A Multimodal Fingers Classification for General Interactive Surfaces

For a university student, the main measure of success is whether or not they pass the modules for which they are enrolled. It is not always easy for a lecturer to determine whether or not a student will be successful as there may be various factors on which the final result of a student depends. Applying rules or common insights might aid a lecturer in identifying students who are struggling in a specific module. This may afford the lecturer the opportunity to devise an intervention; so as to enhance the chances of the student passing the module. To facilitate the gathering of information on which to base possible student academic interventions, this study makes use of an artificial neural network and decision tree to derive a series of insights and rules which a lecturer may use to identify struggling students. The artificial neural network and decision tree are both trained using supervised learning techniques. Individually, both the neural network and decision tree have an accuracy in excess of 77% and a sensitivity in excess of 83% on a test data set. Both models show a moderate level of agreement with a pre-labelled dataset.

Predictive systems use historical and other available data to predict an event. In this paper we tries to compare the power of Artificial Neural Network (ANN) and Decision Tree (DT) in prediction of aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm and ANN. We try to find the effective parameters on events occurrences. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using K-means we add a field to dataset which determines the cluster of each record and after that we predict the events using C5 algorithm and ANN. This framework and time series model can predict future events efficiently. We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This dataset includes 17 types of events. Using this framework the closest event can be predicted. The C5 method is able to predict events with 79.55% accuracy and ANN with 72.87% accuracy. Applying K-means clustering algorithm the prediction increase to 94.59% for C5 and 92.66% accuracies for ANN. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge dataset of aerology and can be extended in many different datasets in any other environments.

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision making. Missing some data for treatment planning may occur. To solve this problem, the proposed study used two algorithms to determine the proper rehabilitation treatment program. Artificial Neural Networks and Decision Trees models were considered. Sensitivity, specificity and accuracy values were computed to define the performance of both algorithms. The results of this study indicated that both techniques can apply for data classification and define the proper treatment programs. However, the results were shown that the specificity and accuracy of decision trees model were higher than neural network model.

A Comparison of Predicting Movie Success between Artificial Neural Network and Decision Tree

Smart cities integrate data collection and communication technologies to operate more efficiently in order to provide better services to citizens, improved road traffic situation, better economic development, etc. However, safety still a main concern of road traffic as road crashes pose serious threats to road users causing death and injury all over the world. Road traffic safety is the process of implementing measures to reduce the number of crashes, death and severe injuries. In this paper, using Machine Learning (ML) algorithms, Off-line, we train and test the Intelligent and Dynamic Adaptation Model (IDAM) on the road traffic data collected for 14 years of car crashes in United Kingdom (UK). IDAM then on-line determines the optimal speed limit for each road segment based on many predictors such as weather, road type, light condition, and many others, stated later. We train and test IDAM using three different machine learning algorithms: Artificial Neural Network (ANN), Decision Tree (DT), and Linear Regression (LR) with Stochastic Gradient Descent (SGD). Using the testing dataset, IDAM achieves prediction accuracy of around 96%. Implemented in an operation algorithm, IDAM can continuously adapt the road speed limit to an optimal value as the predictors change on-line.

In manufacturing practices of most big printing companies there are collected data and records of process parameters. Gathering information from this data in form of developed models and rules such as data mining, which uses statistical methods or Artificial Intelligence, Artificial Neural Networks, Decision Trees, Expert Systems, and others are subjects of inter-disciplinary fields of science called Data Mining. In the shrink sleeve production process, the effects of using data mining tools not only improve the quality of the shrink sleeve and winding process but also reduce manufacturing costs. This paper describes how developed models of data mining tools can be used for prediction of initial tension parameters and winding speed for each every new design of shrink sleeve labels. Every one design of shrink sleeve label has a lot of factors. Some of them are more significant, some of them less. The aim of this paper is to choose significant factors and compute a models in learning process using collected data. Finally when models will be computed, can be used for prediction of key winding parameters of each new shrink sleeve label designs. This will bring for company saved time for experimental selection during converting of winding parameters like tension and speed and also will minimize risk of occurrence of defects with incorrect winding parameters.

In this article, the authors proposed the method of medical diagnosis in gestational diabetes mellitus (GDM) in the initial stages of pregnancy to facilitate diagnoses and prevent the affection. Nowadays, in industrial modern world with changing lifestyle alimental manner the incidence of complex disease has been increasingly grown. GDM is a chronic disease and one of the major health problems that is often diagnosed in middle or late period of pregnancy, when it is too late for prediction. If it is not treated, it will make serious complications and various side effects for mother and child. This article is designed for answering to the question of: “What is the best approach in timely and accurate prediction of GDM?” Thus, the artificial neural network and decision tree are proposed to reduce the amount of error and the level of accuracy in anticipating and improving the precision of prediction. The results illustrate that intelligent diagnosis systems can improve the quality of healthcare, timely prediction, prevention, and knowledge discovery in bioinformatics.

The county level of urbanization quality analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of urbanization quality data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of urbanization quality. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding the county level of urbanization quality classification for Guanzhong urban agglomeration. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county level of urbanization quality classification and prediction.

The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of 165 patients were used. Potential predictors included the patients' race, age, sex, preoperative intraocular pressure (IOP), preoperative visual acuity, number of IOP-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. With a small amount of data, the best classfier was logistic regression, but with more data, the best classifier was the random forest.

Power transformers are devices used in the transmission networks to step up and step down a voltage value. It primarily operates during large loads and has the highest efficiency at full load and near it. Power transformers generally have higher power ratings (>200 MVA) and are present near the generation sites. The power transformer becomes an integral part of the system and has an immediate and direct impact on the stability and reliability of the network. Hence, in order to ensure that the system remains stable, the protection of the power transformer becomes a task of utmost importance. In this paper, we describe a method to optimize the fault detection by differentiating between actual faults and other conditions that mimic a fault with the help of classification algorithms such as artificial neural networks (ANNs) and decision trees.

In contrast to English, automatic hyphenation by computer of Afrikaans words is a problem that still needs to be addressed, since errors are still often encountered in printed text. An initial step in this task is the ability to automatically syllabify words. Since new words are created continuously by joining words, it is necessary to develop an “intelligent” technique for syllabification. As a first phase of the research, we consider only the orthographic information of words, and disregard both syntactic and morphological information. This approach allows us to use machine-learning techniques such as artificial neural networks and decision trees that are known for their pattern recognition abilities. Both these techniques are trained with isolated patterns consisting of input patterns and corresponding outputs (or targets) that indicate whether the input pattern should be split at a certain position, or not. In the process of compiling a list of syllabified words from which to generate training data for the syllabification problem, irregular patterns were identified. The same letter patterns are split differently in different words and complete words that are spelled identically are split differently due to meaning. We also identified irregularities in and between the different dictionaries that we used. We examined the influence range of letters that are involved in irregularities. For example, for their in agter-ente and vaste-rente we have to consider three letters to the left of r to be certain where the hyphen should be inserted. The influence range of the k in verstek-waarde and kleinste-kwadrate is four to the left and three to the right. In an analysis of letter patterns in Afrikaans words we found that the letter e has the highest frequency overall (16,2% of all letters in the word list). The frequency of words starting with s is the highest, while the frequency of words ending with e is the highest. It is important to note that the frequency of words ending with s is even higher than for words starting with s. The two and three letter patterns that occur most are er (10% of all two letter patterns) and ing (4% of all three letter patterns). In an analysis of syllables in Afrikaans words, we found that (as for complete words) syllables most often start with the letter s and end with e, while the frequency of syllables ending with s is almost as high as the frequency of syllables starting with s. This indicates that problems with hyphenation can be expected around the letter s. The two and three letter syllables that occur most often are -ge- and -ver-, respectively.In an attempt to decide on the window length to use to generate training data for machine-learning techniques we also analysed the length of syllables. The results show that two and three letter syllables occur most often, but that four letter syllables have the most unique instances. We also analysed a spectrum of window configurations and found that the ideal configuration will have to be determined empirically. One major problem we identified in this study is that irregular syllabification often occurs where letter patterns include the letter s. The reasons being (i) the use of the combining s when joining words, (ii) almost equal frequencies of syllables starting and ending with s and (iii) vague hyphe- nation rules for letter combinations containing s. To effectively address automatic syllabification in Afrikaans, it is necessary to develop more sophisticated methods to handle vagueness around the letter s.

Coordinated development degree of county socio-economic system analysis and prediction play an important role in urban agglomeration coordinated development and improve benefit of regional coordinated development in China. According to the county socio-economic system data which is large scale and imbalance, this paper presented a support vector machine (SVM) model to predict coordinated development degree of county socio-economic system. In order to improve the discrimination precision of SVM in classification, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding coordinated development degree of county socio-economic system prediction for Guanzhong urban agglomeration. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for coordinated development degree of county socio-economic system classification and prediction.

In machine learning, the learning paradigms of an artificial neural network (ANN) and a decision tree (DT) are different, but they are equivalent in essence. This paper proves the approximate equivalency between feedback neural networks and decision trees. The result provides us a very useful guideline when we perform theoretical research and applications on DT and ANN.

Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naive Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.