Concrete Gravity Dams

Trend based Prediction 0.75 5. Online/Offline Prediction 0.80 6. LCS algorithm 0.85 7. Sequential Pattern Mining with Markov Model 0.86 8. Modified Markov Model with Association Rule Mining 0.89 9.

We were assigned to construct a software that utilizes a classification algorithm that is able to accurately decide a correct classification for a certain sequence of inputs that were provided by the user. The input is to be classified based on a known training set of records of the same attributes as the sequence provided by the user.

In this module, the class label for the testing data is predicted. The n – dimensional feature vector for the testing data is converted from query tree of testing data in the manner similar to the data pre – processing phase. The SQLIA classifier determines the new testing feature vector is normal or malicious, by using optimized SVM classification model.

The major issue filter based feature subset methods are producing feature subset with maximum relevancy and minimum redundancy. The subset of features ‗S‘ are identified based on selecting feature subset at the multiple level . Finally, a subset of 19 % features are produced from the gives the better classifier performance when compared with other feature subsets methods . Extensive experiments are conducted on image data sets and shows that our framework for selecting optimal feature subset produced better efficiency and accuracy. The future scope of the work is to annotate the image regions and can retrieve relevant images based on image

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

Among the hundreds of thousands bridges in the world, two worth mentioning for their history and unique features. These two engineering masterpieces are the Mike O'Callaghan-Pat Tillman Memorial Bridge, as known as Hoover Dam Bypass Bridge and the Akashi Kaikyo Bridge. Through the analysis of their characteristics, it is possible compare and contrast these projects in aspects such as historic facts, design features, and environmental and construction challenges.

Logistic regression model, as a usual approach before, was used to analyze the stroke outcomes' data. Fortunately, because of its potentially more powerful high-level prediction performance, machine learning algorithms have been proposed as an alternative to analyze large-scale multivariate data. Support vector machine (SVM) is one of the most popular machine learning methods to use for recognition or classification. Support vector machine (SVM) is one of the most popular machine learning methods used for recognition or classification.

Using Naïve Bayes If we want to build a model of a problem in probabilistic way, this kind of classification is the best one because it is supervised learning approach

Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study that has its roots in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart or blood vessels Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

The removal of a dam may seem like a daunting task, but in many cases it is cheaper to remove the dam than it would be to repair the issues. The average dam has the design lifespan of 50 years. According to the American Society of Civil Engineers, “The average age of our nation’s dams is 52 years. By 2020, 70% of the total dams in the United States will be over 50 years old” (ASCE). There is a large need for old dams to be removed and replaced with new dams built to current specifications. In order to replace these dams, state and federal governments need to provide a large amount of funding. ASCE has stated in their infrastructure report card, “The Association of State Dam Safety Officials estimates that the total cost to rehabilitate the nation’s non-federaland federal dams is over $57 billion. To rehabilitate just those dams categorized as most critical, or high- hazard, would cost the nation $21 billion, a cost that continues to rise as maintenance, repair, and rehabilitation are delayed” (ASCE). With limited funding, dam owners have moved toward a risk based decision-making process. The funding for rebuilding dams has only been going towards dams that are in the high hazard category and are close to failing. This is a growing concern as only dams that are about to fail are getting repaired. The number of dams that are considered high risk is constantly

Many hospitals are maintaining their patient’s database online like the records related to tests suggested, their results and the prescriptions suggested. This generates huge data which could be in any form like text, numbers as well as images and videos. In fact, all this data is important in making clinical decisions. In order to handle such a large data efficiently, usage of multistage classifier has become necessary. In existing systems, all the features are tested at a time by the classifier in order to detect whether patient is suffering from that particular disease or not. The entire testing consumes lot of time if the system is testing all attributes of a patient who is not actually suffering from the disease. So in such cases if we test the attributes step by step with few attributes in each step then we will be able to arrive at conclusion in primary stage itself leading to more efficient use of time as well as money. Simultaneously patient is also relieved from unnecessary stress as well as fatigue. This system optimizes resources very efficiently. Also it aims to identify the problem in the very preliminary stage and suggest reliable solution to problems increasing life of

The purpose of this paper is to provide a unique and concise solution to a biomedical problem from a supervised learning and statistical perspective. As the solution is purely from a statistical perspective without support from proper biomedical theories, the solution might not serve practical or meaningful biomedical values. In our solution, we have reduced feature dimension to as low as 0.1%. The selected features served as biomarkers might be useful to the diagnosis of LGL leukemia, but it requires further analysis and proof in the field of biomedical

In case of road anomalies, bump testing samples were correctly recognized with accuracy 92.3\%. For driving behaviors, normal driving behaviors testing samples were correctly classified with accuracy 95.3\% while the abnormal behaviors classified with accuracy 98\%. In this experiment, k-NN achieves total recognition rate 95.9\% in determining the type of driving event. k-NN results are shown in Figure \ref{fig:algorithms}.

Wrapper method: wrapper method involve learning algorithm and search for optimal feature subset from original feature set which discover the relationship between relevance and optimal subset selection. It performed better than the filter method. The specific training classifier is used to evaluate the performance of selected features.