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Abstract

The purpose of the work was to predict the selected product parameters of the dry separation process using a pneumatic sorter. From the perspective of application of coal for energy purposes, determination of process parameters of the output as: ash content, moisture content, sulfur content, calorific value is essential. Prediction was carried out using chosen machine learning algorithms that proved to be effective in forecasting output of various technological processes in which the relationships between process parameters are non-linear. The source of data used in the work were experiments of dry separation of coal samples. Multiple linear regression was used as the baseline predictive technique. The results showed that in the case of predicting moisture and sulfur content this technique was sufficient. The more complex machine learning algorithms like support vector machine (SVM) and multilayer perceptron neural network (MPL) were used and analyzed in the case of ash content and calorific value. In addition, k-means clustering technique was applied. The role of cluster analysis was to obtain additional information about coal samples used as feed material. The combination of techniques such as multilayer perceptron neural network (MPL) or support vector machine (SVM) with k-means allowed for the development of a hybrid algorithm. This approach has significantly increased the effectiveness of the predictive models and proved to be a useful tool in the modeling of the coal enrichment process.

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Authors and Affiliations

Alina Żogała
Maciej Rzychoń
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Abstract

Leaf - a significant part of the plant, produces food using the process called photosynthesis. Leaf disease can cause damage to the entire plant and eventually lowers crop production. Machine learning algorithm for classifying five types of diseases, such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew, Leaf Curl and Myrothecium leaf diseases, is proposed in the proposed study. The classification of diseases needs front face of leafs. This paper proposes an automated image acquisition process using a USB camera interfaced with Raspberry PI SoC. The image is transmitted to host PC for classification of diseases using online web server. Pre-processing of the acquired image by host PC to obtain full leaf, and later classification model based on SVM is used to detect type diseases. Results were checked with a 97% accuracy for the collection of acquired images.
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Bibliography

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[2] M. H. Saleem, J. Potgieter, and K. M. Arif, “Plant disease detection and classification by deep learning,” Plants, vol. 8, no. 11, p. 468, 2019.
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Authors and Affiliations

Hiren Mewada
1
Jignesh Patoliaya
2

  1. Faculty of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Kobhar, Kingdom of Saudi Arabai
  2. Charotar University of Science and Technology, Changa, India
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Abstract

Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM) was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE), Non Dominated Sorting Genetic Algorithm (NSGA II) and Simulated Annealing (SA). It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.

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Authors and Affiliations

Deepak Kumar
Sudheer Ch
Shashi Mathur
Jan Adamowski
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Abstract

Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.

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Authors and Affiliations

Xin Xia
Xiaofeng Liu
Jichao Lou
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Abstract

This study focuses on the problem of mapping impervious surfaces in urban areas and aims to use remote sensing data and orthophotos to accurately classify and map these surfaces. Impervious surface indices and green space assessments are widely used in land use and urban planning to evaluate the urban environment. Local governments also rely on impervious surface mapping to calculate stormwater fees and effectively manage stormwater runoff. However, accurately determining the size of impervious surfaces is a significant challenge. This study proposes the use of the Support Vector Machines (SVM) method, a pattern recognition approach that is increasingly used in solving engineering problems, to classify impervious surfaces. The research results demonstrate the effectiveness of the SVM method in accurately estimating impervious surfaces, as evidenced by a high overall accuracy of over 90% (indicated by the Cohen’s Kappa coefficient). A case study of the “Parkowo-Lesne” housing estate in Warsaw, which covers an area of 200,000 m², shows the successful application of the method. In practice, the remote sensing imagery and SVM method allowed accurate calculation of the area of the surface classes studied. The permeable surface represented about 67.4% of the total complex and the impervious surface corresponded to the remaining 32.6%. These results have implications for stormwater management, pollutant control, flood control, emergency management, and the establishment of stormwater fees for individual properties. The use of remote sensing data and the SVM method provides a valuable approach for mapping impervious surfaces and improving urban land use management.
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Authors and Affiliations

Janusz Sobieraj
1
ORCID: ORCID
Marcos Fernández Marín
2
ORCID: ORCID
Dominik Metelski
3
ORCID: ORCID

  1. Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16,00-637 Warsaw, Poland
  2. Universitat Politccnica de Valcncia, Department of Computer Science and Artificial Intelligence,46980 Paterna (Valencia), Spain
  3. University of Granada, Faculty of Economics and Business Sciences, Campus Cartuja, 18071Granada, Spain
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Abstract

This work present an efficient hardware architecture of Support Vector Machine (SVM) for the classification of Hyperspectral remotely sensed data using High Level Synthesis (HLS) method. The high classification time and power consumption in traditional classification of remotely sensed data is the main motivation for this work. Therefore presented work helps to classify the remotely sensed data in real-time and to take immediate action during the natural disaster. An embedded based SVM is designed and implemented on Zynq SoC for classification of hyperspectral images. The data set of remotely sensed data are tested on different platforms and the performance is compared with existing works. Novelty in our proposed work is extend the HLS based FPGA implantation to the onboard classification system in remote sensing. The experimental results for selected data set from different class shows that our architecture on Zynq 7000 implementation generates a delay of 11.26 μs and power consumption of 1.7 Watts, which is extremely better as compared to other Field Programmable Gate Array (FPGA) implementation using Hardware description Language (HDL) and Central Processing Unit (CPU) implementation.
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Authors and Affiliations

H.N. Mahendra
1
S. Mallikarjunaswamy
1

  1. Department of Electronics and Communication Engineering, JSS Academy of Technical Education Bengaluru and Affiliated to Visvesvaraya Technological University, Belagavi, India

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