Machine learning in the prediction of sugarcane production environments

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art.

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The application of machine learning to sensor signals for machine tool and process health assessment

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405420960892 ◽

2020 ◽

pp. 095440542096089

Author(s):

James Moore ◽

Jon Stammers ◽

Javier Dominguez-Caballero

Keyword(s):

Machine Learning ◽

Machine Tool ◽

Manufacturing Industry ◽

Failure Modes ◽

Test Procedure ◽

Machining Process ◽

Sensor Data ◽

Custom Made ◽

Production Environments ◽

Sensor Signals

Due to the latest advancements in monitoring technologies, interest in the possibility of early-detection of quality issues in components has grown considerably in the manufacturing industry. However, implementation of such techniques has been limited outside of the research environment due to the more demanding scenarios posed by production environments. This paper proposes a method of assessing the health of a machining process and the machine tool itself by applying a range of machine learning (ML) techniques to sensor data. The aim of this work is not to provide complete diagnosis of a condition, but to provide a rapid indication that the machine tool or process has changed beyond acceptable limits; making for a more realistic solution for production environments. Prior research by the authors found good visibility of simulated failure modes in a number of machining operations and machine tool fingerprint routines, through the defined sensor suite. The current research set out to utilise this system, and streamline the test procedure to obtain a large dataset to test ML techniques upon. Various supervised and unsupervised ML techniques were implemented using a range of features extracted from the raw sensor signals, principal component analysis and continuous wavelet transform. The latter were classified using convolutional neural networks (CNN); both custom-made networks, and pre-trained networks through transfer learning. The detection and classification accuracies of the simulated failure modes across all classical ML and CNN techniques tested were promising, with all approaching 100% under certain conditions.

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The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Sensors ◽

10.3390/s20010230 ◽

2019 ◽

Vol 20 (1) ◽

pp. 230 ◽

Cited By ~ 2

Author(s):

Ahmed Rady ◽

Joel Fischer ◽

Stuart Reeves ◽

Brian Logan ◽

Nicholas James Watson

Keyword(s):

Machine Learning ◽

Light Intensity ◽

Prediction Accuracy ◽

Positive Impact ◽

Food Allergens ◽

Processing Methods ◽

Linear Discriminant ◽

Powdered Food ◽

Production Environments ◽

Classification Prediction

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.

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Flame: an open source framework for model development, hosting, and usage in production environments

Journal of Cheminformatics ◽

10.1186/s13321-021-00509-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Manuel Pastor ◽

José Carlos Gómez-Tamayo ◽

Ferran Sanz

Keyword(s):

Machine Learning ◽

Open Source ◽

Predictive Models ◽

Web Application ◽

Model Building ◽

Molecular Descriptor ◽

Model Development ◽

Graphic Interface ◽

Chemical Structures ◽

Production Environments

AbstractThis article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

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The Learning Path to Neural Network Industrial Application in Distributed Environments

Processes ◽

10.3390/pr9122247 ◽

2021 ◽

Vol 9 (12) ◽

pp. 2247

Author(s):

Lenka Landryová ◽

Jan Sikora ◽

Renata Wagnerová

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Legacy Systems ◽

Industrial Companies ◽

Applied Machine Learning ◽

Learning Path ◽

Production Environments ◽

Distributed Production

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data.

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Flame: An Open Source Framework for Model Development, Hosting, and Usage in Production Environments

10.21203/rs.3.rs-107430/v1 ◽

2020 ◽

Author(s):

Manuel Pastor ◽

José Carlos Gómez-Tamayo ◽

Ferran Sanz

Keyword(s):

Machine Learning ◽

Open Source ◽

Predictive Models ◽

Web Application ◽

Model Building ◽

Molecular Descriptor ◽

Model Development ◽

Graphic Interface ◽

Chemical Structures ◽

Production Environments

Abstract This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application, with a Python backend and a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures, since the software supports structural normalization, molecular descriptor generation and machine learning building, using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code adding unlimited model customization. Models generated with Flame can be easily exported facilitating collaborative model development. All models are stored in a persistent model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly three years of development in collaboration with users from pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high quality predictive models based on shared legacy data for assessing the safety of drug candidates.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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