scholarly journals An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091920 ◽  
Author(s):  
Ebru Turanoglu Bekar ◽  
Per Nyqvist ◽  
Anders Skoogh
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Industrial Case Study ◽  
Intelligent Approach

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

scholarly journals Machine Learning Algorithms Comparison for Manufacturing Applications

2021 ◽  
Author(s):  
Mohammed Almanei ◽  
Omogbai Oleghe ◽  
Sandeep Jagtap ◽  
Konstantinos Salonitis
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Manufacturing Industry ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Manufacturing Applications

With the vast amount of data available, and its increasing complexity in manufacturing processes, traditional statistical approaches have started to fall short. This is where machine learning plays a key role, addressing the challenges by bringing the ability to analyse large and complex datasets from multiple sources, finding non-linear and intricate patterns on data, relationships between several factors and their influence on the manufacturing process outputs. This paper demonstrates the advantages and applications of using supervised machine learning techniques in the manufacturing industry. It focuses on binary classification and compares the performance of three different machine learning algorithms: logistic regression, support vector machine, and neural networks. A case study has been conducted on a manufacturing company, using the techniques and algorithms mentioned. The case study focuses on analysing the relationship between different manufacturing process variables and their impact on one key output variable of a product, which in this case is the result of a quality test that measures product performance. The modelling problem has been oriented towards a Boolean goal to predict whether the parts will pass this test.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

scholarly journals Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

2020 ◽  
pp. 458-467
Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Dynamic Environment ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Mechanisms

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

scholarly journals Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software

2019 ◽  
Vol 8 (5S3) ◽  
pp. 192-195
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Software Applications ◽  
Learning Techniques ◽  
Software Maintainability ◽  
Quality Aspect

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

In-game behaviour analysis of football players using machine learning techniques based on player statistics

2020 ◽  
pp. 174795412095976
Author(s):  
Abraham García-Aliaga ◽  
Moisés Marquina ◽  
Javier Coterón ◽  
Asier Rodríguez-González ◽  
Sergio Luengo-Sánchez
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Football Players ◽  
Machine Learning Algorithm ◽  
Reduction Techniques ◽  
Learning Techniques ◽  
Dimensionality Reduction Techniques ◽  
Player Positions

The purpose of this research was to determine the on-field playing positions of a group of football players based on their technical-tactical behaviour using machine learning algorithms. Each player was characterized according to a set of 52 non-spatiotemporal descriptors including offensive, defensive and build-up variables that were computed from OPTA’s on-ball event records of the matches for 18 national leagues between the 2012 and 2019 seasons. To test whether positions could be identified from the statistical performance of the players, the dimensionality reduction techniques were used. To better understand the differences between the player positions, the most discriminatory variables for each group were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. From the combination of both techniques, we obtained useful conclusions to enhance the performance of players and to identify positions on the field. The study demonstrates the suitability and potential of artificial intelligence to characterize players' positions according to their technical-tactical behaviour, providing valuable information to the professionals of this sport.

Estimation of annual runoff using selected data machine learning algorithm

2020 ◽  
Author(s):  
ujjwal singh ◽  
Rajani Kumar Pradhan ◽  
Shailendra Pratap ◽  
Martin Hanel ◽  
Ioannis Markonis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Runoff ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
Large River ◽  
Annual Runoff ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Run Off ◽  
Basin Scale

<p>Annual runoff is important information on water balance in the catchment and large river basin scale. It forms the boundary conditions for mathematical modelling of hydrological balance on a finer temporal and spatial scale. It is important for the assessment of climate change on water resources. Currently, there are several datasets on global gridded runoff fields available. GRUN and E-RUN provide monthly estimates of runoff rate with the spatial resolution of 0.5 degree. The GRUN is global dataset and E-RUN is covering Europe <sup>1</sup><sup>,2</sup>.In this study, we evaluate the capability of paleoclimate reconstructions on precipitation, PDSI, and temperature, which are available in the form of gridded fields, to estimate annual surface runoff using selected machine learning techniques. For this purpose, we use as a benchmark runoff information GRUN and E-RUN data sets. Both data are aggregated on the annual time scale for the period 1902 – 2014 (GRUN) and 1952-2015 (E-RUN). Following machine learning algorithms were tested: Random forests, SVM, MLP, LDA and Extra Trees. Reconstructed precipitation, temperature, PDSI<sup>3</sup> and runoff estimated using selected Budyko models with different spatial aggregation served as inputs<sup>4–7</sup> . Different combinations of inputs were analysed.Our results show that the estimated surface runoff is in good agreement with E-RUN and GRUN datasets for analysed periods. The result and newly tested approach based on derived machine learning models can be further applied to the estimation of paleoclimatic reconstructions of runoff fields.</p><p> </p><p>References:</p><ol><li>Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. GRUN: an observation-based global gridded runoff dataset from 1902 to 2014. Earth Syst. Sci. Data <strong>11</strong>, 1655–1674 (2019).</li> <li>Gudmundsson, L. & Seneviratne, S. I. Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth Syst. Sci. Data <strong>8</strong>, 279–295 (2016).</li> <li>Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era, Sci. Adv., 1, e1500561. (2015).</li> <li>Schreiber, P. Über die Beziehungen zwischen dem Niederschlag und der Wasserführung der Flüsse in Mitteleuropa. Z Meteorol <strong>21</strong>, 441–452 (1904).</li> <li>Ol’Dekop, E. M. On evaporation from the surface of river basins. Trans. Meteorol. Obs. <strong>4</strong>, 200 (1911).</li> <li>Turc, L. Le bilan d’eau des sols: relations entre les précipitations, l’évaporation et l’écoulement. (1953).</li> <li>Pike, J. G. The estimation of annual run-off from meteorological data in a tropical climate. J. Hydrol. <strong>2</strong>, 116–123 (1964).</li> </ol><p> </p>

scholarly journals Machine learning algorithms: a background artifact

2017 ◽  
Vol 7 (1.1) ◽  
pp. 143 ◽  
Author(s):  
J. Deepika ◽  
T. Senthil ◽  
C. Rajan ◽  
A. Surendar
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Knowledge Discovery ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Intelligence ◽  
Machine Learning Algorithm ◽  
Human History ◽  
Learning Techniques

With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.  

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