Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

Since consuming gutter oil does great harm to people’s health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.

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MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS

Radiation Protection Dosimetry ◽

10.1093/rpd/ncaa108 ◽

2020 ◽

Vol 190 (3) ◽

pp. 342-351

Author(s):

Munir S Pathan ◽

S M Pradhan ◽

T Palani Selvam

Keyword(s):

Machine Learning ◽

Glow Curve ◽

Good Accuracy ◽

Machine Learning Algorithms ◽

Support Vector ◽

Computationally Efficient ◽

Artificial Neural Network Ann ◽

First Time

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

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Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

10.1101/317073 ◽

2018 ◽

Cited By ~ 3

Author(s):

Adam Hakim ◽

Shira Klorfeld ◽

Tal Sela ◽

Doron Friedman ◽

Maytal Shabat-Simon ◽

...

Keyword(s):

Machine Learning ◽

Predictive Power ◽

Rank Order ◽

Marketing Research ◽

Machine Learning Algorithms ◽

Great Promise ◽

Neural Signals ◽

Novel Approach ◽

First Time ◽

Better Than

AbstractA basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects’ individual future preferences over the products and the commercials’ population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials’ effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials’ population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects’ preferences and marketing campaigns’ success.

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Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms

Interactive Learning Environments ◽

10.1080/10494820.2020.1826982 ◽

2020 ◽

pp. 1-15

Author(s):

Muhammad Alshurideh ◽

Barween Al Kurdi ◽

Said A. Salloum ◽

Ibrahim Arpaci ◽

Mostafa Al-Emran

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Learning Systems ◽

Machine Learning Algorithms ◽

Comparative Approach ◽

Actual Use

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A Quality-assured Approximate Hardware Accelerators–based on Machine Learning and Dynamic Partial Reconfiguration

ACM Journal on Emerging Technologies in Computing Systems ◽

10.1145/3462329 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-19

Author(s):

Mahmoud Masadeh ◽

Yassmeen Elderhalli ◽

Osman Hasan ◽

Sofiene Tahar

Keyword(s):

Machine Learning ◽

Power Consumption ◽

Error Resilience ◽

User Preferences ◽

Machine Learning Algorithms ◽

Approximate Computing ◽

Partial Reconfiguration ◽

Efficient Design ◽

Fpga Design ◽

Dynamic Partial Reconfiguration

Machine learning is widely used these days to extract meaningful information out of the Zettabytes of sensors data collected daily. All applications require analyzing and understanding the data to identify trends, e.g., surveillance, exhibit some error tolerance. Approximate computing has emerged as an energy-efficient design paradigm aiming to take advantage of the intrinsic error resilience in a wide set of error-tolerant applications. Thus, inexact results could reduce power consumption, delay, area, and execution time. To increase the energy-efficiency of machine learning on FPGA, we consider approximation at the hardware level, e.g., approximate multipliers. However, errors in approximate computing heavily depend on the application, the applied inputs, and user preferences. However, dynamic partial reconfiguration has been introduced, as a key differentiating capability in recent FPGAs, to significantly reduce design area, power consumption, and reconfiguration time by adaptively changing a selective part of the FPGA design without interrupting the remaining system. Thus, integrating “Dynamic Partial Reconfiguration” (DPR) with “Approximate Computing” (AC) will significantly ameliorate the efficiency of FPGA-based design approximation. In this article, we propose hardware-efficient quality-controlled approximate accelerators, which are suitable to be implemented in FPGA-based machine learning algorithms as well as any error-resilient applications. Experimental results using three case studies of image blending, audio blending, and image filtering applications demonstrate that the proposed adaptive approximate accelerator satisfies the required quality with an accuracy of 81.82%, 80.4%, and 89.4%, respectively. On average, the partial bitstream was found to be 28.6 smaller than the full bitstream .

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Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva

Scientific Reports ◽

10.1038/s41598-021-84924-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Silvia Alonso ◽

Sara Cáceres ◽

Daniel Vélez ◽

Luis Sanz ◽

Gema Silvan ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Random Forest Model ◽

Forest Model ◽

Spontaneous Labour ◽

Hormonal Mechanism ◽

First Time ◽

Estrone Sulphate

AbstractSteroidal hormone interaction in pregnancy is crucial for adequate fetal evolution and preparation for childbirth and extrauterine life. Estrone sulphate, estriol, progesterone and cortisol play important roles in the initiation of labour mechanism at the start of contractions and cervical effacement. However, their interaction remains uncertain. Although several studies regarding the hormonal mechanism of labour have been reported, the prediction of date of birth remains a challenge. In this study, we present for the first time machine learning algorithms for the prediction of whether spontaneous labour will occur from week 37 onwards. Estrone sulphate, estriol, progesterone and cortisol were analysed in saliva samples collected from 106 pregnant women since week 34 by enzyme-immunoassay (EIA) techniques. We compared a random forest model with a traditional logistic regression over a dataset constructed with the values observed of these measures. We observed that the results, evaluated in terms of accuracy and area under the curve (AUC) metrics, are sensibly better in the random forest model. For this reason, we consider that machine learning methods contribute in an important way to the obstetric practice.

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Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination

Scientific Reports ◽

10.1038/s41598-020-78159-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Camelia Berghian-Grosan ◽

Dana Alina Magdas

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Model Accuracy ◽

Region I ◽

Geographical Discrimination ◽

First Time ◽

Region Ii ◽

Fruit Spirits

AbstractThrough this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I—200–600 cm−1 and region II—1200–1400 cm−1) appeared to have the higher discrimination potential for the investigated distillates. The proposed approach proved to be a very effective one for trademark fingerprint differentiation, a model accuracy of 95.5% being obtained (only one sample was misclassified). A comparable model accuracy (90.9%) was achieved for the geographical discrimination of the fruit spirits which can be considered as a very good one taking into account that this classification was made inside Transylvania region, among neighbouring areas. Because the trademark fingerprint is the prevailing one, the successfully distillate type differentiation, with respect to the fruit variety, was possible to be made only inside of each producing entity.

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Energy Profile Prediction of Milling Processes Using Machine Learning Techniques

Machine Learning for Cyber Physical Systems - Technologien für die intelligente Automation ◽

10.1007/978-3-662-62746-4_1 ◽

2020 ◽

pp. 1-11

Author(s):

Matthias Mühlbauer ◽

Hubert Würschinger ◽

Dominik Polzer ◽

Nico Hanenkamp

Keyword(s):

Machine Learning ◽

Power Consumption ◽

Model Building ◽

Milling Process ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Accurate Forecast ◽

The Individual ◽

Cutting Processes

AbstractThe prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.

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Driving Factors of CO2 Emissions: Further Study Based on Machine Learning

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.721517 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shanshan Li ◽

Yam Wing Siu ◽

Guoqin Zhao

Keyword(s):

Machine Learning ◽

Economic Growth ◽

Carbon Dioxide ◽

Growth Rate ◽

Foreign Capital ◽

Machine Learning Algorithms ◽

Optimal Range ◽

Policy Makers ◽

Actual Use ◽

D Investment

Greenhouse gases, especially carbon dioxide (CO2) emissions, are viewed as one of the core causes of climate change, and it has become one of the most important environmental problems in the world. This paper attempts to investigate the relation between CO2 emissions and economic growth, industry structure, urbanization, research and development (R&D) investment, actual use of foreign capital, and growth rate of energy consumption in China between 2000 and 2018. This study is important for China as it has pledged to peak its carbon dioxide emissions (CO2) by 2030 and achieve carbon neutrality by 2060. We apply a suite of machine learning algorithms on the training set of data, 2000–2015, and predict the levels of CO2 emissions for the testing set, 2016–2018. Employing rmse for model selection, results show that the nonlinear model of k-nearest neighbors (KNN) model performs the best among linear models, nonlinear models, ensemble models, and artificial neural networks for the present dataset. Using KNN model, sensitivity analysis of CO2 emissions around its centroid position was conducted. The findings indicate that not all provinces should develop its industrialization. Some provinces should stay at relatively mild industrialization stage while selected others should develop theirs as quickly as possible. It is because CO2 emissions will eventually decrease after saturation point. In terms of urbanization, there is an optimal range for a province. At the optimal range, the CO2 emissions would be at a minimum, and it is likely a result of technological innovation in energy usage and efficiency. Moreover, China should increase its R&D investment intensity from the present level as it will decrease CO2 emissions. If R&D reinvestment is associated with actual use of foreign capital, policy makers should prioritize the use of foreign capital for R&D investment on green technology. Last, economic growth requires consuming energy. However, policy makers must refrain from consuming energy beyond a certain optimal growth rate. The above findings provide a guide to policy makers to achieve dual-carbon strategy while sustaining economic development.

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