scholarly journals Aflatoxin detection on direction of the 4.0 age at 3.0 costs

2019 ◽  
Vol 7 (7) ◽  
pp. 338-346 ◽  
Author(s):  
Mariana Matulovic ◽  
Flávio José de Oliveira Morais ◽  
Angela Vacaro de Souza ◽  
Cleber Aalexandre de Amorim ◽  
Luiz Fernando Sommaggio Coletta
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Environmental Variables ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Toxic Metabolite ◽  
Monitoring And Control ◽  
Carcinogenic Effects ◽  
And Control

Articulate the most diverse and sophisticated technologies, such as Remote Sensing, Big Data, Cloud Computing, Internet of Things, 3D Printing, among others, is part of universe 4.0, whether industrial or agricultural. Focusing on agricultural context, this paper proposes a low-cost 4.0 device to perform the monitoring and control of certain environmental variables for the detection of aflatoxins in peanut crops. Aflatoxins are toxic metabolite of fungi genus Aspergillus that can cause toxic and carcinogenic effects in humans and animals. The device developed was able to monitor temperature and humidity variations helping the aflatoxins identification. The equipment portability allows its use in silos with encapsulation via Additive Manufacturing, besides the aflatoxin prediction from Machine Learning algorithms.

scholarly journals MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 29-34
Author(s):  
V. Ayma ◽  
C. Beltrán ◽  
P. N. Happ ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Local Mode ◽  
Ground Truth Data ◽  
Computational Performance ◽  
Thematic Accuracy ◽  
Computing Infrastructure

<p><strong>Abstract.</strong> Climate change and its effects are taking more importance nowadays; and glaciers are one of the most affected ecosystems by that, considering that the energy of Earth’s surface and its temperature may be directly related to glacier temporal changes. Then, the comprehension of glaciers behaviour, by its retreating or melting critical conditions, can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by satellites sensors, we can refer to this analysis as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithms. To validate our proposal, we analysed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance achieved by our proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas by the clustering approaches against the manually selected ground truth data. We compared the computational load involved in executing the clustering processes sequentially and in a distributed fashion, using a local mode and cluster configuration over a cloud computing infrastructure.</p>

AI-Based Cloud Computing Application for Smart Earthmoving Operations

2020 ◽  
Author(s):  
Ashraf Salem ◽  
Osama Moselhi
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Project Managers ◽  
Monitoring And Control ◽  
The Status ◽  
Artificial Neural Network Ann ◽  
Data Acquisition Module ◽  
And Control

This paper introduces a newly developed model for automated monitoring and control of productivity in earthmoving operations. The model makes use of advancements in wireless sensing networks, Internet of Things (IoT), and artificial intelligence. It utilizes data analytics and a dashboard to provide project managers with actionable data on the status of these operations in near-real time. The model consists of two modules; the first is a low-cost open-source remote sensing data acquisition module for collecting data throughout the execution of earthmoving operations. The collected data is sent to a cloud-based MySQL database, in which the second module is designed to (1) measure actual productivity in near-real-time, (2) detecting the location and condition of hauling roads and (3) monitoring and reporting driving conditions over these roads. Artificial Neural Network (ANN) is used in cloud computing for analyzing the productivity to determine and prioritize causes behind experienced loss of productivity from that planned

scholarly journals Monitoring and control of polymer production line based on machine learning

2021 ◽  
Vol 2119 (1) ◽  
pp. 012159
Author(s):  
S Abdurakipov
Keyword(s):  
Machine Learning ◽  
Process Parameters ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Real Problem ◽  
Monitoring And Control ◽  
Machine Learning Model ◽  
Statistical Relationships ◽  
And Control

Abstract The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

scholarly journals Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3515
Author(s):  
Sung-Ho Sim ◽  
Yoon-Su Jeong
Keyword(s):  
Low Cost ◽  
Third Party ◽  
Monitoring And Control ◽  
Constant Size ◽  
Computing Environments ◽  
Iot Devices ◽  
Cloud Servers ◽  
The Cost ◽  
Processing Techniques ◽  
And Control

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

&lt;p&gt;Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R&lt;sup&gt;2&lt;/sup&gt; = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R&lt;sup&gt;2&lt;/sup&gt; = 0.75 and 0.78 respectively) while DL performs better in TDS (R&lt;sup&gt;2&lt;/sup&gt; =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.&lt;/p&gt;

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