. Priakanth ◽

S. Gopikrishnan

Keyword(s):

Machine Learning ◽

Data Science ◽

Learning Algorithms ◽

Independent Learning ◽

Analytical Models ◽

Guided Learning ◽

Learning Techniques ◽

Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?