Describes a sequence of methods to improve the running-time of the Isodata and K-means algorithms. The methods are compared on a range of remotely-sensed data sets, and show consistent and dramatic speedups.
Describes a sequence of methods to improve the running-time of the Isodata and K-means algorithms. The methods are compared on a range of remotely-sensed data sets, and show consistent and dramatic speedups.
In computer science education, teaching and learning programming is difficult. Understanding and coding programmes are regarded as extremely difficult in computer science education. This is because practical ability i...
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In computer science education, teaching and learning programming is difficult. Understanding and coding programmes are regarded as extremely difficult in computer science education. This is because practical ability is valued more than theoretical knowledge. According to research, students with metacognitive management skills outperform lower-performing students in programming. The more difficult the programming task, the more important it is for the programmer to have metacognitive control skills. The cognitive processes involved in learning computer programming necessitate the development of metacognitive skills in the novice programmer. This study’s main objective is to predict computer programming students’ academic grades using classification algorithms. The predictive analysis used 151 records that were gathered and used. Three methods are considered in order to find the best classifier: Rule Based classification, Decision Tree classification, and Nave Bayesian classification. The confusion matrix, common metrics like precision, recall, ROC curve, kappa statistics, mean absolute error, root mean squared error, relative absolute error, and root relative squared error are all used to assess how well each classifier classified the dataset. Conclusion: With 95% accuracy, the ID3 classifier outperformed the other six predictive models.
A bstract-Coronavirus disease has been declared as an infectious pandemic affecting the life and health of millions across the globe. It has caused high number of mortalities giving birth to exceptional state of emerg...
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ISBN:
(数字)9781728186351
ISBN:
(纸本)9781728186368
A bstract-Coronavirus disease has been declared as an infectious pandemic affecting the life and health of millions across the globe. It has caused high number of mortalities giving birth to exceptional state of emergency worldwide. It has not affected the people but also has damaged infrastructure of different countries, especially causing an expectational situation in health care systems globally. Due to unavailability of vaccination and faster human to human transmission of virus, healthcare facilities are at high risk of exceeding their limit and capacity, especially in developing countries like Pakistan. Therefore, it is important to manage resources properly in these countries to control high mortality rate and damage it can cause. In this paper we have taken a case study of small city in Pakistan, where healthcare facilities are not enough to deal with pandemic. Most of the COVID-19 patients have to be refer to big cities based on their severity. We have taken data of COVID-19 positive patients from this small city, developed and applied machine learning classification model to predict the severity of patient, in order to deal with the shortage of resources. Among all seven taken and tested algorithms, we have chosen SVM to predict severity of patients. Model has shown 60% of accuracy and have divided patient's severity into mild, moderate and severe levels.
Data mining is the discovery of interesting and valuable information hidden in large data sets. Data mining, whose usage area is expanding day by day, is also widely used in the shopping sector. In this paper, a data ...
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Data mining is the discovery of interesting and valuable information hidden in large data sets. Data mining, whose usage area is expanding day by day, is also widely used in the shopping sector. In this paper, a data collection form related to shopping habits was prepared and applied to individuals and a data set was obtained. The data obtained from this form were analyzed using data mining techniques. Thus, it was tried to determine what kinds of products people spend their money, tendency to save money according to gender and what they attach importance to shopping. In this study, many classification algorithms were used and as a result, J48, Naive Bayes, SMO and Random Forest classification algorithms were found to be the highest performing algorithms. The results revealed that gender and occupational knowledge affect the shopping rate and that the budget allocated to shopping varies according to gender. In addition, it was observed that the educational status and place of residence did not affect shopping tendency.
Detecting autism spectrum disorder (ASD) is challenging due to its varying symptoms across individuals. This research assessed four common machine learning classifiers - Logistic Regression, Naïve Bayes, Support ...
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ISBN:
(数字)9798350394962
ISBN:
(纸本)9798350394979
Detecting autism spectrum disorder (ASD) is challenging due to its varying symptoms across individuals. This research assessed four common machine learning classifiers - Logistic Regression, Naïve Bayes, Support Vector Machines, and K-Nearest Neighbors - using four ASD datasets. Two experiments evaluated the algorithms on the original datasets and versions modified by combining three datasets, adding an age feature, and reducing features. Support vector machines showed the most consistently strong performance on all metrics in both experiments. The adult dataset performed best among original datasets, while the modified combined dataset was most successful, with substantial gains across measures. Overall, the results demonstrate machine learning's promise for developing more accurate and accessible ASD screening, complementing standard diagnostic practices. Further research should evaluate these techniques on expanded and diverse ASD data.
We propose an efficient strategy for qualitative analysis of measured polymer spectra based on fast wavelet transforms and fuzzy set theory. First, a wavelet transform is applied to the measured data acting as a featu...
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We propose an efficient strategy for qualitative analysis of measured polymer spectra based on fast wavelet transforms and fuzzy set theory. First, a wavelet transform is applied to the measured data acting as a feature extractor. Then, a fuzzy classification algorithm separates different spectra into various clusters thus giving a qualitative interpretation of the ingredients due to calculated membership values. For that we implemented a special rule based approach with fuzzy if-then rules, a fuzzy c-means algorithm and a fuzzy Kohonen cluster algorithm. The three classification algorithms are compared and results are given.
Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefi...
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ISBN:
(纸本)9781479968329
Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper concentrates on the four most famous algorithms, i.e., Decision Tree, Naïve Bayes, K Nearest Neighbour and Genetic Programming and the effect on their performance of time and accuracy when the number of instances are incrementally decreased. This paper will also investigate the difference in result when working with binary class or multiclass datasets and suggest the algorithms to follow when using certain kind of dataset.
Machine Learning (ML) has become a vast umbrella of various algorithms. Certainly, even for classification models, there are numerous algorithms such as Logistic Regression, Naïve Bayes Classifier, K-Nearest Neig...
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ISBN:
(数字)9781728173665
ISBN:
(纸本)9781728173672
Machine Learning (ML) has become a vast umbrella of various algorithms. Certainly, even for classification models, there are numerous algorithms such as Logistic Regression, Naïve Bayes Classifier, K-Nearest Neighbors, Decision tree and Random Forest Classifiers. The proposed works present a comparative study of various binary classifier and have implemented various boosting algorithms and finally have summarized the related arguments for optimal performance of the presented classification models.
In the last decade, a large number of computer aided diagnosis (CAD) tools are developed for the identification of different algorithm. For the disease diagnosis, classification algorithms based on ML (ML) techniques ...
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ISBN:
(纸本)9781538658741
In the last decade, a large number of computer aided diagnosis (CAD) tools are developed for the identification of different algorithm. For the disease diagnosis, classification algorithms based on ML (ML) techniques are commonly used. The classification algorithm uses a supervised learning methodology to makes the system or computer program to learn from the given input data and then employ the learning knowledge to identify the upcoming observations. This paper intends to evaluate the different classification algorithms namely radial basis function (RBF), Naive Bayes (NB), J48 and Olex-GA on the identification of Lymph diseases. For the performance evaluation of different classifiers, a benchmark Lymph dataset is used interms of different performance measures. The obtained results proved that the RBF network attained better performance compared to NB, J48 and Olex-GA.
Collaborative Healthcare data analytics is a method of methodical data analysis that allows healthcare specialists to discovery opportunities used for development in health system management processing the various inf...
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Collaborative Healthcare data analytics is a method of methodical data analysis that allows healthcare specialists to discovery opportunities used for development in health system management processing the various information are stored. This proposed approach entails three parts comparable to preprocessing, attribute selection, classification algorithms. The goal of this work is to plan a machine-based diagnostic approach using machine learning technique. This method is developed to mining the risk factors of chronic kidney diseases. There are many improved algorithms consume stayed proposed in every recent years. These algorithms improve the effectiveness of mining risk of Chronic Kidney Disease, but there are also shortcomings. In this work, Random forest, SVM and ANN algorithms were used to identify an early diagnosis of CKD patients with good accuracy.
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