In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage ...
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In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine's disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine's health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decisiontree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine's health.
A hybrid model based on the combination of k-means method, BP neural network and decision tree algorithm is proposed for predicting the end-point phosphorus content of electric arc furnace. The industrial data from el...
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ISBN:
(纸本)9783030652531;9783030652524
A hybrid model based on the combination of k-means method, BP neural network and decision tree algorithm is proposed for predicting the end-point phosphorus content of electric arc furnace. The industrial data from electric arc furnace is filtered firstly by the box-plot method, and the processed data of end-point phosphorus content is classified into three clusters by k-means analysis method. Then, three BP neural networks with different parameters for each cluster are established to deal with the data overlapping problem and increase the model accuracy. In order to obtain the optimum prediction result, a new method combined with the posterior knowledge of dephosphorization ratio and the decision tree algorithm is employed. With this method, the results predicted, respectively, by the three different BP neural networks are merged according to the merging rule set established by decision tree algorithm and the identified result is taken as the final end-point phosphorus content. In comparison with the traditional BP neural network and deep layer neural network, the hybrid model increases the prediction accuracy of end-point P content to 97.8% with +/- 0.006% error range, and meanwhile for the error ranges of +/- 0.005% and +/- 0.004%, the prediction accuracy is 94.2% and 83.0%, respectively.
The challenges of internationalization, the diversion to outcome-based education, and the emergence of the COVID-19 pandemic triggered a growing demand for quality educators. Hence, educational institutions shall ensu...
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ISBN:
(纸本)9781665495325
The challenges of internationalization, the diversion to outcome-based education, and the emergence of the COVID-19 pandemic triggered a growing demand for quality educators. Hence, educational institutions shall ensure continuous evaluation of faculty performance and use its data as a tool to capacitate learning providers and enhance instruction in the classroom. Using the identified performance indicators, this study aims to elicit insights from the dataset extracted from the Faculty Performance Evaluation System (FPES) of the Camarines Sur Polytechnic Colleges (CSPC) to understand how the students perceived their respective instructors' performance levels prior to and at the onset of the COVID-19 pandemic. Generated patterns were uncovered using descriptive analysis based on the students' ratings. Meanwhile, the students' comments, suggestions, and recommendations were analyzed using Sentiment Analysis through TextBlob. The same dataset was further examined to recommend a prescribed action using a supervised learning method (decision tree algorithm). With 98% model accuracy, faculty performance testing dataset were provided with prescribed actions with the following rules: Outstanding & Very Satisfactory Ratings = Re-Hire/No Action Needed;Satisfactory = Mentorship;Unsatisfactory & Poor = Re-Training & Re-Evaluation. The study discovered a decline in the faculty performance evaluation results at the onset of the COVID-19 pandemic. However, the students' sentiments were considerate to the faculty's endeavor as most of its polarity scores fell under "positive." Recommendations to strengthen and boost faculty performance were incorporated based on the findings of the prescriptive analysis.
Machine learning is widely being used in medical field for disease diagnostics and *** area of machine learning is mainly classified into 3 parts: supervised, unsupervised and reinforcement *** machine learning (ML) a...
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ISBN:
(纸本)9781665416504
Machine learning is widely being used in medical field for disease diagnostics and *** area of machine learning is mainly classified into 3 parts: supervised, unsupervised and reinforcement *** machine learning (ML) algorithms are used in this paper for modeling and showing the impact of increased testing on the number of daily confirmed cases of COVID-19. The algorithms used to carry out this study are decisiontree regression and random forest regression. Machine learning for modeling has proven to be significant for forecasting and hence decision making over the future course of actions. In this paper, Gaussian process regression has been used for modeling as well as forecasting the daily confirmed cases in South Korea. The results obtained show that if the number of tests conducted is increased to the population of South Korea, approximately equal to 51, 286, 183, the peak in the daily cases is obtained earlier and hence the overall number of daily cases is less compared to current cases.
Identifying file type of file fragments has been investigated for a long time but it is still a challenge. It is found in the literature that high-entropy file fragments make the problem more complicated. Especially, ...
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Identifying file type of file fragments has been investigated for a long time but it is still a challenge. It is found in the literature that high-entropy file fragments make the problem more complicated. Especially, existing popular file types share same compression algorithms such as deflate algorithm that causes file type identification for file fragment become harder. Applying machine learning or empirical techniques is to deal with this problem. Compression algorithms are used to reduce the size of files that have big data size and include image files. Many research work of file type identification have been done for JPEG format, and the Rate of Change feature is proven to work effectively for it. Conversely, few efforts have been made for PNG although this is a popular image format and widely used nowadays. In this article, we propose a new approach based on the deflate-encoded data detection, entropy-based clustering, and decisiontree techniques to identify PNG data fragments which are the deflate-encoded fragments. Experiments showed high accuracy rates for the proposed method.
we are presenting a predictive analysis of the academic performance of the students in Kongu Engineering College based on the academic performance of the passed out students. The performance of the students both in ac...
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ISBN:
(纸本)9781665414517
we are presenting a predictive analysis of the academic performance of the students in Kongu Engineering College based on the academic performance of the passed out students. The performance of the students both in academic and in the placement is degrading every year which needs to be analyzed in order to improve the performance of the students for the upcoming batches. The placement performance of the students purely depends on academic performance and also on co-curricular performance. So, both will be taken for consideration. The involvement in the co-curricular activities and extra-curricular activities also affects the placement performance of the students. Thus, the data relating in the academic results, co-curricular activities, extracurricular involvement details and other personal details has to be considered for the prediction. The datasets are collected from various resources and the primary thirty six parameters were considered as parameters that have high impact on placement performance. The missing details were filled manually with the help of the faculties. Chi-square method is used for selecting the features that matches both the placed and non-placed students. Classification models were created to predict the placement performance of the students at the end of the last semester for each dataset.
Feature Selection, a critical data preprocessing step in machine learning, is an effective way in removing irrelevant variables, thus reducing the dimensionality of input features. Removing uninformative or, even wors...
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Feature Selection, a critical data preprocessing step in machine learning, is an effective way in removing irrelevant variables, thus reducing the dimensionality of input features. Removing uninformative or, even worse, misinformative input columns helps train a machine learning model on a more generalised data with better performances on new and unseen data. In this paper, eight feature selection techniques paired with the gradient boosting regressor model were evaluated based on the statistical comparison of their prediction errors and computational efficiency in characterising a shallow marine reservoir. Analysis of the results shows that the best technique in selecting relevant logs for permeability, porosity and water saturation prediction was the Random Forest, SelectKBest and Lasso regularisation methods, respectively. These techniques did not only reduce the features of the high dimensional dataset but also achieved low prediction errors based on MAE and RMSE and improved computational efficiency. This indicates that the Random Forest, SelectKBest, and Lasso regularisation can identify the best input features for permeability, porosity and water saturation predictions, respectively.
With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the prot...
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With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the protection of personal privacy during the mining of massive amounts of data has become a significant challenge. This paper examines the relationship between data mining techniques and privacy protection measures through a review of the pertinent literature. It provides a concise analysis of the benefits and drawbacks of commonly utilized classification algorithms in data mining. In addition, it examines the interplay between data mining techniques and privacy protection and summarizes important privacy protection *** privacy protection methods. These techniques include data anonymization,association rule concealing, data perturbation, etc. By comprehending these privacy protection techniques, appropriate privacy safeguards can be selected to ensure the privacy and security of the data when conducting data mining.
The increasing importance of Veterinary Informatics is driving the implementation of integrated veterinary information management systems (VIMS) for the capture, storage, analysis and retrieval of animal data. In this...
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ISBN:
(纸本)9789897585210
The increasing importance of Veterinary Informatics is driving the implementation of integrated veterinary information management systems (VIMS) for the capture, storage, analysis and retrieval of animal data. In this paper, a decision tree algorithm was implemented, starting from the database of the University Veterinary Hospital at Federico II University of Naples, aiming at building a predictive model for an effective recognition of neoplastic diseases and zoonoses for cats and dogs focusing to Campania Region, in order to figure out, according to the One (Digital) Health perspective specifics, the connection between humans, animals, and surrounding environment.
In this study, expecting to form a virtuous circle conducive to socio-economic development. The problems of environmental cost control mode, and the relationship between the value chain and environmental cost control ...
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In this study, expecting to form a virtuous circle conducive to socio-economic development. The problems of environmental cost control mode, and the relationship between the value chain and environmental cost control was discussed. The decision tree algorithm of artificial intelligence (AI) was applied in designing the environmental cost control system of manufacturing enterprises to realise the internalisation of environmental cost. The external environmental consumption cost was calculated by taking an oil production plant as the research object according to the environmental cost report of the oil production plant, and relevant suggestions were proposed for the enterprise's environmental cost scheme control.
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