Machine-learning techniques and their applications in manufacturing

2005 ◽  
Vol 219 (5) ◽  
pp. 395-412 ◽  
Author(s):  
D T Pham ◽  
A A Afify
Keyword(s):  
Machine Learning ◽  
Inductive Learning ◽  
Computer Programs ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Issues ◽  
Current Trends ◽  
Learning Techniques ◽  
Recent Developments ◽  
Learning Research

Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.

Overview of Machine Learning Approaches for Wireless Communication

2021 ◽  
pp. 1579-1597
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

Investigating Machine Learning Techniques for User Sentiment Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Nimesh V Patel ◽  
Hitesh Chhinkaniwala
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Learning Approaches ◽  
Current Trends ◽  
Learning Techniques ◽  
Benchmark Datasets

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.

Overview of Machine Learning Approaches for Wireless Communication

2019 ◽  
pp. 123-140 ◽  
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

scholarly journals Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2717
Author(s):  
Nusrat Rouf ◽  
Majid Bashir Malik ◽  
Tasleem Arif ◽  
Sparsh Sharma ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Digital Libraries ◽  
Stock Price ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Stock Market Prediction ◽  
Research Areas ◽  
Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

scholarly journals Machine Learning Frameworks in Cancer Detection

2021 ◽  
Vol 297 ◽  
pp. 01073
Author(s):  
Sabyasachi Pramanik ◽  
K. Martin Sagayam ◽  
Om Prakash Jena
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Cancer Development ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Fact Finding ◽  
Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

scholarly journals Machine learning for metagenomics: methods and tools

Metagenomics ◽  
2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Hayssam Soueidan ◽  
Macha Nikolski
Keyword(s):  
Machine Learning ◽  
Gene Prediction ◽  
Full Range ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Unified Framework ◽  
Comparative Metagenomics ◽  
Learning Techniques ◽  
Ngs Data ◽  
Modern Machine

AbstractOwing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2012 ◽  
pp. 155-165
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

scholarly journals A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 170 ◽  
Author(s):  
Zhixi Li ◽  
Vincent Tam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Predictive Ability ◽  
Trading Strategies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

scholarly journals Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5213 ◽  
Author(s):  
Donato Impedovo ◽  
Fabrizio Balducci ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Machine Learning Techniques ◽  
Visual Features ◽  
Learning Approaches ◽  
Traffic State ◽  
State Classification ◽  
Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

Sign in / Sign up

Export Citation Format

Share Document